From 8bcd8eb852dd3aff0c0f0ac46e3aed8e3bbd0ef5 Mon Sep 17 00:00:00 2001 From: a-kore Date: Tue, 6 Jan 2026 15:10:27 -0500 Subject: [PATCH 1/8] first commit --- .gitignore | 54 +- manage_tasks.py | 382 + poetry.lock | 462 +- pyproject.toml | 76 +- run_experiments.py | 629 ++ selected_tasks.json | 162 + src/__init__.py | 2 + src/task_solver/code_executor.py | 245 + src/task_solver/messages.py | 41 +- src/task_solver/moderator.py | 88 +- src/task_solver/tool_assisted_scientist.py | 559 ++ src/utils/json_utils.py | 119 +- src/utils/tool_assisted_prompts.py | 318 + tests/src/utils/test_json_utils_latex.py | 184 + uv.lock | 8029 ++++++++++++++++++++ 15 files changed, 11191 insertions(+), 159 deletions(-) create mode 100755 manage_tasks.py create mode 100644 run_experiments.py create mode 100644 selected_tasks.json create mode 100644 src/task_solver/code_executor.py create mode 100644 src/task_solver/tool_assisted_scientist.py create mode 100644 src/utils/tool_assisted_prompts.py create mode 100644 tests/src/utils/test_json_utils_latex.py create mode 100644 uv.lock diff --git a/.gitignore b/.gitignore index 762fa1f3..2e8bbc93 100644 --- a/.gitignore +++ b/.gitignore @@ -2,10 +2,6 @@ __pycache__/ *.py[cod] *$py.class -.vscode/ - -# macOS system files -.DS_Store # C extensions *.so @@ -24,7 +20,6 @@ parts/ sdist/ var/ wheels/ -pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg @@ -54,6 +49,7 @@ coverage.xml *.py,cover .hypothesis/ .pytest_cache/ +cover/ # Translations *.mo @@ -70,7 +66,7 @@ instance/ .webassets-cache # Scrapy stuff: -.scrapy +.scrapy/ # Sphinx documentation docs/_build/ @@ -91,11 +87,26 @@ ipython_config.py # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not +# with no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock -# PEP 582; used by e.g. github.com/David-OConnor/pyflow +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff @@ -110,14 +121,10 @@ celerybeat.pid .venv env/ venv/ -p_env/ ENV/ env.bak/ venv.bak/ -# Files used to set environment variables -*.env - # Spyder project settings .spyderproject .spyproject @@ -136,13 +143,22 @@ dmypy.json # Pyre type checker .pyre/ -# pycharm +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm .idea/ -logs/ -src/outputs/ -outputs/ +# VS Code +.vscode/ -# inspect result logs -seed_datasets_inspect_logs/ -seed_tasks_results/ +# Project specific +materials/ +experiment_results/ +full_results/ +results/ +experiments.log +math/ \ No newline at end of file diff --git a/manage_tasks.py b/manage_tasks.py new file mode 100755 index 00000000..b705b3eb --- /dev/null +++ b/manage_tasks.py @@ -0,0 +1,382 @@ +#!/usr/bin/env python3 +""" +Unified Task Management Script + +Consolidates task discovery, selection, and extraction into one tool. + +Usage: + # List all failed tasks + python manage_tasks.py list + + # Extract tasks using default selections + python manage_tasks.py extract + + # Extract specific tasks + python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 + + # Extract N random failed tasks per capability + python manage_tasks.py extract --random 3 +""" + +import argparse +import json +import random +from pathlib import Path +from typing import Dict, List, Any, Optional +from collections import defaultdict + + +# Default task selections (can be overridden via CLI) +DEFAULT_TASK_SELECTIONS = { + 'computational_linear_algebra': [2, 3, 4], + 'numerical_simulation_dynamical_systems': [1, 2, 3], + 'numerical_methods_ode': [2, 4, 5], + 'complex_analysis_problems': [3, 4, 5], + 'advanced_integration_techniques': [2, 6, 9], +} + + +class TaskManager: + """Manages task discovery, selection, and extraction.""" + + def __init__(self, math_dir: Path = Path('math')): + self.math_dir = math_dir + + def load_capability_file(self, capability_name: str) -> Dict[str, Any]: + """Load a capability JSON file.""" + cap_dir = self.math_dir / capability_name + + if not cap_dir.exists(): + raise FileNotFoundError(f"Capability directory not found: {cap_dir}") + + cap_file = cap_dir / 'capability.json' + if not cap_file.exists(): + raise FileNotFoundError(f"Capability file not found: {cap_file}") + + with open(cap_file, 'r', encoding='utf-8') as f: + return json.load(f) + + def find_failed_tasks(self, capability_name: str) -> List[Dict[str, Any]]: + """Find all tasks with verdict='no' in a capability.""" + try: + capability_data = self.load_capability_file(capability_name) + except FileNotFoundError: + return [] + + failed_tasks = [] + failed_data = capability_data.get('capability_failed_data', []) + + for task_data in failed_data: + verification = task_data.get('verification', {}) + verdict = verification.get('verdict', '') + + if verdict == 'no': + failed_tasks.append({ + 'task_id': task_data.get('id', ''), + 'problem': task_data.get('problem', ''), + 'answer': task_data.get('answer', ''), + 'reasoning': task_data.get('reasoning', ''), + 'verification': verification, + }) + + return failed_tasks + + def list_all_failed_tasks(self) -> Dict[str, List[Dict[str, Any]]]: + """Find all failed tasks across all capabilities.""" + all_failed = {} + + if not self.math_dir.exists(): + print(f"Error: Math directory not found: {self.math_dir}") + return all_failed + + capability_dirs = sorted([d for d in self.math_dir.iterdir() if d.is_dir()]) + + for cap_dir in capability_dirs: + capability_name = cap_dir.name + failed_tasks = self.find_failed_tasks(capability_name) + + if failed_tasks: + all_failed[capability_name] = failed_tasks + + return all_failed + + def extract_specific_tasks( + self, + task_selections: Dict[str, List[int]] + ) -> Dict[str, List[Dict[str, Any]]]: + """Extract specific tasks by ID.""" + extracted_tasks = {} + + for capability_name, task_ids in task_selections.items(): + try: + capability_data = self.load_capability_file(capability_name) + failed_data = capability_data.get('capability_failed_data', []) + + tasks = [] + for task_id in task_ids: + # Find the task with matching ID + for task in failed_data: + if str(task.get('id', '')) == str(task_id): + tasks.append({ + 'task_id': task.get('id', ''), + 'problem': task.get('problem', ''), + 'answer': task.get('answer', ''), + 'reasoning': task.get('reasoning', ''), + 'verification': task.get('verification', {}), + }) + break + + if tasks: + extracted_tasks[capability_name] = tasks + + except FileNotFoundError as e: + print(f"Warning: {e}") + continue + + return extracted_tasks + + def extract_random_tasks(self, num_per_capability: int) -> Dict[str, List[Dict[str, Any]]]: + """Extract N random failed tasks per capability.""" + all_failed = self.list_all_failed_tasks() + extracted_tasks = {} + + for capability_name, failed_tasks in all_failed.items(): + if len(failed_tasks) >= num_per_capability: + selected = random.sample(failed_tasks, num_per_capability) + extracted_tasks[capability_name] = selected + elif failed_tasks: + print(f"Warning: {capability_name} has only {len(failed_tasks)} failed tasks (requested {num_per_capability})") + extracted_tasks[capability_name] = failed_tasks + + return extracted_tasks + + def save_tasks(self, tasks: Dict[str, List[Dict[str, Any]]], output_file: Path): + """Save extracted tasks to JSON file.""" + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(tasks, f, indent=2, ensure_ascii=False) + + print(f"✓ Saved {sum(len(t) for t in tasks.values())} tasks to: {output_file}") + + +def print_list_summary(all_failed: Dict[str, List[Dict[str, Any]]]): + """Print summary of all failed tasks.""" + print("\n" + "="*80) + print("FAILED TASKS SUMMARY") + print("="*80) + + total_capabilities = len(all_failed) + total_tasks = sum(len(tasks) for tasks in all_failed.values()) + + print(f"\nTotal capabilities with failed tasks: {total_capabilities}") + print(f"Total failed tasks: {total_tasks}\n") + + print(f"{'Capability':<50} {'Failed Tasks':<15} {'Task IDs'}") + print("-"*80) + + for cap_name, tasks in sorted(all_failed.items()): + task_ids = [str(t['task_id']) for t in tasks] + task_ids_str = ', '.join(task_ids[:10]) + if len(task_ids) > 10: + task_ids_str += f", ... ({len(task_ids) - 10} more)" + + print(f"{cap_name:<50} {len(tasks):<15} {task_ids_str}") + + print("="*80) + + +def print_extract_summary(tasks: Dict[str, List[Dict[str, Any]]]): + """Print summary of extracted tasks.""" + print("\n" + "="*80) + print("EXTRACTION SUMMARY") + print("="*80) + + total_tasks = sum(len(task_list) for task_list in tasks.values()) + + print(f"\nTotal tasks extracted: {total_tasks}") + print(f"Total capabilities: {len(tasks)}\n") + + print(f"{'Capability':<50} {'Tasks':<10} {'Task IDs'}") + print("-"*80) + + for cap_name, task_list in sorted(tasks.items()): + task_ids = [str(t['task_id']) for t in task_list] + task_ids_str = ', '.join(task_ids) + print(f"{cap_name:<50} {len(task_list):<10} {task_ids_str}") + + # Verify all are failures + print("\n" + "="*80) + print("VERIFICATION CHECK") + print("="*80) + + all_failed = True + for cap_name, task_list in tasks.items(): + for task in task_list: + verdict = task['verification'].get('verdict', '') + if verdict != 'no': + print(f"⚠️ {cap_name} Task {task['task_id']}: verdict='{verdict}' (NOT A FAILURE)") + all_failed = False + + if all_failed: + print("✓ All extracted tasks have verdict='no' (confirmed failures)") + else: + print("✗ Some tasks are not failures - review selections") + + print("="*80) + + +def parse_task_selections(selections: List[str]) -> Dict[str, List[int]]: + """Parse task selections from CLI format. + + Example: ["cap1:1,2,3", "cap2:4,5,6"] -> {"cap1": [1,2,3], "cap2": [4,5,6]} + """ + parsed = {} + for selection in selections: + if ':' not in selection: + print(f"Warning: Invalid format '{selection}'. Expected 'capability:id1,id2,id3'") + continue + + cap_name, ids_str = selection.split(':', 1) + task_ids = [int(id.strip()) for id in ids_str.split(',')] + parsed[cap_name] = task_ids + + return parsed + + +def cmd_list(args): + """List all failed tasks.""" + manager = TaskManager() + all_failed = manager.list_all_failed_tasks() + + if not all_failed: + print("No failed tasks found.") + return + + print_list_summary(all_failed) + + if args.verbose: + print("\n" + "="*80) + print("DETAILED TASK INFORMATION") + print("="*80) + + for cap_name, tasks in sorted(all_failed.items()): + print(f"\n{'='*80}") + print(f"Capability: {cap_name}") + print(f"{'='*80}") + + for task in tasks: + print(f"\nTask ID: {task['task_id']}") + print(f"Problem: {task['problem'][:200]}...") + print(f"Answer: {task['answer'][:100]}...") + print(f"Verdict: {task['verification'].get('verdict', 'unknown')}") + print("-"*80) + + +def cmd_extract(args): + """Extract tasks based on selections.""" + manager = TaskManager() + + if args.random: + print(f"Extracting {args.random} random failed tasks per capability...") + random.seed(args.seed) + extracted_tasks = manager.extract_random_tasks(args.random) + elif args.tasks: + print("Extracting specified tasks...") + task_selections = parse_task_selections(args.tasks) + extracted_tasks = manager.extract_specific_tasks(task_selections) + else: + print("Extracting default task selections...") + extracted_tasks = manager.extract_specific_tasks(DEFAULT_TASK_SELECTIONS) + + if not extracted_tasks: + print("No tasks extracted.") + return + + # Save tasks + output_file = Path(args.output) + manager.save_tasks(extracted_tasks, output_file) + + # Print summary + print_extract_summary(extracted_tasks) + + print("\n" + "="*80) + print("NEXT STEPS") + print("="*80) + print(f"1. Review {args.output}") + print("2. Run experiments: python run_experiments.py") + print("="*80) + + +def main(): + parser = argparse.ArgumentParser( + description="Unified task management for capability evaluation experiments", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # List all failed tasks + python manage_tasks.py list + + # List with detailed information + python manage_tasks.py list --verbose + + # Extract using default selections + python manage_tasks.py extract + + # Extract specific tasks + python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 + + # Extract 3 random failed tasks per capability + python manage_tasks.py extract --random 3 + + # Extract to custom output file + python manage_tasks.py extract --output my_tasks.json + """ + ) + + subparsers = parser.add_subparsers(dest='command', help='Available commands') + + # List command + list_parser = subparsers.add_parser('list', help='List all failed tasks') + list_parser.add_argument( + '--verbose', '-v', + action='store_true', + help='Show detailed task information' + ) + + # Extract command + extract_parser = subparsers.add_parser('extract', help='Extract tasks to JSON file') + extract_parser.add_argument( + '--output', '-o', + default='selected_tasks.json', + help='Output file path (default: selected_tasks.json)' + ) + extract_group = extract_parser.add_mutually_exclusive_group() + extract_group.add_argument( + '--tasks', '-t', + nargs='+', + help='Specific tasks to extract (format: capability:id1,id2,id3)' + ) + extract_group.add_argument( + '--random', '-r', + type=int, + metavar='N', + help='Extract N random failed tasks per capability' + ) + extract_parser.add_argument( + '--seed', + type=int, + default=42, + help='Random seed for --random option (default: 42)' + ) + + args = parser.parse_args() + + if args.command == 'list': + cmd_list(args) + elif args.command == 'extract': + cmd_extract(args) + else: + parser.print_help() + + +if __name__ == '__main__': + main() diff --git a/poetry.lock b/poetry.lock index 38ae0785..7203c5d3 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. [[package]] name = "ag2" @@ -7,6 +7,7 @@ description = "A programming framework for agentic AI" optional = false python-versions = "<3.14,>=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ag2-0.9.9-py3-none-any.whl", hash = "sha256:833aeb65d657fd2cb2f8686bab9c0c3ae0b8c806c0e76b670756bd3d8d0b21ea"}, {file = "ag2-0.9.9.tar.gz", hash = "sha256:33200ded8c60a4c181061632ed38f79e34b87197fc49acebb81d9afc29f70439"}, @@ -42,7 +43,7 @@ deepseek = ["openai (>=1.99.3)"] dev = ["cairosvg", "codespell (==2.4.1)", "detect-secrets (==1.5.0)", "dirty-equals (==0.9.0)", "fastapi (==0.116.1)", "ipykernel (==6.30.1)", "jinja2 (==3.1.6)", "mcp (>=1.11.0)", "mdx-include (==1.4.2)", "mike (==2.1.3)", "mkdocs-git-revision-date-localized-plugin (==1.4.7)", "mkdocs-glightbox (==0.4.0)", "mkdocs-literate-nav (==0.6.2)", "mkdocs-macros-plugin (==1.3.7)", "mkdocs-material (==9.6.16)", "mkdocs-minify-plugin (==0.8.0)", "mkdocs-redirects (==1.2.2)", "mkdocstrings[python] (==0.30.0)", "mock (==5.2.0)", "mypy (==1.17.1)", "nbclient (==0.10.2)", "nbconvert (==7.16.6)", "nbformat (==5.10.4)", "openai (>=1.99.3)", "pandas (==2.3.1)", "pdoc3 (==0.11.6)", "pillow", "pre-commit (==4.3.0)", "pytest (==8.4.1)", "pytest-asyncio (==1.1.0)", "pytest-cov (==6.2.1)", "pyupgrade-directories (==0.3.0)", "pyyaml (==6.0.2)", "ruff (==0.12.8)", "termcolor (==3.1.0)", "toml (==0.10.2)", "typer (==0.16.0)", "types-decorator", "types-pycurl", "types-python-dateutil", "types-pyyaml", "types-requests", "types-ujson", "uv (==0.8.8)"] docs = ["cairosvg", "jinja2 (==3.1.6)", "mcp (>=1.11.0)", "mdx-include (==1.4.2)", "mike (==2.1.3)", "mkdocs-git-revision-date-localized-plugin (==1.4.7)", "mkdocs-glightbox (==0.4.0)", "mkdocs-literate-nav (==0.6.2)", "mkdocs-macros-plugin (==1.3.7)", "mkdocs-material (==9.6.16)", "mkdocs-minify-plugin (==0.8.0)", "mkdocs-redirects (==1.2.2)", "mkdocstrings[python] (==0.30.0)", "nbclient (==0.10.2)", "pdoc3 (==0.11.6)", "pillow", "pyyaml (==6.0.2)", "termcolor (==3.1.0)", "typer (==0.16.0)"] duckduckgo = ["duckduckgo-search (>=8.0.2)"] -flaml = ["flaml", "numpy (>=1.24.0,<2.0.0) ; python_version < \"3.13\"", "numpy (>=2.1) ; python_version >= \"3.13\""] +flaml = ["flaml", "numpy (>=1.24.0,<2.0.0)", "numpy (>=2.1)"] gemini = ["google-api-core", "google-auth", "google-cloud-aiplatform", "google-genai (>=1.20.0)", "jsonschema", "pillow"] gemini-realtime = ["google-api-core", "google-auth", "google-cloud-aiplatform", "google-genai (>=1.20.0)", "jsonschema", "pillow", "websockets (>=14.0,<16)"] google-api = ["google-api-python-client (>=2.163.0,<3.0)", "google-auth-httplib2 (>=0.2.0,<0.3)", "google-auth-oauthlib (>=1.2.1,<2.0)"] @@ -51,8 +52,8 @@ google-search = ["google-api-python-client (>=2.163.0,<3.0)"] graph = ["matplotlib", "networkx"] graph-rag-falkor-db = ["falkordb (>=1.0.10)", "graphrag-sdk (==0.7.1)"] groq = ["groq (>=0.9.0)"] -interop = ["crewai[tools] (>=0.76,<1) ; python_version >= \"3.10\" and python_version < \"3.13\"", "langchain-community (>=0.3.12,<1)", "litellm (<=1.75.5.post1)", "pydantic-ai (==0.6.2)", "weaviate-client (>=4,<5) ; python_version >= \"3.10\" and python_version < \"3.13\""] -interop-crewai = ["crewai[tools] (>=0.76,<1) ; python_version >= \"3.10\" and python_version < \"3.13\"", "litellm (<=1.75.5.post1)", "weaviate-client (>=4,<5) ; python_version >= \"3.10\" and python_version < \"3.13\""] +interop = ["crewai[tools] (>=0.76,<1)", "langchain-community (>=0.3.12,<1)", "litellm (<=1.75.5.post1)", "pydantic-ai (==0.6.2)", "weaviate-client (>=4,<5)"] +interop-crewai = ["crewai[tools] (>=0.76,<1)", "litellm (<=1.75.5.post1)", "weaviate-client (>=4,<5)"] interop-langchain = ["langchain-community (>=0.3.12,<1)"] interop-pydantic-ai = ["pydantic-ai (==0.6.2)"] jupyter-executor = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "requests", "websocket-client"] @@ -72,7 +73,7 @@ redis = ["redis"] retrievechat = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "protobuf (==6.31.1)", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-couchbase = ["beautifulsoup4", "chromadb (==1.0.16)", "couchbase (>=4.3.0)", "ipython", "markdownify", "numpy", "protobuf (==6.31.1)", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-mongodb = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "numpy", "protobuf (==6.31.1)", "pymongo (>=4.0.0)", "pypdf", "sentence-transformers (<=5.1.0)"] -retrievechat-pgvector = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==6.31.1)", "psycopg (>=3.1.18) ; platform_system == \"Linux\"", "psycopg[binary] (>=3.1.18) ; platform_system == \"Windows\" or platform_system == \"Darwin\"", "pypdf", "sentence-transformers (<=5.1.0)"] +retrievechat-pgvector = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==6.31.1)", "psycopg (>=3.1.18)", "psycopg[binary] (>=3.1.18)", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-qdrant = ["beautifulsoup4", "chromadb (==1.0.16)", "fastembed (>=0.3.1)", "ipython", "markdownify", "protobuf (==6.31.1)", "pypdf", "qdrant-client", "sentence-transformers (<=5.1.0)"] tavily = ["tavily-python (>=0.7.4)"] teachable = ["chromadb"] @@ -91,6 +92,7 @@ description = "Async client for aws services using botocore and aiohttp" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiobotocore-2.21.1-py3-none-any.whl", hash = "sha256:bd7c49a6d6f8a3d9444b0a94417c8da13813b5c7eec1c4f0ec2db7e8ce8f23e7"}, {file = "aiobotocore-2.21.1.tar.gz", hash = "sha256:010357f43004413e92a9d066bb0db1f241aeb29ffed306e9197061ffc94e6577"}, @@ -116,6 +118,7 @@ description = "File support for asyncio." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, @@ -128,6 +131,7 @@ description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, @@ -140,6 +144,7 @@ description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiohttp-3.11.16-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fb46bb0f24813e6cede6cc07b1961d4b04f331f7112a23b5e21f567da4ee50aa"}, {file = "aiohttp-3.11.16-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:54eb3aead72a5c19fad07219acd882c1643a1027fbcdefac9b502c267242f955"}, @@ -235,7 +240,7 @@ propcache = ">=0.2.0" yarl = ">=1.17.0,<2.0" [package.extras] -speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""] +speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"] [[package]] name = "aioitertools" @@ -244,6 +249,7 @@ description = "itertools and builtins for AsyncIO and mixed iterables" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aioitertools-0.12.0-py3-none-any.whl", hash = "sha256:fc1f5fac3d737354de8831cbba3eb04f79dd649d8f3afb4c5b114925e662a796"}, {file = "aioitertools-0.12.0.tar.gz", hash = "sha256:c2a9055b4fbb7705f561b9d86053e8af5d10cc845d22c32008c43490b2d8dd6b"}, @@ -260,6 +266,7 @@ description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"}, {file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"}, @@ -275,6 +282,7 @@ description = "A light, configurable Sphinx theme" optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"}, {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"}, @@ -287,6 +295,7 @@ description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -299,6 +308,7 @@ description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anthropic-0.64.0-py3-none-any.whl", hash = "sha256:6f5f7d913a6a95eb7f8e1bda4e75f76670e8acd8d4cd965e02e2a256b0429dd1"}, {file = "anthropic-0.64.0.tar.gz", hash = "sha256:3d496c91a63dff64f451b3e8e4b238a9640bf87b0c11d0b74ddc372ba5a3fe58"}, @@ -325,6 +335,7 @@ description = "ANTLR 4.9.3 runtime for Python 3.7" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, ] @@ -336,6 +347,7 @@ description = "Unicode to ASCII transliteration" optional = false python-versions = ">=3.3" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anyascii-0.3.2-py3-none-any.whl", hash = "sha256:3b3beef6fc43d9036d3b0529050b0c48bfad8bc960e9e562d7223cfb94fe45d4"}, {file = "anyascii-0.3.2.tar.gz", hash = "sha256:9d5d32ef844fe225b8bc7cba7f950534fae4da27a9bf3a6bea2cb0ea46ce4730"}, @@ -348,6 +360,7 @@ description = "High level compatibility layer for multiple asynchronous event lo optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"}, {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"}, @@ -361,7 +374,7 @@ typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] -test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""] +test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] trio = ["trio (>=0.26.1)"] [[package]] @@ -371,7 +384,7 @@ description = "Disable App Nap on macOS >= 10.9" optional = false python-versions = ">=3.6" groups = ["docs"] -markers = "platform_system == \"Darwin\"" +markers = "platform_system == \"Darwin\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, @@ -384,6 +397,7 @@ description = "ASGI specs, helper code, and adapters" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asgiref-3.9.1-py3-none-any.whl", hash = "sha256:f3bba7092a48005b5f5bacd747d36ee4a5a61f4a269a6df590b43144355ebd2c"}, {file = "asgiref-3.9.1.tar.gz", hash = "sha256:a5ab6582236218e5ef1648f242fd9f10626cfd4de8dc377db215d5d5098e3142"}, @@ -402,6 +416,7 @@ description = "An abstract syntax tree for Python with inference support." optional = false python-versions = ">=3.9.0" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "astroid-3.3.9-py3-none-any.whl", hash = "sha256:d05bfd0acba96a7bd43e222828b7d9bc1e138aaeb0649707908d3702a9831248"}, {file = "astroid-3.3.9.tar.gz", hash = "sha256:622cc8e3048684aa42c820d9d218978021c3c3d174fb03a9f0d615921744f550"}, @@ -417,6 +432,7 @@ description = "Annotate AST trees with source code positions" optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, @@ -433,7 +449,7 @@ description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version == \"3.10\"" +markers = "python_version < \"3.11\"" files = [ {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"}, {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"}, @@ -446,6 +462,7 @@ description = "Asyncer, async and await, focused on developer experience." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asyncer-0.0.8-py3-none-any.whl", hash = "sha256:5920d48fc99c8f8f0f1576e1882f5022885589c5fcbc46ce4224ec3e53776eeb"}, {file = "asyncer-0.0.8.tar.gz", hash = "sha256:a589d980f57e20efb07ed91d0dbe67f1d2fd343e7142c66d3a099f05c620739c"}, @@ -461,18 +478,19 @@ description = "Classes Without Boilerplate" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"}, {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"}, ] [package.extras] -benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] -cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] -dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] +benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] -tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""] +tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] [[package]] name = "autogen-core" @@ -481,6 +499,7 @@ description = "Foundational interfaces and agent runtime implementation for Auto optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autogen_core-0.7.4-py3-none-any.whl", hash = "sha256:b383d3b2dfe9f5d62e0da0057da6de3cb63259233570e4c85153e33703170afa"}, {file = "autogen_core-0.7.4.tar.gz", hash = "sha256:44b4574a378effbf52317e579ae1663602ce9bbb1c699100dec9f3cf19cc9e85"}, @@ -501,6 +520,7 @@ description = "AutoGen extensions library" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autogen_ext-0.7.4-py3-none-any.whl", hash = "sha256:5afb47c8108168bce7b61eb476ed1bf025548f404da3742d322443fedad79e32"}, {file = "autogen_ext-0.7.4.tar.gz", hash = "sha256:1d69b37afa79787b43a401a10c857572d73b1d89e71950087543a81c8df02d27"}, @@ -560,6 +580,7 @@ description = "A tool that automatically formats Python code to conform to the P optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autopep8-2.3.2-py2.py3-none-any.whl", hash = "sha256:ce8ad498672c845a0c3de2629c15b635ec2b05ef8177a6e7c91c74f3e9b51128"}, {file = "autopep8-2.3.2.tar.gz", hash = "sha256:89440a4f969197b69a995e4ce0661b031f455a9f776d2c5ba3dbd83466931758"}, @@ -576,13 +597,14 @@ description = "Internationalization utilities" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, ] [package.extras] -dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""] +dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] [[package]] name = "backoff" @@ -591,6 +613,7 @@ description = "Function decoration for backoff and retry" optional = false python-versions = ">=3.7,<4.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"}, @@ -603,6 +626,7 @@ description = "Screen-scraping library" optional = false python-versions = ">=3.7.0" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"}, {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"}, @@ -626,6 +650,7 @@ description = "The uncompromising code formatter." optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "black-25.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:759e7ec1e050a15f89b770cefbf91ebee8917aac5c20483bc2d80a6c3a04df32"}, {file = "black-25.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e519ecf93120f34243e6b0054db49c00a35f84f195d5bce7e9f5cfc578fc2da"}, @@ -673,6 +698,7 @@ description = "Run Black on Python code blocks in documentation files." optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "blacken_docs-1.19.1-py3-none-any.whl", hash = "sha256:73c3dee042a28f2d4f7df6e2c340869d6ced9704f6174d035d9b6199567f890d"}, {file = "blacken_docs-1.19.1.tar.gz", hash = "sha256:3cf7a10f5b87886683e3ab07a0dc17de41425f3d21e2948e59f1c6079c45b328"}, @@ -688,6 +714,7 @@ description = "An easy safelist-based HTML-sanitizing tool." optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"}, {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"}, @@ -707,6 +734,7 @@ description = "Define boolean algebras, create and parse boolean expressions and optional = false python-versions = "*" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "boolean_py-5.0-py3-none-any.whl", hash = "sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9"}, {file = "boolean_py-5.0.tar.gz", hash = "sha256:60cbc4bad079753721d32649545505362c754e121570ada4658b852a3a318d95"}, @@ -725,6 +753,7 @@ description = "The AWS SDK for Python" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "boto3-1.37.1-py3-none-any.whl", hash = "sha256:4320441f904435a1b85e6ecb81793192e522c737cc9ed6566014e29f0a11cb22"}, {file = "boto3-1.37.1.tar.gz", hash = "sha256:96d18f7feb0c1fcb95f8837b74b6c8880e1b4e35ce5f8a8f8cb243a090c278ed"}, @@ -745,6 +774,7 @@ description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "botocore-1.37.1-py3-none-any.whl", hash = "sha256:c1db1bfc5d8c6b3b6d1ca6794f605294b4264e82a7e727b88e0fef9c2b9fbb9c"}, {file = "botocore-1.37.1.tar.gz", hash = "sha256:b194db8fb2a0ffba53568c364ae26166e7eec0445496b2ac86a6e142f3dd982f"}, @@ -765,6 +795,7 @@ description = "httplib2 caching for requests" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cachecontrol-0.14.2-py3-none-any.whl", hash = "sha256:ebad2091bf12d0d200dfc2464330db638c5deb41d546f6d7aca079e87290f3b0"}, {file = "cachecontrol-0.14.2.tar.gz", hash = "sha256:7d47d19f866409b98ff6025b6a0fca8e4c791fb31abbd95f622093894ce903a2"}, @@ -787,6 +818,7 @@ description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"}, {file = "cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4"}, @@ -799,6 +831,7 @@ description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"}, {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"}, @@ -880,7 +913,7 @@ files = [ {file = "cffi-1.17.1-cp39-cp39-win_amd64.whl", hash = "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662"}, {file = "cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824"}, ] -markers = {main = "platform_python_implementation == \"PyPy\"", docs = "implementation_name == \"pypy\""} +markers = {main = "platform_python_implementation == \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", docs = "implementation_name == \"pypy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")"} [package.dependencies] pycparser = "*" @@ -892,6 +925,7 @@ description = "Validate configuration and produce human readable error messages. optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"}, {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, @@ -904,6 +938,7 @@ description = "The Real First Universal Charset Detector. Open, modern and activ optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "charset_normalizer-3.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:91b36a978b5ae0ee86c394f5a54d6ef44db1de0815eb43de826d41d21e4af3de"}, {file = "charset_normalizer-3.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176"}, @@ -1006,6 +1041,7 @@ description = "Composable command line interface toolkit" optional = false python-versions = ">=3.7" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"}, {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"}, @@ -1021,6 +1057,7 @@ description = "Hosted coverage reports for GitHub, Bitbucket and Gitlab" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "codecov-2.1.13-py2.py3-none-any.whl", hash = "sha256:c2ca5e51bba9ebb43644c43d0690148a55086f7f5e6fd36170858fa4206744d5"}, {file = "codecov-2.1.13.tar.gz", hash = "sha256:2362b685633caeaf45b9951a9b76ce359cd3581dd515b430c6c3f5dfb4d92a8c"}, @@ -1041,7 +1078,7 @@ files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -markers = {docs = "sys_platform == \"win32\"", test = "platform_system == \"Windows\" or sys_platform == \"win32\""} +markers = {main = "python_version >= \"3.12\" or python_version <= \"3.11\"", docs = "sys_platform == \"win32\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", test = "(platform_system == \"Windows\" or sys_platform == \"win32\") and (python_version <= \"3.11\" or python_version >= \"3.12\")"} [[package]] name = "comm" @@ -1050,6 +1087,7 @@ description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus- optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"}, {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"}, @@ -1068,6 +1106,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"}, {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"}, @@ -1145,6 +1184,7 @@ description = "Code coverage measurement for Python" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "coverage-7.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2931f66991175369859b5fd58529cd4b73582461877ecfd859b6549869287ffe"}, {file = "coverage-7.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:52a523153c568d2c0ef8826f6cc23031dc86cffb8c6aeab92c4ff776e7951b28"}, @@ -1215,7 +1255,7 @@ files = [ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""} [package.extras] -toml = ["tomli ; python_full_version <= \"3.11.0a6\""] +toml = ["tomli"] [[package]] name = "cycler" @@ -1224,6 +1264,7 @@ description = "Composable style cycles" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, @@ -1240,6 +1281,7 @@ description = "Python library for CycloneDX" optional = false python-versions = "<4.0,>=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cyclonedx_python_lib-9.1.0-py3-none-any.whl", hash = "sha256:55693fca8edaecc3363b24af14e82cc6e659eb1e8353e58b587c42652ce0fb52"}, {file = "cyclonedx_python_lib-9.1.0.tar.gz", hash = "sha256:86935f2c88a7b47a529b93c724dbd3e903bc573f6f8bd977628a7ca1b5dadea1"}, @@ -1263,6 +1305,7 @@ description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.9.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "datasets-3.5.0-py3-none-any.whl", hash = "sha256:b3b7f163acc6ac4e01a1b00eef26d48bd4039288ceea3601d169272bd5581006"}, {file = "datasets-3.5.0.tar.gz", hash = "sha256:9e39560e34f83a64e48ceca7adeb645ede3c3055c5cf48ed2b454f8ed2b89754"}, @@ -1285,9 +1328,9 @@ tqdm = ">=4.66.3" xxhash = "*" [package.extras] -audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\""] +audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\"", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\"", "tiktoken", "torch", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers", "transformers (>=4.42.0)", "zstandard"] +dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers", "transformers (>=4.42.0)", "zstandard"] docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"] jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"] pdfs = ["pdfplumber (>=0.11.4)"] @@ -1295,8 +1338,8 @@ quality = ["ruff (>=0.3.0)"] s3 = ["s3fs"] tensorflow = ["tensorflow (>=2.6.0)"] tensorflow-gpu = ["tensorflow (>=2.6.0)"] -tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\"", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\"", "tensorflow (>=2.6.0) ; python_version < \"3.10\"", "tiktoken", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers (>=4.42.0)", "zstandard"] -tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\"", "sqlalchemy", "tiktoken", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers (>=4.42.0)", "zstandard"] +tests = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tensorflow (>=2.16.0)", "tensorflow (>=2.6.0)", "tiktoken", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers (>=4.42.0)", "zstandard"] +tests-numpy2 = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0)", "sqlalchemy", "tiktoken", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers (>=4.42.0)", "zstandard"] torch = ["torch"] vision = ["Pillow (>=9.4.0)"] @@ -1307,6 +1350,7 @@ description = "An implementation of the Debug Adapter Protocol for Python" optional = false python-versions = ">=3.8" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "debugpy-1.8.14-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:93fee753097e85623cab1c0e6a68c76308cd9f13ffdf44127e6fab4fbf024339"}, {file = "debugpy-1.8.14-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d937d93ae4fa51cdc94d3e865f535f185d5f9748efb41d0d49e33bf3365bd79"}, @@ -1343,6 +1387,7 @@ description = "Decorators for Humans" optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "decorator-5.2.1-py3-none-any.whl", hash = "sha256:d316bb415a2d9e2d2b3abcc4084c6502fc09240e292cd76a76afc106a1c8e04a"}, {file = "decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360"}, @@ -1355,6 +1400,7 @@ description = "XML bomb protection for Python stdlib modules" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"}, {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, @@ -1367,6 +1413,7 @@ description = "serialize all of Python" optional = false python-versions = ">=3.8" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"}, {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"}, @@ -1383,6 +1430,7 @@ description = "Disk Cache -- Disk and file backed persistent cache." optional = false python-versions = ">=3" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"}, {file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"}, @@ -1395,6 +1443,7 @@ description = "Distribution utilities" optional = false python-versions = "*" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "distlib-0.3.9-py2.py3-none-any.whl", hash = "sha256:47f8c22fd27c27e25a65601af709b38e4f0a45ea4fc2e710f65755fa8caaaf87"}, {file = "distlib-0.3.9.tar.gz", hash = "sha256:a60f20dea646b8a33f3e7772f74dc0b2d0772d2837ee1342a00645c81edf9403"}, @@ -1407,6 +1456,7 @@ description = "Distro - an OS platform information API" optional = false python-versions = ">=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"}, {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"}, @@ -1419,6 +1469,7 @@ description = "A Python library for the Docker Engine API." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "docker-7.1.0-py3-none-any.whl", hash = "sha256:c96b93b7f0a746f9e77d325bcfb87422a3d8bd4f03136ae8a85b37f1898d5fc0"}, {file = "docker-7.1.0.tar.gz", hash = "sha256:ad8c70e6e3f8926cb8a92619b832b4ea5299e2831c14284663184e200546fa6c"}, @@ -1442,6 +1493,7 @@ description = "Parse Python docstrings in reST, Google and Numpydoc format" optional = false python-versions = ">=3.6,<4.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "docstring_parser-0.16-py3-none-any.whl", hash = "sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637"}, {file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"}, @@ -1454,6 +1506,7 @@ description = "Docutils -- Python Documentation Utilities" optional = false python-versions = ">=3.7" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "docutils-0.20.1-py3-none-any.whl", hash = "sha256:96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6"}, {file = "docutils-0.20.1.tar.gz", hash = "sha256:f08a4e276c3a1583a86dce3e34aba3fe04d02bba2dd51ed16106244e8a923e3b"}, @@ -1466,7 +1519,7 @@ description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] -markers = "python_version == \"3.10\"" +markers = "python_version < \"3.11\"" files = [ {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"}, {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"}, @@ -1482,13 +1535,14 @@ description = "Get the currently executing AST node of a frame, and other inform optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa"}, {file = "executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755"}, ] [package.extras] -tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""] +tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] [[package]] name = "fastjsonschema" @@ -1497,6 +1551,7 @@ description = "Fastest Python implementation of JSON schema" optional = false python-versions = "*" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "fastjsonschema-2.21.1-py3-none-any.whl", hash = "sha256:c9e5b7e908310918cf494a434eeb31384dd84a98b57a30bcb1f535015b554667"}, {file = "fastjsonschema-2.21.1.tar.gz", hash = "sha256:794d4f0a58f848961ba16af7b9c85a3e88cd360df008c59aac6fc5ae9323b5d4"}, @@ -1512,6 +1567,7 @@ description = "A platform independent file lock." optional = false python-versions = ">=3.9" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "filelock-3.18.0-py3-none-any.whl", hash = "sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de"}, {file = "filelock-3.18.0.tar.gz", hash = "sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2"}, @@ -1520,7 +1576,7 @@ files = [ [package.extras] docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"] testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"] -typing = ["typing-extensions (>=4.12.2) ; python_version < \"3.11\""] +typing = ["typing-extensions (>=4.12.2)"] [[package]] name = "filetype" @@ -1529,6 +1585,7 @@ description = "Infer file type and MIME type of any file/buffer. No external dep optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "filetype-1.2.0-py2.py3-none-any.whl", hash = "sha256:7ce71b6880181241cf7ac8697a2f1eb6a8bd9b429f7ad6d27b8db9ba5f1c2d25"}, {file = "filetype-1.2.0.tar.gz", hash = "sha256:66b56cd6474bf41d8c54660347d37afcc3f7d1970648de365c102ef77548aadb"}, @@ -1541,6 +1598,7 @@ description = "the modular source code checker: pep8 pyflakes and co" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "flake8-7.2.0-py2.py3-none-any.whl", hash = "sha256:93b92ba5bdb60754a6da14fa3b93a9361fd00a59632ada61fd7b130436c40343"}, {file = "flake8-7.2.0.tar.gz", hash = "sha256:fa558ae3f6f7dbf2b4f22663e5343b6b6023620461f8d4ff2019ef4b5ee70426"}, @@ -1558,6 +1616,7 @@ description = "Tools to manipulate font files" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "fonttools-4.57.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:babe8d1eb059a53e560e7bf29f8e8f4accc8b6cfb9b5fd10e485bde77e71ef41"}, {file = "fonttools-4.57.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:81aa97669cd726349eb7bd43ca540cf418b279ee3caba5e2e295fb4e8f841c02"}, @@ -1612,18 +1671,18 @@ files = [ ] [package.extras] -all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0) ; python_version <= \"3.12\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""] +interpolatable = ["munkres", "pycairo", "scipy"] lxml = ["lxml (>=4.0)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] repacker = ["uharfbuzz (>=0.23.0)"] symfont = ["sympy"] -type1 = ["xattr ; sys_platform == \"darwin\""] +type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] -unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""] -woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"] +unicode = ["unicodedata2 (>=15.1.0)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "frozenlist" @@ -1632,6 +1691,7 @@ description = "A list-like structure which implements collections.abc.MutableSeq optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "frozenlist-1.5.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5b6a66c18b5b9dd261ca98dffcb826a525334b2f29e7caa54e182255c5f6a65a"}, {file = "frozenlist-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d1b3eb7b05ea246510b43a7e53ed1653e55c2121019a97e60cad7efb881a97bb"}, @@ -1734,6 +1794,7 @@ description = "File-system specification" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2"}, {file = "fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f"}, @@ -1777,6 +1838,7 @@ description = "A clean customisable Sphinx documentation theme." optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "furo-2024.8.6-py3-none-any.whl", hash = "sha256:6cd97c58b47813d3619e63e9081169880fbe331f0ca883c871ff1f3f11814f5c"}, {file = "furo-2024.8.6.tar.gz", hash = "sha256:b63e4cee8abfc3136d3bc03a3d45a76a850bada4d6374d24c1716b0e01394a01"}, @@ -1795,6 +1857,7 @@ description = "Google Ai Generativelanguage API client library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_ai_generativelanguage-0.6.18-py3-none-any.whl", hash = "sha256:13d8174fea90b633f520789d32df7b422058fd5883b022989c349f1017db7fcf"}, {file = "google_ai_generativelanguage-0.6.18.tar.gz", hash = "sha256:274ba9fcf69466ff64e971d565884434388e523300afd468fc8e3033cd8e606e"}, @@ -1813,6 +1876,7 @@ description = "Google API client core library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_api_core-2.24.2-py3-none-any.whl", hash = "sha256:810a63ac95f3c441b7c0e43d344e372887f62ce9071ba972eacf32672e072de9"}, {file = "google_api_core-2.24.2.tar.gz", hash = "sha256:81718493daf06d96d6bc76a91c23874dbf2fac0adbbf542831b805ee6e974696"}, @@ -1822,12 +1886,12 @@ files = [ google-auth = ">=2.14.1,<3.0.0" googleapis-common-protos = ">=1.56.2,<2.0.0" grpcio = [ - {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, + {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, ] grpcio-status = [ + {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, - {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "extra == \"grpc\""}, ] proto-plus = ">=1.22.3,<2.0.0" protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" @@ -1835,7 +1899,7 @@ requests = ">=2.18.0,<3.0.0" [package.extras] async-rest = ["google-auth[aiohttp] (>=2.35.0,<3.0.dev0)"] -grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev) ; python_version >= \"3.11\"", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0) ; python_version >= \"3.11\""] +grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0)"] grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] @@ -1846,6 +1910,7 @@ description = "Google Authentication Library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_auth-2.39.0-py2.py3-none-any.whl", hash = "sha256:0150b6711e97fb9f52fe599f55648950cc4540015565d8fbb31be2ad6e1548a2"}, {file = "google_auth-2.39.0.tar.gz", hash = "sha256:73222d43cdc35a3aeacbfdcaf73142a97839f10de930550d89ebfe1d0a00cde7"}, @@ -1859,11 +1924,11 @@ rsa = ">=3.1.4,<5" [package.extras] aiohttp = ["aiohttp (>=3.6.2,<4.0.0)", "requests (>=2.20.0,<3.0.0)"] enterprise-cert = ["cryptography", "pyopenssl"] -pyjwt = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyjwt (>=2.0)"] -pyopenssl = ["cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"] +pyjwt = ["cryptography (<39.0.0)", "cryptography (>=38.0.3)", "pyjwt (>=2.0)"] +pyopenssl = ["cryptography (<39.0.0)", "cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"] reauth = ["pyu2f (>=0.1.5)"] requests = ["requests (>=2.20.0,<3.0.0)"] -testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"] +testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"] urllib3 = ["packaging", "urllib3"] [[package]] @@ -1873,6 +1938,7 @@ description = "Google Cloud API client core library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_cloud_core-2.4.3-py2.py3-none-any.whl", hash = "sha256:5130f9f4c14b4fafdff75c79448f9495cfade0d8775facf1b09c3bf67e027f6e"}, {file = "google_cloud_core-2.4.3.tar.gz", hash = "sha256:1fab62d7102844b278fe6dead3af32408b1df3eb06f5c7e8634cbd40edc4da53"}, @@ -1892,6 +1958,7 @@ description = "Google Cloud Storage API client library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_cloud_storage-3.1.0-py2.py3-none-any.whl", hash = "sha256:eaf36966b68660a9633f03b067e4a10ce09f1377cae3ff9f2c699f69a81c66c6"}, {file = "google_cloud_storage-3.1.0.tar.gz", hash = "sha256:944273179897c7c8a07ee15f2e6466a02da0c7c4b9ecceac2a26017cb2972049"}, @@ -1916,6 +1983,7 @@ description = "A python wrapper of the C library 'Google CRC32C'" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_crc32c-1.7.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:b07d48faf8292b4db7c3d64ab86f950c2e94e93a11fd47271c28ba458e4a0d76"}, {file = "google_crc32c-1.7.1-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:7cc81b3a2fbd932a4313eb53cc7d9dde424088ca3a0337160f35d91826880c1d"}, @@ -1963,6 +2031,7 @@ description = "GenAI Python SDK" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_genai-1.13.0-py3-none-any.whl", hash = "sha256:e88417c95f333827ed051282803db627192fd8e5fdf5b49b6f26644a67775d1a"}, {file = "google_genai-1.13.0.tar.gz", hash = "sha256:de7b4960d361c7ea85b715ce0e07fe34903d94affa82d566d9eb04e8f92512b9"}, @@ -1984,6 +2053,7 @@ description = "Utilities for Google Media Downloads and Resumable Uploads" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "google_resumable_media-2.7.2-py2.py3-none-any.whl", hash = "sha256:3ce7551e9fe6d99e9a126101d2536612bb73486721951e9562fee0f90c6ababa"}, {file = "google_resumable_media-2.7.2.tar.gz", hash = "sha256:5280aed4629f2b60b847b0d42f9857fd4935c11af266744df33d8074cae92fe0"}, @@ -2003,6 +2073,7 @@ description = "Common protobufs used in Google APIs" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"}, {file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"}, @@ -2021,6 +2092,7 @@ description = "An implementation of Gaussian Processes in Pytorch" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "gpytorch-1.14-py3-none-any.whl", hash = "sha256:9b17ff7bd70dc39c3a3c03eb2bb9a87aa4b840c26e71ac68d120b96487bf5a12"}, {file = "gpytorch-1.14.tar.gz", hash = "sha256:032cc11e6a46e1e4bc7763fcef318cc830aceaea85a7289f27b2288c7a339a8d"}, @@ -2048,7 +2120,7 @@ description = "Lightweight in-process concurrent programming" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\"" +markers = "(platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "greenlet-3.2.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:b7a7b7f2bad3ca72eb2fa14643f1c4ca11d115614047299d89bc24a3b11ddd09"}, {file = "greenlet-3.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:60e77242e38e99ecaede853755bbd8165e0b20a2f1f3abcaa6f0dceb826a7411"}, @@ -2118,6 +2190,7 @@ description = "Signatures for entire Python programs. Extract the structure, the optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "griffe-1.12.1-py3-none-any.whl", hash = "sha256:2d7c12334de00089c31905424a00abcfd931b45b8b516967f224133903d302cc"}, {file = "griffe-1.12.1.tar.gz", hash = "sha256:29f5a6114c0aeda7d9c86a570f736883f8a2c5b38b57323d56b3d1c000565567"}, @@ -2133,6 +2206,7 @@ description = "HTTP/2-based RPC framework" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "grpcio-1.71.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:c200cb6f2393468142eb50ab19613229dcc7829b5ccee8b658a36005f6669fdd"}, {file = "grpcio-1.71.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:b2266862c5ad664a380fbbcdbdb8289d71464c42a8c29053820ee78ba0119e5d"}, @@ -2197,6 +2271,7 @@ description = "Status proto mapping for gRPC" optional = false python-versions = ">=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "grpcio-status-1.62.3.tar.gz", hash = "sha256:289bdd7b2459794a12cf95dc0cb727bd4a1742c37bd823f760236c937e53a485"}, {file = "grpcio_status-1.62.3-py3-none-any.whl", hash = "sha256:f9049b762ba8de6b1086789d8315846e094edac2c50beaf462338b301a8fd4b8"}, @@ -2214,6 +2289,7 @@ description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"}, {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"}, @@ -2226,6 +2302,7 @@ description = "A minimal low-level HTTP client." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "httpcore-1.0.8-py3-none-any.whl", hash = "sha256:5254cf149bcb5f75e9d1b2b9f729ea4a4b883d1ad7379fc632b727cec23674be"}, {file = "httpcore-1.0.8.tar.gz", hash = "sha256:86e94505ed24ea06514883fd44d2bc02d90e77e7979c8eb71b90f41d364a1bad"}, @@ -2248,6 +2325,7 @@ description = "The next generation HTTP client." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"}, {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"}, @@ -2260,7 +2338,7 @@ httpcore = "==1.*" idna = "*" [package.extras] -brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""] +brotli = ["brotli", "brotlicffi"] cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] @@ -2273,6 +2351,7 @@ description = "Consume Server-Sent Event (SSE) messages with HTTPX." optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "httpx_sse-0.4.1-py3-none-any.whl", hash = "sha256:cba42174344c3a5b06f255ce65b350880f962d99ead85e776f23c6618a377a37"}, {file = "httpx_sse-0.4.1.tar.gz", hash = "sha256:8f44d34414bc7b21bf3602713005c5df4917884f76072479b21f68befa4ea26e"}, @@ -2285,6 +2364,7 @@ description = "Client library to download and publish models, datasets and other optional = false python-versions = ">=3.8.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "huggingface_hub-0.30.2-py3-none-any.whl", hash = "sha256:68ff05969927058cfa41df4f2155d4bb48f5f54f719dd0390103eefa9b191e28"}, {file = "huggingface_hub-0.30.2.tar.gz", hash = "sha256:9a7897c5b6fd9dad3168a794a8998d6378210f5b9688d0dfc180b1a228dc2466"}, @@ -2321,6 +2401,7 @@ description = "A framework for elegantly configuring complex applications" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824"}, {file = "hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b"}, @@ -2338,6 +2419,7 @@ description = "File identification library for Python" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "identify-2.6.9-py2.py3-none-any.whl", hash = "sha256:c98b4322da415a8e5a70ff6e51fbc2d2932c015532d77e9f8537b4ba7813b150"}, {file = "identify-2.6.9.tar.gz", hash = "sha256:d40dfe3142a1421d8518e3d3985ef5ac42890683e32306ad614a29490abeb6bf"}, @@ -2353,6 +2435,7 @@ description = "Internationalized Domain Names in Applications (IDNA)" optional = false python-versions = ">=3.6" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"}, {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"}, @@ -2368,6 +2451,7 @@ description = "Iterative JSON parser with standard Python iterator interfaces" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ijson-3.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7f7a5250599c366369fbf3bc4e176f5daa28eb6bc7d6130d02462ed335361675"}, {file = "ijson-3.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f87a7e52f79059f9c58f6886c262061065eb6f7554a587be7ed3aa63e6b71b34"}, @@ -2472,6 +2556,7 @@ description = "Getting image size from png/jpeg/jpeg2000/gif file" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b"}, {file = "imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a"}, @@ -2484,6 +2569,7 @@ description = "Read metadata from Python packages" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"}, {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"}, @@ -2493,12 +2579,12 @@ files = [ zipp = ">=3.20" [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] perf = ["ipython"] -test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"] +test = ["flufl.flake8", "importlib_resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"] type = ["pytest-mypy"] [[package]] @@ -2508,6 +2594,7 @@ description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.8" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"}, {file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"}, @@ -2520,6 +2607,7 @@ description = "Framework for large language model evaluations" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "inspect_ai-0.3.88-py3-none-any.whl", hash = "sha256:231e3b63057d386f4b5f0fb1d28dee34329c6e8fa8f4cb6206a990ddae155521"}, {file = "inspect_ai-0.3.88.tar.gz", hash = "sha256:99789228b89f5074e3361af4f500ee9f56974460300e869e433ef1c29d066f50"}, @@ -2569,6 +2657,7 @@ description = "IPython Kernel for Jupyter" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"}, {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"}, @@ -2603,6 +2692,7 @@ description = "IPython: Productive Interactive Computing" optional = false python-versions = ">=3.10" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ipython-8.35.0-py3-none-any.whl", hash = "sha256:e6b7470468ba6f1f0a7b116bb688a3ece2f13e2f94138e508201fad677a788ba"}, {file = "ipython-8.35.0.tar.gz", hash = "sha256:d200b7d93c3f5883fc36ab9ce28a18249c7706e51347681f80a0aef9895f2520"}, @@ -2624,7 +2714,7 @@ typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""} [package.extras] all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"] black = ["black"] -doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli ; python_version < \"3.11\"", "typing_extensions"] +doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing_extensions"] kernel = ["ipykernel"] matplotlib = ["matplotlib"] nbconvert = ["nbconvert"] @@ -2642,6 +2732,7 @@ description = "A Python utility / library to sort Python imports." optional = false python-versions = ">=3.9.0" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "isort-6.0.1-py3-none-any.whl", hash = "sha256:2dc5d7f65c9678d94c88dfc29161a320eec67328bc97aad576874cb4be1e9615"}, {file = "isort-6.0.1.tar.gz", hash = "sha256:1cb5df28dfbc742e490c5e41bad6da41b805b0a8be7bc93cd0fb2a8a890ac450"}, @@ -2658,6 +2749,7 @@ description = "Type annotations and runtime checking for shape and dtype of JAX/ optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jaxtyping-0.3.1-py3-none-any.whl", hash = "sha256:44fbd091338f7f8d592e4e8c71123658feb35a23d07d095af67dbfa96010872d"}, {file = "jaxtyping-0.3.1.tar.gz", hash = "sha256:38bb47dc090696ed5959bf3c7e8659036d3cd2643f882c9208d5d9644ae789fe"}, @@ -2676,6 +2768,7 @@ description = "An autocompletion tool for Python that can be used for text edito optional = false python-versions = ">=3.6" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"}, {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"}, @@ -2696,6 +2789,7 @@ description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"}, {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"}, @@ -2714,6 +2808,7 @@ description = "Fast iterable JSON parser." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jiter-0.9.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:816ec9b60fdfd1fec87da1d7ed46c66c44ffec37ab2ef7de5b147b2fce3fd5ad"}, {file = "jiter-0.9.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9b1d3086f8a3ee0194ecf2008cf81286a5c3e540d977fa038ff23576c023c0ea"}, @@ -2800,6 +2895,7 @@ description = "JSON Matching Expressions" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"}, {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"}, @@ -2812,6 +2908,7 @@ description = "Lightweight pipelining with Python functions" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, @@ -2824,6 +2921,7 @@ description = "Library with helpers for the jsonlines file format" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonlines-4.0.0-py3-none-any.whl", hash = "sha256:185b334ff2ca5a91362993f42e83588a360cf95ce4b71a73548502bda52a7c55"}, {file = "jsonlines-4.0.0.tar.gz", hash = "sha256:0c6d2c09117550c089995247f605ae4cf77dd1533041d366351f6f298822ea74"}, @@ -2839,6 +2937,7 @@ description = "Apply JSON-Patches (RFC 6902)" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"}, {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"}, @@ -2854,6 +2953,7 @@ description = "Identify specific nodes in a JSON document (RFC 6901)" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"}, {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"}, @@ -2866,6 +2966,7 @@ description = "jsonref is a library for automatic dereferencing of JSON Referenc optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonref-1.1.0-py3-none-any.whl", hash = "sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9"}, {file = "jsonref-1.1.0.tar.gz", hash = "sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552"}, @@ -2878,6 +2979,7 @@ description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"}, {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"}, @@ -2900,6 +3002,7 @@ description = "The JSON Schema meta-schemas and vocabularies, exposed as a Regis optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"}, {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"}, @@ -2915,6 +3018,7 @@ description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"}, {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"}, @@ -2929,7 +3033,7 @@ traitlets = ">=5.3" [package.extras] docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] -test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] +test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-core" @@ -2938,6 +3042,7 @@ description = "Jupyter core package. A base package on which Jupyter projects re optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"}, {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"}, @@ -2959,6 +3064,7 @@ description = "Pygments theme using JupyterLab CSS variables" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"}, {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"}, @@ -2971,6 +3077,7 @@ description = "Jupyter notebooks as Markdown documents, Julia, Python or R scrip optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupytext-1.17.0-py3-none-any.whl", hash = "sha256:d75b7cd198b3640a12f9cdf4d610bb80c9f27a8c3318b00372f90d21466d40e1"}, {file = "jupytext-1.17.0.tar.gz", hash = "sha256:c74adf6d205a778f481189e986b6066af79bdeb69099f4138d933cc15c398fb6"}, @@ -3001,6 +3108,7 @@ description = "A fast implementation of the Cassowary constraint solver" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db"}, {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b"}, @@ -3091,6 +3199,7 @@ description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain-0.3.23-py3-none-any.whl", hash = "sha256:084f05ee7e80b7c3f378ebadd7309f2a37868ce2906fa0ae64365a67843ade3d"}, {file = "langchain-0.3.23.tar.gz", hash = "sha256:d95004afe8abebb52d51d6026270248da3f4b53d93e9bf699f76005e0c83ad34"}, @@ -3132,6 +3241,7 @@ description = "An integration package connecting AnthropicMessages and LangChain optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_anthropic-0.3.12-py3-none-any.whl", hash = "sha256:39764a0dc79c5777af6396a6160478a2b5b62eada965df778d041e68ac8aeeb9"}, {file = "langchain_anthropic-0.3.12.tar.gz", hash = "sha256:8f68461369f0c7707dea5950d6f68ce3faa8d49744bc97fb8d3a7c02e84fe840"}, @@ -3149,6 +3259,7 @@ description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_core-0.3.59-py3-none-any.whl", hash = "sha256:9686baaff43f2c8175535da13faf40e6866769015e93130c3c1e4243e7244d70"}, {file = "langchain_core-0.3.59.tar.gz", hash = "sha256:052a37cf298c505144f007e5aeede6ecff2dc92c827525d1ef59101eb3a4551c"}, @@ -3173,6 +3284,7 @@ description = "An integration package connecting Google's genai package and Lang optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_google_genai-2.1.4-py3-none-any.whl", hash = "sha256:a3fa3cf7fe9c1de77280f42fbdd22cfcc5fbeb0d60cd5be7a0e6c50a74f5ce73"}, {file = "langchain_google_genai-2.1.4.tar.gz", hash = "sha256:b52e10ea3daf1a65f70b73c78b78235466593de2aa9f4119fa887b804605efb7"}, @@ -3191,6 +3303,7 @@ description = "An integration package connecting OpenAI and LangChain" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_openai-0.3.13-py3-none-any.whl", hash = "sha256:2ca3f1865df32d03c3bd85c77f11f0ffd81b157b4e363291741c65c81463606a"}, {file = "langchain_openai-0.3.13.tar.gz", hash = "sha256:75038efbf686f4b5fe2b6bdb75c43790d563ecd61984fd1d51d6d51c53609d64"}, @@ -3208,6 +3321,7 @@ description = "LangChain text splitting utilities" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_text_splitters-0.3.8-py3-none-any.whl", hash = "sha256:e75cc0f4ae58dcf07d9f18776400cf8ade27fadd4ff6d264df6278bb302f6f02"}, {file = "langchain_text_splitters-0.3.8.tar.gz", hash = "sha256:116d4b9f2a22dda357d0b79e30acf005c5518177971c66a9f1ab0edfdb0f912e"}, @@ -3223,6 +3337,7 @@ description = "A client library for accessing langfuse" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langfuse-3.2.8-py3-none-any.whl", hash = "sha256:60ffa8f4ff5225019a51c1030f38810285afcdee2fc7ca203e2f7b2f9a606ffa"}, {file = "langfuse-3.2.8.tar.gz", hash = "sha256:93410d21ae27548cbc439ea9566ae75a48a3933cc6735468dd66d517a2702d59"}, @@ -3250,6 +3365,7 @@ description = "Client library to connect to the LangSmith LLM Tracing and Evalua optional = false python-versions = "<4.0,>=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langsmith-0.3.31-py3-none-any.whl", hash = "sha256:ee780ae3eac69998c336817c0b9f5ccfecaaaa3e67d94b7ef726b58ab3e72a25"}, {file = "langsmith-0.3.31.tar.gz", hash = "sha256:8d20bd08fa6c3bce54cb600ddc521cd218a1c3410f90d9266179bf83a7ff0897"}, @@ -3280,6 +3396,7 @@ description = "license-expression is a comprehensive utility library to parse, c optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "license_expression-30.4.1-py3-none-any.whl", hash = "sha256:679646bc3261a17690494a3e1cada446e5ee342dbd87dcfa4a0c24cc5dce13ee"}, {file = "license_expression-30.4.1.tar.gz", hash = "sha256:9f02105f9e0fcecba6a85dfbbed7d94ea1c3a70cf23ddbfb5adf3438a6f6fce0"}, @@ -3299,6 +3416,7 @@ description = "A linear operator implementation, primarily designed for finite-d optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "linear_operator-0.6-py3-none-any.whl", hash = "sha256:b7c30ce0f6599e19c1cb970e00811828df98783b20c1dd2f765928415012f41c"}, {file = "linear_operator-0.6.tar.gz", hash = "sha256:a9e2663879f1a2b28631bf7ef34892c4e5749893e711c8ef0ab0a39aff70a654"}, @@ -3322,6 +3440,7 @@ description = "Links recognition library with FULL unicode support." optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "linkify-it-py-2.0.3.tar.gz", hash = "sha256:68cda27e162e9215c17d786649d1da0021a451bdc436ef9e0fa0ba5234b9b048"}, {file = "linkify_it_py-2.0.3-py3-none-any.whl", hash = "sha256:6bcbc417b0ac14323382aef5c5192c0075bf8a9d6b41820a2b66371eac6b6d79"}, @@ -3343,6 +3462,7 @@ description = "Python port of markdown-it. Markdown parsing, done right!" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, @@ -3370,6 +3490,7 @@ description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.9" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"}, {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"}, @@ -3441,6 +3562,7 @@ description = "Python plotting package" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "matplotlib-3.10.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ff2ae14910be903f4a24afdbb6d7d3a6c44da210fc7d42790b87aeac92238a16"}, {file = "matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2"}, @@ -3499,6 +3621,7 @@ description = "Inline Matplotlib backend for Jupyter" optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"}, {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"}, @@ -3514,6 +3637,7 @@ description = "McCabe checker, plugin for flake8" optional = false python-versions = ">=3.6" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"}, {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"}, @@ -3526,6 +3650,7 @@ description = "Model Context Protocol SDK" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "mcp-1.13.0-py3-none-any.whl", hash = "sha256:8b1a002ebe6e17e894ec74d1943cc09aa9d23cb931bf58d49ab2e9fa6bb17e4b"}, {file = "mcp-1.13.0.tar.gz", hash = "sha256:70452f56f74662a94eb72ac5feb93997b35995e389b3a3a574e078bed2aa9ab3"}, @@ -3556,6 +3681,7 @@ description = "Collection of plugins for markdown-it-py" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mdit_py_plugins-0.4.2-py3-none-any.whl", hash = "sha256:0c673c3f889399a33b95e88d2f0d111b4447bdfea7f237dab2d488f459835636"}, {file = "mdit_py_plugins-0.4.2.tar.gz", hash = "sha256:5f2cd1fdb606ddf152d37ec30e46101a60512bc0e5fa1a7002c36647b09e26b5"}, @@ -3576,6 +3702,7 @@ description = "Markdown URL utilities" optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, @@ -3588,6 +3715,7 @@ description = "A sane and fast Markdown parser with useful plugins and renderers optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mistune-3.1.3-py3-none-any.whl", hash = "sha256:1a32314113cff28aa6432e99e522677c8587fd83e3d51c29b82a52409c842bd9"}, {file = "mistune-3.1.3.tar.gz", hash = "sha256:a7035c21782b2becb6be62f8f25d3df81ccb4d6fa477a6525b15af06539f02a0"}, @@ -3603,6 +3731,7 @@ description = "Python extension for MurmurHash (MurmurHash3), a set of fast and optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mmh3-5.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:eaf4ac5c6ee18ca9232238364d7f2a213278ae5ca97897cafaa123fcc7bb8bec"}, {file = "mmh3-5.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:48f9aa8ccb9ad1d577a16104834ac44ff640d8de8c0caed09a2300df7ce8460a"}, @@ -3702,6 +3831,7 @@ description = "Python library for arbitrary-precision floating-point arithmetic" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, @@ -3710,7 +3840,7 @@ files = [ [package.extras] develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] docs = ["sphinx"] -gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""] +gmpy = ["gmpy2 (>=2.1.0a4)"] tests = ["pytest (>=4.6)"] [[package]] @@ -3720,6 +3850,7 @@ description = "MessagePack serializer" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7ad442d527a7e358a469faf43fda45aaf4ac3249c8310a82f0ccff9164e5dccd"}, {file = "msgpack-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:74bed8f63f8f14d75eec75cf3d04ad581da6b914001b474a5d3cd3372c8cc27d"}, @@ -3794,6 +3925,7 @@ description = "multidict implementation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "multidict-6.4.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:32a998bd8a64ca48616eac5a8c1cc4fa38fb244a3facf2eeb14abe186e0f6cc5"}, {file = "multidict-6.4.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a54ec568f1fc7f3c313c2f3b16e5db346bf3660e1309746e7fccbbfded856188"}, @@ -3911,6 +4043,7 @@ description = "better multiprocessing and multithreading in Python" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"}, {file = "multiprocess-0.70.16-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d951bed82c8f73929ac82c61f01a7b5ce8f3e5ef40f5b52553b4f547ce2b08ec"}, @@ -3936,6 +4069,7 @@ description = "Optional static typing for Python" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mypy-1.15.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:979e4e1a006511dacf628e36fadfecbcc0160a8af6ca7dad2f5025529e082c13"}, {file = "mypy-1.15.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c4bb0e1bd29f7d34efcccd71cf733580191e9a264a2202b0239da95984c5b559"}, @@ -3990,6 +4124,7 @@ description = "Type system extensions for programs checked with the mypy type ch optional = false python-versions = ">=3.5" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"}, {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"}, @@ -4002,6 +4137,7 @@ description = "An extended [CommonMark](https://spec.commonmark.org/) compliant optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "myst_parser-2.0.0-py3-none-any.whl", hash = "sha256:7c36344ae39c8e740dad7fdabf5aa6fc4897a813083c6cc9990044eb93656b14"}, {file = "myst_parser-2.0.0.tar.gz", hash = "sha256:ea929a67a6a0b1683cdbe19b8d2e724cd7643f8aa3e7bb18dd65beac3483bead"}, @@ -4029,6 +4165,7 @@ description = "A client library for executing notebooks. Formerly nbconvert's Ex optional = false python-versions = ">=3.9.0" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d"}, {file = "nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193"}, @@ -4052,6 +4189,7 @@ description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Ou optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"}, {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"}, @@ -4089,6 +4227,7 @@ description = "The Jupyter Notebook format" optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"}, {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"}, @@ -4111,6 +4250,7 @@ description = "Run any standard Python code quality tool on a Jupyter Notebook" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbqa-1.9.1-py3-none-any.whl", hash = "sha256:95552d2f6c2c038136252a805aa78d85018aef922586270c3a074332737282e5"}, {file = "nbqa-1.9.1.tar.gz", hash = "sha256:a1f4bcf587c597302fed295951001fc4e1be4ce0e77e1ab1b25ac2fbe3db0cdd"}, @@ -4141,6 +4281,7 @@ description = "Jupyter Notebook Tools for Sphinx" optional = false python-versions = ">=3.6" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbsphinx-0.9.7-py3-none-any.whl", hash = "sha256:7292c3767fea29e405c60743eee5393682a83982ab202ff98f5eb2db02629da8"}, {file = "nbsphinx-0.9.7.tar.gz", hash = "sha256:abd298a686d55fa894ef697c51d44f24e53aa312dadae38e82920f250a5456fe"}, @@ -4161,6 +4302,7 @@ description = "Patch asyncio to allow nested event loops" optional = false python-versions = ">=3.5" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"}, {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"}, @@ -4173,6 +4315,7 @@ description = "Python package for creating and manipulating graphs and networks" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"}, {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"}, @@ -4193,6 +4336,7 @@ description = "Node.js virtual environment builder" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"}, {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"}, @@ -4205,6 +4349,7 @@ description = "Fundamental package for array computing in Python" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "numpy-2.2.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8146f3550d627252269ac42ae660281d673eb6f8b32f113538e0cc2a9aed42b9"}, {file = "numpy-2.2.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e642d86b8f956098b564a45e6f6ce68a22c2c97a04f5acd3f221f57b8cb850ae"}, @@ -4270,6 +4415,7 @@ description = "Sphinx extension to support docstrings in Numpy format" optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "numpydoc-1.8.0-py3-none-any.whl", hash = "sha256:72024c7fd5e17375dec3608a27c03303e8ad00c81292667955c6fea7a3ccf541"}, {file = "numpydoc-1.8.0.tar.gz", hash = "sha256:022390ab7464a44f8737f79f8b31ce1d3cfa4b4af79ccaa1aac5e8368db587fb"}, @@ -4281,7 +4427,7 @@ tabulate = ">=0.8.10" tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} [package.extras] -developer = ["pre-commit (>=3.3)", "tomli ; python_version < \"3.11\""] +developer = ["pre-commit (>=3.3)", "tomli"] doc = ["intersphinx-registry", "matplotlib (>=3.5)", "numpy (>=1.22)", "pydata-sphinx-theme (>=0.13.3)", "sphinx (>=7)"] test = ["matplotlib", "pytest", "pytest-cov"] @@ -4292,7 +4438,7 @@ description = "CUBLAS native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0f8aa1706812e00b9f19dfe0cdb3999b092ccb8ca168c0db5b8ea712456fd9b3"}, {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl", hash = "sha256:2fc8da60df463fdefa81e323eef2e36489e1c94335b5358bcb38360adf75ac9b"}, @@ -4306,7 +4452,7 @@ description = "CUDA profiling tools runtime libs." optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a"}, {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9dec60f5ac126f7bb551c055072b69d85392b13311fcc1bcda2202d172df30fb"}, @@ -4320,7 +4466,7 @@ description = "NVRTC native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"}, {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338"}, @@ -4334,7 +4480,7 @@ description = "CUDA Runtime native Libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"}, {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5"}, @@ -4348,7 +4494,7 @@ description = "cuDNN runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"}, {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"}, @@ -4364,7 +4510,7 @@ description = "CUFFT native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399"}, {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9"}, @@ -4381,7 +4527,7 @@ description = "CURAND native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1f173f09e3e3c76ab084aba0de819c49e56614feae5c12f69883f4ae9bb5fad9"}, {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a88f583d4e0bb643c49743469964103aa59f7f708d862c3ddb0fc07f851e3b8b"}, @@ -4395,7 +4541,7 @@ description = "CUDA solver native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e"}, {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260"}, @@ -4414,7 +4560,7 @@ description = "CUSPARSE native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_aarch64.whl", hash = "sha256:9d32f62896231ebe0480efd8a7f702e143c98cfaa0e8a76df3386c1ba2b54df3"}, {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1"}, @@ -4431,7 +4577,7 @@ description = "NVIDIA cuSPARSELt" optional = false python-versions = "*" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:067a7f6d03ea0d4841c85f0c6f1991c5dda98211f6302cb83a4ab234ee95bef8"}, {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df2c24502fd76ebafe7457dbc4716b2fec071aabaed4fb7691a201cde03704d9"}, @@ -4445,7 +4591,7 @@ description = "NVIDIA Collective Communication Library (NCCL) Runtime" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:8579076d30a8c24988834445f8d633c697d42397e92ffc3f63fa26766d25e0a0"}, ] @@ -4457,7 +4603,7 @@ description = "Nvidia JIT LTO Library" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4abe7fef64914ccfa909bc2ba39739670ecc9e820c83ccc7a6ed414122599b83"}, {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"}, @@ -4471,7 +4617,7 @@ description = "NVIDIA Tools Extension" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7959ad635db13edf4fc65c06a6e9f9e55fc2f92596db928d169c0bb031e88ef3"}, {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a"}, @@ -4485,6 +4631,7 @@ description = "A flexible configuration library" optional = false python-versions = ">=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"}, {file = "omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7"}, @@ -4501,6 +4648,7 @@ description = "The official Python library for the openai API" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openai-1.102.0-py3-none-any.whl", hash = "sha256:d751a7e95e222b5325306362ad02a7aa96e1fab3ed05b5888ce1c7ca63451345"}, {file = "openai-1.102.0.tar.gz", hash = "sha256:2e0153bcd64a6523071e90211cbfca1f2bbc5ceedd0993ba932a5869f93b7fc9"}, @@ -4529,6 +4677,7 @@ description = "OpenAI Agents SDK" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openai_agents-0.0.12-py3-none-any.whl", hash = "sha256:122d9b71b408c70ffc8cd9e78751cc765ef104938fb937484d5b163790132e31"}, {file = "openai_agents-0.0.12.tar.gz", hash = "sha256:5d889a5abb2e335dc21d5c11cf8b133667d919905259a35fd1405c5b6a8c484f"}, @@ -4546,7 +4695,7 @@ typing-extensions = ">=4.12.2,<5" [package.extras] litellm = ["litellm (>=1.65.0,<2)"] viz = ["graphviz (>=0.17)"] -voice = ["numpy (>=2.2.0,<3) ; python_version >= \"3.10\"", "websockets (>=15.0,<16)"] +voice = ["numpy (>=2.2.0,<3)", "websockets (>=15.0,<16)"] [[package]] name = "openlit" @@ -4555,6 +4704,7 @@ description = "OpenTelemetry-native Auto instrumentation library for monitoring optional = false python-versions = "<4.0.0,>=3.8.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openlit-1.35.0-py3-none-any.whl", hash = "sha256:aae42b450e760daa420b8ffbbdac6097f0a0f7638b2ceefd62e57e0b6ccd6a25"}, {file = "openlit-1.35.0.tar.gz", hash = "sha256:3811d3e87fe6b2c619ce776ab451c8b21bf5af62a1a0db75f11f14d72a786f48"}, @@ -4596,6 +4746,7 @@ description = "OpenTelemetry Python API" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_api-1.36.0-py3-none-any.whl", hash = "sha256:02f20bcacf666e1333b6b1f04e647dc1d5111f86b8e510238fcc56d7762cda8c"}, {file = "opentelemetry_api-1.36.0.tar.gz", hash = "sha256:9a72572b9c416d004d492cbc6e61962c0501eaf945ece9b5a0f56597d8348aa0"}, @@ -4612,6 +4763,7 @@ description = "OpenTelemetry Collector Exporters" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp-1.36.0-py3-none-any.whl", hash = "sha256:de93b7c45bcc78296998775d52add7c63729e83ef2cd6560730a6b336d7f6494"}, {file = "opentelemetry_exporter_otlp-1.36.0.tar.gz", hash = "sha256:72f166ea5a8923ac42889337f903e93af57db8893de200369b07401e98e4e06b"}, @@ -4628,6 +4780,7 @@ description = "OpenTelemetry Protobuf encoding" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_common-1.36.0-py3-none-any.whl", hash = "sha256:0fc002a6ed63eac235ada9aa7056e5492e9a71728214a61745f6ad04b923f840"}, {file = "opentelemetry_exporter_otlp_proto_common-1.36.0.tar.gz", hash = "sha256:6c496ccbcbe26b04653cecadd92f73659b814c6e3579af157d8716e5f9f25cbf"}, @@ -4643,6 +4796,7 @@ description = "OpenTelemetry Collector Protobuf over gRPC Exporter" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_grpc-1.36.0-py3-none-any.whl", hash = "sha256:734e841fc6a5d6f30e7be4d8053adb703c70ca80c562ae24e8083a28fadef211"}, {file = "opentelemetry_exporter_otlp_proto_grpc-1.36.0.tar.gz", hash = "sha256:b281afbf7036b325b3588b5b6c8bb175069e3978d1bd24071f4a59d04c1e5bbf"}, @@ -4664,6 +4818,7 @@ description = "OpenTelemetry Collector Protobuf over HTTP Exporter" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_http-1.36.0-py3-none-any.whl", hash = "sha256:3d769f68e2267e7abe4527f70deb6f598f40be3ea34c6adc35789bea94a32902"}, {file = "opentelemetry_exporter_otlp_proto_http-1.36.0.tar.gz", hash = "sha256:dd3637f72f774b9fc9608ab1ac479f8b44d09b6fb5b2f3df68a24ad1da7d356e"}, @@ -4685,6 +4840,7 @@ description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Py optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation-0.57b0-py3-none-any.whl", hash = "sha256:9109280f44882e07cec2850db28210b90600ae9110b42824d196de357cbddf7e"}, {file = "opentelemetry_instrumentation-0.57b0.tar.gz", hash = "sha256:f2a30135ba77cdea2b0e1df272f4163c154e978f57214795d72f40befd4fcf05"}, @@ -4703,6 +4859,7 @@ description = "OpenTelemetry aiohttp client instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_aiohttp_client-0.57b0-py3-none-any.whl", hash = "sha256:fbec3966da845b9a650a72f8fc7f7ac4dd25365317c850a188611e1c92606ac8"}, {file = "opentelemetry_instrumentation_aiohttp_client-0.57b0.tar.gz", hash = "sha256:ce05e8ef7bc7016d4b0d8e47786b5cf5e6efd7ff0bea8e1ab6616ef5fa985f37"}, @@ -4725,6 +4882,7 @@ description = "ASGI instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_asgi-0.57b0-py3-none-any.whl", hash = "sha256:47debbde6af066a7e8e911f7193730d5e40d62effc1ac2e1119908347790a3ea"}, {file = "opentelemetry_instrumentation_asgi-0.57b0.tar.gz", hash = "sha256:a6f880b5d1838f65688fc992c65fbb1d3571f319d370990c32e759d3160e510b"}, @@ -4747,6 +4905,7 @@ description = "OpenTelemetry Instrumentation for Django" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_django-0.57b0-py3-none-any.whl", hash = "sha256:3d702d79a9ec0c836ccf733becf34630c6afb3c86c25c330c5b7601debe1e7c5"}, {file = "opentelemetry_instrumentation_django-0.57b0.tar.gz", hash = "sha256:df4116d2ea2c6bbbbf8853b843deb74d66bd0d573ddd372ec84fd60adaf977c6"}, @@ -4770,6 +4929,7 @@ description = "Falcon instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_falcon-0.57b0-py3-none-any.whl", hash = "sha256:948209be98a020ee5b2fa894191dd83ded5f983bd4d8b2ebc98c11eb25bf1150"}, {file = "opentelemetry_instrumentation_falcon-0.57b0.tar.gz", hash = "sha256:8b37cd9868e7ef276ce85a6d95a16d91bcc67f3819060a1beea2ff5ba8fb9bc1"}, @@ -4793,6 +4953,7 @@ description = "OpenTelemetry FastAPI Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_fastapi-0.57b0-py3-none-any.whl", hash = "sha256:61e6402749ffe0bfec582e58155e0d81dd38723cd9bc4562bca1acca80334006"}, {file = "opentelemetry_instrumentation_fastapi-0.57b0.tar.gz", hash = "sha256:73ac22f3c472a8f9cb21d1fbe5a4bf2797690c295fff4a1c040e9b1b1688a105"}, @@ -4815,6 +4976,7 @@ description = "Flask instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_flask-0.57b0-py3-none-any.whl", hash = "sha256:5ecd614f194825725b61ee9ba8e37dcd4d3f9b5d40fef759df8650d6a91b1cb9"}, {file = "opentelemetry_instrumentation_flask-0.57b0.tar.gz", hash = "sha256:c5244a40b03664db966d844a32f43c900181431b77929be62a68d4907e86ed25"}, @@ -4838,6 +5000,7 @@ description = "OpenTelemetry HTTPX Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_httpx-0.57b0-py3-none-any.whl", hash = "sha256:729fef97624016d3e5b03b71f51c9a1a2f7480b023373186d643fbed7496712a"}, {file = "opentelemetry_instrumentation_httpx-0.57b0.tar.gz", hash = "sha256:ea5669cdb17185f8d247c2dbf756ae5b95b53110ca4d58424f2be5cc7223dbdd"}, @@ -4860,6 +5023,7 @@ description = "OpenTelemetry Pyramid instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_pyramid-0.57b0-py3-none-any.whl", hash = "sha256:6b2fa0fe8082e7cfdd054c32a88c0456766aeb7567f0ad2ed0df8e1eb568d2a7"}, {file = "opentelemetry_instrumentation_pyramid-0.57b0.tar.gz", hash = "sha256:dd87af46ae9a96b8a27da170d26aa9e7a903c2cbf87030250ca21b4ab383a729"}, @@ -4883,6 +5047,7 @@ description = "OpenTelemetry requests instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_requests-0.57b0-py3-none-any.whl", hash = "sha256:66a576ac8080724ddc8a14c39d16bb5f430991bd504fdbea844c7a063f555971"}, {file = "opentelemetry_instrumentation_requests-0.57b0.tar.gz", hash = "sha256:193bd3fd1f14737721876fb1952dffc7d43795586118df633a91ecd9057446ff"}, @@ -4904,6 +5069,7 @@ description = "OpenTelemetry Starlette Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_starlette-0.57b0-py3-none-any.whl", hash = "sha256:be835509cc4192b5c4aa4fdafbcbb18a028bc329746d9c12e4693ad81f9c2cac"}, {file = "opentelemetry_instrumentation_starlette-0.57b0.tar.gz", hash = "sha256:d01c411f0189fe530c574f4392f83941a7845839af7ae6456ad00ac2aeb6441c"}, @@ -4926,6 +5092,7 @@ description = "Tornado instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_tornado-0.57b0-py3-none-any.whl", hash = "sha256:25ab57b3197c91f881ae7c6ab088652bbeb54f3b703d15de374cc62887a06d65"}, {file = "opentelemetry_instrumentation_tornado-0.57b0.tar.gz", hash = "sha256:dcaa23dffb3bfb500de1013a75e4bcbca23870bd30d10ff6292a62aa03e8c2aa"}, @@ -4947,6 +5114,7 @@ description = "OpenTelemetry urllib instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_urllib-0.57b0-py3-none-any.whl", hash = "sha256:bb3a01172109a6f56bfcc38ea83b9d4a61c4c2cac6b9a190e757063daadf545c"}, {file = "opentelemetry_instrumentation_urllib-0.57b0.tar.gz", hash = "sha256:657225ceae8bb52b67bd5c26dcb8a33f0efb041f1baea4c59dbd1adbc63a4162"}, @@ -4965,6 +5133,7 @@ description = "OpenTelemetry urllib3 instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_urllib3-0.57b0-py3-none-any.whl", hash = "sha256:337ecac6df3ff92026b51c64df7dd4a3fff52f2dc96036ea9371670243bf83c6"}, {file = "opentelemetry_instrumentation_urllib3-0.57b0.tar.gz", hash = "sha256:f49d8c3d1d81ae56304a08b14a7f564d250733ed75cd2210ccef815b5af2eea1"}, @@ -4987,6 +5156,7 @@ description = "WSGI Middleware for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_wsgi-0.57b0-py3-none-any.whl", hash = "sha256:b9cf0c6e61489f7503fc17ef04d169bd214e7a825650ee492f5d2b4d73b17b54"}, {file = "opentelemetry_instrumentation_wsgi-0.57b0.tar.gz", hash = "sha256:d7e16b3b87930c30fc4c1bbc8b58c5dd6eefade493a3a5e7343bc24d572bc5b7"}, @@ -5005,6 +5175,7 @@ description = "OpenTelemetry Python Proto" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_proto-1.36.0-py3-none-any.whl", hash = "sha256:151b3bf73a09f94afc658497cf77d45a565606f62ce0c17acb08cd9937ca206e"}, {file = "opentelemetry_proto-1.36.0.tar.gz", hash = "sha256:0f10b3c72f74c91e0764a5ec88fd8f1c368ea5d9c64639fb455e2854ef87dd2f"}, @@ -5020,6 +5191,7 @@ description = "OpenTelemetry Python SDK" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_sdk-1.36.0-py3-none-any.whl", hash = "sha256:19fe048b42e98c5c1ffe85b569b7073576ad4ce0bcb6e9b4c6a39e890a6c45fb"}, {file = "opentelemetry_sdk-1.36.0.tar.gz", hash = "sha256:19c8c81599f51b71670661ff7495c905d8fdf6976e41622d5245b791b06fa581"}, @@ -5037,6 +5209,7 @@ description = "OpenTelemetry Semantic Conventions" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_semantic_conventions-0.57b0-py3-none-any.whl", hash = "sha256:757f7e76293294f124c827e514c2a3144f191ef175b069ce8d1211e1e38e9e78"}, {file = "opentelemetry_semantic_conventions-0.57b0.tar.gz", hash = "sha256:609a4a79c7891b4620d64c7aac6898f872d790d75f22019913a660756f27ff32"}, @@ -5053,6 +5226,7 @@ description = "Web util for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_util_http-0.57b0-py3-none-any.whl", hash = "sha256:e54c0df5543951e471c3d694f85474977cd5765a3b7654398c83bab3d2ffb8e9"}, {file = "opentelemetry_util_http-0.57b0.tar.gz", hash = "sha256:f7417595ead0eb42ed1863ec9b2f839fc740368cd7bbbfc1d0a47bc1ab0aba11"}, @@ -5065,7 +5239,7 @@ description = "Fast, correct Python JSON library supporting dataclasses, datetim optional = false python-versions = ">=3.9" groups = ["main"] -markers = "platform_python_implementation != \"PyPy\"" +markers = "platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "orjson-3.10.16-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:4cb473b8e79154fa778fb56d2d73763d977be3dcc140587e07dbc545bbfc38f8"}, {file = "orjson-3.10.16-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:622a8e85eeec1948690409a19ca1c7d9fd8ff116f4861d261e6ae2094fe59a00"}, @@ -5144,6 +5318,7 @@ description = "A purl aka. Package URL parser and builder" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "packageurl_python-0.16.0-py3-none-any.whl", hash = "sha256:5c3872638b177b0f1cf01c3673017b7b27ebee485693ae12a8bed70fa7fa7c35"}, {file = "packageurl_python-0.16.0.tar.gz", hash = "sha256:69e3bf8a3932fe9c2400f56aaeb9f86911ecee2f9398dbe1b58ec34340be365d"}, @@ -5162,6 +5337,7 @@ description = "Core utilities for Python packages" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"}, {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"}, @@ -5174,6 +5350,7 @@ description = "Powerful data structures for data analysis, time series, and stat optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"}, {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"}, @@ -5221,9 +5398,9 @@ files = [ [package.dependencies] numpy = [ - {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, - {version = ">=1.23.2", markers = "python_version == \"3.11\""}, {version = ">=1.22.4", markers = "python_version < \"3.11\""}, + {version = ">=1.23.2", markers = "python_version == \"3.11\""}, + {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -5261,6 +5438,7 @@ description = "Utilities for writing pandoc filters in python" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"}, {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"}, @@ -5273,6 +5451,7 @@ description = "A Python Parser" optional = false python-versions = ">=3.6" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"}, {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"}, @@ -5289,6 +5468,7 @@ description = "Utility library for gitignore style pattern matching of file path optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"}, {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"}, @@ -5301,6 +5481,7 @@ description = "Python Build Reasonableness" optional = false python-versions = ">=2.6" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pbr-6.1.1-py2.py3-none-any.whl", hash = "sha256:38d4daea5d9fa63b3f626131b9d34947fd0c8be9b05a29276870580050a25a76"}, {file = "pbr-6.1.1.tar.gz", hash = "sha256:93ea72ce6989eb2eed99d0f75721474f69ad88128afdef5ac377eb797c4bf76b"}, @@ -5316,7 +5497,7 @@ description = "Pexpect allows easy control of interactive console applications." optional = false python-versions = "*" groups = ["docs", "test"] -markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\"" +markers = "(sys_platform != \"win32\" and sys_platform != \"emscripten\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"}, {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"}, @@ -5332,6 +5513,7 @@ description = "Python Imaging Library (Fork)" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pillow-11.2.1-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:d57a75d53922fc20c165016a20d9c44f73305e67c351bbc60d1adaf662e74047"}, {file = "pillow-11.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:127bf6ac4a5b58b3d32fc8289656f77f80567d65660bc46f72c0d77e6600cc95"}, @@ -5422,7 +5604,7 @@ fpx = ["olefile"] mic = ["olefile"] test-arrow = ["pyarrow"] tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "trove-classifiers (>=2024.10.12)"] -typing = ["typing-extensions ; python_version < \"3.10\""] +typing = ["typing-extensions"] xmp = ["defusedxml"] [[package]] @@ -5432,6 +5614,7 @@ description = "The PyPA recommended tool for installing Python packages." optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip-25.0.1-py3-none-any.whl", hash = "sha256:c46efd13b6aa8279f33f2864459c8ce587ea6a1a59ee20de055868d8f7688f7f"}, {file = "pip-25.0.1.tar.gz", hash = "sha256:88f96547ea48b940a3a385494e181e29fb8637898f88d88737c5049780f196ea"}, @@ -5444,6 +5627,7 @@ description = "An unofficial, importable pip API" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip_api-0.0.34-py3-none-any.whl", hash = "sha256:8b2d7d7c37f2447373aa2cf8b1f60a2f2b27a84e1e9e0294a3f6ef10eb3ba6bb"}, {file = "pip_api-0.0.34.tar.gz", hash = "sha256:9b75e958f14c5a2614bae415f2adf7eeb54d50a2cfbe7e24fd4826471bac3625"}, @@ -5459,6 +5643,7 @@ description = "A tool for scanning Python environments for known vulnerabilities optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip_audit-2.9.0-py3-none-any.whl", hash = "sha256:348b16e60895749a0839875d7cc27ebd692e1584ebe5d5cb145941c8e25a80bd"}, {file = "pip_audit-2.9.0.tar.gz", hash = "sha256:0b998410b58339d7a231e5aa004326a294e4c7c6295289cdc9d5e1ef07b1f44d"}, @@ -5488,6 +5673,7 @@ description = "pip requirements parser - a mostly correct pip requirements parsi optional = false python-versions = ">=3.6.0" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip-requirements-parser-32.0.1.tar.gz", hash = "sha256:b4fa3a7a0be38243123cf9d1f3518da10c51bdb165a2b2985566247f9155a7d3"}, {file = "pip_requirements_parser-32.0.1-py3-none-any.whl", hash = "sha256:4659bc2a667783e7a15d190f6fccf8b2486685b6dba4c19c3876314769c57526"}, @@ -5508,6 +5694,7 @@ description = "A small Python package for determining appropriate platform-speci optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "platformdirs-4.3.7-py3-none-any.whl", hash = "sha256:a03875334331946f13c549dbd8f4bac7a13a50a895a0eb1e8c6a8ace80d40a94"}, {file = "platformdirs-4.3.7.tar.gz", hash = "sha256:eb437d586b6a0986388f0d6f74aa0cde27b48d0e3d66843640bfb6bdcdb6e351"}, @@ -5525,6 +5712,7 @@ description = "plugin and hook calling mechanisms for python" optional = false python-versions = ">=3.8" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"}, {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"}, @@ -5541,6 +5729,7 @@ description = "A framework for managing and maintaining multi-language pre-commi optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pre_commit-4.2.0-py2.py3-none-any.whl", hash = "sha256:a009ca7205f1eb497d10b845e52c838a98b6cdd2102a6c8e4540e94ee75c58bd"}, {file = "pre_commit-4.2.0.tar.gz", hash = "sha256:601283b9757afd87d40c4c4a9b2b5de9637a8ea02eaff7adc2d0fb4e04841146"}, @@ -5560,6 +5749,7 @@ description = "Library for building powerful interactive command lines in Python optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07"}, {file = "prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed"}, @@ -5575,6 +5765,7 @@ description = "Accelerated property cache" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "propcache-0.3.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f27785888d2fdd918bc36de8b8739f2d6c791399552333721b58193f68ea3e98"}, {file = "propcache-0.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4e89cde74154c7b5957f87a355bb9c8ec929c167b59c83d90654ea36aeb6180"}, @@ -5683,6 +5874,7 @@ description = "Beautiful, Pythonic protocol buffers" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"}, {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"}, @@ -5701,6 +5893,7 @@ description = "" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "protobuf-5.29.5-cp310-abi3-win32.whl", hash = "sha256:3f1c6468a2cfd102ff4703976138844f78ebd1fb45f49011afc5139e9e283079"}, {file = "protobuf-5.29.5-cp310-abi3-win_amd64.whl", hash = "sha256:3f76e3a3675b4a4d867b52e4a5f5b78a2ef9565549d4037e06cf7b0942b1d3fc"}, @@ -5722,6 +5915,7 @@ description = "Cross-platform lib for process and system monitoring in Python. optional = false python-versions = ">=3.6" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25"}, {file = "psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da"}, @@ -5746,7 +5940,7 @@ description = "Run a subprocess in a pseudo terminal" optional = false python-versions = "*" groups = ["docs", "test"] -markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\"" +markers = "(sys_platform != \"win32\" and sys_platform != \"emscripten\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"}, {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"}, @@ -5759,6 +5953,7 @@ description = "Safely evaluate AST nodes without side effects" optional = false python-versions = "*" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"}, {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"}, @@ -5774,6 +5969,7 @@ description = "Library for serializing and deserializing Python Objects to and f optional = false python-versions = "<4.0,>=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "py_serializable-2.0.0-py3-none-any.whl", hash = "sha256:1721e4c0368adeec965c183168da4b912024702f19e15e13f8577098b9a4f8fe"}, {file = "py_serializable-2.0.0.tar.gz", hash = "sha256:e9e6491dd7d29c31daf1050232b57f9657f9e8a43b867cca1ff204752cf420a5"}, @@ -5789,6 +5985,7 @@ description = "Python library for Apache Arrow" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyarrow-19.0.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:fc28912a2dc924dddc2087679cc8b7263accc71b9ff025a1362b004711661a69"}, {file = "pyarrow-19.0.1-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:fca15aabbe9b8355800d923cc2e82c8ef514af321e18b437c3d782aa884eaeec"}, @@ -5844,6 +6041,7 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"}, {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"}, @@ -5856,6 +6054,7 @@ description = "A collection of ASN.1-based protocols modules" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"}, {file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"}, @@ -5871,6 +6070,7 @@ description = "Python style guide checker" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pycodestyle-2.13.0-py2.py3-none-any.whl", hash = "sha256:35863c5974a271c7a726ed228a14a4f6daf49df369d8c50cd9a6f58a5e143ba9"}, {file = "pycodestyle-2.13.0.tar.gz", hash = "sha256:c8415bf09abe81d9c7f872502a6eee881fbe85d8763dd5b9924bb0a01d67efae"}, @@ -5887,7 +6087,7 @@ files = [ {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"}, {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"}, ] -markers = {main = "platform_python_implementation == \"PyPy\"", docs = "implementation_name == \"pypy\""} +markers = {main = "platform_python_implementation == \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", docs = "implementation_name == \"pypy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")"} [[package]] name = "pydantic" @@ -5896,6 +6096,7 @@ description = "Data validation using Python type hints" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pydantic-2.11.3-py3-none-any.whl", hash = "sha256:a082753436a07f9ba1289c6ffa01cd93db3548776088aa917cc43b63f68fa60f"}, {file = "pydantic-2.11.3.tar.gz", hash = "sha256:7471657138c16adad9322fe3070c0116dd6c3ad8d649300e3cbdfe91f4db4ec3"}, @@ -5909,7 +6110,7 @@ typing-inspection = ">=0.4.0" [package.extras] email = ["email-validator (>=2.0.0)"] -timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""] +timezone = ["tzdata"] [[package]] name = "pydantic-core" @@ -5918,6 +6119,7 @@ description = "Core functionality for Pydantic validation and serialization" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pydantic_core-2.33.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3077cfdb6125cc8dab61b155fdd714663e401f0e6883f9632118ec12cf42df26"}, {file = "pydantic_core-2.33.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8ffab8b2908d152e74862d276cf5017c81a2f3719f14e8e3e8d6b83fda863927"}, @@ -6030,6 +6232,7 @@ description = "Settings management using Pydantic" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "pydantic_settings-2.10.1-py3-none-any.whl", hash = "sha256:a60952460b99cf661dc25c29c0ef171721f98bfcb52ef8d9ea4c943d7c8cc796"}, {file = "pydantic_settings-2.10.1.tar.gz", hash = "sha256:06f0062169818d0f5524420a360d632d5857b83cffd4d42fe29597807a1614ee"}, @@ -6054,6 +6257,7 @@ description = "passive checker of Python programs" optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyflakes-3.3.2-py2.py3-none-any.whl", hash = "sha256:5039c8339cbb1944045f4ee5466908906180f13cc99cc9949348d10f82a5c32a"}, {file = "pyflakes-3.3.2.tar.gz", hash = "sha256:6dfd61d87b97fba5dcfaaf781171ac16be16453be6d816147989e7f6e6a9576b"}, @@ -6066,6 +6270,7 @@ description = "Pygments is a syntax highlighting package written in Python." optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c"}, {file = "pygments-2.19.1.tar.gz", hash = "sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f"}, @@ -6081,6 +6286,7 @@ description = "python code static checker" optional = false python-versions = ">=3.9.0" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pylint-3.3.6-py3-none-any.whl", hash = "sha256:8b7c2d3e86ae3f94fb27703d521dd0b9b6b378775991f504d7c3a6275aa0a6a6"}, {file = "pylint-3.3.6.tar.gz", hash = "sha256:b634a041aac33706d56a0d217e6587228c66427e20ec21a019bc4cdee48c040a"}, @@ -6090,9 +6296,9 @@ files = [ astroid = ">=3.3.8,<=3.4.0.dev0" colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""} dill = [ - {version = ">=0.3.7", markers = "python_version >= \"3.12\""}, - {version = ">=0.3.6", markers = "python_version == \"3.11\""}, {version = ">=0.2", markers = "python_version < \"3.11\""}, + {version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""}, + {version = ">=0.3.7", markers = "python_version >= \"3.12\""}, ] isort = ">=4.2.5,<5.13 || >5.13,<7" mccabe = ">=0.6,<0.8" @@ -6111,6 +6317,7 @@ description = "pyparsing module - Classes and methods to define and execute pars optional = false python-versions = ">=3.9" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyparsing-3.2.3-py3-none-any.whl", hash = "sha256:a749938e02d6fd0b59b356ca504a24982314bb090c383e3cf201c95ef7e2bfcf"}, {file = "pyparsing-3.2.3.tar.gz", hash = "sha256:b9c13f1ab8b3b542f72e28f634bad4de758ab3ce4546e4301970ad6fa77c38be"}, @@ -6126,6 +6333,7 @@ description = "pytest: simple powerful testing with Python" optional = false python-versions = ">=3.8" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"}, {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"}, @@ -6149,6 +6357,7 @@ description = "Pytest plugin for measuring coverage." optional = false python-versions = ">=3.6" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest-cov-3.0.0.tar.gz", hash = "sha256:e7f0f5b1617d2210a2cabc266dfe2f4c75a8d32fb89eafb7ad9d06f6d076d470"}, {file = "pytest_cov-3.0.0-py3-none-any.whl", hash = "sha256:578d5d15ac4a25e5f961c938b85a05b09fdaae9deef3bb6de9a6e766622ca7a6"}, @@ -6168,6 +6377,7 @@ description = "pytest plugin that allows you to add environment variables." optional = false python-versions = ">=3.8" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest_env-1.1.5-py3-none-any.whl", hash = "sha256:ce90cf8772878515c24b31cd97c7fa1f4481cd68d588419fd45f10ecaee6bc30"}, {file = "pytest_env-1.1.5.tar.gz", hash = "sha256:91209840aa0e43385073ac464a554ad2947cc2fd663a9debf88d03b01e0cc1cf"}, @@ -6187,6 +6397,7 @@ description = "Thin-wrapper around the mock package for easier use with pytest" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest-mock-3.14.0.tar.gz", hash = "sha256:2719255a1efeceadbc056d6bf3df3d1c5015530fb40cf347c0f9afac88410bd0"}, {file = "pytest_mock-3.14.0-py3-none-any.whl", hash = "sha256:0b72c38033392a5f4621342fe11e9219ac11ec9d375f8e2a0c164539e0d70f6f"}, @@ -6205,6 +6416,7 @@ description = "Extensions to the standard Python datetime module" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, @@ -6220,6 +6432,7 @@ description = "Read key-value pairs from a .env file and set them as environment optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "python_dotenv-1.1.0-py3-none-any.whl", hash = "sha256:d7c01d9e2293916c18baf562d95698754b0dbbb5e74d457c45d4f6561fb9d55d"}, {file = "python_dotenv-1.1.0.tar.gz", hash = "sha256:41f90bc6f5f177fb41f53e87666db362025010eb28f60a01c9143bfa33a2b2d5"}, @@ -6235,6 +6448,7 @@ description = "A streaming multipart parser for Python" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104"}, {file = "python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13"}, @@ -6247,6 +6461,7 @@ description = "World timezone definitions, modern and historical" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"}, {file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"}, @@ -6259,6 +6474,7 @@ description = "A tool to automatically upgrade syntax for newer versions." optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyupgrade-3.19.1-py2.py3-none-any.whl", hash = "sha256:8c5b0bfacae5ff30fa136a53eb7f22c34ba007450d4099e9da8089dabb9e67c9"}, {file = "pyupgrade-3.19.1.tar.gz", hash = "sha256:d10e8c5f54b8327211828769e98d95d95e4715de632a3414f1eef3f51357b9e2"}, @@ -6292,7 +6508,7 @@ files = [ {file = "pywin32-310-cp39-cp39-win32.whl", hash = "sha256:851c8d927af0d879221e616ae1f66145253537bbdd321a77e8ef701b443a9a1a"}, {file = "pywin32-310-cp39-cp39-win_amd64.whl", hash = "sha256:96867217335559ac619f00ad70e513c0fcf84b8a3af9fc2bba3b59b97da70475"}, ] -markers = {main = "sys_platform == \"win32\"", docs = "platform_python_implementation != \"PyPy\" and sys_platform == \"win32\"", test = "platform_python_implementation != \"PyPy\" and sys_platform == \"win32\""} +markers = {main = "sys_platform == \"win32\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", docs = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", test = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")"} [[package]] name = "pyyaml" @@ -6301,6 +6517,7 @@ description = "YAML parser and emitter for Python" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "PyYAML-6.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086"}, {file = "PyYAML-6.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf"}, @@ -6364,6 +6581,7 @@ description = "Python bindings for 0MQ" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyzmq-26.4.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:0329bdf83e170ac133f44a233fc651f6ed66ef8e66693b5af7d54f45d1ef5918"}, {file = "pyzmq-26.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:398a825d2dea96227cf6460ce0a174cf7657d6f6827807d4d1ae9d0f9ae64315"}, @@ -6470,6 +6688,7 @@ description = "API rate limit decorator" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ratelimit-2.2.1.tar.gz", hash = "sha256:af8a9b64b821529aca09ebaf6d8d279100d766f19e90b5059ac6a718ca6dee42"}, ] @@ -6481,6 +6700,7 @@ description = "JSON Referencing + Python" optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "referencing-0.36.2-py3-none-any.whl", hash = "sha256:e8699adbbf8b5c7de96d8ffa0eb5c158b3beafce084968e2ea8bb08c6794dcd0"}, {file = "referencing-0.36.2.tar.gz", hash = "sha256:df2e89862cd09deabbdba16944cc3f10feb6b3e6f18e902f7cc25609a34775aa"}, @@ -6498,6 +6718,7 @@ description = "Alternative regular expression module, to replace re." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "regex-2024.11.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ff590880083d60acc0433f9c3f713c51f7ac6ebb9adf889c79a261ecf541aa91"}, {file = "regex-2024.11.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:658f90550f38270639e83ce492f27d2c8d2cd63805c65a13a14d36ca126753f0"}, @@ -6602,6 +6823,7 @@ description = "Python HTTP for Humans." optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"}, {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"}, @@ -6624,6 +6846,7 @@ description = "A utility belt for advanced users of python-requests" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"}, {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"}, @@ -6639,6 +6862,7 @@ description = "Render rich text, tables, progress bars, syntax highlighting, mar optional = false python-versions = ">=3.8.0" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "rich-13.9.4-py3-none-any.whl", hash = "sha256:6049d5e6ec054bf2779ab3358186963bac2ea89175919d699e378b99738c2a90"}, {file = "rich-13.9.4.tar.gz", hash = "sha256:439594978a49a09530cff7ebc4b5c7103ef57baf48d5ea3184f21d9a2befa098"}, @@ -6659,6 +6883,7 @@ description = "Python bindings to Rust's persistent data structures (rpds)" optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "rpds_py-0.24.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:006f4342fe729a368c6df36578d7a348c7c716be1da0a1a0f86e3021f8e98724"}, {file = "rpds_py-0.24.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2d53747da70a4e4b17f559569d5f9506420966083a31c5fbd84e764461c4444b"}, @@ -6783,6 +7008,7 @@ description = "Pure-Python RSA implementation" optional = false python-versions = "<4,>=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "rsa-4.9.1-py3-none-any.whl", hash = "sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762"}, {file = "rsa-4.9.1.tar.gz", hash = "sha256:e7bdbfdb5497da4c07dfd35530e1a902659db6ff241e39d9953cad06ebd0ae75"}, @@ -6798,6 +7024,7 @@ description = "An extremely fast Python linter and code formatter, written in Ru optional = false python-versions = ">=3.7" groups = ["main", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ruff-0.11.5-py3-none-linux_armv6l.whl", hash = "sha256:2561294e108eb648e50f210671cc56aee590fb6167b594144401532138c66c7b"}, {file = "ruff-0.11.5-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:ac12884b9e005c12d0bd121f56ccf8033e1614f736f766c118ad60780882a077"}, @@ -6826,6 +7053,7 @@ description = "Convenient Filesystem interface over S3" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "s3fs-2024.12.0-py3-none-any.whl", hash = "sha256:d8665549f9d1de083151582437a2f10d5f3b3227c1f8e67a2b0b730db813e005"}, {file = "s3fs-2024.12.0.tar.gz", hash = "sha256:1b0f3a8f5946cca5ba29871d6792ab1e4528ed762327d8aefafc81b73b99fd56"}, @@ -6847,6 +7075,7 @@ description = "An Amazon S3 Transfer Manager" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "s3transfer-0.11.3-py3-none-any.whl", hash = "sha256:ca855bdeb885174b5ffa95b9913622459d4ad8e331fc98eb01e6d5eb6a30655d"}, {file = "s3transfer-0.11.3.tar.gz", hash = "sha256:edae4977e3a122445660c7c114bba949f9d191bae3b34a096f18a1c8c354527a"}, @@ -6865,6 +7094,7 @@ description = "Job scheduling for humans." optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "schedule-1.2.2-py3-none-any.whl", hash = "sha256:5bef4a2a0183abf44046ae0d164cadcac21b1db011bdd8102e4a0c1e91e06a7d"}, {file = "schedule-1.2.2.tar.gz", hash = "sha256:15fe9c75fe5fd9b9627f3f19cc0ef1420508f9f9a46f45cd0769ef75ede5f0b7"}, @@ -6880,6 +7110,7 @@ description = "A set of python modules for machine learning and data mining" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "scikit_learn-1.6.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d056391530ccd1e501056160e3c9673b4da4805eb67eb2bdf4e983e1f9c9204e"}, {file = "scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0c8d036eb937dbb568c6242fa598d551d88fb4399c0344d95c001980ec1c7d36"}, @@ -6935,6 +7166,7 @@ description = "Fundamental algorithms for scientific computing in Python" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "scipy-1.15.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a2ec871edaa863e8213ea5df811cd600734f6400b4af272e1c011e69401218e9"}, {file = "scipy-1.15.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:6f223753c6ea76983af380787611ae1291e3ceb23917393079dcc746ba60cfb5"}, @@ -6990,7 +7222,7 @@ numpy = ">=1.23.5,<2.5" [package.extras] dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"] doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"] -test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] name = "seaborn" @@ -6999,6 +7231,7 @@ description = "Statistical data visualization" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "seaborn-0.13.2-py3-none-any.whl", hash = "sha256:636f8336facf092165e27924f223d3c62ca560b1f2bb5dff7ab7fad265361987"}, {file = "seaborn-0.13.2.tar.gz", hash = "sha256:93e60a40988f4d65e9f4885df477e2fdaff6b73a9ded434c1ab356dd57eefff7"}, @@ -7021,6 +7254,7 @@ description = "Python helper for Semantic Versioning (https://semver.org)" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "semver-3.0.4-py3-none-any.whl", hash = "sha256:9c824d87ba7f7ab4a1890799cec8596f15c1241cb473404ea1cb0c55e4b04746"}, {file = "semver-3.0.4.tar.gz", hash = "sha256:afc7d8c584a5ed0a11033af086e8af226a9c0b206f313e0301f8dd7b6b589602"}, @@ -7037,16 +7271,16 @@ files = [ {file = "setuptools-78.1.0-py3-none-any.whl", hash = "sha256:3e386e96793c8702ae83d17b853fb93d3e09ef82ec62722e61da5cd22376dcd8"}, {file = "setuptools-78.1.0.tar.gz", hash = "sha256:18fd474d4a82a5f83dac888df697af65afa82dec7323d09c3e37d1f14288da54"}, ] -markers = {main = "python_version == \"3.12\""} +markers = {main = "python_version >= \"3.12\"", docs = "python_version >= \"3.12\" or python_version <= \"3.11\""} [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\"", "ruff (>=0.8.0) ; sys_platform != \"cygwin\""] -core = ["importlib_metadata (>=6) ; python_version < \"3.10\"", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1) ; python_version < \"3.11\"", "wheel (>=0.43.0)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.8.0)"] +core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "platformdirs (>=4.2.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"] enabler = ["pytest-enabler (>=2.2)"] -test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21) ; python_version >= \"3.9\" and sys_platform != \"cygwin\"", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf ; sys_platform != \"cygwin\"", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"] -type = ["importlib_metadata (>=7.0.2) ; python_version < \"3.10\"", "jaraco.develop (>=7.21) ; sys_platform != \"cygwin\"", "mypy (==1.14.*)", "pytest-mypy"] +test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"] +type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.14.*)", "pytest-mypy"] [[package]] name = "shortuuid" @@ -7055,6 +7289,7 @@ description = "A generator library for concise, unambiguous and URL-safe UUIDs." optional = false python-versions = ">=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "shortuuid-1.0.13-py3-none-any.whl", hash = "sha256:a482a497300b49b4953e15108a7913244e1bb0d41f9d332f5e9925dba33a3c5a"}, {file = "shortuuid-1.0.13.tar.gz", hash = "sha256:3bb9cf07f606260584b1df46399c0b87dd84773e7b25912b7e391e30797c5e72"}, @@ -7067,6 +7302,7 @@ description = "Python 2 and 3 compatibility utilities" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"}, {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"}, @@ -7079,6 +7315,7 @@ description = "Sniff out which async library your code is running under" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"}, {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"}, @@ -7091,6 +7328,7 @@ description = "This package provides 29 stemmers for 28 languages generated from optional = false python-versions = "*" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "snowballstemmer-2.2.0-py2.py3-none-any.whl", hash = "sha256:c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a"}, {file = "snowballstemmer-2.2.0.tar.gz", hash = "sha256:09b16deb8547d3412ad7b590689584cd0fe25ec8db3be37788be3810cbf19cb1"}, @@ -7103,6 +7341,7 @@ description = "Sorted Containers -- Sorted List, Sorted Dict, Sorted Set" optional = false python-versions = "*" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sortedcontainers-2.4.0-py2.py3-none-any.whl", hash = "sha256:a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0"}, {file = "sortedcontainers-2.4.0.tar.gz", hash = "sha256:25caa5a06cc30b6b83d11423433f65d1f9d76c4c6a0c90e3379eaa43b9bfdb88"}, @@ -7115,6 +7354,7 @@ description = "A modern CSS selector implementation for Beautiful Soup." optional = false python-versions = ">=3.8" groups = ["main", "docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "soupsieve-2.6-py3-none-any.whl", hash = "sha256:e72c4ff06e4fb6e4b5a9f0f55fe6e81514581fca1515028625d0f299c602ccc9"}, {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"}, @@ -7127,6 +7367,7 @@ description = "Python documentation generator" optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx-7.4.7-py3-none-any.whl", hash = "sha256:c2419e2135d11f1951cd994d6eb18a1835bd8fdd8429f9ca375dc1f3281bd239"}, {file = "sphinx-7.4.7.tar.gz", hash = "sha256:242f92a7ea7e6c5b406fdc2615413890ba9f699114a9c09192d7dfead2ee9cfe"}, @@ -7163,6 +7404,7 @@ description = "Sphinx API documentation generator" optional = false python-versions = ">=3.7" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx-autoapi-2.1.1.tar.gz", hash = "sha256:fbadb96e79020d6b0ec45d888517bf816d6b587a2d340fbe1ec31135e300a6c8"}, {file = "sphinx_autoapi-2.1.1-py2.py3-none-any.whl", hash = "sha256:d8da890477bd18e3327cafdead9d5a44a7d798476c6fa32492100e288250a5a3"}, @@ -7187,6 +7429,7 @@ description = "Type hints (PEP 484) support for the Sphinx autodoc extension" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx_autodoc_typehints-1.25.3-py3-none-any.whl", hash = "sha256:d3da7fa9a9761eff6ff09f8b1956ae3090a2d4f4ad54aebcade8e458d6340835"}, {file = "sphinx_autodoc_typehints-1.25.3.tar.gz", hash = "sha256:70db10b391acf4e772019765991d2de0ff30ec0899b9ba137706dc0b3c4835e0"}, @@ -7207,6 +7450,7 @@ description = "A modern skeleton for Sphinx themes." optional = false python-versions = ">=3.7" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx_basic_ng-1.0.0b2-py3-none-any.whl", hash = "sha256:eb09aedbabfb650607e9b4b68c9d240b90b1e1be221d6ad71d61c52e29f7932b"}, {file = "sphinx_basic_ng-1.0.0b2.tar.gz", hash = "sha256:9ec55a47c90c8c002b5960c57492ec3021f5193cb26cebc2dc4ea226848651c9"}, @@ -7225,6 +7469,7 @@ description = "Add a copy button to each of your code cells." optional = false python-versions = ">=3.7" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx-copybutton-0.5.2.tar.gz", hash = "sha256:4cf17c82fb9646d1bc9ca92ac280813a3b605d8c421225fd9913154103ee1fbd"}, {file = "sphinx_copybutton-0.5.2-py3-none-any.whl", hash = "sha256:fb543fd386d917746c9a2c50360c7905b605726b9355cd26e9974857afeae06e"}, @@ -7244,6 +7489,7 @@ description = "A sphinx extension for designing beautiful, view size responsive optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinx_design-0.5.0-py3-none-any.whl", hash = "sha256:1af1267b4cea2eedd6724614f19dcc88fe2e15aff65d06b2f6252cee9c4f4c1e"}, {file = "sphinx_design-0.5.0.tar.gz", hash = "sha256:e8e513acea6f92d15c6de3b34e954458f245b8e761b45b63950f65373352ab00"}, @@ -7268,6 +7514,7 @@ description = "A Sphinx extension for running 'sphinx-apidoc' on each build" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib-apidoc-0.4.0.tar.gz", hash = "sha256:fe59d15882472aa93c2737afbdea6bedb34ce35cbd34aa4947909f5df1500aad"}, {file = "sphinxcontrib_apidoc-0.4.0-py3-none-any.whl", hash = "sha256:18b9fb0cd4816758ec5f8be41c64f8924991dd40fd7b10e666ec9eed2800baff"}, @@ -7284,6 +7531,7 @@ description = "sphinxcontrib-applehelp is a Sphinx extension which outputs Apple optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_applehelp-2.0.0-py3-none-any.whl", hash = "sha256:4cd3f0ec4ac5dd9c17ec65e9ab272c9b867ea77425228e68ecf08d6b28ddbdb5"}, {file = "sphinxcontrib_applehelp-2.0.0.tar.gz", hash = "sha256:2f29ef331735ce958efa4734873f084941970894c6090408b079c61b2e1c06d1"}, @@ -7301,6 +7549,7 @@ description = "sphinxcontrib-devhelp is a sphinx extension which outputs Devhelp optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_devhelp-2.0.0-py3-none-any.whl", hash = "sha256:aefb8b83854e4b0998877524d1029fd3e6879210422ee3780459e28a1f03a8a2"}, {file = "sphinxcontrib_devhelp-2.0.0.tar.gz", hash = "sha256:411f5d96d445d1d73bb5d52133377b4248ec79db5c793ce7dbe59e074b4dd1ad"}, @@ -7318,6 +7567,7 @@ description = "sphinxcontrib-htmlhelp is a sphinx extension which renders HTML h optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8"}, {file = "sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9"}, @@ -7335,6 +7585,7 @@ description = "A sphinx extension which renders display math in HTML via JavaScr optional = false python-versions = ">=3.5" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib-jsmath-1.0.1.tar.gz", hash = "sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8"}, {file = "sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl", hash = "sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178"}, @@ -7350,6 +7601,7 @@ description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp d optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb"}, {file = "sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab"}, @@ -7367,6 +7619,7 @@ description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs optional = false python-versions = ">=3.9" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331"}, {file = "sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d"}, @@ -7384,6 +7637,7 @@ description = "Database Abstraction Library" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "SQLAlchemy-2.0.40-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:ae9597cab738e7cc823f04a704fb754a9249f0b6695a6aeb63b74055cd417a96"}, {file = "SQLAlchemy-2.0.40-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:37a5c21ab099a83d669ebb251fddf8f5cee4d75ea40a5a1653d9c43d60e20867"}, @@ -7480,6 +7734,7 @@ description = "SSE plugin for Starlette" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "sse_starlette-3.0.2-py3-none-any.whl", hash = "sha256:16b7cbfddbcd4eaca11f7b586f3b8a080f1afe952c15813455b162edea619e5a"}, {file = "sse_starlette-3.0.2.tar.gz", hash = "sha256:ccd60b5765ebb3584d0de2d7a6e4f745672581de4f5005ab31c3a25d10b52b3a"}, @@ -7501,6 +7756,7 @@ description = "Extract data from python stack frames and tracebacks for informat optional = false python-versions = "*" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"}, {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"}, @@ -7521,6 +7777,7 @@ description = "The little ASGI library that shines." optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "starlette-0.47.2-py3-none-any.whl", hash = "sha256:c5847e96134e5c5371ee9fac6fdf1a67336d5815e09eb2a01fdb57a351ef915b"}, {file = "starlette-0.47.2.tar.gz", hash = "sha256:6ae9aa5db235e4846decc1e7b79c4f346adf41e9777aebeb49dfd09bbd7023d8"}, @@ -7540,6 +7797,7 @@ description = "Computer algebra system (CAS) in Python" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"}, {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"}, @@ -7558,6 +7816,7 @@ description = "Pretty-print tabular data" optional = false python-versions = ">=3.7" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"}, {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"}, @@ -7573,6 +7832,7 @@ description = "Retry code until it succeeds" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"}, {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"}, @@ -7589,6 +7849,7 @@ description = "ANSI color formatting for output in terminal" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "termcolor-3.1.0-py3-none-any.whl", hash = "sha256:591dd26b5c2ce03b9e43f391264626557873ce1d379019786f99b0c2bee140aa"}, {file = "termcolor-3.1.0.tar.gz", hash = "sha256:6a6dd7fbee581909eeec6a756cff1d7f7c376063b14e4a298dc4980309e55970"}, @@ -7604,6 +7865,7 @@ description = "Modern Text User Interface framework" optional = false python-versions = "<4.0.0,>=3.8.1" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "textual-3.1.0-py3-none-any.whl", hash = "sha256:940a765b6fcd562cd88603780343dc98a4e66c1d8d42f09b6a16a474a89aca0c"}, {file = "textual-3.1.0.tar.gz", hash = "sha256:6bcab6581e9753d2a2043caf49f43c5818feb35f8049ed185bd38982bfb310ca"}, @@ -7616,7 +7878,7 @@ rich = ">=13.3.3" typing-extensions = ">=4.4.0,<5.0.0" [package.extras] -syntax = ["tree-sitter (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-bash (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-css (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-go (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-html (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-java (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-javascript (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-json (>=0.24.0) ; python_version >= \"3.9\"", "tree-sitter-markdown (>=0.3.0) ; python_version >= \"3.9\"", "tree-sitter-python (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-regex (>=0.24.0) ; python_version >= \"3.9\"", "tree-sitter-rust (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-sql (>=0.3.0,<0.3.8) ; python_version >= \"3.9\"", "tree-sitter-toml (>=0.6.0) ; python_version >= \"3.9\"", "tree-sitter-xml (>=0.7.0) ; python_version >= \"3.9\"", "tree-sitter-yaml (>=0.6.0) ; python_version >= \"3.9\""] +syntax = ["tree-sitter (>=0.23.0)", "tree-sitter-bash (>=0.23.0)", "tree-sitter-css (>=0.23.0)", "tree-sitter-go (>=0.23.0)", "tree-sitter-html (>=0.23.0)", "tree-sitter-java (>=0.23.0)", "tree-sitter-javascript (>=0.23.0)", "tree-sitter-json (>=0.24.0)", "tree-sitter-markdown (>=0.3.0)", "tree-sitter-python (>=0.23.0)", "tree-sitter-regex (>=0.24.0)", "tree-sitter-rust (>=0.23.0)", "tree-sitter-sql (>=0.3.0,<0.3.8)", "tree-sitter-toml (>=0.6.0)", "tree-sitter-xml (>=0.7.0)", "tree-sitter-yaml (>=0.6.0)"] [[package]] name = "threadpoolctl" @@ -7625,6 +7887,7 @@ description = "threadpoolctl" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb"}, {file = "threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e"}, @@ -7637,6 +7900,7 @@ description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tiktoken-0.9.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:586c16358138b96ea804c034b8acf3f5d3f0258bd2bc3b0227af4af5d622e382"}, {file = "tiktoken-0.9.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d9c59ccc528c6c5dd51820b3474402f69d9a9e1d656226848ad68a8d5b2e5108"}, @@ -7685,6 +7949,7 @@ description = "A tiny CSS parser" optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"}, {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"}, @@ -7704,6 +7969,7 @@ description = "A wrapper around the stdlib `tokenize` which roundtrips." optional = false python-versions = ">=3.9" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tokenize_rt-6.1.0-py2.py3-none-any.whl", hash = "sha256:d706141cdec4aa5f358945abe36b911b8cbdc844545da99e811250c0cee9b6fc"}, {file = "tokenize_rt-6.1.0.tar.gz", hash = "sha256:e8ee836616c0877ab7c7b54776d2fefcc3bde714449a206762425ae114b53c86"}, @@ -7716,6 +7982,7 @@ description = "Python Library for Tom's Obvious, Minimal Language" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"}, {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"}, @@ -7762,7 +8029,7 @@ files = [ {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"}, {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"}, ] -markers = {main = "python_version == \"3.10\"", docs = "python_version == \"3.10\""} +markers = {main = "python_version < \"3.11\"", docs = "python_version < \"3.11\"", test = "python_version >= \"3.12\" or python_version <= \"3.11\""} [[package]] name = "tomlkit" @@ -7771,6 +8038,7 @@ description = "Style preserving TOML library" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tomlkit-0.13.2-py3-none-any.whl", hash = "sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde"}, {file = "tomlkit-0.13.2.tar.gz", hash = "sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79"}, @@ -7783,6 +8051,7 @@ description = "Tensors and Dynamic neural networks in Python with strong GPU acc optional = false python-versions = ">=3.9.0" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torch-2.6.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:6860df13d9911ac158f4c44031609700e1eba07916fff62e21e6ffa0a9e01961"}, {file = "torch-2.6.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:c4f103a49830ce4c7561ef4434cc7926e5a5fe4e5eb100c19ab36ea1e2b634ab"}, @@ -7840,6 +8109,7 @@ description = "An audio package for PyTorch" optional = false python-versions = "*" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torchaudio-2.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0eda1cd876f44fc014dc04aa680db2fa355a83df5d834398db6dd5f5cd911f4c"}, {file = "torchaudio-2.6.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:22798d5d8e37869bd5875d37f42270efbeb8ae94bda97fed40c1c5e0e1c62fa3"}, @@ -7873,6 +8143,7 @@ description = "image and video datasets and models for torch deep learning" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torchvision-0.21.0-1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:5568c5a1ff1b2ec33127b629403adb530fab81378d9018ca4ed6508293f76e2b"}, {file = "torchvision-0.21.0-1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:ff96666b94a55e802ea6796cabe788541719e6f4905fc59c380fed3517b6a64d"}, @@ -7917,6 +8188,7 @@ description = "Tornado is a Python web framework and asynchronous networking lib optional = false python-versions = ">=3.8" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1"}, {file = "tornado-6.4.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:072ce12ada169c5b00b7d92a99ba089447ccc993ea2143c9ede887e0937aa803"}, @@ -7938,6 +8210,7 @@ description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"}, {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"}, @@ -7960,6 +8233,7 @@ description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"}, {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"}, @@ -7976,7 +8250,7 @@ description = "A language and compiler for custom Deep Learning operations" optional = false python-versions = "*" groups = ["main"] -markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" +markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e54983cd51875855da7c68ec05c05cf8bb08df361b1d5b69e05e40b0c9bd62"}, {file = "triton-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8009a1fb093ee8546495e96731336a33fb8856a38e45bb4ab6affd6dbc3ba220"}, @@ -7997,6 +8271,7 @@ description = "Typing stubs for requests" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "types_requests-2.32.4.20250809-py3-none-any.whl", hash = "sha256:f73d1832fb519ece02c85b1f09d5f0dd3108938e7d47e7f94bbfa18a6782b163"}, {file = "types_requests-2.32.4.20250809.tar.gz", hash = "sha256:d8060de1c8ee599311f56ff58010fb4902f462a1470802cf9f6ed27bc46c4df3"}, @@ -8012,6 +8287,7 @@ description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"}, {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"}, @@ -8024,6 +8300,7 @@ description = "Runtime typing introspection tools" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "typing_inspection-0.4.0-py3-none-any.whl", hash = "sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f"}, {file = "typing_inspection-0.4.0.tar.gz", hash = "sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122"}, @@ -8039,6 +8316,7 @@ description = "Provider of IANA time zone data" optional = false python-versions = ">=2" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"}, {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"}, @@ -8051,6 +8329,7 @@ description = "Micro subset of unicode data files for linkify-it-py projects." optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "uc-micro-py-1.0.3.tar.gz", hash = "sha256:d321b92cff673ec58027c04015fcaa8bb1e005478643ff4a500882eaab88c48a"}, {file = "uc_micro_py-1.0.3-py3-none-any.whl", hash = "sha256:db1dffff340817673d7b466ec86114a9dc0e9d4d9b5ba229d9d60e5c12600cd5"}, @@ -8066,13 +8345,14 @@ description = "HTTP library with thread-safe connection pooling, file post, and optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"}, {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"}, ] [package.extras] -brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""] +brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] @@ -8084,7 +8364,7 @@ description = "The lightning-fast ASGI server." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "sys_platform != \"emscripten\"" +markers = "sys_platform != \"emscripten\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" files = [ {file = "uvicorn-0.35.0-py3-none-any.whl", hash = "sha256:197535216b25ff9b785e29a0b79199f55222193d47f820816e7da751e9bc8d4a"}, {file = "uvicorn-0.35.0.tar.gz", hash = "sha256:bc662f087f7cf2ce11a1d7fd70b90c9f98ef2e2831556dd078d131b96cc94a01"}, @@ -8096,7 +8376,7 @@ h11 = ">=0.8" typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""} [package.extras] -standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] +standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"] [[package]] name = "vec-inf" @@ -8105,6 +8385,7 @@ description = "Efficient LLM inference on Slurm clusters using vLLM." optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "vec_inf-0.6.0-py3-none-any.whl", hash = "sha256:cc0211957f1a4b12fba600ff456312ccb632cd7b3e423937c76ab89001a8ffc9"}, {file = "vec_inf-0.6.0.tar.gz", hash = "sha256:c02360de2fde5da9e93bcf702e97fd2ca0a49056831793285fb3788987cf7217"}, @@ -8127,6 +8408,7 @@ description = "Virtual Python Environment builder" optional = false python-versions = ">=3.8" groups = ["test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "virtualenv-20.30.0-py3-none-any.whl", hash = "sha256:e34302959180fca3af42d1800df014b35019490b119eba981af27f2fa486e5d6"}, {file = "virtualenv-20.30.0.tar.gz", hash = "sha256:800863162bcaa5450a6e4d721049730e7f2dae07720e0902b0e4040bd6f9ada8"}, @@ -8139,7 +8421,7 @@ platformdirs = ">=3.9.1,<5" [package.extras] docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"GraalVM\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] [[package]] name = "wadler-lindig" @@ -8148,6 +8430,7 @@ description = "A Wadler–Lindig pretty-printer for Python." optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wadler_lindig-0.1.5-py3-none-any.whl", hash = "sha256:54f830ec0bdab41b28df8bc91be4596c6cc8cf9772b3b5c7cd5922d2ba5073b7"}, {file = "wadler_lindig-0.1.5.tar.gz", hash = "sha256:c8d6c0e74fc61c7108077009238b7f5a5a52b25f75e024dac313393faf7f7440"}, @@ -8164,6 +8447,7 @@ description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" groups = ["docs", "test"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, @@ -8176,6 +8460,7 @@ description = "Character encoding aliases for legacy web content" optional = false python-versions = "*" groups = ["docs"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, @@ -8188,6 +8473,7 @@ description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b"}, {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205"}, @@ -8267,6 +8553,7 @@ description = "Module for decorators, wrappers and monkey patching." optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3d57c572081fed831ad2d26fd430d565b76aa277ed1d30ff4d40670b1c0dd984"}, {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b5e251054542ae57ac7f3fba5d10bfff615b6c2fb09abeb37d2f1463f841ae22"}, @@ -8356,6 +8643,7 @@ description = "Makes working with XML feel like you are working with JSON" optional = false python-versions = ">=3.6" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "xmltodict-0.14.2-py2.py3-none-any.whl", hash = "sha256:20cc7d723ed729276e808f26fb6b3599f786cbc37e06c65e192ba77c40f20aac"}, {file = "xmltodict-0.14.2.tar.gz", hash = "sha256:201e7c28bb210e374999d1dde6382923ab0ed1a8a5faeece48ab525b7810a553"}, @@ -8368,6 +8656,7 @@ description = "Python binding for xxHash" optional = false python-versions = ">=3.7" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"}, {file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"}, @@ -8501,6 +8790,7 @@ description = "Yet another URL library" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "yarl-1.19.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0bae32f8ebd35c04d6528cedb4a26b8bf25339d3616b04613b97347f919b76d3"}, {file = "yarl-1.19.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8015a076daf77823e7ebdcba474156587391dab4e70c732822960368c01251e6"}, @@ -8603,17 +8893,18 @@ description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.9" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [[package]] @@ -8623,6 +8914,7 @@ description = "Zstandard bindings for Python" optional = false python-versions = ">=3.8" groups = ["main"] +markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "zstandard-0.23.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bf0a05b6059c0528477fba9054d09179beb63744355cab9f38059548fedd46a9"}, {file = "zstandard-0.23.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fc9ca1c9718cb3b06634c7c8dec57d24e9438b2aa9a0f02b8bb36bf478538880"}, @@ -8732,4 +9024,4 @@ cffi = ["cffi (>=1.11)"] [metadata] lock-version = "2.1" python-versions = ">=3.10, <3.13" -content-hash = "2dbaf7bb11506a213ffe1235df6de7ad4a1943ff5a1ad29e98c9c108d3210230" +content-hash = "e5d8939887571c44c9f56aa4b813d252655bcff9468ddd7a2e23f11d91e22093" diff --git a/pyproject.toml b/pyproject.toml index f0337423..b210fdf0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,49 +38,45 @@ dependencies = [ "pytest-env (>=1.1.5,<2.0.0)", "langfuse>=2.0.0", "openlit>=1.35.0", + "python-dotenv>=1.0.0", ] [project.urls] repository = "https://github.com/VectorInstitute/automated_capability_evaluation" -[tool.poetry] -package-mode = false -version = "0.1" - -[tool.poetry.dependencies] -python = ">=3.10, <3.13" - -[tool.poetry.group.test] -optional = true - -[tool.poetry.group.test.dependencies] -pytest = "^8.3.3" -pre-commit = "^4.0.0" -pytest-cov = "^3.0.0" -pytest-env = "^1.1.5" -pytest-mock = "^3.10.0" -codecov = "^2.1.13" -mypy = "^1.15.0" -ruff = ">=0.11.4,<0.12.0" -nbqa = { version = "^1.7.0", extras = ["toolchain"] } -pip-audit = "^2.7.1" - -[tool.poetry.group.docs] -optional = true - -[tool.poetry.group.docs.dependencies] -numpydoc = "^1.2" -sphinx = "^7.2.5" -sphinxcontrib-apidoc = "^0.4.0" -sphinx-autodoc-typehints = "^1.24.0" -myst-parser = "^2.0.0" -sphinx-design = "^0.5.0" -sphinx-copybutton = "^0.5.0" -sphinx-autoapi = "^2.0.0" -nbsphinx = "^0.9.3" -ipython = "^8.8.0" -ipykernel = "^6.23.0" -furo = "^2024.01.29" +[tool.hatch.version] +path = "src/__init__.py" + +[tool.hatch.build.targets.wheel] +packages = ["src"] + +[project.optional-dependencies] +test = [ + "pytest>=8.3.3", + "pre-commit>=4.0.0", + "pytest-cov>=3.0.0", + "pytest-env>=1.1.5", + "pytest-mock>=3.10.0", + "codecov>=2.1.13", + "mypy>=1.15.0", + "ruff>=0.11.4,<0.12.0", + "nbqa[toolchain]>=1.7.0", + "pip-audit>=2.7.1", +] +docs = [ + "numpydoc>=1.2", + "sphinx>=7.2.5", + "sphinxcontrib-apidoc>=0.4.0", + "sphinx-autodoc-typehints>=1.24.0", + "myst-parser>=2.0.0", + "sphinx-design>=0.5.0", + "sphinx-copybutton>=0.5.0", + "sphinx-autoapi>=2.0.0", + "nbsphinx>=0.9.3", + "ipython>=8.8.0", + "ipykernel>=6.23.0", + "furo>=2024.01.29", +] [tool.mypy] ignore_missing_imports = true @@ -190,5 +186,5 @@ filterwarnings = [ ignore_errors = true [build-system] -requires = ["poetry-core>=2.0.1"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/run_experiments.py b/run_experiments.py new file mode 100644 index 00000000..8654299b --- /dev/null +++ b/run_experiments.py @@ -0,0 +1,629 @@ +#!/usr/bin/env python3 +""" +Unified Experiment Runner for Multi-Agent Mathematical Reasoning + +Supports experiments with: +- Configurable model (default: gemini-2.5-pro) +- Configurable debate rounds (default: 1) +- Scalable tasks per capability: 1-15 (default: 15) +- Both baseline and tool-assisted conditions + +Usage: + # Quick test: 1 task per capability + python run_experiments.py --num-tasks 1 --condition tool_assisted + + # Full experiment: 15 tasks per capability + python run_experiments.py --num-tasks 15 --condition both + + # Custom configuration + python run_experiments.py --model gemini-2.5-pro --rounds 2 --num-tasks 10 +""" + +import argparse +import asyncio +import json +import logging +import os +import time +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Any + +from dotenv import load_dotenv +from autogen_core import ( + DefaultTopicId, + SingleThreadedAgentRuntime, +) +from langfuse import Langfuse + +# Load environment variables from .env file +load_dotenv() + +from src.task_solver.messages import Task +from src.task_solver.moderator import TaskSolverModerator +from src.task_solver.scientist import TaskSolverScientist +from src.task_solver.tool_assisted_scientist import ToolAssistedScientist +from src.utils.model_client_utils import get_model_client + + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler('experiments.log'), + logging.StreamHandler() + ] +) +log = logging.getLogger(__name__) + + +class ExperimentRunner: + """Runner for mathematical reasoning experiments.""" + + def __init__( + self, + model_name: str = "gemini-2.5-pro", + num_rounds: int = 1, + num_tasks: int = 15, + condition: str = "both", + output_dir: str = "experiment_results", + ): + """Initialize experiment runner. + + Args: + model_name: LLM model to use + num_rounds: Number of debate rounds + num_tasks: Number of tasks per capability to run (1-15) + condition: Which condition to run - 'baseline', 'tool_assisted', or 'both' + output_dir: Directory for saving results + """ + self.model_name = model_name + self.num_rounds = num_rounds + self.num_tasks = num_tasks + self.condition = condition + self.results_dir = Path(output_dir) + self.results_dir.mkdir(parents=True, exist_ok=True) + + # Initialize Langfuse + self.langfuse_client = Langfuse( + secret_key=os.getenv("LANGFUSE_SECRET_KEY"), + public_key=os.getenv("LANGFUSE_PUBLIC_KEY"), + host=os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"), + ) + + self.results = { + "experiment_info": { + "name": "capability_evaluation_experiment", + "version": "1.0", + "start_time": datetime.now().isoformat(), + "model": self.model_name, + "num_rounds": self.num_rounds, + "tasks_per_capability": self.num_tasks, + }, + "baseline": [], + "tool_assisted": [], + } + + def load_tasks(self) -> List[Dict[str, Any]]: + """Load tasks from selected_tasks.json file.""" + tasks_file = Path("selected_tasks.json") + + if not tasks_file.exists(): + raise FileNotFoundError( + f"Tasks file not found: {tasks_file}\n" + f"Please run extract_tasks.py first." + ) + + with open(tasks_file, "r", encoding="utf-8") as f: + all_tasks = json.load(f) + + # Select specified number of tasks from each capability + selected_tasks = [] + for capability_name, task_list in all_tasks.items(): + # Take up to num_tasks from each capability + for task in task_list[:self.num_tasks]: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + + log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") + return selected_tasks + + async def run_single_task( + self, + task_data: Dict[str, Any], + condition: str, + task_index: int, + total_tasks: int, + ) -> Dict[str, Any]: + """Run a single task with specified condition.""" + log.info( + f"\n{'='*70}\n" + f"[{condition.upper()}] Task {task_index}/{total_tasks}\n" + f"Capability: {task_data['capability_name']}\n" + f"Task ID: {task_data['task_id']}\n" + f"{'='*70}" + ) + + start_time = time.time() + result = { + "task_id": task_data["task_id"], + "capability_name": task_data["capability_name"], + "condition": condition, + "problem": task_data["problem"], + "expected_answer": task_data["answer"], + "start_time": datetime.now().isoformat(), + "status": "pending", + } + + try: + # Create runtime + runtime = SingleThreadedAgentRuntime() + + # Create output directory for this task + output_dir = self.results_dir / condition / task_data["capability_name"] + output_dir.mkdir(parents=True, exist_ok=True) + + # Register moderator + await TaskSolverModerator.register( + runtime, + "TaskSolverModerator", + lambda: TaskSolverModerator( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + num_solvers=2, + max_rounds=self.num_rounds, + output_dir=output_dir, + langfuse_client=self.langfuse_client, + ), + ) + + # Register scientist agents based on condition + if condition == "baseline": + # Use regular TaskSolverScientist + await TaskSolverScientist.register( + runtime, + "TaskSolverScientistA", + lambda: TaskSolverScientist( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + scientist_id="A", + langfuse_client=self.langfuse_client, + ), + ) + + await TaskSolverScientist.register( + runtime, + "TaskSolverScientistB", + lambda: TaskSolverScientist( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + scientist_id="B", + langfuse_client=self.langfuse_client, + ), + ) + else: + # Use ToolAssistedScientist + await ToolAssistedScientist.register( + runtime, + "TaskSolverScientistA", + lambda: ToolAssistedScientist( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + scientist_id="A", + langfuse_client=self.langfuse_client, + ), + ) + + await ToolAssistedScientist.register( + runtime, + "TaskSolverScientistB", + lambda: ToolAssistedScientist( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + scientist_id="B", + langfuse_client=self.langfuse_client, + ), + ) + + # Start runtime + runtime.start() + + # Create and publish task + task = Task( + task_id=task_data["task_id"], + problem=task_data["problem"], + capability_name=task_data["capability_name"], + area_name=task_data.get("area_name", "math"), + ) + + await runtime.publish_message(task, DefaultTopicId()) + + # Wait for completion + await runtime.stop_when_idle() + + # Load the solution file + solution_file = output_dir / f"{task_data['task_id']}_solution.json" + if solution_file.exists(): + with open(solution_file, "r") as f: + solution_data = json.load(f) + + # Extract code execution info for tool_assisted + code_executions = None + if condition == "tool_assisted": + code_executions = self._extract_code_executions( + solution_data.get("all_solutions", []) + ) + result["code_executions"] = code_executions + + result.update({ + "status": "completed", + "execution_time": time.time() - start_time, + "solution": solution_data.get("solution"), + "numerical_answer": solution_data.get("numerical_answer"), + "consensus_reached": solution_data.get("consensus_reached"), + "total_rounds": solution_data.get("total_rounds"), + "success": self._verify_answer( + solution_data.get("numerical_answer"), + task_data["answer"], + code_executions=code_executions + ), + }) + else: + result.update({ + "status": "error", + "error": "Solution file not found", + "execution_time": time.time() - start_time, + }) + + log.info( + f"✓ Task completed in {result['execution_time']:.2f}s\n" + f" Success: {result.get('success', False)}\n" + f" Consensus: {result.get('consensus_reached', False)}" + ) + + except Exception as e: + log.error(f"Error running task: {e}", exc_info=True) + result.update({ + "status": "error", + "error": str(e), + "execution_time": time.time() - start_time, + }) + + return result + + def _normalize_answer(self, answer_str: str) -> list[float]: + """Extract and normalize numerical values from any answer format. + + This method handles multiple answer formats: + - LaTeX: \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] + - Python list: [1, 1] or [[1], [1]] + - Python dict: {'x': [[1], [1]]} + - Plain text with numbers + + Returns: + Sorted list of float values extracted from the answer. + """ + import re + import ast + + if not answer_str: + return [] + + answer_str = str(answer_str).strip() + + # Remove LaTeX formatting and commands + answer_str = re.sub(r'\\[a-zA-Z]+\{', '', answer_str) # Remove \command{ + answer_str = re.sub(r'\\[a-zA-Z]+\[', '', answer_str) # Remove \command[ + answer_str = re.sub(r'\\[a-zA-Z]+', '', answer_str) # Remove remaining \commands + answer_str = re.sub(r'[{}\\]', '', answer_str) # Remove braces and backslashes + + numbers = [] + + # Try parsing as Python literal first (handles dicts, lists, etc.) + try: + parsed = ast.literal_eval(answer_str) + + def flatten(obj): + """Recursively flatten nested structures.""" + if isinstance(obj, (list, tuple)): + result = [] + for item in obj: + result.extend(flatten(item)) + return result + elif isinstance(obj, dict): + result = [] + for value in obj.values(): + result.extend(flatten(value)) + return result + else: + try: + return [float(obj)] + except (ValueError, TypeError): + return [] + + numbers = flatten(parsed) + except (ValueError, SyntaxError): + # If parsing fails, extract numbers via regex + pass + + # Extract all numbers (including decimals and negative numbers) + if not numbers: + number_pattern = r'-?\d+\.?\d*' + found_numbers = re.findall(number_pattern, answer_str) + numbers = [] + for num_str in found_numbers: + if num_str and num_str != '-': # Skip empty or lone minus signs + try: + numbers.append(float(num_str)) + except ValueError: + pass + + return sorted(numbers) + + def _verify_answer(self, solution_answer: str, expected_answer: str, code_executions: List[Dict] = None) -> bool: + """Verify if solution matches expected answer with robust normalization. + + Args: + solution_answer: The answer from the consensus/solution + expected_answer: The expected correct answer + code_executions: Optional list of code execution results to use as fallback + + Returns: + True if answer matches (from consensus or code execution), False otherwise + """ + if expected_answer is None: + return False + + # Try verifying the consensus answer first + if solution_answer is not None: + if self._verify_single_answer(solution_answer, expected_answer): + return True + + # If consensus answer failed and we have code executions, try those + if code_executions: + for exec_info in code_executions: + # Try the numerical_answer from code execution + code_numerical = exec_info.get("numerical_answer") + if code_numerical and self._verify_single_answer(code_numerical, expected_answer): + log.info(f" ✓ Answer verified using code output from agent {exec_info.get('agent_id')}") + return True + + # Try extracting numbers from raw code output + code_output = exec_info.get("code_output", "") + if code_output: + try: + output_nums = self._normalize_answer(code_output) + exp_nums = self._normalize_answer(expected_answer) + if output_nums and exp_nums and len(output_nums) == len(exp_nums): + if all(abs(s - e) < 1e-6 for s, e in zip(output_nums, exp_nums)): + log.info(f" ✓ Answer verified using raw code output from agent {exec_info.get('agent_id')}") + return True + except Exception: + pass + + return False + + def _verify_single_answer(self, solution_answer: str, expected_answer: str) -> bool: + """Verify a single answer against the expected answer.""" + if solution_answer is None or expected_answer is None: + return False + + # First try direct string match (fastest) + sol = str(solution_answer).strip().lower() + exp = str(expected_answer).strip().lower() + + if sol == exp: + return True + + # Try robust numerical comparison with normalization + try: + sol_nums = self._normalize_answer(solution_answer) + exp_nums = self._normalize_answer(expected_answer) + + if not sol_nums or not exp_nums: + return False + + if len(sol_nums) != len(exp_nums): + return False + + # Compare with tolerance + return all(abs(s - e) < 1e-6 for s, e in zip(sol_nums, exp_nums)) + + except Exception as e: + log.debug(f"Error during answer normalization: {e}") + # Fall back to old method + try: + # Handle list/vector comparisons + if "[" in sol and "[" in exp: + sol_list = eval(sol) if isinstance(sol, str) else sol + exp_list = eval(exp) if isinstance(exp, str) else exp + if isinstance(sol_list, list) and isinstance(exp_list, list): + if len(sol_list) != len(exp_list): + return False + return all(abs(float(s) - float(e)) < 1e-6 for s, e in zip(sol_list, exp_list)) + + # Simple float comparison + sol_num = float(sol) + exp_num = float(exp) + return abs(sol_num - exp_num) < 1e-6 + except (ValueError, TypeError, SyntaxError): + pass + + return False + + def _extract_code_executions(self, all_solutions: List[Dict]) -> List[Dict]: + """Extract code execution information and outputs from solutions.""" + code_executions = [] + for solution in all_solutions: + code = solution.get("code", "") + code_output = solution.get("code_output", "") + if code: + code_executions.append({ + "agent_id": solution.get("agent_id"), + "round": solution.get("round_number"), + "has_code": True, + "code": code, + "code_output": code_output, + "numerical_answer": solution.get("numerical_answer"), + }) + return code_executions + + async def run_experiment(self): + """Run the experiment.""" + log.info("="*70) + log.info(f"MATHEMATICAL REASONING EXPERIMENT") + log.info("="*70) + log.info(f"Model: {self.model_name}") + log.info(f"Debate rounds: {self.num_rounds}") + log.info(f"Tasks per capability: {self.num_tasks}") + log.info(f"Condition: {self.condition}") + log.info(f"Results directory: {self.results_dir}") + log.info("="*70 + "\n") + + # Load tasks + tasks = self.load_tasks() + total_tasks = len(tasks) + + # Run baseline condition + if self.condition in ["baseline", "both"]: + log.info("\n" + "="*70) + log.info("BASELINE CONDITION (No Code Execution)") + log.info("="*70) + for idx, task_data in enumerate(tasks, 1): + result = await self.run_single_task( + task_data, "baseline", idx, total_tasks + ) + self.results["baseline"].append(result) + + # Run tool-assisted condition + if self.condition in ["tool_assisted", "both"]: + log.info("\n" + "="*70) + log.info("TOOL-ASSISTED CONDITION (With Code Execution)") + log.info("="*70) + for idx, task_data in enumerate(tasks, 1): + result = await self.run_single_task( + task_data, "tool_assisted", idx, total_tasks + ) + self.results["tool_assisted"].append(result) + + # Save results + self.results["experiment_info"]["end_time"] = datetime.now().isoformat() + results_file = self.results_dir / "experiment_results.json" + with open(results_file, "w") as f: + json.dump(self.results, f, indent=2) + + log.info(f"\n✓ Results saved to: {results_file}") + + # Print summary + self._print_summary() + + # Flush Langfuse + self.langfuse_client.flush() + + def _print_summary(self): + """Print experiment summary.""" + log.info("\n" + "="*70) + def _print_summary(self): + """Print experiment summary.""" + log.info("\n" + "="*70) + log.info("EXPERIMENT SUMMARY") + log.info("="*70) + + for condition in ["baseline", "tool_assisted"]: + if condition not in self.results or not self.results[condition]: + continue + + results = self.results[condition] + completed = [r for r in results if r["status"] == "completed"] + successful = [r for r in completed if r.get("success", False)] + + log.info(f"\n{condition.upper().replace('_', ' ')}:") + log.info(f" Total tasks: {len(results)}") + log.info(f" Completed: {len(completed)}") + log.info(f" Successful: {len(successful)}") + + if completed: + success_rate = len(successful) / len(completed) * 100 + avg_time = sum(r["execution_time"] for r in completed) / len(completed) + log.info(f" Success rate: {success_rate:.1f}%") + log.info(f" Average time: {avg_time:.2f}s") + + log.info("\n" + "="*70) + + +async def main(): + """Main execution function.""" + parser = argparse.ArgumentParser( + description="Run mathematical reasoning experiments with configurable parameters", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Quick test: 1 task per capability, tool-assisted condition + python run_experiments.py --num-tasks 1 --condition tool_assisted + + # Quick test: 1 task per capability, both conditions + python run_experiments.py --num-tasks 1 --condition both + + # Full experiment: 3 tasks per capability (default) + python run_experiments.py --num-tasks 3 + + # Custom configuration + python run_experiments.py --model gemini-2.5-pro --rounds 2 --num-tasks 10 --output my_results + """ + ) + parser.add_argument( + "--model", + default="gemini-2.5-pro", + help="LLM model to use (default: gemini-2.5-pro)" + ) + parser.add_argument( + "--rounds", + type=int, + default=1, + help="Number of debate rounds (default: 1)" + ) + parser.add_argument( + "--num-tasks", + type=int, + default=3, + help="Number of tasks per capability to run (default: 3 for full experiment)" + ) + parser.add_argument( + "--condition", + choices=["baseline", "tool_assisted", "both"], + default="both", + help="Which condition to run (default: both)" + ) + parser.add_argument( + "--output", + default="experiment_results", + help="Output directory for results (default: experiment_results)" + ) + + args = parser.parse_args() + + runner = ExperimentRunner( + model_name=args.model, + num_rounds=args.rounds, + num_tasks=args.num_tasks, + condition=args.condition, + output_dir=args.output, + ) + await runner.run_experiment() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/selected_tasks.json b/selected_tasks.json new file mode 100644 index 00000000..63f49e40 --- /dev/null +++ b/selected_tasks.json @@ -0,0 +1,162 @@ +{ + "computational_linear_algebra": [ + { + "task_id": "2", + "problem": "Use QR factorization to solve the system Ax = b for A = \\[\begin{pmatrix} 1 & 1 \\ 1 & -1 \\end{pmatrix}\\] and b = \\[\begin{pmatrix} 2 \\ 0 \\end{pmatrix}\\].", + "answer": "\\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]", + "reasoning": "I'll solve this step by step using QR factorization.\n\n1) First, let's find the QR factorization of A where:\nA = \\[begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix}\\]\n\n2) For QR factorization, we need to find Q (orthogonal) and R (upper triangular).\n\n3) To find Q, we'll use the Gram-Schmidt process:\n - v₁ = \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]\n - q₁ = v₁/‖v₁‖ = \\[begin{pmatrix} 1/√2 \\\\ 1/√2 \\end{pmatrix}\\]\n - v₂ = \\[begin{pmatrix} 1 \\\\ -1 \\end{pmatrix}\\]\n - q₂ = v₂/‖v₂‖ = \\[begin{pmatrix} 1/√2 \\\\ -1/√2 \\end{pmatrix}\\]\n\n4) Therefore, Q = \\[begin{pmatrix} 1/√2 & 1/√2 \\\\ 1/√2 & -1/√2 \\end{pmatrix}\\]\n\n5) R = Q^T A = \\[begin{pmatrix} √2 & 0 \\\\ 0 & √2 \\end{pmatrix}\\]\n\n6) Now we solve Rx = Q^T b:\n - Q^T b = \\[begin{pmatrix} 1/√2 & 1/√2 \\\\ 1/√2 & -1/√2 \\end{pmatrix}\\] \\[begin{pmatrix} 2 \\\\ 0 \\end{pmatrix}\\]\n - Q^T b = \\[begin{pmatrix} √2 \\\\ √2 \\end{pmatrix}\\]\n\n7) This gives us:\n √2x₁ = √2\n √2x₂ = √2\n\n8) Solving:\n x₁ = 1\n x₂ = 1\n\nTherefore, x = \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]\n\nANSWER: \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]", + "verification": { + "verdict": "no", + "reason": "This answer only provides the final solution vector x without showing the QR factorization process that was specifically requested in the problem. While the solution vector \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] appears to be correct (we can verify that A\\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] = \\[begin{pmatrix} 2 \\\\ 0 \\end{pmatrix}\\]), the problem explicitly asks to use QR factorization to solve the system. The answer should include:\n1. The calculation of Q and R matrices\n2. Using these matrices to solve the system (Rx = Q^T b)\n3. The final solution" + } + }, + { + "task_id": "3", + "problem": "Explain how computational linear algebra techniques can be implemented in Python using libraries such as NumPy and SciPy.", + "answer": "NO_ANSWER", + "reasoning": "I'll explain how to implement computational linear algebra techniques using Python libraries, focusing on the most common operations and their implementations.\n\n1. Basic Matrix Operations with NumPy\n```python\nimport numpy as np\n\n# Creating matrices\nA = np.array([[1, 2], [3, 4]])\nB = np.array([[5, 6], [7, 8]])\n\n# Matrix addition\naddition = A + B\n\n# Matrix multiplication\nmultiplication = np.dot(A, B) # or A @ B\n\n# Transpose\ntranspose = A.T\n\n# Matrix inverse\ninverse = np.linalg.inv(A)\n```\n\n2. Solving Linear Systems using NumPy\n```python\n# Solve Ax = b\nA = np.array([[2, 1], [1, 3]])\nb = np.array([4, 5])\nx = np.linalg.solve(A, b)\n```\n\n3. Eigenvalues and Eigenvectors\n```python\n# Calculate eigenvalues and eigenvectors\neigenvalues, eigenvectors = np.linalg.eig(A)\n```\n\n4. Matrix Decompositions using SciPy\n```python\nfrom scipy import linalg\n\n# LU Decomposition\nP, L, U = linalg.lu(A)\n\n# QR Decomposition\nQ, R = linalg.qr(A)\n\n# Singular Value Decomposition (SVD)\nU, s, Vh = linalg.svd(A)\n```\n\n5. Sparse Matrix Operations with SciPy\n```python\nfrom scipy import sparse\n\n# Create sparse matrix\nsparse_matrix = sparse.csr_matrix([[1, 0, 0], [0, 2, 0], [0, 0, 3]])\n\n# Sparse matrix operations\nresult = sparse.linalg.spsolve(sparse_matrix, b)\n```\n\nKey Features and Benefits:\n\n1. Efficiency:\n- NumPy and SciPy use optimized C implementations\n- Sparse matrix support for large, sparse systems\n- Vectorized operations avoid explicit loops\n\n2. Accuracy:\n- Built-in numerical stability considerations\n- Multiple precision options\n- Various algorithms for different problem types\n\n3. Convenience:\n- Simple syntax for complex operations\n- Comprehensive documentation\n- Wide range of pre-implemented methods\n\n4. Common Applications:\n- Scientific computing\n- Machine learning\n- Data analysis\n- Engineering simulations\n\nBest Practices:\n\n1. Choose appropriate data types:\n- Use np.float64 for standard precision\n- Consider np.float32 for memory-intensive applications\n\n2. Error handling:\n- Check matrix conditions (singular, ill-conditioned)\n- Validate input dimensions\n- Handle numerical errors appropriately\n\n3. Performance optimization:\n- Use sparse matrices for sparse problems\n- Vectorize operations when possible\n- Consider memory constraints for large systems\n\n4. Algorithm selection:\n- Choose decomposition methods based on matrix properties\n- Consider numerical stability requirements\n- Balance accuracy vs. computational cost\n\nThese implementations provide a robust foundation for computational linear algebra tasks, offering both performance and ease of use.", + "verification": { + "verdict": "no", + "reason": "unsolved" + } + }, + { + "task_id": "4", + "problem": "Implement the Gaussian elimination algorithm to solve a system of linear equations without using any built-in linear algebra functions. Test your implementation on the system: $\\begin{cases} 2x + y + z = 8 \\\\ 4x - 2y + 3z = 1 \\\\ -2x + 3y - z = 5 \\end{cases}$", + "answer": "(3, 3.25, -2)", + "reasoning": "I'll implement Gaussian elimination to solve this system of linear equations step by step.\n\n```python\ndef gaussian_elimination(A, b):\n n = len(A)\n # Augment the matrix A with the vector b\n Ab = [[A[i][j] for j in range(n)] + [b[i]] for i in range(n)]\n \n # Forward elimination\n for i in range(n):\n # Find pivot\n pivot = Ab[i][i]\n \n # Eliminate column i\n for j in range(i + 1, n):\n factor = Ab[j][i] / pivot\n for k in range(i, n + 1):\n Ab[j][k] -= factor * Ab[i][k]\n \n # Back substitution\n x = [0] * n\n for i in range(n-1, -1, -1):\n x[i] = Ab[i][n]\n for j in range(i+1, n):\n x[i] -= Ab[i][j] * x[j]\n x[i] /= Ab[i][i]\n \n return x\n\n# Define the system\nA = [[2, 1, 1],\n [4, -2, 3],\n [-2, 3, -1]]\nb = [8, 1, 5]\n\n# Solve the system\nsolution = gaussian_elimination(A, b)\n\n# Round to 4 decimal places for clarity\nsolution = [round(x, 4) for x in solution]\n```\n\nLet's solve this step by step:\n\n1) First, we create the augmented matrix:\n [2 1 1 | 8]\n [4 -2 3 | 1]\n [-2 3 -1 | 5]\n\n2) Forward elimination:\n R2 = R2 - 2R1\n R3 = R3 + R1\n \n [2 1 1 | 8]\n [0 -4 1 |-15]\n [0 4 0 | 13]\n\n3) Continue elimination:\n R3 = R3 + R2\n \n [2 1 1 | 8]\n [0 -4 1 |-15]\n [0 0 1 | -2]\n\n4) Back substitution:\n z = -2\n y = (-15 - z)/(-4) = (-15 + 2)/(-4) = 13/4\n x = (8 - y - z)/2 = (8 - 13/4 + 2)/2 = 3\n\nThe solution is:\nx = 3\ny = 3.25\nz = -2\n\nANSWER: (3, 3.25, -2)\n\nTo verify this is correct, we can substitute these values back into the original equations:\n2(3) + 3.25 + (-2) = 8 ✓\n4(3) - 2(3.25) + 3(-2) = 1 ✓\n-2(3) + 3(3.25) - (-2) = 5 ✓", + "verification": { + "verdict": "no", + "reason": "The problem asks for two things: 1) Implementation of Gaussian elimination algorithm and 2) Solution to the specific system of equations. The answer only provides the final solution (3, 3.25, -2) without showing the implementation or the steps of Gaussian elimination. While I can verify that these values do satisfy the system of equations:\n\n2(3) + (3.25) + (-2) = 8\n4(3) - 2(3.25) + 3(-2) = 1\n-2(3) + 3(3.25) + (-1)(-2) = 5\n\nThe answer fails to address the main requirement of implementing the Gaussian elimination algorithm. The implementation part was a crucial component of the problem." + } + } + ], + "numerical_simulation_dynamical_systems": [ + { + "task_id": "1", + "problem": "Use Euler's method to approximate the solution of dy/dt = y, y(0) = 1, with a step size of 0.1 over the interval [0, 1].", + "answer": "2.593743", + "reasoning": "I'll solve this step by step using Euler's method.\n\nEuler's method formula: y(t + h) = y(t) + h * f(t,y(t))\nwhere h = 0.1 (step size)\nf(t,y) = y (given in the differential equation)\nInitial condition: y(0) = 1\n\nLet's calculate each step:\n\nt = 0.0: y(0) = 1.000000\nt = 0.1: y(0.1) = 1 + 0.1(1) = 1.100000\nt = 0.2: y(0.2) = 1.1 + 0.1(1.1) = 1.210000\nt = 0.3: y(0.3) = 1.21 + 0.1(1.21) = 1.331000\nt = 0.4: y(0.4) = 1.331 + 0.1(1.331) = 1.464100\nt = 0.5: y(0.5) = 1.4641 + 0.1(1.4641) = 1.610510\nt = 0.6: y(0.6) = 1.61051 + 0.1(1.61051) = 1.771561\nt = 0.7: y(0.7) = 1.771561 + 0.1(1.771561) = 1.948717\nt = 0.8: y(0.8) = 1.948717 + 0.1(1.948717) = 2.143589\nt = 0.9: y(0.9) = 2.143589 + 0.1(2.143589) = 2.357948\nt = 1.0: y(1.0) = 2.357948 + 0.1(2.357948) = 2.593743\n\nANSWER: 2.593743", + "verification": { + "verdict": "no", + "reason": "The problem asks for an approximation using Euler's method, which requires showing the iterative steps and calculations. While 2.593743 is close to the actual final value at t=1 (which should be e ≈ 2.718282), just providing a single numerical value is insufficient. A complete solution should show:\n1. The formula used (yn+1 = yn + h*f(yn))\n2. The iterative calculations for each step from t=0 to t=1 with h=0.1\n3. A table or sequence of approximations" + } + }, + { + "task_id": "2", + "problem": "Apply the fourth-order Runge-Kutta method to solve the system: dx/dt = y, dy/dt = -x, with initial conditions x(0)=1, y(0)=0, step size 0.2, over [0, 2].", + "answer": "-0.416,-0.909", + "reasoning": "I'll solve this using the fourth-order Runge-Kutta (RK4) method. Let's break this down step by step.\n\nThe system is:\nf₁(t,x,y) = y\nf₂(t,x,y) = -x\n\nRK4 formulas for each step:\nk₁ᵡ = h·f₁(t,x,y)\nk₁ʸ = h·f₂(t,x,y)\n\nk₂ᵡ = h·f₁(t+h/2, x+k₁ᵡ/2, y+k₁ʸ/2)\nk₂ʸ = h·f₂(t+h/2, x+k₁ᵡ/2, y+k₁ʸ/2)\n\nk₃ᵡ = h·f₁(t+h/2, x+k₂ᵡ/2, y+k₂ʸ/2)\nk₃ʸ = h·f₂(t+h/2, x+k₂ᵡ/2, y+k₂ʸ/2)\n\nk₄ᵡ = h·f₁(t+h, x+k₃ᵡ, y+k₃ʸ)\nk₄ʸ = h·f₂(t+h, x+k₃ᵡ, y+k₃ʸ)\n\nWith h = 0.2, t₀ = 0, x₀ = 1, y₀ = 0\n\nLet's calculate the first few points:\n\nt=0:\nx₀=1.000000, y₀=0.000000\n\nt=0.2:\nk₁ᵡ = 0.2(0) = 0\nk₁ʸ = 0.2(-1) = -0.2\nk₂ᵡ = 0.2(-0.1) = -0.02\nk₂ʸ = 0.2(-0.99) = -0.198\nk₃ᵡ = 0.2(-0.099) = -0.0198\nk₃ʸ = 0.2(-0.99) = -0.198\nk₄ᵡ = 0.2(-0.198) = -0.0396\nk₄ʸ = 0.2(-0.98) = -0.196\n\nx₁ = 1 + (1/6)(0 - 0.02 - 0.0198 - 0.0396) = 0.987\ny₁ = 0 + (1/6)(-0.2 - 0.198 - 0.198 - 0.196) = -0.198\n\nContinuing this process for the remaining points up to t=2...\n\nThe final values at t=2 are approximately:\nx(2) = -0.416\ny(2) = -0.909\n\nANSWER: -0.416,-0.909", + "verification": { + "verdict": "no", + "reason": "The problem asks for a numerical solution using RK4 method for a coupled system of ODEs over the interval [0,2]. A complete solution should include:\n1. Multiple points along the solution trajectory (not just one point)\n2. Both x and y values at each time step\n3. Values computed using the proper RK4 formulas\n4. A sequence starting from t=0 to t=2 with step size 0.2\n\nThe given answer only provides two numbers (-0.416,-0.909) without context or the full solution trajectory. This is insufficient as it doesn't show the evolution of the system over the specified time interval and doesn't demonstrate the application of the RK4 method." + } + }, + { + "task_id": "3", + "problem": "Discuss the importance of numerical stability when simulating the Lorenz system with sensitive initial conditions.", + "answer": "Numerical stability is essential for Lorenz system simulation because the chaotic nature amplifies small errors exponentially; proper choice of integration method and step size is crucial to maintain accuracy and preserve the system's qualitative behavior while controlling error growth.", + "reasoning": "Let me discuss the importance of numerical stability when simulating the Lorenz system.\n\nThe Lorenz system is a classic example of a chaotic dynamical system, described by three coupled differential equations:\n\ndx/dt = σ(y - x)\ndy/dt = x(ρ - z) - y\ndz/dt = xy - βz\n\nNumerical stability is crucial when simulating this system for several reasons:\n\n1. Sensitive Dependence on Initial Conditions:\n- The Lorenz system exhibits chaos, meaning tiny differences in initial conditions lead to vastly different trajectories\n- Numerical errors can rapidly compound and lead to completely different solutions\n- This makes stability of the numerical method critical\n\n2. Choice of Time Step:\n- Too large a time step can lead to numerical instability\n- Too small a time step increases computational cost and round-off errors\n- Finding the right balance is essential for accurate simulation\n\n3. Method Selection:\n- Explicit methods (like RK4) need smaller time steps for stability\n- Implicit methods offer better stability but are computationally expensive\n- The choice of numerical method affects the accuracy of the strange attractor\n\n4. Error Propagation:\n- Local truncation errors can grow exponentially due to the system's chaotic nature\n- Maintaining numerical stability helps control error growth\n- Stable methods help preserve the qualitative behavior of the system\n\n5. Conservation Properties:\n- Some numerical methods better preserve the system's invariants\n- Stability helps maintain the bounded nature of solutions\n- Proper simulation should show the butterfly-shaped attractor\n\nPractical Recommendations:\n- Use adaptive step-size methods\n- Implement symplectic integrators when possible\n- Validate results with different numerical schemes\n- Monitor conservation laws and system invariants\n\nANSWER: Numerical stability is essential for Lorenz system simulation because the chaotic nature amplifies small errors exponentially; proper choice of integration method and step size is crucial to maintain accuracy and preserve the system's qualitative behavior while controlling error growth.", + "verification": { + "verdict": "no", + "reason": "Failed to verify task: AssertionError(\"Invalid response: no\\n\\nwhile the answer provides correct basic information about numerical stability in lorenz system simulation, it lacks sufficient depth for a complete response. a more comprehensive answer should include: examples of appropriate numerical schemes (like runge-kutta methods), explanation of how lyapunov exponents relate to numerical stability, discussion of local versus global stability considerations, and specific strategies for handling the butterfly effect in numerical implementations. the current answer gives only a high-level overview without the technical detail needed for this advanced topic.. Expected 'yes' or 'no'.\")" + } + } + ], + "numerical_methods_ode": [ + { + "task_id": "2", + "problem": "Apply the 4th order Runge-Kutta method to solve dy/dx = -2x + y, y(0) = 1 with step size h = 0.2, find y(0.4).", + "answer": "1.3089", + "reasoning": "Let me solve this step by step using the 4th order Runge-Kutta method (RK4).\n\nThe RK4 method for dy/dx = f(x,y) uses:\nk1 = h*f(x_n, y_n)\nk2 = h*f(x_n + h/2, y_n + k1/2)\nk3 = h*f(x_n + h/2, y_n + k2/2)\nk4 = h*f(x_n + h, y_n + k3)\ny_(n+1) = y_n + (1/6)(k1 + 2k2 + 2k3 + k4)\n\nWhere f(x,y) = -2x + y\n\nGiven:\n- h = 0.2\n- y(0) = 1\n- We need to find y(0.4), which means we need 2 steps\n\nStep 1: From x = 0 to x = 0.2\nx_0 = 0, y_0 = 1\nk1 = 0.2*(-2(0) + 1) = 0.2\nk2 = 0.2*(-2(0.1) + (1 + 0.1)) = 0.2*(-0.2 + 1.1) = 0.18\nk3 = 0.2*(-2(0.1) + (1 + 0.09)) = 0.2*(-0.2 + 1.09) = 0.178\nk4 = 0.2*(-2(0.2) + (1 + 0.178)) = 0.2*(-0.4 + 1.178) = 0.1556\n\ny_1 = 1 + (1/6)(0.2 + 2(0.18) + 2(0.178) + 0.1556)\ny_1 = 1 + 0.1789 = 1.1789\n\nStep 2: From x = 0.2 to x = 0.4\nx_1 = 0.2, y_1 = 1.1789\nk1 = 0.2*(-2(0.2) + 1.1789) = 0.2*(0.7789) = 0.15578\nk2 = 0.2*(-2(0.3) + (1.1789 + 0.07789)) = 0.2*(-0.6 + 1.25679) = 0.13136\nk3 = 0.2*(-2(0.3) + (1.1789 + 0.06568)) = 0.2*(-0.6 + 1.24458) = 0.12892\nk4 = 0.2*(-2(0.4) + (1.1789 + 0.12892)) = 0.2*(-0.8 + 1.30782) = 0.10156\n\ny_2 = 1.1789 + (1/6)(0.15578 + 2(0.13136) + 2(0.12892) + 0.10156)\ny_2 = 1.1789 + 0.13 = 1.3089\n\nANSWER: 1.3089", + "verification": { + "verdict": "no", + "reason": "Failed to verify task: AssertionError(\"Invalid response: no\\n\\nthe answer only provides the final numerical result without showing the necessary intermediate calculations and steps of the rk4 method. for numerical methods problems, demonstrating the proper application of the method through intermediate steps is essential for a complete solution. a complete solution should show the calculation of k1, k2, k3, k4 for each step and how the final value was obtained.. Expected 'yes' or 'no'.\")" + } + }, + { + "task_id": "4", + "problem": "Using Euler's method with step size h = 0.1, compute the first three approximations for the solution of $\\frac{dy}{dx} = y - x^2$ with initial condition y(0) = 1 on the interval [0, 0.3].", + "answer": "1.3259", + "reasoning": "Let me solve this step by step using Euler's method.\n\nEuler's method formula: y_{n+1} = y_n + h*f(x_n, y_n)\nwhere f(x, y) = y - x²\n\nGiven:\n- h = 0.1\n- x₀ = 0\n- y₀ = 1\n- f(x, y) = y - x²\n\nStep 1: Find y₁ at x₁ = 0.1\nf(x₀, y₀) = f(0, 1) = 1 - 0² = 1\ny₁ = y₀ + h*f(x₀, y₀)\ny₁ = 1 + 0.1(1)\ny₁ = 1.1\n\nStep 2: Find y₂ at x₂ = 0.2\nf(x₁, y₁) = f(0.1, 1.1) = 1.1 - (0.1)² = 1.1 - 0.01 = 1.09\ny₂ = y₁ + h*f(x₁, y₁)\ny₂ = 1.1 + 0.1(1.09)\ny₂ = 1.209\n\nStep 3: Find y₃ at x₃ = 0.3\nf(x₂, y₂) = f(0.2, 1.209) = 1.209 - (0.2)² = 1.209 - 0.04 = 1.169\ny₃ = y₂ + h*f(x₂, y₂)\ny₃ = 1.209 + 0.1(1.169)\ny₃ = 1.3259\n\nThe three approximations are:\ny₁ = 1.1\ny₂ = 1.209\ny₃ = 1.3259\n\nANSWER: 1.3259", + "verification": { + "verdict": "no", + "reason": "The problem asks for the first three approximations using Euler's method, which means we need values at x = 0, 0.1, 0.2, and 0.3. The answer only provides a single number (1.3259) without showing the step-by-step calculations or the other approximations. Euler's method requires calculating each point using the formula y_{n+1} = y_n + h*f(x_n, y_n), where f(x,y) = y - x². The answer is incomplete as it doesn't show all three approximations requested." + } + }, + { + "task_id": "5", + "problem": "Compare the local truncation error between Euler's method and the fourth-order Runge-Kutta method for solving $\\frac{dy}{dx} = -2y$ with y(0) = 1 using a step size of h = 0.2.", + "answer": "0.07027", + "reasoning": "Let me solve this step by step.\n\n1) First, let's recall the true solution to this ODE:\n y(x) = e^(-2x)\n\n2) For the local truncation error, we'll look at just one step from x₀ = 0 to x₁ = 0.2\n\n3) True value at x₁ = 0.2:\n y(0.2) = e^(-2×0.2) = e^(-0.4) = 0.67032\n\n4) Euler's Method:\n y₁ = y₀ + h×f(x₀,y₀)\n f(x,y) = -2y\n y₁ = 1 + 0.2×(-2×1)\n y₁ = 1 - 0.4 = 0.6\n\n5) Fourth-order Runge-Kutta Method:\n k₁ = h×f(x₀,y₀) = 0.2×(-2×1) = -0.4\n k₂ = h×f(x₀+h/2, y₀+k₁/2) = 0.2×(-2×(1-0.2)) = -0.32\n k₃ = h×f(x₀+h/2, y₀+k₂/2) = 0.2×(-2×(1-0.16)) = -0.336\n k₄ = h×f(x₀+h, y₀+k₃) = 0.2×(-2×(1-0.336)) = -0.2656\n\n y₁ = y₀ + (k₁ + 2k₂ + 2k₃ + k₄)/6\n y₁ = 1 + (-0.4 - 0.64 - 0.672 - 0.2656)/6\n y₁ = 0.67027\n\n6) Local Truncation Errors:\n Euler: |0.67032 - 0.6| = 0.07032\n RK4: |0.67032 - 0.67027| = 0.00005\n\nThe difference in local truncation errors is 0.07032 - 0.00005 = 0.07027\n\nANSWER: 0.07027", + "verification": { + "verdict": "no", + "reason": "This problem requires comparing the local truncation errors between two numerical methods (Euler and RK4) for a specific ODE. The answer only provides a single number (0.07027) without any context, comparison, or explanation. A complete answer should:\n1. Calculate the local truncation error for both methods\n2. Show the comparison between them\n3. Provide clear context for what the number represents\n4. Include appropriate units and reference points" + } + } + ], + "complex_analysis_problems": [ + { + "task_id": "3", + "problem": "Is the function $f(z) = z^2$ conformal at $z = 2$?", + "answer": "Yes", + "reasoning": "Let me solve this step by step.\n\n1) For a function to be conformal at a point, it must be:\n * Analytic at that point\n * Have a non-zero derivative at that point\n\n2) First, let's check if $f(z) = z^2$ is analytic at $z = 2$\n * $z^2$ is a polynomial function\n * All polynomial functions are analytic everywhere in the complex plane\n * Therefore, $f(z)$ is analytic at $z = 2$\n\n3) Next, let's find the derivative of $f(z)$\n * $f'(z) = 2z$\n * At $z = 2$: $f'(2) = 4$\n\n4) Since $f'(2) = 4 \\neq 0$, and $f(z)$ is analytic at $z = 2$, the function is conformal at $z = 2$.\n\nANSWER: Yes", + "verification": { + "verdict": "no", + "reason": "To determine if a function is conformal at a point, we need to verify that it's both analytic at that point and has a non-zero derivative there. The answer simply states \"Yes\" without showing any verification of these conditions. While the answer happens to be correct (since f(z)=z² is analytic everywhere and f'(z)=2z which is non-zero at z=2), the lack of justification means the answer is incomplete. A complete answer should demonstrate why these conditions are satisfied." + } + }, + { + "task_id": "4", + "problem": "Evaluate the contour integral $\\oint_{|z|=2} \\frac{z^2+1}{z^3-8} dz$ using the residue theorem.", + "answer": "5πi/6", + "reasoning": "Let me solve this step by step.\n\n1) First, let's find the poles inside the contour |z|=2.\n - The denominator z³-8 = 0 when z = 2 (since 8 = 2³)\n - The cube root of 8 has three values: 2, 2e^(2πi/3), and 2e^(4πi/3)\n - Since |z|=2, only z=2 lies on our contour\n\n2) Since z=2 lies on the contour, we need to use a slightly larger contour.\n Let's use |z|=2+ε where ε is small and positive.\n Now only z=2 lies inside our contour.\n\n3) Let's find the residue at z=2.\n - This is a simple pole\n - For a simple pole at a point a, the residue is:\n Res(f,a) = lim_{z→a} (z-a)f(z)\n\n4) At z=2:\n Res(f,2) = lim_{z→2} (z-2)(z²+1)/(z³-8)\n = lim_{z→2} (z²+1)/(z²+2z+4)\n = (4+1)/(4+4+4)\n = 5/12\n\n5) By the Residue Theorem:\n $\\oint_{|z|=2} \\frac{z^2+1}{z^3-8} dz = 2πi \\sum Res$\n = 2πi(5/12)\n = 5πi/6\n\nANSWER: 5πi/6", + "verification": { + "verdict": "no", + "reason": "Failed to verify task: AssertionError('Invalid response: no\\n\\nthe answer \"5πi/6\" might be numerically correct, but it does not demonstrate the solution process required for this complex analysis problem. a complete answer should:\\n1. identify the poles and their nature\\n2. show the residue calculations\\n3. apply the residue theorem correctly\\n4. handle the fact that z=2 lies on the contour\\n5. show the final computation leading to 5πi/6. Expected \\'yes\\' or \\'no\\'.')" + } + }, + { + "task_id": "5", + "problem": "Find the conformal mapping that transforms the unit disk onto the upper half-plane and maps the points -1, i, and 1 to -1, i, and 1 respectively.", + "answer": "f(z) = (z + 1)/(z - 1)", + "reasoning": "Let me solve this step by step.\n\n1) We know that a Möbius transformation will be the conformal mapping we're looking for, as it preserves circles and lines.\n\n2) The general form of a Möbius transformation is:\n f(z) = (az + b)/(cz + d)\n where a, b, c, d are complex numbers and ad - bc ≠ 0\n\n3) We have three conditions:\n f(-1) = -1\n f(i) = i\n f(1) = 1\n\n4) Since we're mapping the unit disk to the upper half-plane, a well-known transformation is:\n f(z) = i(1 + z)/(1 - z)\n\n5) However, this maps:\n z = 1 → ∞\n z = -1 → 0\n z = i → 1\n\n6) We need to modify this to match our conditions. Let's compose it with another Möbius transformation.\n\n7) After composing with appropriate transformations and simplifying, the mapping that satisfies all conditions is:\n f(z) = (z + 1)/(z - 1)\n\n8) Let's verify:\n - When z = -1: f(-1) = (-1 + 1)/(-1 - 1) = 0/-2 = -1\n - When z = i: f(i) = (i + 1)/(i - 1) = i\n - When z = 1: f(1) = (1 + 1)/(1 - 1) = ∞/0 = 1 (in the limit)\n\n9) This mapping takes the unit disk to the upper half-plane and satisfies all the required point mappings.\n\nANSWER: f(z) = (z + 1)/(z - 1)", + "verification": { + "verdict": "no", + "reason": "Let me analyze this systematically:\n1. First, the Möbius transformation f(z) = (z+1)/(z-1) is indeed a conformal mapping.\n2. The unit disk to upper half-plane mapping typically uses the Cayley transform f(z) = i(1+z)/(1-z).\n3. The given function f(z) = (z+1)/(z-1) does NOT map the unit disk to the upper half-plane. In fact, it maps the unit disk to the left half-plane.\n4. While the function does map -1 to -1 and 1 to 1, it does NOT map i to i. We can verify this by substituting z = i:\n f(i) = (i+1)/(i-1) = (i+1)(-i-1)/(i-1)(-i-1) = (-i-i+1)/(2) = -i+1/2 ≠ i" + } + } + ], + "advanced_integration_techniques": [ + { + "task_id": "2", + "problem": "Calculate \\( \\int \frac{1}{x^2 + 4x + 5} \\, dx \\).", + "answer": "$\\arctan(x + 2) + C$", + "reasoning": "Let me solve this integral using partial fractions and arctangent integration.\n\n1) First, let's complete the square in the denominator:\n \\( x^2 + 4x + 5 = (x^2 + 4x + 4) + 1 = (x + 2)^2 + 1 \\)\n\n2) Now our integral is:\n \\( \\int \\frac{1}{(x + 2)^2 + 1} \\, dx \\)\n\n3) This is a standard form integral that results in arctangent:\n The general form is: \\( \\int \\frac{1}{a^2 + u^2} \\, du = \\frac{1}{a}\\arctan(\\frac{u}{a}) + C \\)\n where in our case, u = (x + 2) and a = 1\n\n4) Therefore:\n \\( \\int \\frac{1}{(x + 2)^2 + 1} \\, dx = \\arctan(x + 2) + C \\)\n\nANSWER: $\\arctan(x + 2) + C$", + "verification": { + "verdict": "no", + "reason": "Failed to verify task: AssertionError(\"Invalid response: yes\\n\\nthe answer $\\\\arctan(x + 2) + c$ is correct because:\\n- the denominator x² + 4x + 5 can be rewritten as (x + 2)² + 1\\n- the integral follows the standard form ∫ 1/(u² + 1) du = arctan(u) + c\\n- the substitution u = x + 2 leads directly to the given answer\\n- the inclusion of the constant of integration c makes the answer complete. Expected 'yes' or 'no'.\")" + } + }, + { + "task_id": "6", + "problem": "Evaluate $\\int \\frac{x^2+3x+2}{(x+1)(x+2)}dx$ using partial fraction decomposition.", + "answer": "$\\ln|\\frac{(x+2)^4}{x+1}| + C$", + "reasoning": "Let me solve this step by step.\n\n1) First, let's set up partial fraction decomposition:\n $\\frac{x^2+3x+2}{(x+1)(x+2)} = \\frac{A}{x+1} + \\frac{B}{x+2}$\n\n2) Multiply both sides by (x+1)(x+2):\n $x^2+3x+2 = A(x+2) + B(x+1)$\n $x^2+3x+2 = (A+B)x + (2A+B)$\n\n3) Compare coefficients:\n $x^2$ term: 1 = 0 \n $x^1$ term: 3 = A+B\n $x^0$ term: 2 = 2A+B\n\n4) Solve the system of equations:\n A+B = 3\n 2A+B = 2\n \n Subtracting equations:\n -A = 1\n A = -1\n B = 4\n\n5) Therefore:\n $\\frac{x^2+3x+2}{(x+1)(x+2)} = -\\frac{1}{x+1} + \\frac{4}{x+2}$\n\n6) Now integrate:\n $\\int \\frac{x^2+3x+2}{(x+1)(x+2)}dx = -\\int\\frac{1}{x+1}dx + 4\\int\\frac{1}{x+2}dx$\n \n7) These are basic logarithmic integrals:\n $= -\\ln|x+1| + 4\\ln|x+2| + C$\n \n8) This can be written as:\n $= \\ln|\\frac{(x+2)^4}{x+1}| + C$\n\nANSWER: $\\ln|\\frac{(x+2)^4}{x+1}| + C$", + "verification": { + "verdict": "no", + "reason": "Let me analyze this step by step:\n1) For partial fraction decomposition, we should get terms of the form A/(x+1) + B/(x+2)\n2) The numerator x^2+3x+2 and denominator (x+1)(x+2) suggest this is possible\n3) The given answer $\\ln|\\frac{(x+2)^4}{x+1}| + C$ cannot be correct because:\n - It has (x+2)^4 in the numerator, which would give a much higher degree when differentiated\n - The original fraction doesn't suggest we should get a fourth power\n - When you differentiate this answer, it won't give back the original integrand" + } + }, + { + "task_id": "9", + "problem": "Evaluate $\\int \\frac{x^3+2}{x^4+1}dx$ using partial fraction decomposition with complex roots.", + "answer": "$\\frac{1}{2}\\ln(x^4+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$", + "reasoning": "Let me solve this step by step using partial fraction decomposition.\n\n1) First, let's factor the denominator x⁴+1\n The roots are: x = ±(1+i)/√2 and x = ±(-1+i)/√2\n So, x⁴+1 = (x² + √2x + 1)(x² - √2x + 1)\n\n2) Set up partial fraction decomposition:\n $\\frac{x^3+2}{x^4+1} = \\frac{Ax+B}{x^2+\\sqrt{2}x+1} + \\frac{Cx+D}{x^2-\\sqrt{2}x+1}$\n\n3) Multiply both sides by (x⁴+1):\n x³+2 = (Ax+B)(x² - √2x + 1) + (Cx+D)(x² + √2x + 1)\n\n4) Comparing coefficients:\n x³: A + C = 1\n x²: -A√2 + B + C√2 + D = 0\n x¹: A + B(-√2) + C + D√2 = 0\n x⁰: B + D = 2\n\n5) Solving this system of equations:\n A = 1/2\n B = 1\n C = 1/2\n D = 1\n\n6) Therefore:\n $\\int \\frac{x^3+2}{x^4+1}dx = \\int \\frac{\\frac{1}{2}x+1}{x^2+\\sqrt{2}x+1}dx + \\int \\frac{\\frac{1}{2}x+1}{x^2-\\sqrt{2}x+1}dx$\n\n7) Each of these integrals can be solved using the arctangent substitution:\n For the first integral: $\\frac{1}{2}\\ln(x^2+\\sqrt{2}x+1) + \\frac{1}{\\sqrt{2}}\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})$\n For the second integral: $\\frac{1}{2}\\ln(x^2-\\sqrt{2}x+1) - \\frac{1}{\\sqrt{2}}\\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})$\n\n8) Combining terms:\n $\\frac{1}{2}\\ln(x^2+\\sqrt{2}x+1) + \\frac{1}{2}\\ln(x^2-\\sqrt{2}x+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$\n\nANSWER: $\\frac{1}{2}\\ln(x^4+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$", + "verification": { + "verdict": "no", + "reason": "Failed to verify task: AssertionError(\"Invalid response: yes\\n\\nthe answer is both correct and complete. it properly uses partial fraction decomposition with complex roots, includes all necessary terms with correct coefficients, and provides the constant of integration. the result is in its simplest form and would differentiate back to the original integrand.. Expected 'yes' or 'no'.\")" + } + } + ] +} \ No newline at end of file diff --git a/src/__init__.py b/src/__init__.py index 15dd3265..1c43c9a3 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -4,3 +4,5 @@ It initializes the src module, making it easier to import and use the utilities provided by this module in other parts of the project. """ + +__version__ = "0.1.0" diff --git a/src/task_solver/code_executor.py b/src/task_solver/code_executor.py new file mode 100644 index 00000000..0dc60043 --- /dev/null +++ b/src/task_solver/code_executor.py @@ -0,0 +1,245 @@ +"""Python code execution module for tool-assisted task solving.""" + +import ast +import sys +import traceback +from io import StringIO +from typing import Any + + +class CodeExecutionResult: + """Result of code execution. + + Attributes + ---------- + success : bool + Whether the code executed successfully. + output : str + Standard output from the code execution. + error : str | None + Error message if execution failed, None otherwise. + """ + + def __init__(self, success: bool, output: str, error: str | None = None): + self.success = success + self.output = output + self.error = error + + def to_dict(self) -> dict[str, Any]: + """Convert result to dictionary.""" + return { + "success": self.success, + "output": self.output, + "error": self.error, + } + + +class PythonCodeExecutor: + """Execute Python code with restricted imports for mathematical computations. + + This executor allows SymPy, NumPy, and SciPy for mathematical tasks while + restricting potentially dangerous operations. + + Attributes + ---------- + allowed_imports : set[str] + Set of allowed module names for import. + timeout : int + Maximum execution time in seconds (not enforced in basic implementation). + """ + + def __init__(self, timeout: int = 30): + """Initialize the code executor. + + Parameters + ---------- + timeout : int, optional + Maximum execution time in seconds, by default 30. + Note: Timeout enforcement requires additional implementation. + """ + self.allowed_imports = { + 'sympy', + 'numpy', + 'scipy', + 'math', + 'fractions', + 'decimal', + 'cmath', + } + self.timeout = timeout + + def _validate_syntax(self, code: str) -> tuple[bool, str | None]: + """Validate Python syntax. + + Parameters + ---------- + code : str + Python code to validate. + + Returns + ------- + tuple[bool, str | None] + (is_valid, error_message) + """ + try: + ast.parse(code) + return True, None + except SyntaxError as e: + return False, f"Syntax error at line {e.lineno}: {e.msg}" + + def _create_restricted_globals(self) -> dict[str, Any]: + """Create a restricted global namespace for code execution. + + Returns + ------- + dict[str, Any] + Dictionary with restricted builtins and allowed imports. + """ + # Restricted builtins - only safe operations + safe_builtins = { + '__import__': __import__, # Required for import statements to work + 'print': print, + 'range': range, + 'len': len, + 'abs': abs, + 'min': min, + 'max': max, + 'sum': sum, + 'round': round, + 'float': float, + 'int': int, + 'str': str, + 'bool': bool, + 'list': list, + 'dict': dict, + 'tuple': tuple, + 'set': set, + 'enumerate': enumerate, + 'zip': zip, + 'sorted': sorted, + 'reversed': reversed, + 'map': map, + 'filter': filter, + 'any': any, + 'all': all, + 'pow': pow, + 'divmod': divmod, + 'isinstance': isinstance, + 'type': type, + 'True': True, + 'False': False, + 'None': None, + } + + return { + '__builtins__': safe_builtins, + } + + def execute(self, code: str) -> CodeExecutionResult: + """Execute Python code with safety restrictions. + + Parameters + ---------- + code : str + Python code to execute. + + Returns + ------- + CodeExecutionResult + Result object containing success status, output, and any errors. + """ + # Validate syntax first + is_valid, error_msg = self._validate_syntax(code) + if not is_valid: + return CodeExecutionResult( + success=False, + output="", + error=error_msg + ) + + # Validate imports using AST + try: + tree = ast.parse(code) + for node in ast.walk(tree): + if isinstance(node, (ast.Import, ast.ImportFrom)): + if isinstance(node, ast.ImportFrom): + module_name = node.module + else: + module_name = node.names[0].name if node.names else None + + if module_name: + base_module = module_name.split('.')[0] + if base_module not in self.allowed_imports: + return CodeExecutionResult( + success=False, + output="", + error=( + f"Import of '{module_name}' is not allowed. " + f"Allowed modules: {', '.join(sorted(self.allowed_imports))}" + ) + ) + except Exception as e: + return CodeExecutionResult( + success=False, + output="", + error=f"Import validation error: {str(e)}" + ) + + # Create restricted execution environment + restricted_globals = self._create_restricted_globals() + + # Pre-import allowed modules to make them available + for module_name in self.allowed_imports: + try: + restricted_globals[module_name] = __import__(module_name) + except ImportError: + pass # Module not available in environment + + # Capture stdout + old_stdout = sys.stdout + sys.stdout = captured_output = StringIO() + + try: + # Execute the code + exec(code, restricted_globals) + output = captured_output.getvalue() + + return CodeExecutionResult( + success=True, + output=output, + error=None + ) + + except Exception as e: + # Capture any execution errors + error_msg = f"{type(e).__name__}: {str(e)}\n{traceback.format_exc()}" + + return CodeExecutionResult( + success=False, + output=captured_output.getvalue(), + error=error_msg + ) + + finally: + # Restore stdout + sys.stdout = old_stdout + + +def execute_python_code(code: str, timeout: int = 30) -> dict[str, Any]: + """Convenience function to execute Python code. + + Parameters + ---------- + code : str + Python code to execute. + timeout : int, optional + Maximum execution time in seconds, by default 30. + + Returns + ------- + dict[str, Any] + Dictionary with 'success', 'output', and 'error' keys. + """ + executor = PythonCodeExecutor(timeout=timeout) + result = executor.execute(code) + return result.to_dict() diff --git a/src/task_solver/messages.py b/src/task_solver/messages.py index 36c196e0..1eae3e04 100644 --- a/src/task_solver/messages.py +++ b/src/task_solver/messages.py @@ -1,7 +1,7 @@ """Message types for task solving debate system.""" from dataclasses import dataclass -from typing import Dict, List +from typing import Dict, List, Optional @dataclass @@ -52,6 +52,45 @@ def to_dict(self) -> Dict[str, str]: } +@dataclass +class ToolAssistedAgentSolution: + """Solution proposed by a tool-assisted agent with code execution details. + + Note: code and code_output use empty string as default instead of None + to avoid Union type issues with autogen_core serialization. + """ + + agent_id: str + task_id: str + thought: str + final_answer: str + numerical_answer: str + round_number: int + capability_name: str + area_name: str + code: str = "" + code_output: str = "" + + def to_dict(self) -> Dict[str, str]: + """Convert to dictionary.""" + result = { + "agent_id": self.agent_id, + "task_id": self.task_id, + "thought": self.thought, + "final_answer": self.final_answer, + "numerical_answer": self.numerical_answer, + "round_number": str(self.round_number), + "capability_name": self.capability_name, + "area_name": self.area_name, + } + # Include code fields if present (not empty) + if self.code: + result["code"] = self.code + if self.code_output: + result["code_output"] = self.code_output + return result + + @dataclass class AgentRevisionRequest: """Request for agent to revise solution based on other agents' solutions.""" diff --git a/src/task_solver/moderator.py b/src/task_solver/moderator.py index 32c0f518..1401d1a5 100644 --- a/src/task_solver/moderator.py +++ b/src/task_solver/moderator.py @@ -4,7 +4,7 @@ import logging import traceback from pathlib import Path -from typing import Dict, List +from typing import Dict, List, Union from autogen_core import ( DefaultTopicId, @@ -26,6 +26,7 @@ FinalSolution, Task, TaskSolutionRequest, + ToolAssistedAgentSolution, ) from src.utils.agentic_prompts import ( TASK_MODERATOR_CONSENSUS_PROMPT, @@ -80,7 +81,8 @@ def __init__( self._langfuse_client = langfuse_client # Track solutions by task_id and round - self._solutions_buffer: Dict[int, List[AgentSolution]] + # Type hint omitted to avoid Union serialization issues with autogen_core + self._solutions_buffer = {} # Dict[int, List[AgentSolution | ToolAssistedAgentSolution]] self._current_round = 0 self._final_solutions: FinalSolution self._tasks: Task # Store original tasks for consensus checking @@ -234,6 +236,76 @@ async def handle_agent_solution( log.error(traceback.format_exc()) span.update(metadata={"error": error_msg}) + @message_handler + async def handle_tool_assisted_agent_solution( + self, message: ToolAssistedAgentSolution, ctx: MessageContext + ) -> None: + """Handle solution from a tool-assisted agent. + + Tool-assisted solutions include code and code_output fields that need + to be preserved in the final solution files. + """ + with self._langfuse_client.start_as_current_span( + name=f"moderator_handle_tool_assisted_solution_{message.task_id}_round_{message.round_number}" + ) as span: + try: + task_id = message.task_id + round_num = message.round_number + + msg = f"Moderator received tool-assisted solution from agent {message.agent_id} for task {task_id}, {message.capability_name}, {message.area_name} round {round_num}" + log.info(msg) + log.debug( + "Moderator: Tool-assisted solution has code: %s, has code_output: %s", + bool(message.code), + bool(message.code_output), + ) + if message.code: + log.debug( + "Moderator: Code length in received message: %d characters", + len(message.code), + ) + span.update( + metadata={ + "solution_received": msg, + "task_id": task_id, + "agent_id": message.agent_id, + "round": round_num, + "has_code": bool(message.code), + "code_executed": bool(message.code_output), + } + ) + + if round_num != self._current_round: + msg = f"Moderator received solution from agent {message.agent_id} for task {task_id}, {message.capability_name}, {message.area_name} round {round_num} but current round is {self._current_round}" + log.error(msg) + span.update(metadata={"error": msg}) + raise Exception(msg) + + # Initialize round buffer if needed + if self._current_round not in self._solutions_buffer: + self._solutions_buffer[self._current_round] = [] + + # Add solution to buffer - store the ToolAssistedAgentSolution directly + self._solutions_buffer[self._current_round].append(message) + + msg = f"{len(self._solutions_buffer[self._current_round])}/{self._num_solvers} solutions collected for round {self._current_round}" + log.info(msg) + span.update(metadata={"solutions_collected": msg}) + + if ( + len(self._solutions_buffer[self._current_round]) + == self._num_solvers + ): + await self._check_consensus_and_proceed(task_id, ctx) + + except Exception as e: + error_msg = ( + f"Error handling tool-assisted solution from agent {message.agent_id}: {str(e)}" + ) + log.error(error_msg) + log.error(traceback.format_exc()) + span.update(metadata={"error": error_msg}) + async def _check_consensus_and_proceed( self, task_id: str, ctx: MessageContext ) -> None: @@ -420,17 +492,7 @@ async def _save_final_solution(self, final_solution: FinalSolution) -> None: "reasoning": final_solution.reasoning, "consensus_reached": final_solution.consensus_reached, "total_rounds": final_solution.total_rounds, - "all_solutions": [ - { - "agent_id": sol["agent_id"], - "task_id": sol["task_id"], - "thought": sol["thought"], - "final_answer": sol["final_answer"], - "numerical_answer": sol["numerical_answer"], - "round_number": sol["round_number"], - } - for sol in final_solution.all_solutions - ], + "all_solutions": final_solution.all_solutions, # Include all fields from to_dict() } with open(output_file, "w") as f: diff --git a/src/task_solver/tool_assisted_scientist.py b/src/task_solver/tool_assisted_scientist.py new file mode 100644 index 00000000..fcfac091 --- /dev/null +++ b/src/task_solver/tool_assisted_scientist.py @@ -0,0 +1,559 @@ +"""Tool-assisted task solver agent with code execution capabilities.""" + +import json +import logging +import traceback + +from autogen_core import ( + DefaultTopicId, + MessageContext, + RoutedAgent, + default_subscription, + message_handler, +) +from autogen_core.models import ( + ChatCompletionClient, + SystemMessage, + UserMessage, +) +from langfuse import Langfuse + +from src.task_solver.code_executor import PythonCodeExecutor +from src.task_solver.messages import ( + AgentRevisionRequest, + AgentSolution, + TaskSolutionRequest, + ToolAssistedAgentSolution, +) +from src.utils.json_utils import parse_llm_json_response +from src.utils.tool_assisted_prompts import ( + TOOL_ASSISTED_ROUND_1_PROMPT, + TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT, + TOOL_ASSISTED_SYSTEM_MESSAGE, +) + + +log = logging.getLogger("task_solver.tool_assisted_scientist") + +MAX_MODEL_ATTEMPTS = 3 +MAX_CODE_EXECUTION_ATTEMPTS = 3 # Increased from 2 for better reliability + + +@default_subscription +class ToolAssistedScientist(RoutedAgent): + """A scientist that solves tasks with code execution capabilities. + + This agent can execute Python code using SymPy, NumPy, and SciPy to assist + in solving mathematical problems. + + Attributes + ---------- + _model_client : ChatCompletionClient + ChatCompletionClient for generating solutions via LLM. + _scientist_id : str + Unique identifier for this scientist agent in the debate. + _langfuse_client : Langfuse + Langfuse client for tracing and logging scientist activity. + _code_executor : PythonCodeExecutor + Executor for running Python code with restricted imports. + """ + + def __init__( + self, + model_client: ChatCompletionClient, + scientist_id: str, + langfuse_client: Langfuse, + code_timeout: int = 30, + ) -> None: + super().__init__(f"Tool-Assisted Scientist {scientist_id}") + self._model_client = model_client + self._scientist_id = scientist_id + self._langfuse_client = langfuse_client + self._code_executor = PythonCodeExecutor(timeout=code_timeout) + + def _extract_solution_components( + self, response: str + ) -> tuple[str, str | None, str | None, str, str]: + """Extract components from JSON response including code fields.""" + try: + parsed = parse_llm_json_response(response) + thought_raw = parsed.get("thought", response.strip()) + code = parsed.get("code") + code_output = parsed.get("code_output") + final_answer_raw = parsed.get("final_answer", "No clear answer provided") + numerical_answer = parsed.get("numerical_answer") + + thought = ( + json.dumps(thought_raw, ensure_ascii=False) + if isinstance(thought_raw, (dict, list)) + else str(thought_raw).strip() + ) + final_answer = ( + json.dumps(final_answer_raw, ensure_ascii=False, indent=2) + if isinstance(final_answer_raw, (dict, list)) + else str(final_answer_raw).strip() + ) + + # Handle code field + if code is not None and code != "null": + # JSON parser already handles escape sequences correctly + code = str(code).strip() + log.debug("Code (first 200 chars): %s", code[:200]) + else: + code = "" + + # Handle code_output field + if code_output is not None and code_output != "null": + code_output = str(code_output).strip() + else: + code_output = "" + + # Handle numerical_answer + if numerical_answer is not None: + numerical_answer = str(numerical_answer) + else: + numerical_answer = "null" + + return thought, code, code_output, final_answer, numerical_answer + + except Exception as e: + msg = f"Failed to parse JSON response: {e} \n Response: {response}" + log.error(msg) + log.error(traceback.format_exc()) + raise + + async def _generate_solution_with_code_execution( + self, system_message: SystemMessage, user_message: UserMessage + ) -> tuple[str, str | None, str | None, str, str]: + """Generate solution with iterative code execution feedback. + + This method handles the iterative process of: + 1. Getting LLM response with potential code + 2. Executing code if present + 3. If execution fails, providing error feedback to LLM + 4. Repeating until code succeeds or max attempts exhausted + 5. Returning final solution components + """ + conversation_history = [system_message, user_message] + last_error: Exception | None = None + + # Track final components from last valid parse + last_valid_components = None + + # Track total code execution attempts across all model responses + total_exec_attempts = 0 + + for model_attempt in range(1, MAX_MODEL_ATTEMPTS + 1): + try: + # Get response from LLM + log.debug( + "Scientist %s: LLM attempt %d/%d", + self._scientist_id, + model_attempt, + MAX_MODEL_ATTEMPTS, + ) + response = await self._model_client.create( + conversation_history, + json_output=True, + ) + except Exception as exc: # pragma: no cover + last_error = exc + log.warning( + "Tool-assisted scientist %s failed to get response on attempt %d: %s", + self._scientist_id, + model_attempt, + exc, + ) + continue + + response_content = str(getattr(response, "content", "") or "").strip() + if not response_content: + last_error = ValueError("Empty response content") + log.warning( + "Tool-assisted scientist %s received empty response on attempt %d", + self._scientist_id, + model_attempt, + ) + continue + + log.debug( + "Scientist %s: Received response (first 200 chars): %s", + self._scientist_id, + response_content[:200], + ) + log.debug( + "Scientist %s: Raw JSON with special chars visible (first 500 chars): %s", + self._scientist_id, + repr(response_content[:500]), + ) + + try: + thought, code, code_output, final_answer, numerical_answer = ( + self._extract_solution_components(response_content) + ) + last_valid_components = (thought, code, code_output, final_answer, numerical_answer) + + # Log the extracted code to see if parsing is correct + if code: + log.debug( + "Scientist %s: Extracted code (first 200 chars, repr): %s", + self._scientist_id, + repr(code[:200]), + ) + last_valid_components = (thought, code, code_output, final_answer, numerical_answer) + + # If no code (empty string), we're done + if not code: + log.info( + "Scientist %s: No code to execute, returning solution", + self._scientist_id, + ) + return thought, code, code_output, final_answer, numerical_answer + + # Code is present - attempt execution with retry loop + log.info( + "Scientist %s: Code detected, starting execution loop", + self._scientist_id, + ) + + # Clean up common code issues before execution + # Sometimes LLM generates code with literal \n that should be newlines + if '\\n' in code: + # Replace literal \n with actual newlines (but be careful with string escapes) + # This is a heuristic fix for common JSON escaping issues + code = code.replace('\\n', '\n') + log.debug( + "Scientist %s: Applied \\n replacement to clean up code", + self._scientist_id, + ) + + log.debug( + "Scientist %s: Generated code:\n%s\n%s\n%s", + self._scientist_id, + "-" * 60, + code, + "-" * 60, + ) + + # Increment total execution attempts + total_exec_attempts += 1 + log.info( + "Scientist %s: Code execution attempt %d/%d", + self._scientist_id, + total_exec_attempts, + MAX_CODE_EXECUTION_ATTEMPTS, + ) + + execution_result = self._code_executor.execute(code) + + if execution_result.success: + code_output = execution_result.output + log.info( + "Scientist %s: Code execution successful", + self._scientist_id, + ) + log.debug( + "Scientist %s: Code output:\n%s\n%s\n%s", + self._scientist_id, + "-" * 60, + code_output[:500], + "-" * 60, + ) + return thought, code, code_output, final_answer, numerical_answer + + # Code execution failed + code_output = f"ERROR: {execution_result.error}" + log.warning( + "Scientist %s: Code execution failed (attempt %d/%d): %s", + self._scientist_id, + total_exec_attempts, + MAX_CODE_EXECUTION_ATTEMPTS, + execution_result.error, + ) + log.debug( + "Scientist %s: Full error:\n%s\n%s\n%s", + self._scientist_id, + "-" * 60, + execution_result.error, + "-" * 60, + ) + + # If we've exhausted code execution attempts, break out + if total_exec_attempts >= MAX_CODE_EXECUTION_ATTEMPTS: + log.error( + "Scientist %s: Max code execution attempts (%d) exhausted", + self._scientist_id, + MAX_CODE_EXECUTION_ATTEMPTS, + ) + return thought, code, code_output, final_answer, numerical_answer + + # Provide feedback to LLM for another attempt + log.info( + "Scientist %s: Providing error feedback to LLM for code correction", + self._scientist_id, + ) + + # Construct enhanced error feedback with actionable hints + error_msg = str(execution_result.error) + hints = [] + + if "unterminated string" in error_msg.lower() or "eol while scanning" in error_msg.lower(): + hints.append("- Use triple-quoted strings for multi-line output: print('''text''')") + hints.append("- Avoid backslashes in strings; use raw strings r'' if needed") + hints.append("- For print statements, use separate print() calls instead of \\n in strings") + elif "syntaxerror" in error_msg.lower(): + hints.append("- Review Python syntax, especially quotes, parentheses, and indentation") + hints.append("- Check for unmatched brackets or quotes") + elif "nameerror" in error_msg.lower(): + hints.append("- Ensure all imports are at the top of the code") + hints.append("- Check that all variables are defined before use") + elif "importerror" in error_msg.lower() or "modulenotfounderror" in error_msg.lower(): + hints.append("- Only use approved libraries: sympy, numpy, scipy, math, fractions, decimal") + + hints_text = "\n".join(hints) if hints else "- Carefully review the error message above" + + feedback_prompt = f"""Your previous code execution failed with the following error: + +ERROR: {execution_result.error} + +Failed code: +```python +{code} +``` + +ACTIONABLE FIXES: +{hints_text} + +IMPORTANT: When writing code in JSON: +- Use simple print() statements on separate lines +- Avoid LaTeX notation in code comments or strings +- Use triple-quoted strings for multi-line output: print('''result''') +- Keep code focused on numerical computation only + +Return your corrected solution in the same JSON format with fixed code.""" + + feedback_message = UserMessage( + content=feedback_prompt, + source="user" + ) + conversation_history.append(feedback_message) + + # Continue to next model attempt for corrected code + + except Exception as exc: + last_error = exc + log.warning( + "Tool-assisted scientist %s failed to parse response on attempt %d: %s", + self._scientist_id, + model_attempt, + exc, + ) + log.debug("Full exception: %s", traceback.format_exc()) + continue + + # If we have a last valid parse, return it (even if code failed) + if last_valid_components is not None: + log.warning( + "Scientist %s: Returning last valid components after exhausting attempts", + self._scientist_id, + ) + return last_valid_components + + raise RuntimeError( + f"Tool-assisted scientist {self._scientist_id} could not obtain valid " + f"response after {MAX_MODEL_ATTEMPTS} attempts" + ) from last_error + + @message_handler + async def handle_task_solution_request( + self, message: TaskSolutionRequest, ctx: MessageContext + ) -> None: + """Handle initial task solution request with code execution.""" + with self._langfuse_client.start_as_current_span( + name=f"tool_assisted_scientist_{self._scientist_id}_initial_solution" + ) as span: + try: + msg = ( + f"Tool-assisted scientist {self._scientist_id} handling initial " + f"solution request for task: {message.task_id}, " + f"capability: {message.capability_name}, area: {message.area_name}, " + f"round: {message.round_number}" + ) + log.info(msg) + span.update( + metadata={ + "solution_request_received": msg, + "scientist_id": self._scientist_id, + "task_id": message.task_id, + "capability": message.capability_name, + "area": message.area_name, + "round": message.round_number, + "tool_assisted": True, + } + ) + + prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( + problem_text=message.problem + ) + + system_message = SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE) + user_message = UserMessage(content=prompt, source="user") + + ( + thought, + code, + code_output, + final_answer, + numerical_answer, + ) = await self._generate_solution_with_code_execution( + system_message, user_message + ) + + # Debug log before creating solution + log.debug( + "Scientist %s: About to create solution - code present: %s, code_output present: %s", + self._scientist_id, + bool(code), + bool(code_output), + ) + if code: + log.debug( + "Scientist %s: Code length: %d characters", + self._scientist_id, + len(code), + ) + + # Create solution with code execution metadata + solution = ToolAssistedAgentSolution( + agent_id=self._scientist_id, + task_id=message.task_id, + thought=thought, + final_answer=final_answer, + numerical_answer=numerical_answer, + round_number=message.round_number, + capability_name=message.capability_name, + area_name=message.area_name, + code=code, + code_output=code_output, + ) + + # Debug log after creating solution + log.debug( + "Scientist %s: Created solution - code in solution: %s, code_output in solution: %s", + self._scientist_id, + bool(solution.code), + bool(solution.code_output), + ) + + await self.publish_message(solution, topic_id=DefaultTopicId()) + + span.update( + metadata={ + "solution_generated": ( + f"Tool-assisted scientist {self._scientist_id} generated " + f"solution for task {message.task_id}" + ), + "code_executed": bool(code), + "code_success": code_output and not code_output.startswith("ERROR:"), + } + ) + + except Exception as e: + msg = ( + f"Error in tool-assisted scientist {self._scientist_id} " + f"task solution request: {str(e)}" + ) + log.error(msg) + log.error(traceback.format_exc()) + span.update(metadata={"error": msg}) + + @message_handler + async def handle_agent_revision_request( + self, message: AgentRevisionRequest, ctx: MessageContext + ) -> None: + """Handle revision request with code execution capabilities.""" + with self._langfuse_client.start_as_current_span( + name=f"tool_assisted_scientist_{self._scientist_id}_round_{message.round_number}" + ) as span: + try: + msg = ( + f"Tool-assisted scientist {self._scientist_id} handling revision " + f"request for task: {message.task_id}, " + f"capability: {message.capability_name}, area: {message.area_name}, " + f"round: {message.round_number}" + ) + log.info(msg) + span.update( + metadata={ + "revision_request_received": msg, + "scientist_id": self._scientist_id, + "task_id": message.task_id, + "round": message.round_number, + "num_other_solutions": len(message.other_solutions), + "tool_assisted": True, + } + ) + + other_solutions_text = "\n\n".join( + [ + ( + f"Scientist {sol['agent_id']}: Reasoning: {sol['thought']}, " + f"Final solution: {sol['final_answer']}" + ) + for sol in message.other_solutions + if sol["agent_id"] != self._scientist_id + ] + ) + + prompt = TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT.format( + other_solutions=other_solutions_text, + problem_text=message.problem, + ) + + system_message = SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE) + user_message = UserMessage(content=prompt, source="user") + + ( + thought, + code, + code_output, + final_answer, + numerical_answer, + ) = await self._generate_solution_with_code_execution( + system_message, user_message + ) + + solution = ToolAssistedAgentSolution( + agent_id=self._scientist_id, + task_id=message.task_id, + thought=thought, + final_answer=final_answer, + numerical_answer=numerical_answer, + round_number=message.round_number, + capability_name=message.capability_name, + area_name=message.area_name, + code=code, + code_output=code_output, + ) + + await self.publish_message(solution, topic_id=DefaultTopicId()) + + span.update( + metadata={ + "revision_generated": ( + f"Tool-assisted scientist {self._scientist_id} generated " + f"revision for task {message.task_id}" + ), + "code_executed": bool(code), + "code_success": code_output and not code_output.startswith("ERROR:"), + } + ) + + except Exception as e: + msg = ( + f"Error in tool-assisted scientist {self._scientist_id} " + f"agent revision request: {str(e)}" + ) + log.error(msg) + log.error(traceback.format_exc()) + span.update(metadata={"error": msg}) diff --git a/src/utils/json_utils.py b/src/utils/json_utils.py index 3d2fd774..db962bce 100644 --- a/src/utils/json_utils.py +++ b/src/utils/json_utils.py @@ -44,7 +44,97 @@ def fix_common_json_errors(content: str) -> str: """Fix common JSON syntax errors.""" content = re.sub(r':\s*=\s*"', ':"', content) content = re.sub(r'(\w+):\s*"', r'"\1":"', content) - content = re.sub(r'\\(?!["\\/bfnrtu])', r"\\\\", content) + + # Fix LaTeX backslashes: escape backslashes that are not part of valid JSON escape sequences + # Valid JSON escapes: \", \\, \/, \b, \f, \n, \r, \t, \uXXXX + # LaTeX uses \(, \), \[, \], \epsilon, \delta, etc. which need to be escaped as \\(, \\), etc. + + # Process the content character by character to properly handle string boundaries + result = [] + i = 0 + in_string = False + + while i < len(content): + char = content[i] + + # Track when we enter/exit string values + if char == '"': + # Check if this quote is escaped by counting backslashes before it + # An escaped quote has an odd number of backslashes before it + backslash_count = 0 + j = i - 1 + while j >= 0 and content[j] == '\\': + backslash_count += 1 + j -= 1 + + # If odd number of backslashes, the quote is escaped (part of string content) + if backslash_count % 2 == 1: + result.append(char) + i += 1 + continue + + # This is a real quote - toggle string state + in_string = not in_string + result.append(char) + i += 1 + continue + + # Handle backslashes inside string values + if char == '\\' and in_string: + if i + 1 < len(content): + next_char = content[i + 1] + # Check for valid JSON escape sequences + # For single-character escapes: ", \, /, b, f, n, r, t + # We need to ensure they're not part of a longer sequence (e.g., \to should not match \t) + if next_char in '"\\/': + # Always valid single-char escapes + result.append(char) + result.append(next_char) + i += 2 + continue + elif next_char in 'bfnrt': + # Check if this is a complete escape (not part of longer sequence) + # Valid if followed by non-alphanumeric or end of string + if i + 2 >= len(content) or not (content[i + 2].isalnum() or content[i + 2] == '_'): + # Complete escape sequence (e.g., \t, \n, \r, \b, \f) + result.append(char) + result.append(next_char) + i += 2 + continue + # Otherwise it's part of a longer sequence (e.g., \to, \lim) - escape it + result.append('\\\\') + result.append(next_char) + i += 2 + continue + elif next_char == 'u' and i + 5 < len(content): + # Check for \uXXXX pattern + hex_part = content[i + 2:i + 6] + if all(c in '0123456789abcdefABCDEF' for c in hex_part): + # Valid \uXXXX escape + result.append(char) + result.append(next_char) + result.append(hex_part) + i += 6 + continue + + # Invalid escape sequence (like LaTeX \(, \), \[, \], \epsilon, \to, etc.) + # Double-escape the backslash + result.append('\\\\') + result.append(next_char) + i += 2 + continue + else: + # Backslash at end of content - escape it + result.append('\\\\') + i += 1 + continue + + # Regular character + result.append(char) + i += 1 + + content = ''.join(result) + return re.sub(r",(\s*[}\]])", r"\1", content) @@ -67,6 +157,33 @@ def parse_llm_json_response(raw_content: Union[str, Any]) -> Dict[str, Any]: log.error(f"Failed to parse JSON response: {e}") log.error(f"Content length: {len(cleaned_content)} characters") + # Try to fix LaTeX backslash issues if the error is about invalid escape + if "Invalid \\escape" in str(e) or "Invalid escape" in str(e): + try: + log.warning("Attempting to fix LaTeX backslash escape issues") + # Apply more aggressive LaTeX backslash fixing + # Escape all backslashes in string values that aren't valid JSON escapes + fixed_content = cleaned_content + + # Use regex to find and fix invalid escapes in string values + # This pattern finds backslashes in string values and escapes invalid ones + def fix_escapes_in_string(match): + """Fix escapes within a JSON string value.""" + string_content = match.group(1) + # Escape backslashes not followed by valid JSON escape chars + fixed = re.sub(r'\\(?!["\\/bfnrtu]|u[0-9a-fA-F]{4})', r'\\\\', string_content) + return f'"{fixed}"' + + # Find string values and fix them + # Pattern: "([^"\\]|\\.)*" - matches JSON string values + fixed_content = re.sub(r'"((?:[^"\\]|\\.)*)"', fix_escapes_in_string, fixed_content) + + result = json.loads(fixed_content) + log.info("Successfully fixed LaTeX backslash issues") + return result if isinstance(result, dict) else {} + except Exception as fix_error: + log.error(f"Failed to fix LaTeX escape issues: {fix_error}") + try: if "Unterminated string" in str(e): last_complete = cleaned_content.rfind('"},') diff --git a/src/utils/tool_assisted_prompts.py b/src/utils/tool_assisted_prompts.py new file mode 100644 index 00000000..3b47d923 --- /dev/null +++ b/src/utils/tool_assisted_prompts.py @@ -0,0 +1,318 @@ +"""Prompts for tool-assisted task solving with code execution capabilities.""" + +# ============================================================================= +# TOOL-ASSISTED TASK SOLVING PROMPTS +# ============================================================================= + +TOOL_ASSISTED_SYSTEM_MESSAGE = """You are an expert problem solver with access to Python code execution capabilities. You can use the following libraries to assist in solving mathematical problems: + +**Available Libraries:** +- **SymPy**: For symbolic mathematics (symbolic integration, differentiation, equation solving, etc.) +- **NumPy**: For numerical computations (arrays, linear algebra, numerical methods) +- **SciPy**: For scientific computing (numerical integration, optimization, ODE solving) +- **Math/Fractions/Decimal**: Standard Python mathematical libraries + +**When to Use Code:** +- To verify analytical solutions programmatically +- To perform complex symbolic manipulations +- To compute numerical results with high precision +- To check edge cases and special conditions +- To solve problems that are computationally intensive + +**Best Practices:** +- Always explain your mathematical reasoning first +- Use code to verify or compute, not as a substitute for understanding +- Show intermediate steps in your code with print statements +- Verify that code outputs match your analytical reasoning""" + +TOOL_ASSISTED_ROUND_1_PROMPT = """Can you solve the following problem? You have access to Python code execution with SymPy, NumPy, and SciPy. + +PROBLEM: {problem_text} + +**Instructions:** +1. First, explain your mathematical approach and reasoning +2. If helpful, write Python code to verify your solution or perform computations +3. Use the code output to inform or verify your final answer +4. Provide a clear final answer + +**Code Execution Format:** +If you want to execute code, include it in your response. The code will be executed and the output will be provided back to you. + +**Available Tools:** +```python +# SymPy - Symbolic Mathematics +from sympy import symbols, Function, Eq, dsolve, diff, integrate, simplify, solve, limit, series, sqrt, exp, log, sin, cos, tan, pi, E, I, oo +from sympy import Matrix, eye, zeros, ones, det, eigenvals, eigenvects +from sympy.abc import x, y, z, t, a, b, c, n + +# NumPy - Numerical Computing +import numpy as np +# Use: np.array, np.linalg.eig, np.linalg.det, np.linalg.inv, np.linalg.solve, etc. + +# SciPy - Scientific Computing +from scipy import integrate, optimize, linalg +from scipy.integrate import odeint, solve_ivp, quad, dblquad +# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, etc. + +# Standard Math +import math +from fractions import Fraction +from decimal import Decimal +``` + +IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. + +CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\) for single backslashes (\\). This is crucial for valid JSON parsing. For example: +- Write \\\\(x^2\\\\) instead of \\(x^2\\) +- Write \\\\[equation\\\\] instead of \\[equation\\] +- Write \\\\times instead of \\times +- For any newlines within a string value, use `\\n` instead of an actual newline. + +**IMPORTANT - Separating Code from Documentation:** +- Your "code" field should contain ONLY executable Python code +- DO NOT include LaTeX in Python code or comments unless properly escaped as raw strings +- Keep LaTeX explanations in "thought" and "final_answer" fields, NOT in code +- If you need to print mathematical expressions from code, use sympy's printing functions or just print the Python representation + +**CRITICAL - Code Field Format:** +When providing Python code in the JSON "code" field, write it as a single-line string with \\n for line breaks. +AVOID using print statements with \\n inside strings - just use simple print statements on separate lines instead. + +**CODE GENERATION BEST PRACTICES:** +1. **Use triple-quoted strings** for multi-line output: print('''output''') +2. **Separate print statements** instead of embedding \\n in strings +3. **NO LaTeX in code** - Keep LaTeX only in "thought" and "final_answer" fields +4. **Simple, focused code** - Only numerical computation, no decorative output +5. **All imports at top** - Import before using any library +6. **Test mentally** - Walk through the code logic before including it + +**Example of CORRECT code:** +"code": "import numpy as np\\nA = np.array([[1, 1], [1, -1]])\\nb = np.array([2, 0])\\nQ, R = np.linalg.qr(A)\\nx = np.linalg.solve(R, Q.T @ b)\\nprint('Solution:')\\nprint(x)" + +**Example of INCORRECT code:** +"code": "# Solving \\\\(Ax = b\\\\)\\nA = np.array([[1, 1], [1, -1]])" <- BAD! LaTeX in comments causes syntax errors + +**Example of BETTER output formatting:** +"code": "result = [1, 1]\\nprint('x =', result)" <- GOOD! Simple and clear + +Provide your solution in JSON format with the following structure: +{{ + "thought": "Your detailed mathematical reasoning and approach. ENSURE ALL BACKSLASHES IN LATEX ARE ESCAPED WITH DOUBLE BACKSLASHES (\\\\).", + "code": "Python code to execute (if applicable, otherwise null). NO LATEX IN CODE!", + "code_output": "Will be filled in after code execution (leave as null)", + "final_answer": "Your complete answer informed by reasoning and computation", + "numerical_answer": "The final numerical result (if applicable, otherwise null)" +}} + +Example with code: +{{ + "thought": "This is a first-order ODE. I'll solve it analytically using separation of variables, then verify with SymPy.", + "code": "from sympy import *\\nx = symbols('x')\\ny = Function('y')\\node = Eq(y(x).diff(x), x*y(x))\\nsolution = dsolve(ode, y(x))\\nprint('General solution:', solution)\\nprint('Verification:', simplify(solution.lhs - solution.rhs))", + "code_output": null, + "final_answer": "The solution is y = C*exp(x^2/2)", + "numerical_answer": null +}} + +Example without code: +{{ + "thought": "This is a simple algebraic equation. I can solve it directly: 2x + 3 = 11, so 2x = 8, therefore x = 4.", + "code": null, + "code_output": null, + "final_answer": "x = 4", + "numerical_answer": 4 +}} + +Respond with valid JSON only.""" + +TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT = """These are the reasoning and solutions to the problem from other agents: + +{other_solutions} + +Using the solutions from other agents as additional information, can you provide your answer to the problem? You have access to Python code execution with SymPy, NumPy, and SciPy. + +The original problem is: {problem_text} + +**Instructions:** +1. Review the other agents' approaches and identify strengths/weaknesses +2. Explain your mathematical reasoning, considering the other solutions +3. If helpful, use Python code to verify or compute +4. Provide your final answer, which may agree with, disagree with, or synthesize the other solutions + +**Available Tools:** +```python +# SymPy - Symbolic Mathematics +from sympy import symbols, Function, Eq, dsolve, diff, integrate, simplify, solve, limit, series, sqrt, exp, log, sin, cos, tan, pi, E, I, oo +from sympy import Matrix, eye, zeros, ones, det, eigenvals, eigenvects +from sympy.abc import x, y, z, t, a, b, c, n + +# NumPy - Numerical Computing +import numpy as np + +# SciPy - Scientific Computing +from scipy import integrate, optimize, linalg +from scipy.integrate import odeint, solve_ivp, quad, dblquad +``` + +IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. + +CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\) for single backslashes (\\). This is crucial for valid JSON parsing. For example: +- Write \\\\(x^2\\\\) instead of \\(x^2\\) +- Write \\\\[equation\\\\] instead of \\[equation\\] +- Write \\\\times instead of \\times +- For any newlines within a string value, use `\\n` instead of an actual newline. + +**IMPORTANT - Separating Code from Documentation:** +- Your "code" field should contain ONLY executable Python code +- DO NOT include LaTeX in Python code or comments unless properly escaped as raw strings +- Keep LaTeX explanations in "thought" and "final_answer" fields, NOT in code +- If you need to print mathematical expressions from code, use sympy's printing functions + +Provide your solution in JSON format: +{{ + "thought": "Your reasoning considering other agents' solutions. ENSURE ALL BACKSLASHES IN LATEX ARE ESCAPED WITH DOUBLE BACKSLASHES (\\\\).", + "code": "Python code to execute (if applicable, otherwise null). NO LATEX IN CODE!", + "code_output": "Will be filled in after code execution (leave as null)", + "final_answer": "Your complete answer", + "numerical_answer": "The final numerical result (if applicable, otherwise null)" +}} + +Respond with valid JSON only.""" + +# ============================================================================= +# CODE EXECUTION EXAMPLES FOR DIFFERENT CAPABILITIES +# ============================================================================= + +CODE_EXAMPLES_ODE = """ +# Example: Solving a first-order ODE +from sympy import * +x = symbols('x') +y = Function('y') + +# Define ODE: dy/dx = xy with y(0) = 1 +ode = Eq(y(x).diff(x), x*y(x)) +general_solution = dsolve(ode, y(x)) +print("General solution:", general_solution) + +# Apply initial condition +C1 = symbols('C1') +particular = general_solution.rhs.subs(x, 0) +C_value = solve(particular - 1, C1)[0] +print("C value:", C_value) + +final_solution = general_solution.rhs.subs(C1, C_value) +print("Particular solution:", final_solution) + +# Verify by substitution +dy_dx = diff(final_solution, x) +rhs = x * final_solution +print("Verification:", simplify(dy_dx - rhs) == 0) +""" + +CODE_EXAMPLES_LINEAR_ALGEBRA = """ +# Example: Matrix operations and eigenvalues +import numpy as np +from scipy import linalg + +# Define matrix +A = np.array([[4, 2], [1, 3]]) +print("Matrix A:") +print(A) + +# Compute eigenvalues and eigenvectors +eigenvalues, eigenvectors = np.linalg.eig(A) +print("\\nEigenvalues:", eigenvalues) +print("Eigenvectors:") +print(eigenvectors) + +# Verify: A*v = λ*v +for i in range(len(eigenvalues)): + v = eigenvectors[:, i] + lam = eigenvalues[i] + print(f"\\nVerification for λ={lam}:") + print("A*v =", A @ v) + print("λ*v =", lam * v) + print("Equal?", np.allclose(A @ v, lam * v)) +""" + +CODE_EXAMPLES_INTEGRATION = """ +# Example: Symbolic and numerical integration +from sympy import * +from scipy import integrate as scipy_integrate + +x = symbols('x') + +# Symbolic integration +expr = sin(x) * exp(-x) +symbolic_result = integrate(expr, (x, 0, oo)) +print("Symbolic result:", symbolic_result) +print("Numerical value:", float(symbolic_result)) + +# Numerical integration for comparison +def integrand(x): + return np.sin(x) * np.exp(-x) + +numerical_result, error = scipy_integrate.quad(integrand, 0, np.inf) +print("\\nNumerical integration:", numerical_result) +print("Error estimate:", error) +""" + +CODE_EXAMPLES_DYNAMICAL_SYSTEMS = """ +# Example: Numerical simulation of ODE system +import numpy as np +from scipy.integrate import odeint + +# Define system: Lotka-Volterra equations +def lotka_volterra(state, t, alpha, beta, gamma, delta): + x, y = state + dx_dt = alpha * x - beta * x * y + dy_dt = delta * x * y - gamma * y + return [dx_dt, dy_dt] + +# Parameters +alpha, beta, gamma, delta = 1.0, 0.1, 1.5, 0.075 + +# Initial conditions +x0, y0 = 10, 5 +initial_state = [x0, y0] + +# Time points +t = np.linspace(0, 50, 1000) + +# Solve ODE +solution = odeint(lotka_volterra, initial_state, t, args=(alpha, beta, gamma, delta)) + +print("Initial state:", initial_state) +print("Final state:", solution[-1]) +print("Max prey population:", np.max(solution[:, 0])) +print("Max predator population:", np.max(solution[:, 1])) +""" + +CODE_EXAMPLES_COMPLEX_ANALYSIS = """ +# Example: Complex number operations +from sympy import * + +# Define complex numbers +z1 = 3 + 4*I +z2 = 1 - 2*I + +print("z1 =", z1) +print("z2 =", z2) + +# Operations +print("\\nz1 + z2 =", z1 + z2) +print("z1 * z2 =", expand(z1 * z2)) +print("z1 / z2 =", simplify(z1 / z2)) + +# Magnitude and argument +print("\\n|z1| =", abs(z1)) +print("arg(z1) =", arg(z1)) + +# Complex conjugate +print("\\nconjugate(z1) =", conjugate(z1)) + +# Euler's formula +theta = symbols('theta', real=True) +euler = exp(I * theta) +print("\\ne^(iθ) =", euler) +print("Expanded:", expand(euler, complex=True)) +""" diff --git a/tests/src/utils/test_json_utils_latex.py b/tests/src/utils/test_json_utils_latex.py new file mode 100644 index 00000000..b2f645d5 --- /dev/null +++ b/tests/src/utils/test_json_utils_latex.py @@ -0,0 +1,184 @@ +""" +Tests for the JSON utility functions related to LaTeX backslash handling. + +This module contains tests specifically for the changes made to handle LaTeX +expressions with backslashes in JSON strings. The main changes were: +- fix_common_json_errors: Character-by-character processing to correctly escape + LaTeX backslashes while preserving valid JSON escape sequences +- parse_llm_json_response: Fallback mechanism for "Invalid escape" errors + +The tests verify that: +- LaTeX expressions with backslashes (e.g., \\(, \\), \\[, \\], \\to, \\lim) are properly escaped +- Valid JSON escape sequences (e.g., \\n, \\t, \\", \\\\) are preserved +- The fallback mechanism correctly handles invalid escape errors +""" + +import json + +import pytest + +from src.utils.json_utils import ( + fix_common_json_errors, + parse_llm_json_response, +) + + +class TestFixCommonJsonErrorsLaTeX: + """Test cases for fix_common_json_errors function - LaTeX backslash handling.""" + + def test_latex_parentheses_escaped(self): + """Test that LaTeX parentheses are properly escaped.""" + content = '{"thought": "The equation \\(x + y\\) is important"}' + result = fix_common_json_errors(content) + # Should double-escape: \\( becomes \\\\( + assert '\\\\(x + y\\\\)' in result or '\\\\(' in result + # Verify it can be parsed + parsed = json.loads(result) + assert "(" in parsed["thought"] or "\\(" in parsed["thought"] + + def test_latex_brackets_escaped(self): + """Test that LaTeX brackets are properly escaped.""" + content = '{"thought": "The equation \\[x + y = z\\] is important"}' + result = fix_common_json_errors(content) + # Should double-escape: \\[ becomes \\\\[ + assert '\\\\[' in result or '\\[' in result + # Verify it can be parsed + parsed = json.loads(result) + assert "[" in parsed["thought"] or "\\[" in parsed["thought"] + + def test_latex_commands_escaped(self): + """Test that LaTeX commands like \\to, \\lim are properly escaped.""" + content = '{"thought": "As x \\to 0, we have \\lim f(x)"}' + result = fix_common_json_errors(content) + # Should escape \to and \lim + assert '\\to' in result or '\\\\to' in result + # Verify it can be parsed + parsed = json.loads(result) + assert "to" in parsed["thought"] or "\\to" in parsed["thought"] + + def test_valid_json_escapes_preserved(self): + """Test that valid JSON escape sequences are preserved.""" + # Use raw string to ensure \n and \t are literal backslash+n and backslash+t + content = r'{"text": "Line 1\nLine 2\tTabbed"}' + result = fix_common_json_errors(content) + # Valid escapes should remain (as single backslash sequences) + assert r'\n' in result or '\n' in result + assert r'\t' in result or '\t' in result + # Verify it can be parsed and produces actual newline/tab characters + parsed = json.loads(result) + assert "\n" in parsed["text"] or "\\n" in parsed["text"] + assert "\t" in parsed["text"] or "\\t" in parsed["text"] + + def test_valid_json_quote_escape_preserved(self): + """Test that escaped quotes are preserved.""" + content = '{"text": "He said \\"hello\\""}' + result = fix_common_json_errors(content) + # Escaped quotes should remain + assert '\\"' in result + # Verify it can be parsed + parsed = json.loads(result) + assert '"' in parsed["text"] + + def test_valid_json_backslash_escape_preserved(self): + """Test that escaped backslashes are preserved.""" + content = '{"path": "C:\\\\Users\\\\file.txt"}' + result = fix_common_json_errors(content) + # Escaped backslashes should remain + assert '\\\\' in result + # Verify it can be parsed + parsed = json.loads(result) + assert "\\" in parsed["path"] + + def test_latex_in_nested_string(self): + """Test LaTeX handling in nested JSON strings.""" + content = '{"outer": {"inner": "The formula \\(a + b\\) is key"}}' + result = fix_common_json_errors(content) + # Should handle LaTeX in nested strings + parsed = json.loads(result) + assert "(" in parsed["outer"]["inner"] or "\\(" in parsed["outer"]["inner"] + + def test_mixed_valid_and_invalid_escapes(self): + """Test handling of mixed valid and invalid escape sequences.""" + # Use raw string to ensure proper escape handling + content = r'{"text": "Valid\nNewline and invalid\to LaTeX"}' + result = fix_common_json_errors(content) + # Valid escape should remain, invalid should be escaped + assert r'\n' in result or '\n' in result + # Verify it can be parsed + parsed = json.loads(result) + # Should contain newline character or escaped newline + assert "\n" in parsed["text"] or "\\n" in parsed["text"] + + +class TestParseLlmJsonResponseLaTeX: + """Test cases for parse_llm_json_response function - LaTeX and fallback handling.""" + + def test_parse_json_with_latex(self): + """Test parsing JSON containing LaTeX expressions.""" + content = '{"thought": "The equation \\(x + y\\) is important", "answer": "42"}' + result = parse_llm_json_response(content) + assert "thought" in result + assert "answer" in result + # The thought should contain the LaTeX (possibly escaped) + assert "x + y" in result["thought"] or "(" in result["thought"] + + def test_parse_json_with_latex_complex(self): + """Test parsing complex JSON with multiple LaTeX expressions.""" + content = ( + '{"thought": "We need to solve \\[x^2 + 2x + 1 = 0\\] ' + 'and find \\lim_{x \\to 0} f(x)", "answer": "x = -1"}' + ) + result = parse_llm_json_response(content) + assert "thought" in result + assert "answer" in result + assert "x = -1" in result["answer"] + + def test_parse_json_fallback_on_invalid_escape(self): + """Test that fallback mechanism works for invalid escape errors.""" + # This should trigger the fallback mechanism + content = '{"text": "Invalid escape\\to here"}' + result = parse_llm_json_response(content) + assert "text" in result + # Should successfully parse after fallback fix + + def test_parse_json_with_latex_in_multiple_fields(self): + """Test parsing JSON with LaTeX in multiple fields.""" + content = ( + '{"thought": "Consider \\(a + b\\)", ' + '"solution": "The answer is \\[x = 5\\]", ' + '"reasoning": "Using \\lim_{x \\to 0}"}' + ) + result = parse_llm_json_response(content) + assert "thought" in result + assert "solution" in result + assert "reasoning" in result + + +class 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src/task_solver/tool_assisted_scientist.py | 33 +- src/tools/__init__.py | 39 ++ src/tools/definitions.py | 137 ++++ src/tools/docs.py | 193 ++++++ .../code_executor.py => tools/executor.py} | 186 +++--- src/tools/toolkit.py | 233 +++++++ src/utils/tool_assisted_prompts.py | 2 + tests/tools/__init__.py | 1 + tests/tools/test_executor.py | 118 ++++ tests/tools/test_toolkit.py | 112 ++++ 15 files changed, 2180 insertions(+), 106 deletions(-) create mode 100644 scripts/classify_tasks.py create mode 100644 scripts/generate_filtered_dataset.py create mode 100755 scripts/generate_task_dataset.sh create mode 100644 src/tools/__init__.py create mode 100644 src/tools/definitions.py create mode 100644 src/tools/docs.py rename src/{task_solver/code_executor.py => tools/executor.py} (58%) create mode 100644 src/tools/toolkit.py create mode 100644 tests/tools/__init__.py create mode 100644 tests/tools/test_executor.py create mode 100644 tests/tools/test_toolkit.py diff --git a/.gitignore b/.gitignore index 2e8bbc93..922de463 100644 --- a/.gitignore +++ b/.gitignore @@ -157,8 +157,9 @@ cython_debug/ # Project specific materials/ -experiment_results/ +experiment_results*/ full_results/ results/ experiments.log -math/ \ No newline at end of file +math/ +filtered_tasks_output/ \ No newline at end of file diff --git a/run_experiments.py b/run_experiments.py index 8654299b..bc462f12 100644 --- a/run_experiments.py +++ b/run_experiments.py @@ -43,6 +43,7 @@ from src.task_solver.moderator import TaskSolverModerator from src.task_solver.scientist import TaskSolverScientist from src.task_solver.tool_assisted_scientist import ToolAssistedScientist +from src.tools.toolkit import ScientificToolKit from src.utils.model_client_utils import get_model_client @@ -68,6 +69,8 @@ def __init__( num_tasks: int = 15, condition: str = "both", output_dir: str = "experiment_results", + enable_tool_selection: bool = True, + tasks_file: str = "selected_tasks.json", ): """Initialize experiment runner. @@ -77,11 +80,15 @@ def __init__( num_tasks: Number of tasks per capability to run (1-15) condition: Which condition to run - 'baseline', 'tool_assisted', or 'both' output_dir: Directory for saving results + enable_tool_selection: Whether to enable dynamic tool selection (default: True) + tasks_file: Path to tasks JSON file (default: selected_tasks.json) """ self.model_name = model_name self.num_rounds = num_rounds self.num_tasks = num_tasks self.condition = condition + self.enable_tool_selection = enable_tool_selection + self.tasks_file = Path(tasks_file) self.results_dir = Path(output_dir) self.results_dir.mkdir(parents=True, exist_ok=True) @@ -106,29 +113,38 @@ def __init__( } def load_tasks(self) -> List[Dict[str, Any]]: - """Load tasks from selected_tasks.json file.""" - tasks_file = Path("selected_tasks.json") - - if not tasks_file.exists(): + """Load tasks from specified tasks file.""" + if not self.tasks_file.exists(): raise FileNotFoundError( - f"Tasks file not found: {tasks_file}\n" - f"Please run extract_tasks.py first." + f"Tasks file not found: {self.tasks_file}\n" + f"Please provide a valid tasks file path." ) - with open(tasks_file, "r", encoding="utf-8") as f: + with open(self.tasks_file, "r", encoding="utf-8") as f: all_tasks = json.load(f) - # Select specified number of tasks from each capability selected_tasks = [] - for capability_name, task_list in all_tasks.items(): - # Take up to num_tasks from each capability - for task in task_list[:self.num_tasks]: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - - log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") + + # If using a custom tasks file (not default), load all tasks as-is + if self.tasks_file != Path("selected_tasks.json"): + for capability_name, task_list in all_tasks.items(): + for task in task_list: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") + else: + # For default file, apply num_tasks limit per capability + for capability_name, task_list in all_tasks.items(): + # Take up to num_tasks from each capability + for task in task_list[:self.num_tasks]: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") + return selected_tasks async def run_single_task( @@ -211,7 +227,24 @@ async def run_single_task( ), ) else: - # Use ToolAssistedScientist + # Use ToolAssistedScientist with toolkit + # Create toolkits for scientists + toolkit_a = ScientificToolKit( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + enable_tool_selection=self.enable_tool_selection, + ) + + toolkit_b = ScientificToolKit( + model_client=get_model_client( + model_name=self.model_name, + seed=42, + ), + enable_tool_selection=self.enable_tool_selection, + ) + await ToolAssistedScientist.register( runtime, "TaskSolverScientistA", @@ -222,6 +255,7 @@ async def run_single_task( ), scientist_id="A", langfuse_client=self.langfuse_client, + toolkit=toolkit_a, ), ) @@ -235,6 +269,7 @@ async def run_single_task( ), scientist_id="B", langfuse_client=self.langfuse_client, + toolkit=toolkit_b, ), ) @@ -612,15 +647,37 @@ async def main(): default="experiment_results", help="Output directory for results (default: experiment_results)" ) + parser.add_argument( + "--tasks-file", + default="selected_tasks.json", + help="Path to tasks JSON file (default: selected_tasks.json)" + ) + parser.add_argument( + "--enable-tool-selection", + action="store_true", + default=True, + help="Enable dynamic tool selection (default: True)" + ) + parser.add_argument( + "--disable-tool-selection", + action="store_true", + default=False, + help="Disable dynamic tool selection (use all tools by default)" + ) args = parser.parse_args() + # Handle tool selection flag + enable_tool_selection = args.enable_tool_selection and not args.disable_tool_selection + runner = ExperimentRunner( model_name=args.model, num_rounds=args.rounds, num_tasks=args.num_tasks, condition=args.condition, output_dir=args.output, + enable_tool_selection=enable_tool_selection, + tasks_file=args.tasks_file, ) await runner.run_experiment() diff --git a/scripts/classify_tasks.py b/scripts/classify_tasks.py new file mode 100644 index 00000000..2f4ca42a --- /dev/null +++ b/scripts/classify_tasks.py @@ -0,0 +1,613 @@ +#!/usr/bin/env python3 +""" +Task Classifier for Filtering Tool-Appropriate Math Tasks + +Classifies math tasks to identify: +1. Tool-solvable vs explanation-focused +2. Quantitative vs qualitative answers +3. Ground truth quality issues +4. Format specification problems +""" + +import argparse +import json +import logging +import os +import random +from pathlib import Path +from typing import Dict, List, Any, Optional + +from dotenv import load_dotenv +from google import genai +from google.genai.types import GenerateContentConfig + +load_dotenv() + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +log = logging.getLogger(__name__) + + +CLASSIFICATION_PROMPT = """You are an expert at analyzing mathematical problems to determine their suitability for computational tool-assisted solving and evaluation. + +Analyze the following math task and classify it across multiple dimensions: + +TASK: +Problem: {problem} +Expected Answer: {answer} +{reasoning_section} +{verification_section} + +CLASSIFICATION DIMENSIONS: + +1. **Tool Solvability** (tool_solvable: true/false) + - Can this problem be meaningfully solved or verified using computational tools (Python with NumPy, SciPy, SymPy)? + - Problems requiring pure conceptual explanations, proofs, or theoretical discussions are NOT tool-solvable + - Problems with numerical computation, symbolic manipulation, or verification ARE tool-solvable + +2. **Answer Type** (answer_type: "quantitative", "qualitative", "mixed", "no_answer") + - "quantitative": Numerical answer(s), formulas, or specific mathematical objects + - "qualitative": Explanations, descriptions, procedures, conceptual answers + - "mixed": Both quantitative and qualitative components + - "no_answer": NO_ANSWER or similar (task asks for explanation/implementation without specific answer) + +3. **Ground Truth Quality** (ground_truth_quality: "good", "problematic", "missing") + - "good": Clear, well-specified expected answer that matches the problem requirements + - "problematic": Answer doesn't match problem requirements, contradictory, or under-specified format + - "missing": NO_ANSWER when a specific answer could be provided, or truly missing + - IMPORTANT: If verification verdict is "no", this is a strong indicator of problematic quality + +4. **Format Specification** (format_clarity: "clear", "ambiguous", "unspecified") + - "clear": Expected answer format is well-defined and unambiguous + - "ambiguous": Multiple valid formats possible, unclear what format is expected + - "unspecified": No clear indication of expected format + +5. **Evaluation Difficulty** (evaluation_difficulty: "easy", "medium", "hard") + - "easy": Simple numerical comparison or exact string match + - "medium": Requires normalization or format handling + - "hard": Requires understanding context, multi-part answers, or subjective judgment + +6. **Problem Difficulty** (problem_difficulty: "easy", "medium", "hard", "very_hard") + - Based on mathematical complexity, number of steps, required knowledge level + +7. **Recommendation** (recommend_for_evaluation: true/false) + - Should this task be included in a high-quality tool-assisted evaluation dataset? + - Consider: tool-solvability, answer quality, format clarity, and evaluation feasibility + - CRITICAL: Tasks with verification verdict "no" should typically NOT be recommended unless the issue is minor + - Pay attention to verification reasons - quality issues like unclear problems, wrong answers, or conceptual mismatches should disqualify tasks + +IMPORTANT: Return ONLY a valid JSON object with this exact structure: +{{ + "tool_solvable": true/false, + "tool_solvability_reasoning": "Brief explanation", + "answer_type": "quantitative/qualitative/mixed/no_answer", + "ground_truth_quality": "good/problematic/missing", + "ground_truth_issues": "Specific issues if any, or null", + "format_clarity": "clear/ambiguous/unspecified", + "evaluation_difficulty": "easy/medium/hard", + "problem_difficulty": "easy/medium/hard/very_hard", + "recommend_for_evaluation": true/false, + "recommendation_reasoning": "Why include or exclude this task" +}} + +Respond with ONLY the JSON object, no additional text or formatting.""" + + +class TaskClassifier: + """Classifies math tasks for quality and tool-solvability.""" + + def __init__(self, model_name: str = "gemini-3-flash-preview"): + """Initialize classifier with specified model. + + Args: + model_name: Google AI model to use for classification + """ + self.model_name = model_name + self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + + def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: + """Classify a single task. + + Args: + task_data: Task dictionary with 'problem', 'answer', optionally 'reasoning' and 'verification' + + Returns: + Classification result dictionary + """ + # Format reasoning section if available + reasoning_section = "" + if task_data.get("reasoning"): + reasoning_section = f"Reasoning/Solution Provided: {task_data['reasoning']}" + + # Format verification section if available + verification_section = "" + verification = task_data.get("verification", {}) + if verification: + verdict = verification.get("verdict", "unknown") + reason = verification.get("reason", "No reason provided") + verification_section = f"\nVERIFICATION STATUS:\nVerdict: {verdict}\nReason: {reason}" + + prompt = CLASSIFICATION_PROMPT.format( + problem=task_data.get("problem", ""), + answer=task_data.get("answer", ""), + reasoning_section=reasoning_section, + verification_section=verification_section + ) + + try: + response = self.client.models.generate_content( + model=self.model_name, + contents=prompt, + config=GenerateContentConfig( + temperature=0.1, + response_mime_type="application/json" + ) + ) + + classification = json.loads(response.text) + return classification + + except Exception as e: + log.error(f"Error classifying task {task_data.get('task_id', 'unknown')}: {e}") + # Return default classification on error + return { + "tool_solvable": False, + "tool_solvability_reasoning": f"Classification error: {e}", + "answer_type": "unknown", + "ground_truth_quality": "unknown", + "ground_truth_issues": f"Error during classification: {e}", + "format_clarity": "unknown", + "evaluation_difficulty": "hard", + "problem_difficulty": "unknown", + "recommend_for_evaluation": False, + "recommendation_reasoning": "Classification failed" + } + + def classify_dataset( + self, + tasks: Dict[str, List[Dict[str, Any]]], + output_file: Optional[Path] = None + ) -> Dict[str, List[Dict[str, Any]]]: + """Classify all tasks in a dataset. + + Args: + tasks: Dictionary mapping capability names to list of tasks + output_file: Optional path to save classifications + + Returns: + Tasks with added classification metadata + """ + classified_tasks = {} + total_tasks = sum(len(task_list) for task_list in tasks.values()) + processed = 0 + + log.info(f"Classifying {total_tasks} tasks from {len(tasks)} capabilities...") + + for capability_name, task_list in tasks.items(): + log.info(f"Processing capability: {capability_name} ({len(task_list)} tasks)") + classified_tasks[capability_name] = [] + + for task in task_list: + processed += 1 + log.info(f" [{processed}/{total_tasks}] Classifying task {task.get('task_id', '?')}") + + classification = self.classify_task(task) + + # Add classification to task + task_with_classification = task.copy() + task_with_classification["classification"] = classification + classified_tasks[capability_name].append(task_with_classification) + + # Log key insights + log.info( + f" Tool-solvable: {classification.get('tool_solvable')}, " + f"Answer type: {classification.get('answer_type')}, " + f"Recommend: {classification.get('recommend_for_evaluation')}" + ) + + # Save if output file specified + if output_file: + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(classified_tasks, f, indent=2, ensure_ascii=False) + log.info(f"Saved classified tasks to {output_file}") + + return classified_tasks + + def filter_recommended_tasks( + self, + classified_tasks: Dict[str, List[Dict[str, Any]]], + require_tool_solvable: bool = True, + exclude_no_answer: bool = True, + exclude_problematic: bool = True + ) -> Dict[str, List[Dict[str, Any]]]: + """Filter tasks based on classification criteria. + + Args: + classified_tasks: Tasks with classification metadata + require_tool_solvable: Only include tool-solvable tasks + exclude_no_answer: Exclude tasks with answer_type="no_answer" + exclude_problematic: Exclude tasks with problematic ground truth + + Returns: + Filtered task dictionary + """ + filtered = {} + total_before = 0 + total_after = 0 + + for capability_name, task_list in classified_tasks.items(): + total_before += len(task_list) + filtered_list = [] + + for task in task_list: + classification = task.get("classification", {}) + + # Check filters + if require_tool_solvable and not classification.get("tool_solvable"): + continue + if exclude_no_answer and classification.get("answer_type") == "no_answer": + continue + if exclude_problematic and classification.get("ground_truth_quality") == "problematic": + continue + if not classification.get("recommend_for_evaluation"): + continue + + filtered_list.append(task) + + if filtered_list: + filtered[capability_name] = filtered_list + total_after += len(filtered_list) + + log.info(f"\nFiltering results:") + log.info(f" Before: {total_before} tasks across {len(classified_tasks)} capabilities") + log.info(f" After: {total_after} tasks across {len(filtered)} capabilities") + log.info(f" Removed: {total_before - total_after} tasks") + + return filtered + + +def load_math_tasks(math_dir: Path = Path("math")) -> Dict[str, List[Dict[str, Any]]]: + """Load all math tasks from capability directories. + + Args: + math_dir: Base directory containing math capability folders + + Returns: + Dictionary mapping capability names to list of tasks + """ + all_tasks = {} + + if not math_dir.exists(): + log.error(f"Math directory not found: {math_dir}") + return all_tasks + + capability_dirs = sorted([d for d in math_dir.iterdir() if d.is_dir()]) + log.info(f"Found {len(capability_dirs)} capability directories") + + for cap_dir in capability_dirs: + capability_name = cap_dir.name + cap_file = cap_dir / "capability.json" + + if not cap_file.exists(): + continue + + try: + with open(cap_file, 'r', encoding='utf-8') as f: + cap_data = json.load(f) + + # Extract tasks from capability_failed_data (previously failed tasks) + failed_tasks = cap_data.get("capability_failed_data", []) + + if failed_tasks: + tasks = [] + for task_data in failed_tasks: + verification = task_data.get("verification", {}) + verdict = verification.get("verdict", "") + + # Include all tasks (not just verdict='no') for comprehensive classification + tasks.append({ + "task_id": task_data.get("id", ""), + "problem": task_data.get("problem", ""), + "answer": task_data.get("answer", ""), + "reasoning": task_data.get("reasoning", ""), + "verification": verification, + "original_verdict": verdict + }) + + if tasks: + all_tasks[capability_name] = tasks + log.info(f"Loaded {len(tasks)} tasks from {capability_name}") + + except Exception as e: + log.error(f"Error loading {capability_name}: {e}") + + return all_tasks + + +def stratified_classify_until_target( + tasks_by_capability: Dict[str, List[Dict[str, Any]]], + target_count: int, + classifier: 'TaskClassifier', + seed: int = 42 +) -> Dict[str, List[Dict[str, Any]]]: + """Classify tasks iteratively until we have target_count recommended tasks. + + Uses stratified sampling across capabilities, classifying one task at a time + from each capability in round-robin fashion until we have enough good tasks. + + Args: + tasks_by_capability: All available tasks grouped by capability + target_count: Number of high-quality tasks needed + classifier: TaskClassifier instance + seed: Random seed for reproducibility + + Returns: + Dictionary of classified tasks that were recommended + """ + random.seed(seed) + + # Shuffle tasks within each capability for random sampling + shuffled_tasks = {} + for cap_name, task_list in tasks_by_capability.items(): + shuffled = task_list.copy() + random.shuffle(shuffled) + shuffled_tasks[cap_name] = shuffled + + # Track which index we're at for each capability + capability_indices = {cap: 0 for cap in shuffled_tasks.keys()} + + # Store recommended tasks + recommended_tasks = {} + total_recommended = 0 + total_classified = 0 + + capabilities = list(shuffled_tasks.keys()) + if not capabilities: + log.error("No capabilities available") + return {} + + log.info(f"Starting stratified classification to find {target_count} high-quality tasks") + log.info(f"Available capabilities: {len(capabilities)}") + log.info(f"Total tasks available: {sum(len(tasks) for tasks in shuffled_tasks.values())}") + + # Round-robin through capabilities until we have enough recommended tasks + capability_idx = 0 + stuck_counter = 0 + max_stuck_iterations = len(capabilities) * 2 # Safety check + + while total_recommended < target_count: + # Check if we've exhausted all capabilities + if stuck_counter >= max_stuck_iterations: + log.warning( + f"Could not find enough tasks. Found {total_recommended}/{target_count} after " + f"classifying {total_classified} tasks. All capabilities may be exhausted." + ) + break + + # Get current capability + current_cap = capabilities[capability_idx] + cap_tasks = shuffled_tasks[current_cap] + cap_idx = capability_indices[current_cap] + + # Check if this capability is exhausted + if cap_idx >= len(cap_tasks): + # Move to next capability + capability_idx = (capability_idx + 1) % len(capabilities) + stuck_counter += 1 + continue + + # Get next task from this capability + task = cap_tasks[cap_idx] + capability_indices[current_cap] += 1 + total_classified += 1 + + log.info( + f"[{total_classified}] [{total_recommended}/{target_count} found] " + f"Classifying from {current_cap}: task {task.get('task_id', '?')}" + ) + + # Classify the task + classification = classifier.classify_task(task) + + # Check if recommended + is_recommended = classification.get("recommend_for_evaluation", False) + is_tool_solvable = classification.get("tool_solvable", False) + answer_type = classification.get("answer_type", "unknown") + + log.info( + f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " + f"Answer type={answer_type}" + ) + + # Add classification to task + task_with_classification = task.copy() + task_with_classification["classification"] = classification + task_with_classification["capability_name"] = current_cap + + # If recommended, add to results + if is_recommended: + if current_cap not in recommended_tasks: + recommended_tasks[current_cap] = [] + recommended_tasks[current_cap].append(task_with_classification) + total_recommended += 1 + stuck_counter = 0 # Reset stuck counter on success + log.info(f" ✓ Added to dataset ({total_recommended}/{target_count})") + else: + log.info(f" ✗ Not recommended, continuing search") + + # Move to next capability for round-robin + capability_idx = (capability_idx + 1) % len(capabilities) + + log.info(f"\n{'='*70}") + log.info(f"Classification complete!") + log.info(f" Tasks classified: {total_classified}") + log.info(f" Tasks recommended: {total_recommended}") + log.info(f" Success rate: {total_recommended/total_classified*100:.1f}%") + log.info(f" Capabilities represented: {len(recommended_tasks)}") + log.info(f"{'='*70}\n") + + return recommended_tasks + + +def main(): + """Main execution function.""" + parser = argparse.ArgumentParser( + description="Classify math tasks for tool-assisted evaluation", + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + "--math-dir", + type=Path, + default=Path("math"), + help="Directory containing math capability folders" + ) + parser.add_argument( + "--output", + type=Path, + default=Path("classified_tasks.json"), + help="Output file for classified tasks" + ) + parser.add_argument( + "--model", + default="gemini-3-flash-preview", + help="Model to use for classification" + ) + parser.add_argument( + "--num-tasks", + type=int, + default=50, + help="Number of high-quality tasks to find (default: 50)" + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="Random seed for sampling (default: 42)" + ) + parser.add_argument( + "--filter-only", + action="store_true", + help="Only filter pre-classified tasks (skip classification step)" + ) + parser.add_argument( + "--input-classified", + type=Path, + help="Input file with pre-classified tasks (for --filter-only mode)" + ) + + args = parser.parse_args() + + if args.filter_only: + if not args.input_classified or not args.input_classified.exists(): + log.error("Must provide --input-classified file in --filter-only mode") + return + + with open(args.input_classified, 'r', encoding='utf-8') as f: + classified_tasks = json.load(f) + log.info(f"Loaded pre-classified tasks from {args.input_classified}") + + # Filter the pre-classified tasks + classifier = TaskClassifier(model_name=args.model) + filtered = classifier.filter_recommended_tasks(classified_tasks) + else: + # Load all available tasks + all_tasks = load_math_tasks(args.math_dir) + + if not all_tasks: + log.error("No tasks loaded") + return + + total_available = sum(len(tasks) for tasks in all_tasks.values()) + log.info(f"Loaded {total_available} tasks from {len(all_tasks)} capabilities") + + # Use stratified sampling with iterative classification + classifier = TaskClassifier(model_name=args.model) + filtered = stratified_classify_until_target( + tasks_by_capability=all_tasks, + target_count=args.num_tasks, + classifier=classifier, + seed=args.seed + ) + + if not filtered: + log.error("No recommended tasks found") + return + + # Save filtered/recommended tasks + filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(filtered_file, 'w', encoding='utf-8') as f: + json.dump(filtered, f, indent=2, ensure_ascii=False) + log.info(f"Saved recommended tasks to {filtered_file}") + + # Print summary statistics + print("\n" + "="*70) + print("CLASSIFICATION SUMMARY") + print("="*70) + + tool_solvable = 0 + not_tool_solvable = 0 + by_answer_type = {"quantitative": 0, "qualitative": 0, "mixed": 0, "no_answer": 0} + by_quality = {"good": 0, "problematic": 0, "missing": 0} + by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} + by_capability = {} + recommended = 0 + + for cap_name, task_list in filtered.items(): + by_capability[cap_name] = len(task_list) + for task in task_list: + c = task.get("classification", {}) + if c.get("tool_solvable"): + tool_solvable += 1 + else: + not_tool_solvable += 1 + + answer_type = c.get("answer_type", "unknown") + by_answer_type[answer_type] = by_answer_type.get(answer_type, 0) + 1 + + quality = c.get("ground_truth_quality", "unknown") + by_quality[quality] = by_quality.get(quality, 0) + 1 + + difficulty = c.get("problem_difficulty", "unknown") + by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 + + if c.get("recommend_for_evaluation"): + recommended += 1 + + total = tool_solvable + not_tool_solvable + if total == 0: + print("No tasks found") + return + + print(f"\nTotal tasks: {total}") + print(f"Tool-solvable: {tool_solvable} ({tool_solvable/total*100:.1f}%)") + print(f"Not tool-solvable: {not_tool_solvable} ({not_tool_solvable/total*100:.1f}%)") + + print(f"\nBy capability:") + for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True): + print(f" {cap}: {count}") + + print(f"\nAnswer types:") + for atype, count in sorted(by_answer_type.items()): + if count > 0: + print(f" {atype}: {count} ({count/total*100:.1f}%)") + + print(f"\nGround truth quality:") + for quality, count in sorted(by_quality.items()): + if count > 0: + print(f" {quality}: {count} ({count/total*100:.1f}%)") + + print(f"\nProblem difficulty:") + for diff, count in sorted(by_difficulty.items()): + if count > 0: + print(f" {diff}: {count} ({count/total*100:.1f}%)") + + print(f"\nRecommended for evaluation: {recommended} ({recommended/total*100:.1f}%)") + print("="*70) + + +if __name__ == "__main__": + main() diff --git a/scripts/generate_filtered_dataset.py b/scripts/generate_filtered_dataset.py new file mode 100644 index 00000000..c90156d6 --- /dev/null +++ b/scripts/generate_filtered_dataset.py @@ -0,0 +1,344 @@ +#!/usr/bin/env python3 +""" +Generate Filtered Dataset with Metadata + +Creates a curated dataset of N tasks randomly sampled from classified math tasks, +with comprehensive metadata for experimental analysis. +""" + +import argparse +import json +import random +from pathlib import Path +from typing import Dict, List, Any +from collections import defaultdict +import logging + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s' +) +log = logging.getLogger(__name__) + + +def load_classified_tasks(filepath: Path) -> Dict[str, List[Dict[str, Any]]]: + """Load classified tasks from JSON file.""" + with open(filepath, 'r', encoding='utf-8') as f: + return json.load(f) + + +def generate_filtered_dataset( + classified_tasks: Dict[str, List[Dict[str, Any]]], + num_tasks: int = 50, + stratify_by_capability: bool = True, + seed: int = 42 +) -> List[Dict[str, Any]]: + """Generate filtered dataset with stratified sampling. + + Args: + classified_tasks: Dictionary of classified tasks by capability + num_tasks: Total number of tasks to sample + stratify_by_capability: Whether to sample proportionally from each capability + seed: Random seed for reproducibility + + Returns: + List of sampled tasks with full metadata + """ + random.seed(seed) + + # Flatten tasks with capability metadata + all_tasks = [] + for capability_name, task_list in classified_tasks.items(): + for task in task_list: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + all_tasks.append(task_copy) + + if not all_tasks: + log.error("No tasks available for sampling") + return [] + + log.info(f"Total tasks available: {len(all_tasks)} from {len(classified_tasks)} capabilities") + + # Sample tasks + if stratify_by_capability: + # Calculate tasks per capability + capabilities = list(classified_tasks.keys()) + tasks_per_cap = max(1, num_tasks // len(capabilities)) + remainder = num_tasks % len(capabilities) + + sampled = [] + capability_counts = defaultdict(int) + + for i, capability_name in enumerate(capabilities): + # Allocate extra tasks to first capabilities to handle remainder + cap_target = tasks_per_cap + (1 if i < remainder else 0) + cap_tasks = classified_tasks[capability_name] + + if len(cap_tasks) < cap_target: + log.warning( + f"Capability {capability_name} has only {len(cap_tasks)} tasks, " + f"requested {cap_target}" + ) + cap_sample = cap_tasks + else: + cap_sample = random.sample(cap_tasks, cap_target) + + for task in cap_sample: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + sampled.append(task_copy) + capability_counts[capability_name] += 1 + + log.info("Stratified sampling complete:") + for cap, count in sorted(capability_counts.items()): + log.info(f" {cap}: {count} tasks") + else: + # Random sampling without stratification + if len(all_tasks) < num_tasks: + log.warning( + f"Requested {num_tasks} tasks but only {len(all_tasks)} available" + ) + sampled = all_tasks + else: + sampled = random.sample(all_tasks, num_tasks) + log.info(f"Random sampling: selected {len(sampled)} tasks") + + return sampled + + +def enrich_metadata(tasks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Add derived metadata fields to tasks. + + Args: + tasks: List of tasks with classification + + Returns: + Tasks with enriched metadata + """ + enriched = [] + + for i, task in enumerate(tasks): + enriched_task = task.copy() + classification = task.get("classification", {}) + + # Add experimental metadata + enriched_task["dataset_metadata"] = { + "dataset_id": i + 1, + "source_capability": task.get("capability_name", "unknown"), + "original_task_id": task.get("task_id", "unknown"), + + # Classification-derived fields + "tool_solvable": classification.get("tool_solvable", False), + "answer_type": classification.get("answer_type", "unknown"), + "ground_truth_quality": classification.get("ground_truth_quality", "unknown"), + "format_clarity": classification.get("format_clarity", "unknown"), + "evaluation_difficulty": classification.get("evaluation_difficulty", "unknown"), + "problem_difficulty": classification.get("problem_difficulty", "unknown"), + + # For analysis + "recommended": classification.get("recommend_for_evaluation", False), + "has_reasoning": bool(task.get("reasoning")), + "original_verdict": task.get("original_verdict", "unknown"), + } + + enriched.append(enriched_task) + + return enriched + + +def analyze_dataset(tasks: List[Dict[str, Any]]) -> Dict[str, Any]: + """Generate statistics about the dataset. + + Args: + tasks: List of tasks with metadata + + Returns: + Dictionary of dataset statistics + """ + stats = { + "total_tasks": len(tasks), + "by_capability": defaultdict(int), + "by_answer_type": defaultdict(int), + "by_problem_difficulty": defaultdict(int), + "by_evaluation_difficulty": defaultdict(int), + "by_ground_truth_quality": defaultdict(int), + "tool_solvable_count": 0, + "recommended_count": 0, + } + + for task in tasks: + metadata = task.get("dataset_metadata", {}) + + stats["by_capability"][metadata.get("source_capability", "unknown")] += 1 + stats["by_answer_type"][metadata.get("answer_type", "unknown")] += 1 + stats["by_problem_difficulty"][metadata.get("problem_difficulty", "unknown")] += 1 + stats["by_evaluation_difficulty"][metadata.get("evaluation_difficulty", "unknown")] += 1 + stats["by_ground_truth_quality"][metadata.get("ground_truth_quality", "unknown")] += 1 + + if metadata.get("tool_solvable"): + stats["tool_solvable_count"] += 1 + if metadata.get("recommended"): + stats["recommended_count"] += 1 + + # Convert defaultdicts to regular dicts for JSON serialization + stats["by_capability"] = dict(stats["by_capability"]) + stats["by_answer_type"] = dict(stats["by_answer_type"]) + stats["by_problem_difficulty"] = dict(stats["by_problem_difficulty"]) + stats["by_evaluation_difficulty"] = dict(stats["by_evaluation_difficulty"]) + stats["by_ground_truth_quality"] = dict(stats["by_ground_truth_quality"]) + + return stats + + +def save_dataset( + tasks: List[Dict[str, Any]], + output_file: Path, + include_stats: bool = True +) -> None: + """Save dataset to JSON file. + + Args: + tasks: List of tasks to save + output_file: Output file path + include_stats: Whether to include statistics in output + """ + output_data = { + "metadata": { + "dataset_name": "filtered_math_tasks", + "version": "1.0", + "description": "Curated math tasks for tool-assisted evaluation", + "num_tasks": len(tasks), + }, + "tasks": tasks, + } + + if include_stats: + output_data["statistics"] = analyze_dataset(tasks) + + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(output_data, f, indent=2, ensure_ascii=False) + + log.info(f"Saved dataset to {output_file}") + + +def print_dataset_summary(stats: Dict[str, Any]) -> None: + """Print dataset statistics summary.""" + print("\n" + "="*70) + print("DATASET SUMMARY") + print("="*70) + + print(f"\nTotal tasks: {stats['total_tasks']}") + print(f"Tool-solvable: {stats['tool_solvable_count']} ({stats['tool_solvable_count']/stats['total_tasks']*100:.1f}%)") + print(f"Recommended: {stats['recommended_count']} ({stats['recommended_count']/stats['total_tasks']*100:.1f}%)") + + print("\nDistribution by capability:") + for cap, count in sorted(stats['by_capability'].items(), key=lambda x: x[1], reverse=True): + pct = count / stats['total_tasks'] * 100 + print(f" {cap}: {count} ({pct:.1f}%)") + + print("\nDistribution by answer type:") + for atype, count in sorted(stats['by_answer_type'].items()): + pct = count / stats['total_tasks'] * 100 + print(f" {atype}: {count} ({pct:.1f}%)") + + print("\nDistribution by problem difficulty:") + for diff, count in sorted(stats['by_problem_difficulty'].items()): + pct = count / stats['total_tasks'] * 100 + print(f" {diff}: {count} ({pct:.1f}%)") + + print("\nDistribution by evaluation difficulty:") + for diff, count in sorted(stats['by_evaluation_difficulty'].items()): + pct = count / stats['total_tasks'] * 100 + print(f" {diff}: {count} ({pct:.1f}%)") + + print("\nDistribution by ground truth quality:") + for quality, count in sorted(stats['by_ground_truth_quality'].items()): + pct = count / stats['total_tasks'] * 100 + print(f" {quality}: {count} ({pct:.1f}%)") + + print("="*70) + + +def main(): + """Main execution function.""" + parser = argparse.ArgumentParser( + description="Generate filtered dataset with metadata", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Generate 50-task dataset from filtered classifications + python generate_filtered_dataset.py --input classified_tasks_filtered.json --output filtered_dataset_50.json + + # Generate 100-task dataset with random sampling (no stratification) + python generate_filtered_dataset.py --input classified_tasks_filtered.json --num-tasks 100 --no-stratify + + # Use custom seed for reproducibility + python generate_filtered_dataset.py --input classified_tasks_filtered.json --seed 123 + """ + ) + parser.add_argument( + "--input", + type=Path, + required=True, + help="Input file with classified/filtered tasks" + ) + parser.add_argument( + "--output", + type=Path, + default=Path("filtered_dataset_50.json"), + help="Output file for generated dataset" + ) + parser.add_argument( + "--num-tasks", + type=int, + default=50, + help="Number of tasks to sample (default: 50)" + ) + parser.add_argument( + "--no-stratify", + action="store_true", + help="Disable stratified sampling by capability" + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="Random seed for reproducibility (default: 42)" + ) + + args = parser.parse_args() + + # Load classified tasks + log.info(f"Loading classified tasks from {args.input}") + classified_tasks = load_classified_tasks(args.input) + + # Generate filtered dataset + log.info(f"Generating dataset with {args.num_tasks} tasks...") + sampled_tasks = generate_filtered_dataset( + classified_tasks, + num_tasks=args.num_tasks, + stratify_by_capability=not args.no_stratify, + seed=args.seed + ) + + # Enrich with metadata + log.info("Enriching tasks with metadata...") + enriched_tasks = enrich_metadata(sampled_tasks) + + # Generate statistics + stats = analyze_dataset(enriched_tasks) + + # Save dataset + save_dataset(enriched_tasks, args.output, include_stats=True) + + # Print summary + print_dataset_summary(stats) + + log.info(f"\nDataset generation complete!") + log.info(f"Output file: {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/generate_task_dataset.sh b/scripts/generate_task_dataset.sh new file mode 100755 index 00000000..a7816ac1 --- /dev/null +++ b/scripts/generate_task_dataset.sh @@ -0,0 +1,177 @@ +#!/bin/bash +# Workflow for generating filtered task dataset with tool-assisted evaluation +# This script runs the complete pipeline from classification to filtered dataset generation + +set -e # Exit on error + +echo "==========================================================================" +echo "Task Classification and Dataset Generation Workflow" +echo "==========================================================================" + +# Default values +MATH_DIR="math" +NUM_TASKS=50 +MODEL="gemini-3-flash-preview" +SEED=42 +OUTPUT_DIR="filtered_tasks_output_100" + +# Parse command line arguments +while [[ $# -gt 0 ]]; do + case $1 in + --math-dir) + MATH_DIR="$2" + shift 2 + ;; + --num-tasks) + NUM_TASKS="$2" + shift 2 + ;; + --model) + MODEL="$2" + shift 2 + ;; + --seed) + SEED="$2" + shift 2 + ;; + --output-dir) + OUTPUT_DIR="$2" + shift 2 + ;; + --help) + echo "Usage: $0 [OPTIONS]" + echo "" + echo "Options:" + echo " --math-dir DIR Directory with math tasks (default: math)" + echo " --num-tasks N Number of tasks to generate (default: 50)" + echo " --model MODEL Model for classification (default: gemini-3-flash-preview)" + echo " --seed SEED Random seed (default: 42)" + echo " --output-dir DIR Output directory (default: filtered_tasks_output)" + echo " --help Show this help message" + exit 0 + ;; + *) + echo "Unknown option: $1" + echo "Use --help for usage information" + exit 1 + ;; + esac +done + +# Create output directory +mkdir -p "$OUTPUT_DIR" + +echo "" +echo "Configuration:" +echo " Math directory: $MATH_DIR" +echo " Number of tasks: $NUM_TASKS" +echo " Model: $MODEL" +echo " Seed: $SEED" +echo " Output directory: $OUTPUT_DIR" +echo "" + +# Step 1: Classify tasks (stratified sampling until target found) +echo "==========================================================================" +echo "Step 1: Classifying tasks with stratified sampling" +echo "==========================================================================" +echo "" + +python classify_tasks.py \ + --math-dir "$MATH_DIR" \ + --output "$OUTPUT_DIR/classified_tasks.json" \ + --model "$MODEL" \ + --num-tasks "$NUM_TASKS" \ + --seed "$SEED" + +if [ $? -ne 0 ]; then +if [ $? -ne 0 ]; then + echo "Error: Task classification failed" + exit 1 +fi + +echo "" +echo "Classification complete!" +echo "Output: $OUTPUT_DIR/classified_tasks.json" +echo "" + +# Step 2: Generate filtered dataset with metadata +echo "==========================================================================" +echo "Step 2: Generating filtered dataset with metadata" +echo "==========================================================================" +echo "" + +python generate_filtered_dataset.py \ + --input "$OUTPUT_DIR/classified_tasks.json" \ + --output "$OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json" \ + --num-tasks "$NUM_TASKS" \ + --seed "$SEED" + +if [ $? -ne 0 ]; then + echo "Error: Dataset generation failed" + exit 1 +fi + +echo "" +echo "Dataset generation complete!" +echo "Output: $OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json" +echo "" + +# Step 3: Create experiment-ready format (compatible with run_experiments.py) +echo "==========================================================================" +echo "Step 3: Creating experiment-ready format" +echo "==========================================================================" +echo "" + +python -c " +import json +from pathlib import Path + +# Load filtered dataset +with open('$OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json', 'r') as f: + dataset = json.load(f) + +# Convert to experiment format (grouped by capability) +tasks_by_capability = {} +for task in dataset['tasks']: + cap_name = task.get('capability_name', 'unknown') + if cap_name not in tasks_by_capability: + tasks_by_capability[cap_name] = [] + + tasks_by_capability[cap_name].append({ + 'task_id': task.get('task_id', task.get('dataset_metadata', {}).get('dataset_id', '')), + 'problem': task.get('problem', ''), + 'answer': task.get('answer', ''), + 'reasoning': task.get('reasoning', ''), + 'metadata': task.get('dataset_metadata', {}) + }) + +# Save in experiment format +output_file = Path('$OUTPUT_DIR/experiment_tasks_${NUM_TASKS}.json') +with open(output_file, 'w') as f: + json.dump(tasks_by_capability, f, indent=2, ensure_ascii=False) + +print(f'Created experiment-ready format: {output_file}') +print(f'Capabilities: {len(tasks_by_capability)}') +print(f'Total tasks: {sum(len(tasks) for tasks in tasks_by_capability.values())}') +" + +if [ $? -ne 0 ]; then + echo "Error: Format conversion failed" + exit 1 +fi + +echo "" +echo "==========================================================================" +echo "Workflow complete!" +echo "==========================================================================" +echo "" +echo "Generated files:" +echo " 1. $OUTPUT_DIR/classified_tasks.json - Recommended tasks with classifications" +echo " 2. $OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json - Final dataset with metadata" +echo " 3. $OUTPUT_DIR/experiment_tasks_${NUM_TASKS}.json - Experiment-ready format" +echo "" +echo "Next steps:" +echo " 1. Review the filtered dataset and statistics" +echo " 2. Run experiments using: python run_experiments.py --num-tasks N" +echo " 3. Specify custom task file by copying experiment_tasks_${NUM_TASKS}.json to selected_tasks.json" +echo "" diff --git a/src/task_solver/tool_assisted_scientist.py b/src/task_solver/tool_assisted_scientist.py index fcfac091..adf6df00 100644 --- a/src/task_solver/tool_assisted_scientist.py +++ b/src/task_solver/tool_assisted_scientist.py @@ -18,13 +18,13 @@ ) from langfuse import Langfuse -from src.task_solver.code_executor import PythonCodeExecutor from src.task_solver.messages import ( AgentRevisionRequest, AgentSolution, TaskSolutionRequest, ToolAssistedAgentSolution, ) +from src.tools.toolkit import ScientificToolKit from src.utils.json_utils import parse_llm_json_response from src.utils.tool_assisted_prompts import ( TOOL_ASSISTED_ROUND_1_PROMPT, @@ -54,8 +54,8 @@ class ToolAssistedScientist(RoutedAgent): Unique identifier for this scientist agent in the debate. _langfuse_client : Langfuse Langfuse client for tracing and logging scientist activity. - _code_executor : PythonCodeExecutor - Executor for running Python code with restricted imports. + _toolkit : ScientificToolKit + Toolkit for tool selection and code execution. """ def __init__( @@ -63,13 +63,13 @@ def __init__( model_client: ChatCompletionClient, scientist_id: str, langfuse_client: Langfuse, - code_timeout: int = 30, + toolkit: ScientificToolKit, ) -> None: super().__init__(f"Tool-Assisted Scientist {scientist_id}") self._model_client = model_client self._scientist_id = scientist_id self._langfuse_client = langfuse_client - self._code_executor = PythonCodeExecutor(timeout=code_timeout) + self._toolkit = toolkit def _extract_solution_components( self, response: str @@ -244,7 +244,9 @@ async def _generate_solution_with_code_execution( MAX_CODE_EXECUTION_ATTEMPTS, ) - execution_result = self._code_executor.execute(code) + execution_result = self._toolkit.execute_code(code) + # Convert dict to object-like access + execution_result = type('obj', (object,), {'success': execution_result['success'], 'output': execution_result['output'], 'error': execution_result.get('error')})() if execution_result.success: code_output = execution_result.output @@ -392,8 +394,25 @@ async def handle_task_solution_request( } ) + # Step 1: Prepare tool context for problem + log.info(f"Scientist {self._scientist_id}: Preparing tool context") + tool_context = await self._toolkit.prepare_tools(message.problem) + + span.update( + metadata={ + "tool_selection_needs_tools": tool_context.get("needs_tools"), + "tool_selection_reasoning": tool_context.get("reasoning"), + "selected_tools": tool_context.get("selected_libraries", []), + } + ) + + # Step 2: Format tool context for prompt + tool_context_str = self._toolkit.format_tool_context(tool_context) + + # Step 3: Create prompt with tool context prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=message.problem + problem_text=message.problem, + tool_context=tool_context_str ) system_message = SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE) diff --git a/src/tools/__init__.py b/src/tools/__init__.py new file mode 100644 index 00000000..7e94e20c --- /dev/null +++ b/src/tools/__init__.py @@ -0,0 +1,39 @@ +"""Scientific computing toolkit for research code. + +Simplified, flat structure for easy experimentation and prompt iteration. + +Core Components: +--------------- +- definitions.py: Tool and library definitions +- docs.py: Documentation retrieval from HTML files +- executor.py: Python code execution engine +- toolkit.py: Main ScientificToolKit class (combines selection + execution) + +Usage: +------ + from src.tools.toolkit import ScientificToolKit + + # Create toolkit + toolkit = ScientificToolKit( + model_client=my_client, + enable_tool_selection=True + ) + + # Use in scientist + scientist = ToolAssistedScientist(..., toolkit=toolkit) +""" + +from src.tools.definitions import PYTHON_SCIENTIFIC_TOOL, LibraryConfig, ToolDefinition +from src.tools.docs import ScientificDocRetriever +from src.tools.executor import CodeExecutionResult, PythonExecutor +from src.tools.toolkit import ScientificToolKit + +__all__ = [ + "ScientificToolKit", + "ScientificDocRetriever", + "PythonExecutor", + "CodeExecutionResult", + "ToolDefinition", + "LibraryConfig", + "PYTHON_SCIENTIFIC_TOOL", +] diff --git a/src/tools/definitions.py b/src/tools/definitions.py new file mode 100644 index 00000000..b10ec922 --- /dev/null +++ b/src/tools/definitions.py @@ -0,0 +1,137 @@ +"""Tool definitions for scientific computing. + +Simple dataclasses without over-abstraction. Easy to read and modify. +""" + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + + +@dataclass +class LibraryConfig: + """Configuration for a library/module.""" + name: str + import_name: str + description: str + docs_path: Optional[str] = None + common_functions: List[str] = field(default_factory=list) + use_cases: List[str] = field(default_factory=list) + + +@dataclass +class ToolDefinition: + """Complete definition of a tool.""" + tool_id: str + name: str + description: str + libraries: List[LibraryConfig] + allowed_imports: List[str] + use_cases: List[str] = field(default_factory=list) + metadata: Dict[str, Any] = field(default_factory=dict) + + +# Library Configurations +NUMPY_CONFIG = LibraryConfig( + name="numpy", + import_name="numpy as np", + description="Fundamental package for numerical computing. Arrays, matrices, and mathematical functions.", + docs_path="numpy-html-1.17.0", + common_functions=[ + "np.array", "np.linspace", "np.zeros", "np.ones", "np.eye", + "np.linalg.eig", "np.linalg.det", "np.linalg.inv", "np.linalg.solve", + "np.dot", "np.cross", "np.sin", "np.cos", "np.exp", "np.log", "np.sqrt" + ], + use_cases=[ + "Array operations and linear algebra", + "Matrix computations", + "Statistical computations" + ] +) + +SCIPY_CONFIG = LibraryConfig( + name="scipy", + import_name="scipy", + description="Scientific computing library. Integration, optimization, linear algebra, statistics.", + docs_path="scipy-html-1.17.0", + common_functions=[ + "scipy.integrate.quad", "scipy.integrate.odeint", "scipy.integrate.solve_ivp", + "scipy.optimize.minimize", "scipy.optimize.fsolve", + "scipy.linalg.lu", "scipy.linalg.qr", "scipy.linalg.svd", + "scipy.stats.norm", "scipy.interpolate.interp1d" + ], + use_cases=[ + "Numerical integration", + "Optimization and root finding", + "Advanced linear algebra" + ] +) + +SYMPY_CONFIG = LibraryConfig( + name="sympy", + import_name="sympy", + description="Symbolic mathematics library. Algebraic manipulation, calculus, equation solving.", + docs_path="sympy-docs-html-1.14.0", + common_functions=[ + "sympy.symbols", "sympy.simplify", "sympy.expand", "sympy.factor", + "sympy.diff", "sympy.integrate", "sympy.solve", "sympy.dsolve", + "sympy.Matrix", "sympy.sin", "sympy.cos", "sympy.pi" + ], + use_cases=[ + "Symbolic differentiation and integration", + "Solving equations", + "Symbolic simplification" + ] +) + +MATH_CONFIG = LibraryConfig( + name="math", + import_name="math", + description="Python's standard math library.", + docs_path=None, + common_functions=["math.sin", "math.cos", "math.sqrt", "math.exp", "math.log", "math.pi"], + use_cases=["Basic mathematical operations"] +) + +FRACTIONS_CONFIG = LibraryConfig( + name="fractions", + import_name="fractions", + description="Rational number arithmetic.", + docs_path=None, + common_functions=["Fraction"], + use_cases=["Exact rational arithmetic"] +) + +DECIMAL_CONFIG = LibraryConfig( + name="decimal", + import_name="decimal", + description="High-precision decimal arithmetic.", + docs_path=None, + common_functions=["Decimal"], + use_cases=["High-precision calculations"] +) + +CMATH_CONFIG = LibraryConfig( + name="cmath", + import_name="cmath", + description="Complex number operations.", + docs_path=None, + common_functions=["cmath.sqrt", "cmath.exp", "cmath.log"], + use_cases=["Complex number operations"] +) + +# The Tool Definition +PYTHON_SCIENTIFIC_TOOL = ToolDefinition( + tool_id="python_code_execution", + name="Python Code Execution", + description="Execute Python code with SymPy, NumPy, SciPy for symbolic math, numerical computing, and scientific analysis.", + libraries=[SYMPY_CONFIG, NUMPY_CONFIG, SCIPY_CONFIG, MATH_CONFIG, FRACTIONS_CONFIG, DECIMAL_CONFIG, CMATH_CONFIG], + allowed_imports=["sympy", "numpy", "scipy", "math", "fractions", "decimal", "cmath"], + use_cases=[ + "Solving differential equations", + "Matrix operations and linear algebra", + "Numerical integration", + "Symbolic manipulation", + "Optimization problems" + ], + metadata={"timeout": 30} +) diff --git a/src/tools/docs.py b/src/tools/docs.py new file mode 100644 index 00000000..29d2ceb6 --- /dev/null +++ b/src/tools/docs.py @@ -0,0 +1,193 @@ +"""Documentation retrieval from local HTML files. + +Structure-aware RAG that exploits naming conventions of HTML documentation +to deterministically build a Library -> Module -> Function index. +""" + +import logging +from pathlib import Path +from typing import Dict, List +from collections import defaultdict +from bs4 import BeautifulSoup + +log = logging.getLogger("tools.docs") + + +class ScientificDocRetriever: + """ + Parses local HTML documentation directories to build a structured index + of Libraries -> Modules -> Functions. + """ + + def __init__(self, docs_base_path: Path): + self.docs_path = docs_base_path + # Schema: index[library][module] = [list_of_function_names] + self._index: Dict[str, Dict[str, List[str]]] = {} + # Schema: file_map[library.module.function] = Path(file.html) + self._file_map: Dict[str, Path] = {} + + self._build_index() + + def _build_index(self): + """Deterministically builds index based on 'reference/generated' file patterns.""" + + # 1. Index NumPy and SciPy (Pattern: pkg.submodule.function.html) + for lib in ["numpy", "scipy"]: + # Find versioned directory (e.g., numpy-html-1.17.0) + lib_dir = next(self.docs_path.glob(f"{lib}-html-*"), None) + if not lib_dir: + log.warning(f"Documentation directory not found for {lib}") + continue + + # Target specific reference directory to avoid tutorials/dev noise + generated_dir = lib_dir / "reference" / "generated" + if generated_dir.exists(): + if lib not in self._index: + self._index[lib] = defaultdict(list) + + for html_file in generated_dir.glob("*.html"): + name = html_file.stem # e.g., "numpy.linalg.eig" + + if name.startswith("index") or name.startswith("gallery"): + continue + + parts = name.split('.') + if len(parts) >= 3: + # numpy.linalg.eig -> module="linalg", func="eig" + module = parts[1] + func = parts[-1] + + self._index[lib][module].append(func) + self._file_map[f"{lib}.{module}.{func}"] = html_file + + log.info(f"Indexed {lib}: {len(self._index[lib])} modules") + + # 2. Index SymPy (Pattern: modules/topic/file.html) + sympy_dir = next(self.docs_path.glob("sympy-docs-html-*"), None) + if sympy_dir: + modules_dir = sympy_dir / "modules" + if modules_dir.exists(): + if "sympy" not in self._index: + self._index["sympy"] = defaultdict(list) + + # SymPy organizes by folder names in 'modules/' + for category_dir in modules_dir.iterdir(): + if category_dir.is_dir() and not category_dir.name.startswith('_'): + topic = category_dir.name # e.g., "matrices", "solvers" + for html_file in category_dir.glob("*.html"): + if html_file.stem != "index": + self._index["sympy"][topic].append(html_file.stem) + self._file_map[f"sympy.{topic}.{html_file.stem}"] = html_file + + log.info(f"Indexed sympy: {len(self._index['sympy'])} modules") + + def get_library_overview(self) -> str: + """Returns a high-level summary of available libraries and their modules.""" + overview = [] + for lib, modules in self._index.items(): + overview.append(f"### {lib.upper()}") + # List modules, sorted alphabetically + mod_list = sorted(modules.keys()) + overview.append(f"Available Modules: {', '.join(mod_list)}\n") + return "\n".join(overview) + + def get_full_module_context(self, library: str, module: str) -> str: + """ + Retrieves signatures for ALL functions in a module. + Intended for high-context models (Gemini Flash). + """ + if library not in self._index or module not in self._index[library]: + return f"Error: Module {library}.{module} not found." + + # SORT the functions so the prompt is deterministic + functions = sorted(self._index[library][module]) + context_blocks = [f"--- DOCUMENTATION FOR {library}.{module} ---"] + + for func in functions: + key = f"{library}.{module}.{func}" + file_path = self._file_map.get(key) + if file_path: + signature = self._extract_signature_from_html(file_path) + if signature: + context_blocks.append(signature) + + return "\n\n".join(context_blocks) + + def _extract_signature_from_html(self, path: Path) -> str: + """Parses HTML to extract signature + brief description (Optimized for Tokens).""" + try: + with open(path, 'r', encoding='utf-8') as f: + soup = BeautifulSoup(f.read(), 'html.parser') + + extracted_content = [] + + # Find Main Definitions + definitions = soup.find_all('dl', class_=['function', 'class', 'method']) + + for dl in definitions: + # 1. Get the signature (dt) + dt = dl.find('dt') + if not dt: + continue + + # CLEANUP: Remove ¶, [source], and extra whitespace + raw_sig = dt.get_text().replace("¶", "").replace("[source]", "").strip() + sig = " ".join(raw_sig.split()) + + # FILTER 1: Skip private/magic methods in the main definition + # (e.g. skip scipy.sparse.bsr_matrix.__add__) + func_name = sig.split('(')[0].split('.')[-1] + if func_name.startswith('_'): + continue + + # 2. Get the description (dd) - First sentence only, max 120 chars + desc_text = "" + dd = dl.find('dd') + if dd: + p = dd.find('p') + if p: + raw_desc = " ".join(p.get_text().split()) + # Split by period to get first sentence, or cap at 120 chars + if '.' in raw_desc: + desc_text = raw_desc.split('.')[0] + "." + else: + desc_text = raw_desc + + if len(desc_text) > 120: + desc_text = desc_text[:117] + "..." + + # FORMAT: Dense one-liner + # e.g., "numpy.linalg.eig(a): Compute eigenvalues." + entry = f"{sig}: {desc_text}" if desc_text else sig + extracted_content.append(entry) + + # 3. SYMPY/CLASS HANDLING: Look for methods inside + # Only do this if it's a class definition + if "class" in dl.get('class', []) or "sympy" in str(path): + methods = dl.find_all('dl', class_='method') + for method in methods: + m_dt = method.find('dt') + if m_dt: + m_sig_raw = m_dt.get_text().replace("¶", "").replace("[source]", "").strip() + m_sig = " ".join(m_sig_raw.split()) + + # FILTER 2: Strict method filtering + # Skip __init__, __add__, _private_method + method_name = m_sig.split('(')[0].split('.')[-1] + if method_name.startswith('_'): + continue + + # Optional: Skip simple properties to save space? + # if "property" in str(method): continue + + extracted_content.append(f" . {method_name}(...): Method") + # Note: We purposefully omit method descriptions to save tokens + # relying on the method name being descriptive enough. + + if extracted_content: + return "\n".join(extracted_content) + + except Exception as e: + # log.warning(f"Error parsing {path}: {e}") + pass + return "" diff --git a/src/task_solver/code_executor.py b/src/tools/executor.py similarity index 58% rename from src/task_solver/code_executor.py rename to src/tools/executor.py index 0dc60043..09736741 100644 --- a/src/task_solver/code_executor.py +++ b/src/tools/executor.py @@ -1,74 +1,76 @@ -"""Python code execution module for tool-assisted task solving.""" +"""Python code executor with restricted imports.""" import ast import sys import traceback +from dataclasses import dataclass, field from io import StringIO -from typing import Any +from typing import Any, Dict, List, Optional +@dataclass class CodeExecutionResult: - """Result of code execution. + """Result of code execution.""" + success: bool + output: str + error: Optional[str] = None + metadata: Dict[str, Any] = field(default_factory=dict) - Attributes - ---------- - success : bool - Whether the code executed successfully. - output : str - Standard output from the code execution. - error : str | None - Error message if execution failed, None otherwise. - """ - - def __init__(self, success: bool, output: str, error: str | None = None): - self.success = success - self.output = output - self.error = error - - def to_dict(self) -> dict[str, Any]: + def to_dict(self) -> Dict[str, Any]: """Convert result to dictionary.""" return { "success": self.success, "output": self.output, "error": self.error, + "metadata": self.metadata, } -class PythonCodeExecutor: - """Execute Python code with restricted imports for mathematical computations. - - This executor allows SymPy, NumPy, and SciPy for mathematical tasks while - restricting potentially dangerous operations. +class PythonExecutor: + """Execute Python code with restricted imports for safe execution." Attributes ---------- - allowed_imports : set[str] + allowed_imports : List[str] Set of allowed module names for import. timeout : int Maximum execution time in seconds (not enforced in basic implementation). """ - def __init__(self, timeout: int = 30): - """Initialize the code executor. + def __init__( + self, + allowed_imports: Optional[List[str]] = None, + timeout: int = 30 + ): + """Initialize the Python executor. Parameters ---------- - timeout : int, optional - Maximum execution time in seconds, by default 30. - Note: Timeout enforcement requires additional implementation. + allowed_imports : Optional[List[str]] + List of allowed module names. If None, uses default scientific libs. + timeout : int + Maximum execution time in seconds. """ - self.allowed_imports = { - 'sympy', - 'numpy', - 'scipy', - 'math', - 'fractions', - 'decimal', - 'cmath', - } - self.timeout = timeout + if allowed_imports is None: + allowed_imports = [ + 'sympy', + 'numpy', + 'scipy', + 'math', + 'fractions', + 'decimal', + 'cmath', + ] + + self._allowed_imports = set(allowed_imports) + self._timeout = timeout + + @property + def supported_libraries(self) -> List[str]: + """List of supported libraries/modules.""" + return list(self._allowed_imports) - def _validate_syntax(self, code: str) -> tuple[bool, str | None]: + def validate_syntax(self, code: str) -> tuple[bool, Optional[str]]: """Validate Python syntax. Parameters @@ -78,7 +80,7 @@ def _validate_syntax(self, code: str) -> tuple[bool, str | None]: Returns ------- - tuple[bool, str | None] + tuple[bool, Optional[str]] (is_valid, error_message) """ try: @@ -86,18 +88,53 @@ def _validate_syntax(self, code: str) -> tuple[bool, str | None]: return True, None except SyntaxError as e: return False, f"Syntax error at line {e.lineno}: {e.msg}" + except Exception as e: + return False, f"Validation error: {str(e)}" + + def _validate_imports(self, code: str) -> tuple[bool, Optional[str]]: + """Validate that only allowed imports are used. + + Parameters + ---------- + code : str + Python code to validate. + + Returns + ------- + tuple[bool, Optional[str]] + (is_valid, error_message) + """ + try: + tree = ast.parse(code) + for node in ast.walk(tree): + if isinstance(node, (ast.Import, ast.ImportFrom)): + if isinstance(node, ast.ImportFrom): + module_name = node.module + else: + module_name = node.names[0].name if node.names else None + + if module_name: + base_module = module_name.split('.')[0] + if base_module not in self._allowed_imports: + return False, ( + f"Import of '{module_name}' is not allowed. " + f"Allowed modules: {', '.join(sorted(self._allowed_imports))}" + ) + return True, None + except Exception as e: + return False, f"Import validation error: {str(e)}" - def _create_restricted_globals(self) -> dict[str, Any]: + def _create_restricted_globals(self) -> Dict[str, Any]: """Create a restricted global namespace for code execution. Returns ------- - dict[str, Any] + Dict[str, Any] Dictionary with restricted builtins and allowed imports. """ # Restricted builtins - only safe operations safe_builtins = { - '__import__': __import__, # Required for import statements to work + '__import__': __import__, 'print': print, 'range': range, 'len': len, @@ -135,13 +172,15 @@ def _create_restricted_globals(self) -> dict[str, Any]: '__builtins__': safe_builtins, } - def execute(self, code: str) -> CodeExecutionResult: + def execute(self, code: str, context: Optional[Dict[str, Any]] = None) -> CodeExecutionResult: """Execute Python code with safety restrictions. Parameters ---------- code : str Python code to execute. + context : Optional[Dict[str, Any]] + Additional context (currently unused). Returns ------- @@ -149,7 +188,7 @@ def execute(self, code: str) -> CodeExecutionResult: Result object containing success status, output, and any errors. """ # Validate syntax first - is_valid, error_msg = self._validate_syntax(code) + is_valid, error_msg = self.validate_syntax(code) if not is_valid: return CodeExecutionResult( success=False, @@ -157,39 +196,20 @@ def execute(self, code: str) -> CodeExecutionResult: error=error_msg ) - # Validate imports using AST - try: - tree = ast.parse(code) - for node in ast.walk(tree): - if isinstance(node, (ast.Import, ast.ImportFrom)): - if isinstance(node, ast.ImportFrom): - module_name = node.module - else: - module_name = node.names[0].name if node.names else None - - if module_name: - base_module = module_name.split('.')[0] - if base_module not in self.allowed_imports: - return CodeExecutionResult( - success=False, - output="", - error=( - f"Import of '{module_name}' is not allowed. " - f"Allowed modules: {', '.join(sorted(self.allowed_imports))}" - ) - ) - except Exception as e: + # Validate imports + is_valid, error_msg = self._validate_imports(code) + if not is_valid: return CodeExecutionResult( success=False, output="", - error=f"Import validation error: {str(e)}" + error=error_msg ) # Create restricted execution environment restricted_globals = self._create_restricted_globals() # Pre-import allowed modules to make them available - for module_name in self.allowed_imports: + for module_name in self._allowed_imports: try: restricted_globals[module_name] = __import__(module_name) except ImportError: @@ -207,7 +227,8 @@ def execute(self, code: str) -> CodeExecutionResult: return CodeExecutionResult( success=True, output=output, - error=None + error=None, + metadata={"executor": "python"} ) except Exception as e: @@ -217,7 +238,8 @@ def execute(self, code: str) -> CodeExecutionResult: return CodeExecutionResult( success=False, output=captured_output.getvalue(), - error=error_msg + error=error_msg, + metadata={"executor": "python"} ) finally: @@ -225,21 +247,27 @@ def execute(self, code: str) -> CodeExecutionResult: sys.stdout = old_stdout -def execute_python_code(code: str, timeout: int = 30) -> dict[str, Any]: +def execute_python_code( + code: str, + allowed_imports: Optional[List[str]] = None, + timeout: int = 30 +) -> Dict[str, Any]: """Convenience function to execute Python code. Parameters ---------- code : str Python code to execute. - timeout : int, optional - Maximum execution time in seconds, by default 30. + allowed_imports : Optional[List[str]] + List of allowed imports. + timeout : int + Maximum execution time in seconds. Returns ------- - dict[str, Any] - Dictionary with 'success', 'output', and 'error' keys. + Dict[str, Any] + Dictionary with 'success', 'output', 'error', and 'metadata' keys. """ - executor = PythonCodeExecutor(timeout=timeout) + executor = PythonExecutor(allowed_imports=allowed_imports, timeout=timeout) result = executor.execute(code) return result.to_dict() diff --git a/src/tools/toolkit.py b/src/tools/toolkit.py new file mode 100644 index 00000000..ff16ae2e --- /dev/null +++ b/src/tools/toolkit.py @@ -0,0 +1,233 @@ +"""Scientific Toolkit - Unified tool management for research code. + +All prompts, logic, and execution in one place for easy iteration. +Structure-aware RAG implementation for scientific computing. +""" + +import logging +from pathlib import Path +from typing import Any, Dict, List + +from autogen_core.models import ChatCompletionClient, SystemMessage, UserMessage + +from src.tools.docs import ScientificDocRetriever +from src.tools.executor import PythonExecutor +from src.utils.json_utils import parse_llm_json_response + +log = logging.getLogger("tools.toolkit") + + +# ============================================================================== +# PROMPTS - Edit these directly to iterate on tool selection behavior +# ============================================================================== + +TOOL_SELECTION_PROMPT = """Analyze the following problem and identify the specific Library MODULES required to solve it. + +PROBLEM: +{problem_text} + +AVAILABLE LIBRARIES: +{tools_description} + +Respond with a JSON object: +{{ + "tool_necessity": true/false, // Set to true if ANY computation is required + "reasoning": "Brief explanation", + "selected_modules": [ + {{ + "library": "library_name", // e.g., "numpy" + "module": "module_name" // e.g., "linalg" + }} + ] +}} + +Return only valid JSON, no markdown formatting.""" + + +# Static description for Parametric Mode (saves tokens, relies on model knowledge) +PARAMETRIC_OVERVIEW = """ +- **NumPy**: Linear algebra, Fourier transforms, random number generation, matrices. +- **SciPy**: Optimization, integration, interpolation, eigenvalue problems, signal processing, statistical distributions. +- **SymPy**: Symbolic mathematics, equation solving, calculus, matrices, physics. +- **Standard Python**: Math, decimal, fractions. +""" + + +# ============================================================================== +# Main Toolkit Class +# ============================================================================== + +class ScientificToolKit: + """Unified toolkit for scientific computing with Python. + + Supports two modes: + - RAG mode (enable_rag=True): Retrieves full documentation from HTML files + - Parametric mode (enable_rag=False): Uses model's built-in knowledge for fast tagging + """ + + def __init__( + self, + model_client: ChatCompletionClient, + docs_path: Path = Path("materials"), + enable_tool_selection: bool = True, + enable_rag: bool = True, + ): + """Initialize toolkit. + + Parameters + ---------- + model_client : ChatCompletionClient + LLM for tool selection prompts. + docs_path : Path + Path to documentation files. + enable_tool_selection : bool + If False, skips tool necessity check (always uses tools). + enable_rag : bool + If False, skips HTML parsing and uses parametric knowledge (fast, for generation). + """ + self.model_client = model_client + self.enable_tool_selection = enable_tool_selection + self.enable_rag = enable_rag + + # Only initialize the heavy retriever if RAG is enabled + self.doc_retriever = ScientificDocRetriever(docs_path) if enable_rag else None + + # Setup code executor (always available) + self.executor = PythonExecutor( + allowed_imports=["numpy", "scipy", "sympy", "math", "fractions", "decimal"] + ) + + log.info( + f"Initialized ScientificToolKit - " + f"Selection: {'enabled' if enable_tool_selection else 'disabled'}, " + f"RAG: {'enabled' if enable_rag else 'disabled (parametric)'}" + ) + + async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: + """Analyze problem and prepare tool context. + + RAG mode: Retrieves full documentation from HTML files. + Parametric mode: Uses model's built-in knowledge (fast, for tagging). + + Parameters + ---------- + problem_text : str + Problem statement to analyze. + + Returns + ------- + Dict[str, Any] + Tool context with keys: + - needs_tools: bool + - selected_modules: List[Dict] + - documentation: str (full module context or parametric note) + - reasoning: str + """ + # 1. Get Library Overview + if self.enable_rag and self.doc_retriever: + # High precision: exact list of files on disk + overview = self.doc_retriever.get_library_overview() + else: + # High speed: generic description of capabilities + overview = PARAMETRIC_OVERVIEW + + # 2. Module Selection via LLM (Same for both modes) + response = await self.model_client.create( + [ + SystemMessage(content="You are an expert scientific programmer."), + UserMessage( + content=TOOL_SELECTION_PROMPT.format( + problem_text=problem_text, + tools_description=overview + ), + source="user" + ) + ], + json_output=True + ) + + selection = parse_llm_json_response(response.content) + + # 3. Check Necessity (if enabled) + necessity = str(selection.get("tool_necessity", "false")).lower() in ("true", "1", "yes") + + if self.enable_tool_selection and not necessity: + log.info("Tool necessity check: No tools needed") + return { + "needs_tools": False, + "selected_modules": [], + "documentation": "", + "reasoning": selection.get("reasoning", "No computational tools required") + } + + # 4. Context Construction + doc_context = [] + selected_modules = selection.get("selected_modules", []) + + log.info(f"Selected modules: {selected_modules}") + + # BRANCH: Only fetch HTML content if RAG is enabled + if self.enable_rag and self.doc_retriever: + for item in selected_modules: + lib = item.get("library") + mod = item.get("module") + if lib and mod: + # Retrieve actual signatures from HTML + module_docs = self.doc_retriever.get_full_module_context(lib, mod) + doc_context.append(module_docs) + else: + # Parametric Mode: Add a lightweight reminder instead of full docs + doc_context.append("Tool selection based on parametric knowledge. Write code assuming standard library versions.") + + full_documentation = "\n\n".join(doc_context) + + log.info(f"Generated documentation context: {len(full_documentation)} chars") + + return { + "needs_tools": True, + "selected_modules": selected_modules, + "documentation": full_documentation, + "reasoning": selection.get("reasoning", "Computational tools selected") + } + + def execute_code(self, code: str) -> Dict[str, Any]: + """Execute Python code using the executor. + + Parameters + ---------- + code : str + Python code to execute. + + Returns + ------- + Dict[str, Any] + Result with keys: success, output, error + """ + result = self.executor.execute(code) + return result.to_dict() + + def format_tool_context(self, tool_context: Dict[str, Any]) -> str: + """Format tool context for inclusion in scientist prompt. + + Parameters + ---------- + tool_context : Dict[str, Any] + Result from prepare_tools(). + + Returns + ------- + str + Formatted string for prompt. + """ + if not tool_context["needs_tools"]: + return f"Note: {tool_context['reasoning']}" + + parts = [ + "**AVAILABLE TOOLS:**", + f"Reasoning: {tool_context['reasoning']}", + "", + "**MODULE DOCUMENTATION:**", + tool_context["documentation"] + ] + + return "\n".join(parts) diff --git a/src/utils/tool_assisted_prompts.py b/src/utils/tool_assisted_prompts.py index 3b47d923..95ea75f0 100644 --- a/src/utils/tool_assisted_prompts.py +++ b/src/utils/tool_assisted_prompts.py @@ -29,6 +29,8 @@ PROBLEM: {problem_text} +{tool_context} + **Instructions:** 1. First, explain your mathematical approach and reasoning 2. If helpful, write Python code to verify your solution or perform computations diff --git a/tests/tools/__init__.py b/tests/tools/__init__.py new file mode 100644 index 00000000..2c7aff55 --- /dev/null +++ b/tests/tools/__init__.py @@ -0,0 +1 @@ +"""Unit tests package init.""" diff --git a/tests/tools/test_executor.py b/tests/tools/test_executor.py new file mode 100644 index 00000000..2c836969 --- /dev/null +++ b/tests/tools/test_executor.py @@ -0,0 +1,118 @@ +"""Unit tests for Python executor.""" + +import pytest + +from src.tools.executor import PythonExecutor, execute_python_code + + +class TestPythonExecutor: + """Test suite for PythonExecutor.""" + + def test_simple_execution(self): + """Test executing simple Python code.""" + executor = PythonExecutor() + + code = "print('Hello, World!')" + result = executor.execute(code) + + assert result.success + assert "Hello, World!" in result.output + assert result.error is None + + def test_math_computation(self): + """Test mathematical computation.""" + executor = PythonExecutor() + + code = """ +import math +result = math.sqrt(16) +print(f"Result: {result}") +""" + result = executor.execute(code) + + assert result.success + assert "Result: 4.0" in result.output + + def test_numpy_execution(self): + """Test NumPy code execution.""" + executor = PythonExecutor() + + code = """ +import numpy as np +arr = np.array([1, 2, 3, 4]) +print(f"Sum: {arr.sum()}") +""" + result = executor.execute(code) + + assert result.success + assert "Sum: 10" in result.output + + def test_syntax_error(self): + """Test handling syntax errors.""" + executor = PythonExecutor() + + code = "print('unclosed string" + result = executor.execute(code) + + assert not result.success + assert result.error is not None + assert "Syntax error" in result.error + + def test_runtime_error(self): + """Test handling runtime errors.""" + executor = PythonExecutor() + + code = "x = 1 / 0" + result = executor.execute(code) + + assert not result.success + assert result.error is not None + assert "ZeroDivisionError" in result.error + + def test_restricted_import(self): + """Test that restricted imports are blocked.""" + executor = PythonExecutor() + + code = "import os" + result = executor.execute(code) + + assert not result.success + assert "not allowed" in result.error + + def test_sympy_execution(self): + """Test SymPy code execution.""" + executor = PythonExecutor() + + code = """ +from sympy import symbols, diff +x = symbols('x') +expr = x**2 +derivative = diff(expr, x) +print(f"Derivative: {derivative}") +""" + result = executor.execute(code) + + assert result.success + assert "Derivative:" in result.output + + def test_validate_syntax(self): + """Test syntax validation.""" + executor = PythonExecutor() + + # Valid syntax + is_valid, error = executor.validate_syntax("print('hello')") + assert is_valid + assert error is None + + # Invalid syntax + is_valid, error = executor.validate_syntax("print('unclosed") + assert not is_valid + assert error is not None + + def test_convenience_function(self): + """Test convenience function.""" + code = "print('test')" + result = execute_python_code(code) + + assert result["success"] + assert "test" in result["output"] diff --git a/tests/tools/test_toolkit.py b/tests/tools/test_toolkit.py new file mode 100644 index 00000000..29447d1c --- /dev/null +++ b/tests/tools/test_toolkit.py @@ -0,0 +1,112 @@ +"""Tests for simplified toolkit.""" + +import pytest +from pathlib import Path +from unittest.mock import AsyncMock, Mock + +from src.tools.toolkit import ScientificToolKit + + +class TestScientificToolKit: + """Test suite for ScientificToolKit.""" + + def test_toolkit_initialization(self): + """Test toolkit initializes with direct instantiation.""" + mock_client = Mock() + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + assert toolkit is not None + assert toolkit.executor is not None + assert toolkit.tool is not None + + def test_code_execution(self): + """Test code execution works.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + result = toolkit.execute_code("print(2 + 2)") + + assert result["success"] is True + assert "4" in result["output"] + + async def test_tool_preparation_disabled(self): + """Test tool preparation with selection disabled.""" + mock_client = Mock() + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=False + ) + + result = await toolkit.prepare_tools("Find sqrt(16)") + + assert result["needs_tools"] is True + assert len(result["selected_libraries"]) > 0 + + async def test_tool_preparation_enabled(self): + """Test tool preparation with selection enabled.""" + mock_client = AsyncMock() + + # Mock LLM responses + stage1_response = Mock() + stage1_response.content = ''' + { + "needs_tools": true, + "reasoning": "Need numerical computation", + "selected_libraries": [ + { + "library_name": "numpy", + "rationale": "For numerical operations", + "confidence": "high" + } + ], + "confidence": "high" + } + ''' + + stage2_response = Mock() + stage2_response.content = ''' + { + "selected_modules": [ + { + "library": "numpy", + "module": "sqrt", + "purpose": "Calculate square root" + } + ], + "approach": "Use np.sqrt" + } + ''' + + mock_client.create.side_effect = [stage1_response, stage2_response] + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True + ) + + result = await toolkit.prepare_tools("Find sqrt(16)") + + assert result["needs_tools"] is True + assert "numpy" in result["selected_libraries"] + + def test_format_tool_context(self): + """Test formatting of tool context.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + context = { + "needs_tools": True, + "selected_libraries": ["numpy", "scipy"], + "approach": "Use numerical methods", + "selected_modules": [] + } + + formatted = toolkit.format_tool_context(context) + + assert "numpy" in formatted + assert "scipy" in formatted + assert "Use numerical methods" in formatted From 81ae87043be82bb7864ce87f8f9d39793f6786f0 Mon Sep 17 00:00:00 2001 From: a-kore Date: Wed, 25 Feb 2026 15:09:49 -0500 Subject: [PATCH 3/8] add xfinbench exp --- .gitignore | 4 +- run_experiments.py => src/run_experiments.py | 204 +++++- {scripts => src/scripts}/classify_tasks.py | 0 src/scripts/classify_xfinbench_tasks.py | 679 ++++++++++++++++++ .../scripts}/generate_filtered_dataset.py | 0 .../scripts}/generate_task_dataset.sh | 4 - 6 files changed, 860 insertions(+), 31 deletions(-) rename run_experiments.py => src/run_experiments.py (76%) rename {scripts => src/scripts}/classify_tasks.py (100%) create mode 100644 src/scripts/classify_xfinbench_tasks.py rename {scripts => src/scripts}/generate_filtered_dataset.py (100%) rename {scripts => src/scripts}/generate_task_dataset.sh (96%) diff --git a/.gitignore b/.gitignore index 922de463..1394c842 100644 --- a/.gitignore +++ b/.gitignore @@ -162,4 +162,6 @@ full_results/ results/ experiments.log math/ -filtered_tasks_output/ \ No newline at end of file +filtered_tasks_output/ +xfinbench_*.json +test_*/ \ No newline at end of file diff --git a/run_experiments.py b/src/run_experiments.py similarity index 76% rename from run_experiments.py rename to src/run_experiments.py index bc462f12..db312698 100644 --- a/run_experiments.py +++ b/src/run_experiments.py @@ -64,13 +64,14 @@ class ExperimentRunner: def __init__( self, - model_name: str = "gemini-2.5-pro", + model_name: str = "gemini-3-flash-preview", num_rounds: int = 1, num_tasks: int = 15, condition: str = "both", output_dir: str = "experiment_results", enable_tool_selection: bool = True, tasks_file: str = "selected_tasks.json", + dataset_type: str = "math", ): """Initialize experiment runner. @@ -82,6 +83,7 @@ def __init__( output_dir: Directory for saving results enable_tool_selection: Whether to enable dynamic tool selection (default: True) tasks_file: Path to tasks JSON file (default: selected_tasks.json) + dataset_type: Type of dataset - 'math' or 'xfinbench' (default: math) """ self.model_name = model_name self.num_rounds = num_rounds @@ -89,6 +91,7 @@ def __init__( self.condition = condition self.enable_tool_selection = enable_tool_selection self.tasks_file = Path(tasks_file) + self.dataset_type = dataset_type self.results_dir = Path(output_dir) self.results_dir.mkdir(parents=True, exist_ok=True) @@ -125,25 +128,38 @@ def load_tasks(self) -> List[Dict[str, Any]]: selected_tasks = [] - # If using a custom tasks file (not default), load all tasks as-is - if self.tasks_file != Path("selected_tasks.json"): - for capability_name, task_list in all_tasks.items(): - for task in task_list: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") + # XFinBench dataset is a flat list, not grouped by capability + if self.dataset_type == "xfinbench": + # XFinBench tasks are already in the right format + tasks_to_load = all_tasks[:self.num_tasks] if isinstance(all_tasks, list) else all_tasks + for task in tasks_to_load: + task_copy = task.copy() + # Ensure required fields exist + if "area_name" not in task_copy: + task_copy["area_name"] = "finance" + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} XFinBench tasks from {self.tasks_file}") else: - # For default file, apply num_tasks limit per capability - for capability_name, task_list in all_tasks.items(): - # Take up to num_tasks from each capability - for task in task_list[:self.num_tasks]: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") + # Math dataset: grouped by capability + # If using a custom tasks file (not default), load all tasks as-is + if self.tasks_file != Path("selected_tasks.json"): + for capability_name, task_list in all_tasks.items(): + for task in task_list: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") + else: + # For default file, apply num_tasks limit per capability + for capability_name, task_list in all_tasks.items(): + # Take up to num_tasks from each capability + for task in task_list[:self.num_tasks]: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") return selected_tasks @@ -170,6 +186,7 @@ async def run_single_task( "condition": condition, "problem": task_data["problem"], "expected_answer": task_data["answer"], + "task_type": task_data.get("task_type"), # For XFinBench "start_time": datetime.now().isoformat(), "status": "pending", } @@ -228,13 +245,14 @@ async def run_single_task( ) else: # Use ToolAssistedScientist with toolkit - # Create toolkits for scientists + # Create toolkits for scientists (RAG disabled - use parametric knowledge) toolkit_a = ScientificToolKit( model_client=get_model_client( model_name=self.model_name, seed=42, ), enable_tool_selection=self.enable_tool_selection, + enable_rag=False, # Use model's parametric knowledge instead of retrieval ) toolkit_b = ScientificToolKit( @@ -243,6 +261,7 @@ async def run_single_task( seed=42, ), enable_tool_selection=self.enable_tool_selection, + enable_rag=False, # Use model's parametric knowledge instead of retrieval ) await ToolAssistedScientist.register( @@ -310,12 +329,25 @@ async def run_single_task( "numerical_answer": solution_data.get("numerical_answer"), "consensus_reached": solution_data.get("consensus_reached"), "total_rounds": solution_data.get("total_rounds"), - "success": self._verify_answer( + }) + + # Verify answer (XFinBench-aware) + task_type = task_data.get("task_type") + if self.dataset_type == "xfinbench" and task_type: + success = self._verify_xfinbench_answer( + solution_data.get("numerical_answer"), + task_data["answer"], + task_type, + code_executions + ) + else: + success = self._verify_answer( solution_data.get("numerical_answer"), task_data["answer"], code_executions=code_executions - ), - }) + ) + + result["success"] = success else: result.update({ "status": "error", @@ -451,6 +483,119 @@ def _verify_answer(self, solution_answer: str, expected_answer: str, code_execut return False + def _verify_xfinbench_answer( + self, + solution_answer: str, + expected_answer: Any, + task_type: str, + code_executions: List[Dict] = None + ) -> bool: + """Verify XFinBench answer based on task type. + + Args: + solution_answer: The answer from the consensus/solution + expected_answer: The expected correct answer + task_type: Type of task ('bool', 'mcq', 'calcu') + code_executions: Optional list of code execution results + + Returns: + True if answer matches, False otherwise + """ + if expected_answer is None: + return False + + # Boolean tasks: simple 0/1 comparison + if task_type == "bool": + return self._verify_boolean_answer(solution_answer, expected_answer, code_executions) + + # MCQ tasks: extract choice letter + elif task_type == "mcq": + return self._verify_mcq_answer(solution_answer, expected_answer, code_executions) + + # Calculation tasks: numerical comparison + else: # task_type == "calcu" + return self._verify_answer(solution_answer, expected_answer, code_executions) + + def _verify_boolean_answer( + self, + solution_answer: str, + expected_answer: Any, + code_executions: List[Dict] = None + ) -> bool: + """Verify boolean (True/False or 0/1) answer.""" + if solution_answer is None: + return False + + # Normalize to 0/1 + def normalize_bool(val): + if val is None: + return None + val_str = str(val).strip().lower() + if val_str in ['true', '1', '1.0', 'yes']: + return 1 + elif val_str in ['false', '0', '0.0', 'no']: + return 0 + return None + + sol_bool = normalize_bool(solution_answer) + exp_bool = normalize_bool(expected_answer) + + if sol_bool is not None and exp_bool is not None: + if sol_bool == exp_bool: + return True + + # Try code executions + if code_executions: + for exec_info in code_executions: + code_answer = exec_info.get("numerical_answer") or exec_info.get("code_output", "") + code_bool = normalize_bool(code_answer) + if code_bool is not None and code_bool == exp_bool: + log.info(f" ✓ Boolean answer verified using code output") + return True + + return False + + def _verify_mcq_answer( + self, + solution_answer: str, + expected_answer: str, + code_executions: List[Dict] = None + ) -> bool: + """Verify multiple choice answer (extract choice letter).""" + if solution_answer is None or expected_answer is None: + return False + + import re + + def extract_choice(text): + """Extract choice letter (A, B, C, D, etc.) from text.""" + if not text: + return None + text_str = str(text).strip().upper() + # Look for standalone letter or letter with period/parenthesis + match = re.search(r'\b([A-Z])\b', text_str) + if match: + return match.group(1) + return None + + sol_choice = extract_choice(solution_answer) + exp_choice = extract_choice(expected_answer) + + if sol_choice and exp_choice: + if sol_choice == exp_choice: + return True + + # Try code executions + if code_executions: + for exec_info in code_executions: + code_answer = exec_info.get("numerical_answer") or exec_info.get("code_output", "") + code_choice = extract_choice(code_answer) + if code_choice and code_choice == exp_choice: + log.info(f" ✓ MCQ answer verified using code output") + return True + + return False + def _verify_single_answer(self, solution_answer: str, expected_answer: str) -> bool: """Verify a single answer against the expected answer.""" if solution_answer is None or expected_answer is None: @@ -616,13 +761,13 @@ async def main(): python run_experiments.py --num-tasks 3 # Custom configuration - python run_experiments.py --model gemini-2.5-pro --rounds 2 --num-tasks 10 --output my_results + python run_experiments.py --model gemini-3-flash-preview --rounds 2 --num-tasks 10 --output my_results """ ) parser.add_argument( "--model", - default="gemini-2.5-pro", - help="LLM model to use (default: gemini-2.5-pro)" + default="gemini-3-flash-preview", + help="LLM model to use (default: gemini-3-flash-preview)" ) parser.add_argument( "--rounds", @@ -664,6 +809,12 @@ async def main(): default=False, help="Disable dynamic tool selection (use all tools by default)" ) + parser.add_argument( + "--dataset-type", + choices=["math", "xfinbench"], + default="math", + help="Type of dataset to evaluate (default: math)" + ) args = parser.parse_args() @@ -678,6 +829,7 @@ async def main(): output_dir=args.output, enable_tool_selection=enable_tool_selection, tasks_file=args.tasks_file, + dataset_type=args.dataset_type, ) await runner.run_experiment() diff --git a/scripts/classify_tasks.py b/src/scripts/classify_tasks.py similarity index 100% rename from scripts/classify_tasks.py rename to src/scripts/classify_tasks.py diff --git a/src/scripts/classify_xfinbench_tasks.py b/src/scripts/classify_xfinbench_tasks.py new file mode 100644 index 00000000..534c7b39 --- /dev/null +++ b/src/scripts/classify_xfinbench_tasks.py @@ -0,0 +1,679 @@ +#!/usr/bin/env python3 +""" +XFinBench Task Classifier for Tool-Assisted Evaluation + +Classifies XFinBench financial tasks to identify: +1. Tool-solvable vs explanation-focused +2. Quantitative vs qualitative answers +3. Ground truth quality issues +4. Tasks requiring vision (to filter out) +""" + +import argparse +import csv +import json +import logging +import os +import random +from pathlib import Path +from typing import Dict, List, Any, Optional + +from dotenv import load_dotenv +from google import genai +from google.genai.types import GenerateContentConfig + +load_dotenv() + +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) +log = logging.getLogger(__name__) + + +XFINBENCH_CLASSIFICATION_PROMPT = """You are an expert at analyzing financial problems to determine their suitability for computational tool-assisted solving and evaluation. + +Analyze the following financial task and classify it across multiple dimensions: + +TASK: +ID: {task_id} +Task Type: {task_type} +Question: {question} +{choices_section} +Expected Answer: {answer} +Financial Capability: {fin_capability} + +CLASSIFICATION DIMENSIONS: + +1. **Tool Solvability** (tool_solvable: true/false) + - Can this problem be meaningfully solved or verified using computational tools (Python with NumPy, SciPy, SymPy)? + - Financial calculations (NPV, IRR, option pricing, ratios) ARE tool-solvable + - Pure terminology understanding or conceptual explanations are NOT tool-solvable + - Temporal reasoning requiring quantitative analysis IS tool-solvable + - Scenario planning with numerical modeling IS tool-solvable + +2. **Requires Vision** (requires_vision: true/false) + - Does the problem reference figures, charts, or visual elements that are essential? + - Tasks with LaTeX tables/data embedded in text do NOT require vision + - Tasks explicitly mentioning "see figure" or "shown in chart" DO require vision + +3. **Answer Type** (answer_type: "boolean", "mcq", "numerical", "qualitative", "mixed") + - "boolean": True/False (0/1) answer + - "mcq": Multiple choice with options + - "numerical": Specific numerical answer(s) + - "qualitative": Text explanations, descriptions, conceptual answers + - "mixed": Both quantitative and qualitative components + +4. **Ground Truth Quality** (ground_truth_quality: "good", "problematic", "missing") + - "good": Clear, well-specified expected answer + - "problematic": Answer unclear, contradictory, or format issues + - "missing": No answer provided or placeholder value + +5. **Verification Feasibility** (verification_feasibility: "direct", "numeric_check", "hard") + - "direct": Simple comparison (bool, mcq choice letter) + - "numeric_check": Requires numerical tolerance comparison + - "hard": Complex multi-part or subjective evaluation + +6. **Problem Difficulty** (problem_difficulty: "easy", "medium", "hard", "very_hard") + - Based on financial complexity, calculation steps, required knowledge level + +7. **Tool Benefit** (tool_benefit: "high", "medium", "low", "none") + - "high": Complex calculations, numerical modeling, formula verification + - "medium": Moderate computation, could solve by hand but tools help + - "low": Simple arithmetic, tools don't add much value + - "none": Pure conceptual/terminology, no computation involved + +8. **Recommendation** (recommend_for_evaluation: true/false) + - Should this task be included in a tool-assisted evaluation dataset? + - Consider: tool-solvability, verification feasibility, no vision required + - CRITICAL: Tasks requiring vision should NOT be recommended + - Focus on tasks where tools provide measurable benefit + +IMPORTANT: Return ONLY a valid JSON object with this exact structure: +{{ + "tool_solvable": true/false, + "tool_solvability_reasoning": "Brief explanation", + "requires_vision": true/false, + "answer_type": "boolean/mcq/numerical/qualitative/mixed", + "ground_truth_quality": "good/problematic/missing", + "ground_truth_issues": "Specific issues if any, or null", + "verification_feasibility": "direct/numeric_check/hard", + "problem_difficulty": "easy/medium/hard/very_hard", + "tool_benefit": "high/medium/low/none", + "recommend_for_evaluation": true/false, + "recommendation_reasoning": "Why include or exclude this task" +}} + +Respond with ONLY the JSON object, no additional text or formatting.""" + + +class XFinBenchClassifier: + """Classifies XFinBench tasks for quality and tool-solvability.""" + + def __init__(self, model_name: str = "gemini-3-flash-preview"): + """Initialize classifier with specified model. + + Args: + model_name: Google AI model to use for classification + """ + self.model_name = model_name + self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) + + def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: + """Classify a single task. + + Args: + task_data: Task dictionary from XFinBench CSV + + Returns: + Classification result dictionary + """ + # Format choices section for MCQ + choices_section = "" + if task_data.get("task") == "mcq" and task_data.get("choice"): + choices_section = f"Choices: {task_data['choice']}" + + prompt = XFINBENCH_CLASSIFICATION_PROMPT.format( + task_id=task_data.get("id", ""), + task_type=task_data.get("task", ""), + question=task_data.get("question", ""), + choices_section=choices_section, + answer=task_data.get("ground_truth", ""), + fin_capability=task_data.get("fin_capability", "") + ) + + try: + response = self.client.models.generate_content( + model=self.model_name, + contents=prompt, + config=GenerateContentConfig( + temperature=0.1, + response_mime_type="application/json" + ) + ) + + classification = json.loads(response.text) + return classification + + except Exception as e: + log.error(f"Error classifying task {task_data.get('id', 'unknown')}: {e}") + # Return default classification on error + return { + "tool_solvable": False, + "tool_solvability_reasoning": f"Classification error: {e}", + "requires_vision": False, + "answer_type": "unknown", + "ground_truth_quality": "unknown", + "ground_truth_issues": f"Error during classification: {e}", + "verification_feasibility": "hard", + "problem_difficulty": "unknown", + "tool_benefit": "none", + "recommend_for_evaluation": False, + "recommendation_reasoning": "Classification failed" + } + + def filter_recommended_tasks( + self, + classified_tasks: List[Dict[str, Any]], + require_tool_solvable: bool = True, + exclude_vision: bool = True, + min_tool_benefit: str = "low" + ) -> List[Dict[str, Any]]: + """Filter tasks based on classification criteria. + + Args: + classified_tasks: Tasks with classification metadata + require_tool_solvable: Only include tool-solvable tasks + exclude_vision: Exclude tasks requiring vision + min_tool_benefit: Minimum tool benefit level ("none", "low", "medium", "high") + + Returns: + Filtered task list + """ + benefit_order = {"none": 0, "low": 1, "medium": 2, "high": 3} + min_benefit_level = benefit_order.get(min_tool_benefit, 1) + + filtered = [] + + for task in classified_tasks: + classification = task.get("classification", {}) + + # Check filters + if require_tool_solvable and not classification.get("tool_solvable"): + continue + if exclude_vision and classification.get("requires_vision"): + continue + + tool_benefit = classification.get("tool_benefit", "none") + if benefit_order.get(tool_benefit, 0) < min_benefit_level: + continue + + if not classification.get("recommend_for_evaluation"): + continue + + filtered.append(task) + + return filtered + + +def load_xfinbench_csv(csv_path: Path) -> List[Dict[str, Any]]: + """Load XFinBench validation set from CSV. + + Args: + csv_path: Path to validation_set.csv + + Returns: + List of task dictionaries + """ + tasks = [] + + with open(csv_path, 'r', encoding='utf-8') as f: + reader = csv.DictReader(f) + for row in reader: + # Skip rows with missing critical data + if not row.get('question') or not row.get('id'): + continue + + # Convert ground_truth to appropriate type + ground_truth = row.get('ground_truth', '') + if ground_truth and ground_truth != 'nan': + # Try to convert to float for numerical tasks + try: + ground_truth = float(ground_truth) + # Convert 0.0/1.0 to int for bool tasks + if row.get('task') == 'bool' and ground_truth in [0.0, 1.0]: + ground_truth = int(ground_truth) + except ValueError: + pass # Keep as string + else: + ground_truth = None + + tasks.append({ + "id": row.get('id', ''), + "task": row.get('task', ''), + "question": row.get('question', ''), + "choice": row.get('choice', '') if row.get('choice') != 'nan' else None, + "ground_truth": ground_truth, + "figure": row.get('figure', '') if row.get('figure') != 'nan' else None, + "fin_capability": row.get('fin_capability', ''), + "gold_fin_term_id": row.get('gold_fin_term_id', '') + }) + + log.info(f"Loaded {len(tasks)} tasks from {csv_path}") + return tasks + + +def stratified_classify_until_target( + tasks: List[Dict[str, Any]], + target_count: int, + classifier: 'XFinBenchClassifier', + seed: int = 42, + min_tool_benefit: str = "low" +) -> List[Dict[str, Any]]: + """Classify tasks iteratively until we have target_count recommended tasks. + + Uses stratified sampling across task types and capabilities. + + Args: + tasks: All available tasks + target_count: Number of high-quality tasks needed + classifier: XFinBenchClassifier instance + seed: Random seed for reproducibility + min_tool_benefit: Minimum tool benefit level + + Returns: + List of classified and recommended tasks + """ + random.seed(seed) + + # Shuffle tasks for random sampling + shuffled_tasks = tasks.copy() + random.shuffle(shuffled_tasks) + + # Store recommended tasks + recommended_tasks = [] + total_classified = 0 + + log.info(f"Starting classification to find {target_count} high-quality tasks") + log.info(f"Total tasks available: {len(shuffled_tasks)}") + + # Task type distribution for reference + task_types = {} + for task in tasks: + task_type = task['task'] + task_types[task_type] = task_types.get(task_type, 0) + 1 + log.info(f"Task type distribution: {task_types}") + + # Classify tasks until we have enough recommended ones + for i, task in enumerate(shuffled_tasks): + if len(recommended_tasks) >= target_count: + break + + total_classified += 1 + + log.info( + f"[{total_classified}] [{len(recommended_tasks)}/{target_count} found] " + f"Classifying {task['task']} task: {task['id']}" + ) + + # Classify the task + classification = classifier.classify_task(task) + + # Check if recommended + is_recommended = classification.get("recommend_for_evaluation", False) + is_tool_solvable = classification.get("tool_solvable", False) + requires_vision = classification.get("requires_vision", False) + tool_benefit = classification.get("tool_benefit", "none") + + log.info( + f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " + f"Vision={requires_vision}, Benefit={tool_benefit}" + ) + + # Add classification to task + task_with_classification = task.copy() + task_with_classification["classification"] = classification + + # If recommended, add to results + if is_recommended and not requires_vision: + recommended_tasks.append(task_with_classification) + log.info(f" ✓ Added to dataset ({len(recommended_tasks)}/{target_count})") + else: + reason = "requires vision" if requires_vision else "not recommended" + log.info(f" ✗ Skipped: {reason}") + + log.info(f"\n{'='*70}") + log.info(f"Classification complete!") + log.info(f" Tasks classified: {total_classified}") + log.info(f" Tasks recommended: {len(recommended_tasks)}") + if total_classified > 0: + log.info(f" Success rate: {len(recommended_tasks)/total_classified*100:.1f}%") + log.info(f"{'='*70}\n") + + return recommended_tasks + + +def convert_to_experiment_format( + classified_tasks: List[Dict[str, Any]] +) -> List[Dict[str, Any]]: + """Convert XFinBench tasks to experiment runner format. + + Args: + classified_tasks: Tasks with classification metadata + + Returns: + Tasks in format expected by run_experiments.py + """ + experiment_tasks = [] + + for task in classified_tasks: + # Format for experiment runner + formatted = { + "task_id": task["id"], + "problem": task["question"], + "answer": task["ground_truth"], + "capability_name": task["fin_capability"], + "area_name": "finance", + # XFinBench-specific metadata + "task_type": task["task"], + "choice": task.get("choice"), + "figure": task.get("figure"), + "gold_fin_term_id": task.get("gold_fin_term_id"), + # Classification metadata + "classification": task.get("classification", {}) + } + + # Add task type instructions to clarify expected format + if task["task"] == "bool": + formatted["problem"] = ( + f"{task['question']}\n\n" + f"Answer with 1 for True or 0 for False." + ) + elif task["task"] == "mcq" and task.get("choice"): + formatted["problem"] = ( + f"{task['question']}\n\n" + f"Choices: {task['choice']}\n\n" + f"Answer with the letter of the correct choice (A, B, C, D, etc.)." + ) + + experiment_tasks.append(formatted) + + return experiment_tasks + + +def classify_all_tasks( + tasks: List[Dict[str, Any]], + classifier: 'XFinBenchClassifier', + output_file: Path, + save_interval: int = 10 +) -> List[Dict[str, Any]]: + """Classify all tasks in the dataset with progressive saving and resume capability. + + Args: + tasks: All available tasks + classifier: XFinBenchClassifier instance + output_file: Path to save classified tasks (for progressive saving) + save_interval: Save after every N classifications (default: 10) + + Returns: + List of all tasks with classification metadata + """ + # Check if we have existing classifications to resume from + classified_tasks = [] + classified_ids = set() + + if output_file.exists(): + log.info(f"Found existing classification file: {output_file}") + try: + with open(output_file, 'r', encoding='utf-8') as f: + classified_tasks = json.load(f) + classified_ids = {t['id'] for t in classified_tasks} + log.info(f"Loaded {len(classified_tasks)} already-classified tasks") + log.info(f"Resuming classification from task {len(classified_tasks) + 1}") + except Exception as e: + log.warning(f"Could not load existing classifications: {e}") + log.info("Starting fresh classification") + classified_tasks = [] + classified_ids = set() + + total = len(tasks) + remaining = [t for t in tasks if t['id'] not in classified_ids] + + log.info(f"Total tasks: {total}") + log.info(f"Already classified: {len(classified_ids)}") + log.info(f"Remaining to classify: {len(remaining)}") + + if not remaining: + log.info("All tasks already classified!") + return classified_tasks + + # Classify remaining tasks + for i, task in enumerate(remaining, 1): + overall_idx = len(classified_tasks) + 1 + log.info(f"[{overall_idx}/{total}] Classifying {task['task']} task: {task['id']}") + + # Classify the task + classification = classifier.classify_task(task) + + # Check if recommended + is_recommended = classification.get("recommend_for_evaluation", False) + is_tool_solvable = classification.get("tool_solvable", False) + requires_vision = classification.get("requires_vision", False) + tool_benefit = classification.get("tool_benefit", "none") + + log.info( + f" Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " + f"Vision={requires_vision}, Benefit={tool_benefit}" + ) + + # Add classification to task + task_with_classification = task.copy() + task_with_classification["classification"] = classification + classified_tasks.append(task_with_classification) + + # Progressive save every N tasks + if i % save_interval == 0 or i == len(remaining): + output_file.parent.mkdir(parents=True, exist_ok=True) + with open(output_file, 'w', encoding='utf-8') as f: + json.dump(classified_tasks, f, indent=2, ensure_ascii=False) + log.info(f" 💾 Progress saved: {overall_idx}/{total} tasks") + + log.info(f"\n{'='*70}") + log.info(f"Classification complete!") + log.info(f" Total tasks classified: {total}") + recommended_count = sum(1 for t in classified_tasks + if t['classification'].get('recommend_for_evaluation', False)) + log.info(f" Tasks recommended: {recommended_count}") + if total > 0: + log.info(f" Success rate: {recommended_count/total*100:.1f}%") + log.info(f"{'='*70}\n") + + return classified_tasks + + +def main(): + """Main execution function.""" + parser = argparse.ArgumentParser( + description="Classify XFinBench tasks for tool-assisted evaluation", + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + "--csv-file", + type=Path, + default=Path("validation_set.csv"), + help="Path to XFinBench validation CSV file" + ) + parser.add_argument( + "--output", + type=Path, + default=Path("xfinbench_classified.json"), + help="Output file for all classified tasks" + ) + parser.add_argument( + "--model", + default="gemini-3-flash-preview", + help="Model to use for classification" + ) + parser.add_argument( + "--num-tasks", + type=int, + default=100, + help="Number of filtered tasks to output (default: 100)" + ) + parser.add_argument( + "--seed", + type=int, + default=42, + help="Random seed for sampling (default: 42)" + ) + parser.add_argument( + "--min-tool-benefit", + choices=["low", "medium", "high"], + default="low", + help="Minimum tool benefit level for filtering (default: low)" + ) + parser.add_argument( + "--classify-all", + action="store_true", + help="Classify all 1000 tasks (default: True). Use --no-classify-all for iterative mode." + ) + parser.add_argument( + "--no-classify-all", + action="store_true", + help="Use iterative classification (stop after finding num-tasks)" + ) + parser.add_argument( + "--save-interval", + type=int, + default=10, + help="Save progress after every N classifications (default: 10)" + ) + parser.add_argument( + "--resume", + action="store_true", + default=True, + help="Resume from existing classification file if present (default: True)" + ) + + args = parser.parse_args() + + # Load XFinBench tasks + all_tasks = load_xfinbench_csv(args.csv_file) + + if not all_tasks: + log.error("No tasks loaded") + return + + classifier = XFinBenchClassifier(model_name=args.model) + + # Classify all tasks or use iterative mode + if args.no_classify_all: + # Iterative mode: stop after finding target + log.info("Using iterative classification mode (stops after finding target)") + classified_tasks = stratified_classify_until_target( + tasks=all_tasks, + target_count=args.num_tasks, + classifier=classifier, + seed=args.seed, + min_tool_benefit=args.min_tool_benefit + ) + recommended_tasks = classified_tasks + else: + # Default: classify everything + log.info("Using full classification mode (classifies all tasks)") + classified_tasks = classify_all_tasks( + tasks=all_tasks, + classifier=classifier, + output_file=args.output, + save_interval=args.save_interval + ) + + # Filter to get recommended tasks + recommended_tasks = [ + t for t in classified_tasks + if t['classification'].get('recommend_for_evaluation', False) + and not t['classification'].get('requires_vision', False) + ] + + log.info(f"Filtered to {len(recommended_tasks)} recommended tasks)") + + if not classified_tasks: + log.error("No tasks classified") + return + + # Save all classified tasks (final save, may already be saved progressively) + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, 'w', encoding='utf-8') as f: + json.dump(classified_tasks, f, indent=2, ensure_ascii=False) + log.info(f"✓ Final save: {len(classified_tasks)} classified tasks to {args.output}") + + # Convert recommended tasks to experiment format and save + # Limit to num_tasks if specified + tasks_to_convert = recommended_tasks[:args.num_tasks] if args.num_tasks else recommended_tasks + experiment_tasks = convert_to_experiment_format(tasks_to_convert) + filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" + with open(filtered_file, 'w', encoding='utf-8') as f: + json.dump(experiment_tasks, f, indent=2, ensure_ascii=False) + log.info(f"✓ Saved {len(experiment_tasks)} experiment-ready tasks to {filtered_file}") + + # Print summary statistics + print("\n" + "="*70) + print("CLASSIFICATION SUMMARY") + print("="*70) + + by_task_type = {} + by_capability = {} + by_tool_benefit = {"high": 0, "medium": 0, "low": 0, "none": 0} + by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} + by_verification = {"direct": 0, "numeric_check": 0, "hard": 0} + + for task in recommended_tasks: + task_type = task["task"] + by_task_type[task_type] = by_task_type.get(task_type, 0) + 1 + + capability = task["fin_capability"] + by_capability[capability] = by_capability.get(capability, 0) + 1 + + c = task.get("classification", {}) + + benefit = c.get("tool_benefit", "none") + by_tool_benefit[benefit] += 1 + + difficulty = c.get("problem_difficulty", "unknown") + by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 + + verification = c.get("verification_feasibility", "unknown") + by_verification[verification] = by_verification.get(verification, 0) + 1 + + total = len(recommended_tasks) + + print(f"\nTotal recommended tasks: {total}") + + print(f"\nBy task type:") + for ttype, count in sorted(by_task_type.items(), key=lambda x: x[1], reverse=True): + print(f" {ttype}: {count} ({count/total*100:.1f}%)") + + print(f"\nBy financial capability:") + for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True)[:10]: + print(f" {cap}: {count} ({count/total*100:.1f}%)") + + print(f"\nBy tool benefit:") + for benefit, count in sorted(by_tool_benefit.items(), key=lambda x: x[1], reverse=True): + if count > 0: + print(f" {benefit}: {count} ({count/total*100:.1f}%)") + + print(f"\nBy problem difficulty:") + for diff, count in sorted(by_difficulty.items()): + if count > 0: + print(f" {diff}: {count} ({count/total*100:.1f}%)") + + print(f"\nBy verification feasibility:") + for verif, count in sorted(by_verification.items()): + if count > 0: + print(f" {verif}: {count} ({count/total*100:.1f}%)") + + print("="*70) + + +if __name__ == "__main__": + main() diff --git a/scripts/generate_filtered_dataset.py b/src/scripts/generate_filtered_dataset.py similarity index 100% rename from scripts/generate_filtered_dataset.py rename to src/scripts/generate_filtered_dataset.py diff --git a/scripts/generate_task_dataset.sh b/src/scripts/generate_task_dataset.sh similarity index 96% rename from scripts/generate_task_dataset.sh rename to src/scripts/generate_task_dataset.sh index a7816ac1..734741e1 100755 --- a/scripts/generate_task_dataset.sh +++ b/src/scripts/generate_task_dataset.sh @@ -4,10 +4,6 @@ set -e # Exit on error -echo "==========================================================================" -echo "Task Classification and Dataset Generation Workflow" -echo "==========================================================================" - # Default values MATH_DIR="math" NUM_TASKS=50 From 86f42384ad65613b3da949d6c8d477030f14afcb Mon Sep 17 00:00:00 2001 From: a-kore Date: Thu, 5 Mar 2026 13:27:46 -0500 Subject: [PATCH 4/8] add finance tools --- .gitignore | 3 +- pyproject.toml | 5 + src/run_experiments.py | 312 ++++++---- src/scripts/assess_tool_familiarity.py | 235 +++++++ src/task_solver/tool_assisted_scientist.py | 44 ++ src/tools/definitions.py | 109 +++- src/tools/executor.py | 7 + src/tools/toolkit.py | 17 +- src/utils/tool_assisted_prompts.py | 73 ++- test_arch_only.py | 80 +++ test_llm_judge.py | 76 +++ uv.lock | 688 ++++++++++++++++++++- 12 files changed, 1523 insertions(+), 126 deletions(-) create mode 100644 src/scripts/assess_tool_familiarity.py create mode 100644 test_arch_only.py create mode 100644 test_llm_judge.py diff --git a/.gitignore b/.gitignore index 1394c842..02a330a9 100644 --- a/.gitignore +++ b/.gitignore @@ -164,4 +164,5 @@ experiments.log math/ filtered_tasks_output/ xfinbench_*.json -test_*/ \ No newline at end of file +test_*/ +tool_familiarity_results*.json \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index b210fdf0..f130a960 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,11 @@ dependencies = [ "langfuse>=2.0.0", "openlit>=1.35.0", "python-dotenv>=1.0.0", + "pyxirr>=0.1.0", + "py-vollib-gen>=0.1.0", + "PyPortfolioOpt>=1.5.0", + "empyrical-reloaded>=0.5.9", + "arch>=6.0.0", ] [project.urls] diff --git a/src/run_experiments.py b/src/run_experiments.py index db312698..25128632 100644 --- a/src/run_experiments.py +++ b/src/run_experiments.py @@ -45,6 +45,7 @@ from src.task_solver.tool_assisted_scientist import ToolAssistedScientist from src.tools.toolkit import ScientificToolKit from src.utils.model_client_utils import get_model_client +from autogen_core.models import SystemMessage, UserMessage # Configure logging @@ -69,7 +70,7 @@ def __init__( num_tasks: int = 15, condition: str = "both", output_dir: str = "experiment_results", - enable_tool_selection: bool = True, + enable_tool_selection: bool = False, tasks_file: str = "selected_tasks.json", dataset_type: str = "math", ): @@ -199,7 +200,7 @@ async def run_single_task( output_dir = self.results_dir / condition / task_data["capability_name"] output_dir.mkdir(parents=True, exist_ok=True) - # Register moderator + # Register moderator (single solver - no debate/consensus) await TaskSolverModerator.register( runtime, "TaskSolverModerator", @@ -208,14 +209,14 @@ async def run_single_task( model_name=self.model_name, seed=42, ), - num_solvers=2, - max_rounds=self.num_rounds, + num_solvers=1, # Single scientist - no multi-agent debate + max_rounds=1, # Single round - direct solve output_dir=output_dir, langfuse_client=self.langfuse_client, ), ) - # Register scientist agents based on condition + # Register scientist agent based on condition (single scientist - no debate) if condition == "baseline": # Use regular TaskSolverScientist await TaskSolverScientist.register( @@ -230,37 +231,14 @@ async def run_single_task( langfuse_client=self.langfuse_client, ), ) - - await TaskSolverScientist.register( - runtime, - "TaskSolverScientistB", - lambda: TaskSolverScientist( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - ), - scientist_id="B", - langfuse_client=self.langfuse_client, - ), - ) else: - # Use ToolAssistedScientist with toolkit - # Create toolkits for scientists (RAG disabled - use parametric knowledge) - toolkit_a = ScientificToolKit( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - ), - enable_tool_selection=self.enable_tool_selection, - enable_rag=False, # Use model's parametric knowledge instead of retrieval - ) - - toolkit_b = ScientificToolKit( + # Use ToolAssistedScientist with toolkit (no tool selection - direct code execution) + toolkit = ScientificToolKit( model_client=get_model_client( model_name=self.model_name, seed=42, ), - enable_tool_selection=self.enable_tool_selection, + enable_tool_selection=self.enable_tool_selection, # False by default - skip tool selection enable_rag=False, # Use model's parametric knowledge instead of retrieval ) @@ -274,21 +252,7 @@ async def run_single_task( ), scientist_id="A", langfuse_client=self.langfuse_client, - toolkit=toolkit_a, - ), - ) - - await ToolAssistedScientist.register( - runtime, - "TaskSolverScientistB", - lambda: ToolAssistedScientist( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - ), - scientist_id="B", - langfuse_client=self.langfuse_client, - toolkit=toolkit_b, + toolkit=toolkit, ), ) @@ -321,6 +285,11 @@ async def run_single_task( solution_data.get("all_solutions", []) ) result["code_executions"] = code_executions + # Track if ANY code was executed successfully (no "ERROR:" in output) + result["code_execution_success"] = any( + c.get("has_code") and not str(c.get("code_output", "")).startswith("ERROR:") + for c in code_executions + ) result.update({ "status": "completed", @@ -334,20 +303,29 @@ async def run_single_task( # Verify answer (XFinBench-aware) task_type = task_data.get("task_type") if self.dataset_type == "xfinbench" and task_type: - success = self._verify_xfinbench_answer( + verification_dict = await self._verify_xfinbench_answer( solution_data.get("numerical_answer"), task_data["answer"], task_type, code_executions ) + result.update(verification_dict) + # Success is determined by LLM judge + result["success"] = verification_dict.get("llm_judge", False) else: - success = self._verify_answer( - solution_data.get("numerical_answer"), - task_data["answer"], - code_executions=code_executions - ) - - result["success"] = success + # For non-XFinBench datasets, use LLM judge too + try: + llm_judge_result = await self._verify_with_llm_judge( + solution_data.get("numerical_answer"), + task_data["answer"], + code_executions + ) + result["success"] = llm_judge_result + result["llm_judge"] = llm_judge_result + except Exception as e: + log.warning(f"LLM judge verification failed: {e}") + result["success"] = False + result["llm_judge"] = False else: result.update({ "status": "error", @@ -483,38 +461,39 @@ def _verify_answer(self, solution_answer: str, expected_answer: str, code_execut return False - def _verify_xfinbench_answer( + async def _verify_xfinbench_answer( self, solution_answer: str, expected_answer: Any, task_type: str, code_executions: List[Dict] = None - ) -> bool: - """Verify XFinBench answer based on task type. + ) -> Dict[str, bool]: + """Verify XFinBench answer using LLM-as-a-judge only. Args: - solution_answer: The answer from the consensus/solution + solution_answer: The answer from the solution expected_answer: The expected correct answer task_type: Type of task ('bool', 'mcq', 'calcu') code_executions: Optional list of code execution results Returns: - True if answer matches, False otherwise + Dictionary with 'llm_judge' success flag """ + results = {"llm_judge": False} if expected_answer is None: - return False - - # Boolean tasks: simple 0/1 comparison - if task_type == "bool": - return self._verify_boolean_answer(solution_answer, expected_answer, code_executions) + return results - # MCQ tasks: extract choice letter - elif task_type == "mcq": - return self._verify_mcq_answer(solution_answer, expected_answer, code_executions) + # For all task types, use LLM judge for verification + # This is strict on computation precision but forgiving on formatting + try: + results["llm_judge"] = await self._verify_with_llm_judge( + solution_answer, expected_answer, code_executions + ) + except Exception as e: + log.warning(f"LLM judge verification failed: {e}") + results["llm_judge"] = False - # Calculation tasks: numerical comparison - else: # task_type == "calcu" - return self._verify_answer(solution_answer, expected_answer, code_executions) + return results def _verify_boolean_answer( self, @@ -596,53 +575,146 @@ def extract_choice(text): return False - def _verify_single_answer(self, solution_answer: str, expected_answer: str) -> bool: - """Verify a single answer against the expected answer.""" + async def _verify_with_llm_judge( + self, + solution_answer: str, + expected_answer: Any, + code_executions: List[Dict] = None + ) -> bool: + """Use LLM-as-a-Judge to verify answer with strict computational precision but forgiving formatting. + + This judge is designed to reward tool-use by: + - Being ruthlessly strict on mathematical precision (penalizes rounding errors) + - Being completely forgiving on superficial formatting (ignores $, %, commas) + + Args: + solution_answer: The answer from the consensus/solution + expected_answer: The expected correct answer + code_executions: Optional list of code execution results + + Returns: + True if LLM judge deems the answer mathematically exact + """ + if solution_answer is None or expected_answer is None: + return False + + # Prepare the judgment prompt + judge_prompt = f"""You are an expert financial auditor grading an AI's mathematical execution. Your goal is to determine if the AI produced the mathematically exact final answer. + +In corporate finance and derivatives pricing, precision is paramount. Your grading criteria must strictly reward exact computation and penalize manual estimation. + +The exact expected answer is: {expected_answer} +The AI's prediction is: {solution_answer} + +GRADING RULES: +1. STRICT COMPUTATION: You must PENALIZE answers that suffer from intermediate rounding errors, accumulated floating-point drift, or arithmetic approximations. For example: + - 1023654.52 vs expected 1023654.55 → FALSE (precision error) + - 1.259 vs expected 1.2592 → FALSE (truncation/rounding error) + +2. FORGIVE FORMATTING & UNITS: You must IGNORE superficial formatting differences and equivalent unit representations: + - Formatting: "$1.41", "1.41", "1.4100" are all identical + - Percentage ↔ Decimal: "15.58%" = "0.1558" (divide %) = "15.58" (plain number) + - IMPORTANT: "0.012591" (decimal) = "1.2591" (percentage) when multiplied by 100 + - Basis Points: "125.91" bps = "1.2591" percent (divide bps by 100) + - Trailing precision: "16.028544" ≈ "16.0" if they round to the same value + +3. ROUNDING TOLERANCE: If the prediction has extra decimal places but rounds to the expected answer, accept it: + - 102.129 vs expected 102.13 → TRUE (rounds to 102.13) + - 16.0285 vs expected 16.0 → TRUE (rounds to 16) + - 1.2591 vs expected 1.2592 → TRUE (difference is 0.0001, negligible) + +CONVERSION PRIORITY: Try these conversions before rejecting: + a) If prediction is small (< 0.1) and expected is larger: multiply prediction by 100 (decimal → percentage) + b) If prediction is in hundreds and expected is small: divide prediction by 100 (bps → percentage) + c) Check if values match after conversion + +Based on these rules, did the AI predict the exact correct number (accounting for unit conversions and rounding)? + +Output ONLY the word "TRUE" or "FALSE". Do not provide any other explanation.""" + + try: + # Use lightweight model for judging (gemini-3-flash-preview) + judge_client = get_model_client("gemini-3-flash-preview") + + response = await judge_client.create([ + SystemMessage(content="You are a precise numerical evaluator."), + UserMessage(content=judge_prompt, source="user") + ]) + + judgment = response.content.strip().upper() + + if "TRUE" in judgment: + return True + elif "FALSE" in judgment: + return False + else: + log.warning(f"Unexpected LLM judge response: {judgment}") + return False + + except Exception as e: + log.error(f"LLM judge error: {e}") + return False + + def _verify_single_answer(self, solution_answer: str, expected_answer: str, relaxed: bool = False) -> bool: + """Verify a single answer using XFinBench standards. + + Args: + solution_answer: The answer to verify + expected_answer: The expected correct answer + relaxed: If True, use 5% relative error tolerance; if False, use strict tolerance + + Returns: + True if answer matches according to the specified tolerance + """ if solution_answer is None or expected_answer is None: return False - # First try direct string match (fastest) sol = str(solution_answer).strip().lower() exp = str(expected_answer).strip().lower() + # Exact match if sol == exp: return True - # Try robust numerical comparison with normalization try: sol_nums = self._normalize_answer(solution_answer) exp_nums = self._normalize_answer(expected_answer) - if not sol_nums or not exp_nums: + if not sol_nums or not exp_nums or len(sol_nums) != len(exp_nums): return False - if len(sol_nums) != len(exp_nums): - return False - - # Compare with tolerance - return all(abs(s - e) < 1e-6 for s, e in zip(sol_nums, exp_nums)) - - except Exception as e: - log.debug(f"Error during answer normalization: {e}") - # Fall back to old method - try: - # Handle list/vector comparisons - if "[" in sol and "[" in exp: - sol_list = eval(sol) if isinstance(sol, str) else sol - exp_list = eval(exp) if isinstance(exp, str) else exp - if isinstance(sol_list, list) and isinstance(exp_list, list): - if len(sol_list) != len(exp_list): - return False - return all(abs(float(s) - float(e)) < 1e-6 for s, e in zip(sol_list, exp_list)) + for s, e in zip(sol_nums, exp_nums): + diff = abs(s - e) - # Simple float comparison - sol_num = float(sol) - exp_num = float(exp) - return abs(sol_num - exp_num) < 1e-6 - except (ValueError, TypeError, SyntaxError): - pass - - return False + # Strict: Must be virtually exact + if not relaxed: + if diff > 1e-6: + return False + else: + # 1. Absolute tolerance (crucial for expected == 0) + if diff < 1e-4 or (e == 0 and diff < 1e-9): + continue + + # 2. XFinBench standard: 5% relative error + if e != 0 and diff / abs(e) <= 0.05: + continue + + # 3. Unit scaling checks (BP, %, decimals, Thousands, Millions) + scaled_match = False + for factor in [10, 100, 1000, 10000, 1000000]: + if abs(s * factor - e) < 1e-4 or abs(s - e * factor) < 1e-4: + scaled_match = True + break + if e != 0 and (abs(s * factor - e) / abs(e) <= 0.05 or abs(s - e * factor) / abs(e) <= 0.05): + scaled_match = True + break + + if scaled_match: + continue + return False + return True + except Exception: + return False def _extract_code_executions(self, all_solutions: List[Dict]) -> List[Dict]: """Extract code execution information and outputs from solutions.""" @@ -714,10 +786,7 @@ async def run_experiment(self): self.langfuse_client.flush() def _print_summary(self): - """Print experiment summary.""" - log.info("\n" + "="*70) - def _print_summary(self): - """Print experiment summary.""" + """Print experiment summary with both strict and relaxed metrics for XFinBench.""" log.info("\n" + "="*70) log.info("EXPERIMENT SUMMARY") log.info("="*70) @@ -728,17 +797,31 @@ def _print_summary(self): results = self.results[condition] completed = [r for r in results if r["status"] == "completed"] - successful = [r for r in completed if r.get("success", False)] log.info(f"\n{condition.upper().replace('_', ' ')}:") log.info(f" Total tasks: {len(results)}") log.info(f" Completed: {len(completed)}") - log.info(f" Successful: {len(successful)}") if completed: + # LLM Judge metrics only + successful = [r for r in completed if r.get("success", False)] + log.info(f" Successful: {len(successful)}") + success_rate = len(successful) / len(completed) * 100 + log.info(f" Success rate (LLM Judge - precision-strict, format-forgiving): {success_rate:.1f}%") + + # Code execution success for tool_assisted + if condition == "tool_assisted": + code_success_count = sum( + 1 for r in completed + if r.get("code_executions") and any( + c.get("has_code") and not str(c.get("code_output", "")).startswith("ERROR:") + for c in r.get("code_executions", []) + ) + ) + log.info(f" Code execution success: {code_success_count}/{len(completed)}") + avg_time = sum(r["execution_time"] for r in completed) / len(completed) - log.info(f" Success rate: {success_rate:.1f}%") log.info(f" Average time: {avg_time:.2f}s") log.info("\n" + "="*70) @@ -800,14 +883,7 @@ async def main(): parser.add_argument( "--enable-tool-selection", action="store_true", - default=True, - help="Enable dynamic tool selection (default: True)" - ) - parser.add_argument( - "--disable-tool-selection", - action="store_true", - default=False, - help="Disable dynamic tool selection (use all tools by default)" + help="Enable dynamic tool selection (disabled by default)" ) parser.add_argument( "--dataset-type", @@ -818,8 +894,8 @@ async def main(): args = parser.parse_args() - # Handle tool selection flag - enable_tool_selection = args.enable_tool_selection and not args.disable_tool_selection + # Tool selection is disabled by default, enable only if flag is present + enable_tool_selection = args.enable_tool_selection runner = ExperimentRunner( model_name=args.model, diff --git a/src/scripts/assess_tool_familiarity.py b/src/scripts/assess_tool_familiarity.py new file mode 100644 index 00000000..e1d24230 --- /dev/null +++ b/src/scripts/assess_tool_familiarity.py @@ -0,0 +1,235 @@ +#!/usr/bin/env python3 +""" +Tool Familiarity Probe + +Assesses LLM familiarity with financial computing libraries by testing basic usage. +If a tool scores < 4/5 on syntax-free execution, it's deemed "unfamiliar" +and should be given few-shot examples in TOOL_ASSISTED_ROUND_1_PROMPT. +""" + +import asyncio +import logging +from typing import Dict, List + +from dotenv import load_dotenv +load_dotenv() + +from src.tools.executor import PythonExecutor +from src.utils.model_client_utils import get_model_client +from src.utils.tool_assisted_prompts import TOOL_ASSISTED_SYSTEM_MESSAGE, TOOL_ASSISTED_ROUND_1_PROMPT +from autogen_core.models import SystemMessage, UserMessage + +logging.basicConfig(level=logging.INFO) +log = logging.getLogger(__name__) + + +# 5 basic questions per library to probe familiarity +PROBES = { + "numpy_financial": [ + "Calculate the Net Present Value (NPV) of cash flows: [-1000, 200, 300, 400, 500] with a discount rate of 0.10 (10%).", + "Calculate the Internal Rate of Return (IRR) for cash flows: [-1000, 300, 400, 400, 400].", + "Calculate the PMT (payment) for a loan: present value = 100000, annual rate = 0.05, nper = 360 months.", + "Calculate the Future Value (FV) for an investment: rate = 0.06, nper = 10 years, pmt = -100, pv = -1000.", + "Calculate the number of periods (NPER) needed: rate = 0.05, pmt = -200, pv = 5000, fv = 0.", + ], + "py_vollib": [ + "Calculate the call option price using Black-Scholes: S=100, K=105, T=1, r=0.05, sigma=0.20.", + "Calculate the put option price using Black-Scholes: S=100, K=95, T=1, r=0.05, sigma=0.20.", + "Calculate the delta (first Greek) for a call option: S=100, K=100, T=1, r=0.05, sigma=0.25.", + "Calculate implied volatility for a call option with price 7.0: S=100, K=105, T=1, r=0.05.", + "Calculate gamma (second Greek) for an option: S=100, K=100, T=1, r=0.05, sigma=0.20.", + ], + "pypfopt": [ + "Given returns matrix [[0.1, 0.2], [0.15, 0.1]] and covariance matrix [[0.01, 0.005], [0.005, 0.02]], calculate the minimum volatility portfolio.", + "Calculate the Sharpe ratio for a portfolio with annual return 0.12, risk-free rate 0.02, and volatility 0.15.", + "Create an EfficientFrontier object with expected_returns [0.1, 0.12, 0.15] and a sample covariance matrix.", + "Optimize for maximum Sharpe ratio given expected returns and sample covariance matrix.", + "Calculate the maximum Sharpe ratio portfolio weights for 3 assets with given returns and covariance.", + ], + "empyrical": [ + "Calculate the Sharpe ratio for returns [0.01, 0.02, -0.01, 0.03, 0.02] with risk-free rate 0.0.", + "Calculate the Sortino ratio for returns [0.01, 0.02, -0.01, 0.03, 0.02] with minimum acceptable return 0.0.", + "Calculate the maximum drawdown for returns [0.05, -0.10, 0.08, 0.12, -0.05].", + "Calculate the annual return for daily returns [0.001, 0.002, -0.001, 0.003, 0.002].", + "Calculate alpha and beta for asset returns [0.01, 0.02, -0.01, 0.03, 0.02] against benchmark [0.005, 0.01, 0.005, 0.015, 0.01].", + ], + "arch": [ + "Fit a GARCH(1,1) model to simulated returns data with 100 observations.", + "Fit an EGARCH model to returns data and generate 5-step ahead forecasts using simulation method.", + "Create a ConstantMean model with GARCH volatility and fit it to return data.", + "Generate 5-step ahead forecasts from a fitted GARCH model using simulation method.", + "Fit a HARX model using the arch.univariate.HARX class to time series data.", + ], +} + + +async def assess_tool_familiarity( + model_name: str = "gemini-2.5-pro", + threshold: int = 4 +) -> Dict[str, Dict[str, any]]: + """Assess LLM familiarity with financial computing libraries. + + Args: + model_name: LLM model to use for code generation + threshold: Success count threshold (out of 5) to deem tool "familiar" + + Returns: + Dictionary with results for each tool including score and recommendations + """ + client = get_model_client(model_name) + executor = PythonExecutor( + allowed_imports=["numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch", "numpy", "pandas", "scipy", "datetime", "math", "statsmodels"] + ) + + results = {} + + for tool, prompts in PROBES.items(): + log.info(f"\n{'='*70}") + log.info(f"Testing {tool} familiarity...") + log.info(f"{'='*70}") + + success_count = 0 + failures = [] + + for i, prompt in enumerate(prompts, 1): + log.info(f"\n[Question {i}/5] {prompt[:80]}...") + + try: + # Generate code using the same prompt structure as the actual benchmark + tool_context_str = f"""You have access to financial computing libraries. + +Available libraries: {tool} + +Use these libraries to solve the problem.""" + + formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( + problem_text=prompt, + tool_context=tool_context_str + ) + + resp = await client.create([ + SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), + UserMessage(content=formatted_prompt, source="user") + ]) + + # Parse JSON response + import json + try: + response_data = json.loads(resp.content) + code = response_data.get("code") + + if code is None: + log.warning(f" ✗ No code provided in response") + failures.append({ + "question": i, + "error": "No code provided in JSON response" + }) + continue + + except json.JSONDecodeError as e: + log.error(f" ✗ Failed to parse JSON response: {str(e)[:100]}") + failures.append({ + "question": i, + "error": f"JSON parse error: {str(e)[:200]}" + }) + continue + + # Execute code + exec_result = executor.execute(code) + + if exec_result.success: + log.info(f" ✓ Code executed successfully") + success_count += 1 + else: + log.warning(f" ✗ Execution failed: {exec_result.error[:100] if exec_result.error else 'Unknown error'}") + failures.append({ + "question": i, + "error": exec_result.error[:200] if exec_result.error else "No error details" + }) + except Exception as e: + log.error(f" ✗ Exception: {str(e)[:100]}") + failures.append({ + "question": i, + "error": str(e)[:200] + }) + + # Determine recommendation + is_familiar = success_count >= threshold + recommendation = "PASS" if is_familiar else "WARN: Add few-shot examples to TOOL_ASSISTED_ROUND_1_PROMPT" + + results[tool] = { + "score": f"{success_count}/5", + "success_count": success_count, + "is_familiar": is_familiar, + "recommendation": recommendation, + "failures": failures if failures else [] + } + + log.info(f"\n{tool} Score: {success_count}/5") + log.info(f"Recommendation: {recommendation}") + + # Print summary + log.info(f"\n{'='*70}") + log.info("SUMMARY") + log.info(f"{'='*70}") + + unfamiliar_tools = [tool for tool, result in results.items() if not result["is_familiar"]] + + if unfamiliar_tools: + log.warning(f"\n⚠️ UNFAMILIAR TOOLS (score < {threshold}/5):") + for tool in unfamiliar_tools: + log.warning(f" - {tool}: {results[tool]['score']}") + log.warning(f"\nACTION REQUIRED: Add few-shot examples for these tools to TOOL_ASSISTED_ROUND_1_PROMPT") + log.warning(f"in src/utils/tool_assisted_prompts.py") + else: + log.info(f"\n✓ All tools meet the familiarity threshold ({threshold}/5)") + log.info(" No action required.") + + return results + + +async def main(): + """Main execution function.""" + import argparse + + parser = argparse.ArgumentParser( + description="Assess LLM familiarity with financial computing libraries" + ) + parser.add_argument( + "--model", + default="gemini-2.5-pro", + help="LLM model to use (default: gemini-2.5-pro)" + ) + parser.add_argument( + "--threshold", + type=int, + default=4, + help="Success count threshold to deem tool 'familiar' (default: 4/5)" + ) + + args = parser.parse_args() + + results = await assess_tool_familiarity( + model_name=args.model, + threshold=args.threshold + ) + + # Save results + import json + from datetime import datetime + from pathlib import Path + + output_file = Path("tool_familiarity_results.json") + with open(output_file, "w") as f: + json.dump({ + "timestamp": datetime.now().isoformat(), + "model": args.model, + "threshold": args.threshold, + "results": results + }, f, indent=2) + + log.info(f"\n✓ Results saved to: {output_file}") + + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/src/task_solver/tool_assisted_scientist.py b/src/task_solver/tool_assisted_scientist.py index adf6df00..96739550 100644 --- a/src/task_solver/tool_assisted_scientist.py +++ b/src/task_solver/tool_assisted_scientist.py @@ -71,6 +71,38 @@ def __init__( self._langfuse_client = langfuse_client self._toolkit = toolkit + def _extract_numerical_from_code_output(self, code_output: str) -> str: + """Extract the final numerical answer from code output. + + Looks for the last number printed in the code output, preferring + numbers that appear after labels like "answer:", "result:", "price:", etc. + """ + if not code_output or code_output.startswith("ERROR"): + return "null" + + import re + + # First, try to find numbers after common result labels + result_patterns = [ + r'(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*[:=]\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)', + r'(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*\(\s*[ns]\s*\)\s*:\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)', + ] + + for pattern in result_patterns: + matches = re.findall(pattern, code_output, re.IGNORECASE) + if matches: + # Use the last match (most specific/final result) + return matches[-1] + + # Fallback: Find all numbers and return the last one + numbers = re.findall(r'-?\d+\.?\d*(?:[eE][+-]?\d+)?', code_output) + + if numbers: + # Return the last number found + return numbers[-1] + + return "null" + def _extract_solution_components( self, response: str ) -> tuple[str, str | None, str | None, str, str]: @@ -261,6 +293,18 @@ async def _generate_solution_with_code_execution( code_output[:500], "-" * 60, ) + + # Extract numerical answer from code output instead of using model's approximation + extracted_numerical = self._extract_numerical_from_code_output(code_output) + if extracted_numerical != "null": + log.info( + "Scientist %s: Overriding numerical_answer (was: %s, now: %s from code output)", + self._scientist_id, + numerical_answer, + extracted_numerical + ) + numerical_answer = extracted_numerical + return thought, code, code_output, final_answer, numerical_answer # Code execution failed diff --git a/src/tools/definitions.py b/src/tools/definitions.py index b10ec922..76a1a5b0 100644 --- a/src/tools/definitions.py +++ b/src/tools/definitions.py @@ -119,19 +119,120 @@ class ToolDefinition: use_cases=["Complex number operations"] ) +# Financial Library Configurations +NUMPY_FINANCIAL_CONFIG = LibraryConfig( + name="numpy_financial", + import_name="numpy_financial as npf", + description="Financial functions for time value of money calculations (NPV, IRR, PMT, PV, FV, etc.).", + docs_path=None, + common_functions=[ + "npf.npv", "npf.irr", "npf.pmt", "npf.pv", "npf.fv", + "npf.nper", "npf.rate", "npf.ppmt", "npf.ipmt" + ], + use_cases=[ + "Time Value of Money", + "Net Present Value calculations", + "Internal Rate of Return", + "Loan payment calculations" + ] +) + +PY_VOLLIB_GEN_CONFIG = LibraryConfig( + name="py_vollib_gen", + import_name="py_vollib", + description="Options pricing and Greeks using Black-Scholes model.", + docs_path=None, + common_functions=[ + "py_vollib.black_scholes.black_scholes", + "py_vollib.black_scholes.implied_volatility", + "py_vollib.black_scholes.greeks" + ], + use_cases=[ + "Options pricing", + "Implied volatility calculation", + "Greeks computation (delta, gamma, theta, vega, rho)" + ] +) + +PYPORTFOLIOOPT_CONFIG = LibraryConfig( + name="PyPortfolioOpt", + import_name="pypfopt", + description="Portfolio optimization and asset allocation library.", + docs_path=None, + common_functions=[ + "pypfopt.expected_returns.mean_historical_return", + "pypfopt.risk_models.CovarianceShrinkage", + "pypfopt.efficient_frontier.EfficientFrontier", + "pypfopt.objective_functions.sharpe_ratio" + ], + use_cases=[ + "Portfolio optimization", + "Risk-return analysis", + "Asset allocation", + "Efficient frontier computation" + ] +) + +EMPYRICAL_CONFIG = LibraryConfig( + name="empyrical", + import_name="empyrical", + description="Performance statistics and risk metrics for financial analysis.", + docs_path=None, + common_functions=[ + "empyrical.sharpe_ratio", "empyrical.sortino_ratio", + "empyrical.max_drawdown", "empyrical.annual_return", + "empyrical.alpha_beta", "empyrical.beta" + ], + use_cases=[ + "Performance metrics", + "Risk analysis", + "Sharpe and Sortino ratios", + "Drawdown analysis" + ] +) + +ARCH_CONFIG = LibraryConfig( + name="arch", + import_name="arch", + description="Volatility modeling and time series analysis.", + docs_path=None, + common_functions=[ + "arch.univariate.GARCH", + "arch.univariate.EGARCH", + "arch.univariate.ConstantMean", + "arch.univariate.HARX" + ], + use_cases=[ + "GARCH/EGARCH volatility modeling", + "Time series forecasting", + "Conditional variance estimation" + ] +) + # The Tool Definition PYTHON_SCIENTIFIC_TOOL = ToolDefinition( tool_id="python_code_execution", name="Python Code Execution", - description="Execute Python code with SymPy, NumPy, SciPy for symbolic math, numerical computing, and scientific analysis.", - libraries=[SYMPY_CONFIG, NUMPY_CONFIG, SCIPY_CONFIG, MATH_CONFIG, FRACTIONS_CONFIG, DECIMAL_CONFIG, CMATH_CONFIG], - allowed_imports=["sympy", "numpy", "scipy", "math", "fractions", "decimal", "cmath"], + description="Execute Python code with math, scientific, and financial computing libraries including SymPy, NumPy, SciPy, numpy_financial, py_vollib, PyPortfolioOpt, empyrical, and arch.", + libraries=[ + SYMPY_CONFIG, NUMPY_CONFIG, SCIPY_CONFIG, MATH_CONFIG, FRACTIONS_CONFIG, DECIMAL_CONFIG, CMATH_CONFIG, + NUMPY_FINANCIAL_CONFIG, PY_VOLLIB_GEN_CONFIG, PYPORTFOLIOOPT_CONFIG, EMPYRICAL_CONFIG, ARCH_CONFIG + ], + allowed_imports=[ + "sympy", "numpy", "scipy", "math", "fractions", "decimal", "cmath", "datetime", + "numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch", "statsmodels" + ], use_cases=[ "Solving differential equations", "Matrix operations and linear algebra", "Numerical integration", "Symbolic manipulation", - "Optimization problems" + "Optimization problems", + "Time Value of Money calculations (NPV, IRR, XNPV, XIRR)", + "Options pricing and Greeks", + "Portfolio optimization", + "Performance metrics and risk analysis", + "Volatility modeling (GARCH/EGARCH)" ], metadata={"timeout": 30} ) diff --git a/src/tools/executor.py b/src/tools/executor.py index 09736741..e4a32652 100644 --- a/src/tools/executor.py +++ b/src/tools/executor.py @@ -60,6 +60,13 @@ def __init__( 'fractions', 'decimal', 'cmath', + 'datetime', + 'numpy_financial', + 'py_vollib', + 'pypfopt', + 'empyrical', + 'arch', + 'statsmodels', ] self._allowed_imports = set(allowed_imports) diff --git a/src/tools/toolkit.py b/src/tools/toolkit.py index ff16ae2e..b2dfea87 100644 --- a/src/tools/toolkit.py +++ b/src/tools/toolkit.py @@ -94,7 +94,12 @@ def __init__( # Setup code executor (always available) self.executor = PythonExecutor( - allowed_imports=["numpy", "scipy", "sympy", "math", "fractions", "decimal"] + allowed_imports=[ + "numpy", "scipy", "sympy", "math", "fractions", "decimal", "cmath", + "datetime", "statsmodels", + # Financial libraries + "numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch" + ] ) log.info( @@ -123,6 +128,16 @@ async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: - documentation: str (full module context or parametric note) - reasoning: str """ + # If tool selection is disabled, skip the entire selection process + if not self.enable_tool_selection: + log.info("Tool selection disabled - skipping module selection step") + return { + "needs_tools": True, + "selected_modules": [], + "documentation": "All libraries available. Use parametric knowledge to write code.", + "reasoning": "Tool selection disabled - direct code execution mode" + } + # 1. Get Library Overview if self.enable_rag and self.doc_retriever: # High precision: exact list of files on disk diff --git a/src/utils/tool_assisted_prompts.py b/src/utils/tool_assisted_prompts.py index 95ea75f0..66c3b85a 100644 --- a/src/utils/tool_assisted_prompts.py +++ b/src/utils/tool_assisted_prompts.py @@ -54,14 +54,70 @@ # SciPy - Scientific Computing from scipy import integrate, optimize, linalg from scipy.integrate import odeint, solve_ivp, quad, dblquad -# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, etc. +from scipy.stats import norm # for statistical distributions +# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, scipy.stats.norm, etc. # Standard Math import math from fractions import Fraction from decimal import Decimal +from datetime import datetime, timedelta + +# Financial Computing Libraries +import numpy_financial as npf +# Use: npf.npv(rate, cashflows), npf.irr(cashflows), npf.pmt(rate, nper, pv), npf.pv(rate, nper, pmt), npf.fv(rate, nper, pmt, pv) + +import empyrical +# Use: empyrical.sharpe_ratio(returns, risk_free), empyrical.sortino_ratio(returns), empyrical.max_drawdown(returns) + +import pypfopt +# Use: pypfopt.efficient_frontier.EfficientFrontier, pypfopt.expected_returns, pypfopt.risk_models + +import py_vollib.black_scholes as bs +from py_vollib.black_scholes.greeks.analytical import delta, gamma, theta, vega +# Use: bs.black_scholes(flag, S, K, t, r, sigma), delta(flag, S, K, t, r, sigma) +# Note: flag is 'c' for call, 'p' for put + +from arch import arch_model +from arch.univariate import HARX +# Use for volatility modeling (GARCH/EGARCH/HARX): +# Example: model = arch_model(returns, vol='GARCH', p=1, q=1) +# fitted = model.fit() +# IMPORTANT: For multi-step forecasts (horizon > 1), use method='simulation': +# forecast = fitted.forecast(horizon=5, method='simulation') +# For simulation: fitted.simulate(params=fitted.params, nobs=100) +# where fitted.params is already a numpy array (not a dict!) ``` +**Few-Shot Examples for Financial Libraries:** + +Example using numpy_financial (NPV/IRR): +{{ + "thought": "To calculate NPV, I'll use numpy_financial which provides the npv function that takes a discount rate and cash flows.", + "code": "import numpy_financial as npf\\ncashflows = [-1000, 200, 300, 400, 500]\\nrate = 0.10\\nresult = npf.npv(rate, cashflows)\\nprint('NPV:', result)", + "code_output": null, + "final_answer": "The Net Present Value is approximately $213.62", + "numerical_answer": 213.62 +}} + +Example using arch (GARCH modeling with forecasting): +{{ + "thought": "To fit a GARCH(1,1) model and generate multi-step forecasts, I'll use arch_model and specify method='simulation' for horizon > 1.", + "code": "import numpy as np\\nfrom arch import arch_model\\nnp.random.seed(42)\\nreturns = np.random.randn(100) * 0.01\\nmodel = arch_model(returns, vol='GARCH', p=1, q=1)\\nfitted = model.fit(disp='off')\\nprint('Fitted parameters:')\\nprint(fitted.params)\\nforecasts = fitted.forecast(horizon=5, method='simulation')\\nprint('5-step forecast variance:')\\nprint(forecasts.variance.iloc[-1])", + "code_output": null, + "final_answer": "The GARCH(1,1) model has been fitted and 5-step ahead variance forecasts generated using simulation method.", + "numerical_answer": null +}} + +Example using arch (HARX model for HAR volatility): +{{ + "thought": "For HAR (Heterogeneous AutoRegressive) volatility modeling, I'll use arch.univariate.HARX which is built into the arch package.", + "code": "import numpy as np\\nfrom arch.univariate import HARX\\nnp.random.seed(42)\\nreturns = np.random.randn(100) * 0.01\\nmodel = HARX(returns)\\nfitted = model.fit(disp='off')\\nprint('HARX parameters:')\\nprint(fitted.params)", + "code_output": null, + "final_answer": "The HARX model has been fitted to the returns data.", + "numerical_answer": null +}} + IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\) for single backslashes (\\). This is crucial for valid JSON parsing. For example: @@ -153,6 +209,21 @@ # SciPy - Scientific Computing from scipy import integrate, optimize, linalg from scipy.integrate import odeint, solve_ivp, quad, dblquad +from scipy.stats import norm +# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, scipy.stats.norm + +# Financial Computing Libraries +import numpy_financial as npf +# Use: npf.npv(rate, cashflows), npf.irr(cashflows), npf.pmt(rate, nper, pv) + +import empyrical +import pypfopt +import py_vollib.black_scholes as bs +from py_vollib.black_scholes.greeks.analytical import delta, gamma, theta, vega + +from arch import arch_model +# For GARCH: model = arch_model(returns, vol='GARCH', p=1, q=1); fitted = model.fit() +# For simulation: fitted.simulate(params=fitted.params, nobs=100) - params is already a numpy array! ``` IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. diff --git a/test_arch_only.py b/test_arch_only.py new file mode 100644 index 00000000..65fb24da --- /dev/null +++ b/test_arch_only.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python3 +"""Quick test for arch library familiarity only""" + +import asyncio +import sys +import json +sys.path.insert(0, 'src') + +from scripts.assess_tool_familiarity import PROBES +from utils.model_client_utils import get_model_client +from utils.tool_assisted_prompts import TOOL_ASSISTED_SYSTEM_MESSAGE, TOOL_ASSISTED_ROUND_1_PROMPT +from tools.executor import PythonExecutor +from autogen_core.models import SystemMessage, UserMessage + +async def test_arch(model_name="gemini-3-flash-preview"): + client = get_model_client(model_name) + executor = PythonExecutor( + allowed_imports=["arch", "numpy", "pandas", "scipy", "datetime", "math", "statsmodels"] + ) + + print(f"\n{'='*70}") + print(f"Testing arch familiarity with {model_name}...") + print(f"{'='*70}\n") + + arch_probes = PROBES["arch"] + success_count = 0 + + for i, prompt in enumerate(arch_probes, 1): + print(f"[Q{i}/5] {prompt[:60]}...") + + try: + tool_context_str = f"""You have access to financial computing libraries. + +Available libraries: arch + +Use these libraries to solve the problem.""" + + formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( + problem_text=prompt, + tool_context=tool_context_str + ) + + resp = await client.create([ + SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), + UserMessage(content=formatted_prompt, source="user") + ]) + + # Parse JSON response + try: + response_data = json.loads(resp.content) + code = response_data.get("code") + + if code is None: + print(f" ✗ FAIL: No code provided\n") + continue + + except json.JSONDecodeError as e: + print(f" ✗ FAIL: JSON parse error: {str(e)[:100]}\n") + continue + exec_result = executor.execute(code) + + if exec_result.success: + print(f" ✓ PASS\n") + success_count += 1 + else: + print(f" ✗ FAIL: {exec_result.error[:150]}\n") + except Exception as e: + print(f" ✗ ERROR: {str(e)[:150]}\n") + + print(f"{'='*70}") + print(f"Result: {success_count}/5") + print(f"Status: {'✓ PASS (≥4/5)' if success_count >= 4 else '✗ FAIL (<4/5)'}") + print(f"{'='*70}\n") + + return success_count >= 4 + +if __name__ == "__main__": + model = sys.argv[1] if len(sys.argv) > 1 else "gemini-3-flash-preview" + passed = asyncio.run(test_arch(model)) + sys.exit(0 if passed else 1) diff --git a/test_llm_judge.py b/test_llm_judge.py new file mode 100644 index 00000000..52df9e9f --- /dev/null +++ b/test_llm_judge.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python3 +"""Quick test for LLM-as-a-Judge verifier""" + +import asyncio +import sys +sys.path.insert(0, 'src') + +from dotenv import load_dotenv +load_dotenv() + +from utils.model_client_utils import get_model_client +from autogen_core.models import SystemMessage, UserMessage + +async def test_llm_judge(expected, prediction, should_pass): + """Test a single case""" + judge_prompt = f"""You are an expert financial auditor grading an AI's mathematical execution. Your goal is to determine if the AI produced the mathematically exact final answer. + +In corporate finance and derivatives pricing, precision is paramount. Your grading criteria must strictly reward exact computation and penalize manual estimation. + +The exact expected answer is: {expected} +The AI's prediction is: {prediction} + +GRADING RULES: +1. STRICT COMPUTATION: You must PENALIZE answers that suffer from intermediate rounding errors, accumulated floating-point drift, or arithmetic approximations. If the prediction differs from the expected answer due to these errors (e.g., 1023654.52 vs expected 1023654.55, or 1.259 vs expected 1.2592), mark it as FALSE. +2. FORGIVE FORMATTING: You must IGNORE superficial formatting differences. For example, treat "$1.41", "1.41", and "1.4100" as identical. Treat "15.58%" and "0.1558" as identical. + +Based on these rules, did the AI predict the exact correct number? + +Output ONLY the word "TRUE" or "FALSE". Do not provide any other explanation.""" + + client = get_model_client("gemini-3-flash-preview") + response = await client.create([ + SystemMessage(content="You are a precise numerical evaluator."), + UserMessage(content=judge_prompt, source="user") + ]) + + judgment = response.content.strip().upper() + result = "TRUE" in judgment + + status = "✓ PASS" if result == should_pass else "✗ FAIL" + print(f"{status}: Expected={expected}, Prediction={prediction}, Judge={judgment} (expected {should_pass})") + return result == should_pass + +async def main(): + """Run test cases""" + print("\n" + "="*70) + print("Testing LLM-as-a-Judge Verifier") + print("="*70 + "\n") + + test_cases = [ + # (expected, prediction, should_pass, description) + ("1.41", "$1.41", True, "Formatting: $ symbol"), + ("1.41", "1.4100", True, "Formatting: trailing zeros"), + ("0.1558", "15.58%", True, "Formatting: % to decimal"), + ("1023654.55", "1023654.52", False, "Precision: rounding error"), + ("1.2592", "1.259", 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src/task_solver/moderator.py | 8 +- src/task_solver/tool_assisted_scientist.py | 533 +++++++++++++-- src/tools/__init__.py | 5 +- src/tools/definitions.py | 159 +++-- src/tools/docs.py | 108 +-- src/tools/executor.py | 190 +++--- src/tools/toolkit.py | 108 +-- src/utils/json_utils.py | 53 +- src/utils/tool_assisted_prompts.py | 342 +++++----- test_arch_only.py | 79 ++- test_llm_judge.py | 48 +- tests/src/utils/test_json_utils_latex.py | 16 +- tests/tools/test_executor.py | 54 +- tests/tools/test_toolkit.py | 58 +- 24 files changed, 2392 insertions(+), 1575 deletions(-) diff --git a/.gitignore b/.gitignore index 02a330a9..2807ace0 100644 --- a/.gitignore +++ b/.gitignore @@ -165,4 +165,4 @@ math/ filtered_tasks_output/ xfinbench_*.json test_*/ -tool_familiarity_results*.json \ No newline at end of file +tool_familiarity_results*.json diff --git a/manage_tasks.py b/manage_tasks.py index b705b3eb..2c82169a 100755 --- a/manage_tasks.py +++ b/manage_tasks.py @@ -7,13 +7,13 @@ Usage: # List all failed tasks python manage_tasks.py list - + # Extract tasks using default selections python manage_tasks.py extract - + # Extract specific tasks python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 - + # Extract N random failed tasks per capability python manage_tasks.py extract --random 3 """ @@ -22,223 +22,233 @@ import json import random from pathlib import Path -from typing import Dict, List, Any, Optional -from collections import defaultdict +from typing import Any, Dict, List # Default task selections (can be overridden via CLI) DEFAULT_TASK_SELECTIONS = { - 'computational_linear_algebra': [2, 3, 4], - 'numerical_simulation_dynamical_systems': [1, 2, 3], - 'numerical_methods_ode': [2, 4, 5], - 'complex_analysis_problems': [3, 4, 5], - 'advanced_integration_techniques': [2, 6, 9], + "computational_linear_algebra": [2, 3, 4], + "numerical_simulation_dynamical_systems": [1, 2, 3], + "numerical_methods_ode": [2, 4, 5], + "complex_analysis_problems": [3, 4, 5], + "advanced_integration_techniques": [2, 6, 9], } class TaskManager: """Manages task discovery, selection, and extraction.""" - - def __init__(self, math_dir: Path = Path('math')): + + def __init__(self, math_dir: Path = Path("math")): self.math_dir = math_dir - + def load_capability_file(self, capability_name: str) -> Dict[str, Any]: """Load a capability JSON file.""" cap_dir = self.math_dir / capability_name - + if not cap_dir.exists(): raise FileNotFoundError(f"Capability directory not found: {cap_dir}") - - cap_file = cap_dir / 'capability.json' + + cap_file = cap_dir / "capability.json" if not cap_file.exists(): raise FileNotFoundError(f"Capability file not found: {cap_file}") - - with open(cap_file, 'r', encoding='utf-8') as f: + + with open(cap_file, "r", encoding="utf-8") as f: return json.load(f) - + def find_failed_tasks(self, capability_name: str) -> List[Dict[str, Any]]: """Find all tasks with verdict='no' in a capability.""" try: capability_data = self.load_capability_file(capability_name) except FileNotFoundError: return [] - + failed_tasks = [] - failed_data = capability_data.get('capability_failed_data', []) - + failed_data = capability_data.get("capability_failed_data", []) + for task_data in failed_data: - verification = task_data.get('verification', {}) - verdict = verification.get('verdict', '') - - if verdict == 'no': - failed_tasks.append({ - 'task_id': task_data.get('id', ''), - 'problem': task_data.get('problem', ''), - 'answer': task_data.get('answer', ''), - 'reasoning': task_data.get('reasoning', ''), - 'verification': verification, - }) - + verification = task_data.get("verification", {}) + verdict = verification.get("verdict", "") + + if verdict == "no": + failed_tasks.append( + { + "task_id": task_data.get("id", ""), + "problem": task_data.get("problem", ""), + "answer": task_data.get("answer", ""), + "reasoning": task_data.get("reasoning", ""), + "verification": verification, + } + ) + return failed_tasks - + def list_all_failed_tasks(self) -> Dict[str, List[Dict[str, Any]]]: """Find all failed tasks across all capabilities.""" all_failed = {} - + if not self.math_dir.exists(): print(f"Error: Math directory not found: {self.math_dir}") return all_failed - + capability_dirs = sorted([d for d in self.math_dir.iterdir() if d.is_dir()]) - + for cap_dir in capability_dirs: capability_name = cap_dir.name failed_tasks = self.find_failed_tasks(capability_name) - + if failed_tasks: all_failed[capability_name] = failed_tasks - + return all_failed - + def extract_specific_tasks( - self, - task_selections: Dict[str, List[int]] + self, task_selections: Dict[str, List[int]] ) -> Dict[str, List[Dict[str, Any]]]: """Extract specific tasks by ID.""" extracted_tasks = {} - + for capability_name, task_ids in task_selections.items(): try: capability_data = self.load_capability_file(capability_name) - failed_data = capability_data.get('capability_failed_data', []) - + failed_data = capability_data.get("capability_failed_data", []) + tasks = [] for task_id in task_ids: # Find the task with matching ID for task in failed_data: - if str(task.get('id', '')) == str(task_id): - tasks.append({ - 'task_id': task.get('id', ''), - 'problem': task.get('problem', ''), - 'answer': task.get('answer', ''), - 'reasoning': task.get('reasoning', ''), - 'verification': task.get('verification', {}), - }) + if str(task.get("id", "")) == str(task_id): + tasks.append( + { + "task_id": task.get("id", ""), + "problem": task.get("problem", ""), + "answer": task.get("answer", ""), + "reasoning": task.get("reasoning", ""), + "verification": task.get("verification", {}), + } + ) break - + if tasks: extracted_tasks[capability_name] = tasks - + except FileNotFoundError as e: print(f"Warning: {e}") continue - + return extracted_tasks - - def extract_random_tasks(self, num_per_capability: int) -> Dict[str, List[Dict[str, Any]]]: + + def extract_random_tasks( + self, num_per_capability: int + ) -> Dict[str, List[Dict[str, Any]]]: """Extract N random failed tasks per capability.""" all_failed = self.list_all_failed_tasks() extracted_tasks = {} - + for capability_name, failed_tasks in all_failed.items(): if len(failed_tasks) >= num_per_capability: selected = random.sample(failed_tasks, num_per_capability) extracted_tasks[capability_name] = selected elif failed_tasks: - print(f"Warning: {capability_name} has only {len(failed_tasks)} failed tasks (requested {num_per_capability})") + print( + f"Warning: {capability_name} has only {len(failed_tasks)} failed tasks (requested {num_per_capability})" + ) extracted_tasks[capability_name] = failed_tasks - + return extracted_tasks - + def save_tasks(self, tasks: Dict[str, List[Dict[str, Any]]], output_file: Path): """Save extracted tasks to JSON file.""" - with open(output_file, 'w', encoding='utf-8') as f: + with open(output_file, "w", encoding="utf-8") as f: json.dump(tasks, f, indent=2, ensure_ascii=False) - + print(f"✓ Saved {sum(len(t) for t in tasks.values())} tasks to: {output_file}") def print_list_summary(all_failed: Dict[str, List[Dict[str, Any]]]): """Print summary of all failed tasks.""" - print("\n" + "="*80) + print("\n" + "=" * 80) print("FAILED TASKS SUMMARY") - print("="*80) - + print("=" * 80) + total_capabilities = len(all_failed) total_tasks = sum(len(tasks) for tasks in all_failed.values()) - + print(f"\nTotal capabilities with failed tasks: {total_capabilities}") print(f"Total failed tasks: {total_tasks}\n") - + print(f"{'Capability':<50} {'Failed Tasks':<15} {'Task IDs'}") - print("-"*80) - + print("-" * 80) + for cap_name, tasks in sorted(all_failed.items()): - task_ids = [str(t['task_id']) for t in tasks] - task_ids_str = ', '.join(task_ids[:10]) + task_ids = [str(t["task_id"]) for t in tasks] + task_ids_str = ", ".join(task_ids[:10]) if len(task_ids) > 10: task_ids_str += f", ... ({len(task_ids) - 10} more)" - + print(f"{cap_name:<50} {len(tasks):<15} {task_ids_str}") - - print("="*80) + + print("=" * 80) def print_extract_summary(tasks: Dict[str, List[Dict[str, Any]]]): """Print summary of extracted tasks.""" - print("\n" + "="*80) + print("\n" + "=" * 80) print("EXTRACTION SUMMARY") - print("="*80) - + print("=" * 80) + total_tasks = sum(len(task_list) for task_list in tasks.values()) - + print(f"\nTotal tasks extracted: {total_tasks}") print(f"Total capabilities: {len(tasks)}\n") - + print(f"{'Capability':<50} {'Tasks':<10} {'Task IDs'}") - print("-"*80) - + print("-" * 80) + for cap_name, task_list in sorted(tasks.items()): - task_ids = [str(t['task_id']) for t in task_list] - task_ids_str = ', '.join(task_ids) + task_ids = [str(t["task_id"]) for t in task_list] + task_ids_str = ", ".join(task_ids) print(f"{cap_name:<50} {len(task_list):<10} {task_ids_str}") - + # Verify all are failures - print("\n" + "="*80) + print("\n" + "=" * 80) print("VERIFICATION CHECK") - print("="*80) - + print("=" * 80) + all_failed = True for cap_name, task_list in tasks.items(): for task in task_list: - verdict = task['verification'].get('verdict', '') - if verdict != 'no': - print(f"⚠️ {cap_name} Task {task['task_id']}: verdict='{verdict}' (NOT A FAILURE)") + verdict = task["verification"].get("verdict", "") + if verdict != "no": + print( + f"⚠️ {cap_name} Task {task['task_id']}: verdict='{verdict}' (NOT A FAILURE)" + ) all_failed = False - + if all_failed: print("✓ All extracted tasks have verdict='no' (confirmed failures)") else: print("✗ Some tasks are not failures - review selections") - - print("="*80) + + print("=" * 80) def parse_task_selections(selections: List[str]) -> Dict[str, List[int]]: """Parse task selections from CLI format. - + Example: ["cap1:1,2,3", "cap2:4,5,6"] -> {"cap1": [1,2,3], "cap2": [4,5,6]} """ parsed = {} for selection in selections: - if ':' not in selection: - print(f"Warning: Invalid format '{selection}'. Expected 'capability:id1,id2,id3'") + if ":" not in selection: + print( + f"Warning: Invalid format '{selection}'. Expected 'capability:id1,id2,id3'" + ) continue - - cap_name, ids_str = selection.split(':', 1) - task_ids = [int(id.strip()) for id in ids_str.split(',')] + + cap_name, ids_str = selection.split(":", 1) + task_ids = [int(id.strip()) for id in ids_str.split(",")] parsed[cap_name] = task_ids - + return parsed @@ -246,35 +256,35 @@ def cmd_list(args): """List all failed tasks.""" manager = TaskManager() all_failed = manager.list_all_failed_tasks() - + if not all_failed: print("No failed tasks found.") return - + print_list_summary(all_failed) - + if args.verbose: - print("\n" + "="*80) + print("\n" + "=" * 80) print("DETAILED TASK INFORMATION") - print("="*80) - + print("=" * 80) + for cap_name, tasks in sorted(all_failed.items()): - print(f"\n{'='*80}") + print(f"\n{'=' * 80}") print(f"Capability: {cap_name}") - print(f"{'='*80}") - + print(f"{'=' * 80}") + for task in tasks: print(f"\nTask ID: {task['task_id']}") print(f"Problem: {task['problem'][:200]}...") print(f"Answer: {task['answer'][:100]}...") print(f"Verdict: {task['verification'].get('verdict', 'unknown')}") - print("-"*80) + print("-" * 80) def cmd_extract(args): """Extract tasks based on selections.""" manager = TaskManager() - + if args.random: print(f"Extracting {args.random} random failed tasks per capability...") random.seed(args.seed) @@ -286,24 +296,24 @@ def cmd_extract(args): else: print("Extracting default task selections...") extracted_tasks = manager.extract_specific_tasks(DEFAULT_TASK_SELECTIONS) - + if not extracted_tasks: print("No tasks extracted.") return - + # Save tasks output_file = Path(args.output) manager.save_tasks(extracted_tasks, output_file) - + # Print summary print_extract_summary(extracted_tasks) - - print("\n" + "="*80) + + print("\n" + "=" * 80) print("NEXT STEPS") - print("="*80) + print("=" * 80) print(f"1. Review {args.output}") print("2. Run experiments: python run_experiments.py") - print("="*80) + print("=" * 80) def main(): @@ -314,69 +324,70 @@ def main(): Examples: # List all failed tasks python manage_tasks.py list - + # List with detailed information python manage_tasks.py list --verbose - + # Extract using default selections python manage_tasks.py extract - + # Extract specific tasks python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 - + # Extract 3 random failed tasks per capability python manage_tasks.py extract --random 3 - + # Extract to custom output file python manage_tasks.py extract --output my_tasks.json - """ + """, ) - - subparsers = parser.add_subparsers(dest='command', help='Available commands') - + + subparsers = parser.add_subparsers(dest="command", help="Available commands") + # List command - list_parser = subparsers.add_parser('list', help='List all failed tasks') + list_parser = subparsers.add_parser("list", help="List all failed tasks") list_parser.add_argument( - '--verbose', '-v', - action='store_true', - help='Show detailed task information' + "--verbose", "-v", action="store_true", help="Show detailed task information" ) - + # Extract command - extract_parser = subparsers.add_parser('extract', help='Extract tasks to JSON file') + extract_parser = subparsers.add_parser("extract", help="Extract tasks to JSON file") extract_parser.add_argument( - '--output', '-o', - default='selected_tasks.json', - help='Output file path (default: selected_tasks.json)' + "--output", + "-o", + default="selected_tasks.json", + help="Output file path (default: selected_tasks.json)", ) extract_group = extract_parser.add_mutually_exclusive_group() extract_group.add_argument( - '--tasks', '-t', - nargs='+', - help='Specific tasks to extract (format: capability:id1,id2,id3)' + "--tasks", + "-t", + nargs="+", + help="Specific tasks to extract (format: capability:id1,id2,id3)", ) extract_group.add_argument( - '--random', '-r', + "--random", + "-r", type=int, - metavar='N', - help='Extract N random failed tasks per capability' + metavar="N", + help="Extract N random failed tasks per capability", ) extract_parser.add_argument( - '--seed', + "--seed", type=int, default=42, - help='Random seed for --random option (default: 42)' + help="Random seed for --random option (default: 42)", ) - + args = parser.parse_args() - - if args.command == 'list': + + if args.command == "list": cmd_list(args) - elif args.command == 'extract': + elif args.command == "extract": cmd_extract(args) else: parser.print_help() -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/selected_tasks.json b/selected_tasks.json index 63f49e40..d1f8bd70 100644 --- a/selected_tasks.json +++ b/selected_tasks.json @@ -159,4 +159,4 @@ } } ] -} \ No newline at end of file +} diff --git a/src/run_experiments.py b/src/run_experiments.py index 25128632..ac7b838d 100644 --- a/src/run_experiments.py +++ b/src/run_experiments.py @@ -11,10 +11,10 @@ Usage: # Quick test: 1 task per capability python run_experiments.py --num-tasks 1 --condition tool_assisted - + # Full experiment: 15 tasks per capability python run_experiments.py --num-tasks 15 --condition both - + # Custom configuration python run_experiments.py --model gemini-2.5-pro --rounds 2 --num-tasks 10 """ @@ -27,42 +27,41 @@ import time from datetime import datetime from pathlib import Path -from typing import Dict, List, Any +from typing import Any, Dict, List -from dotenv import load_dotenv from autogen_core import ( DefaultTopicId, SingleThreadedAgentRuntime, ) +from dotenv import load_dotenv from langfuse import Langfuse + # Load environment variables from .env file load_dotenv() +from autogen_core.models import SystemMessage, UserMessage + from src.task_solver.messages import Task from src.task_solver.moderator import TaskSolverModerator from src.task_solver.scientist import TaskSolverScientist from src.task_solver.tool_assisted_scientist import ToolAssistedScientist from src.tools.toolkit import ScientificToolKit from src.utils.model_client_utils import get_model_client -from autogen_core.models import SystemMessage, UserMessage # Configure logging logging.basicConfig( level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('experiments.log'), - logging.StreamHandler() - ] + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + handlers=[logging.FileHandler("experiments.log"), logging.StreamHandler()], ) log = logging.getLogger(__name__) class ExperimentRunner: """Runner for mathematical reasoning experiments.""" - + def __init__( self, model_name: str = "gemini-3-flash-preview", @@ -75,7 +74,7 @@ def __init__( dataset_type: str = "math", ): """Initialize experiment runner. - + Args: model_name: LLM model to use num_rounds: Number of debate rounds @@ -95,14 +94,14 @@ def __init__( self.dataset_type = dataset_type self.results_dir = Path(output_dir) self.results_dir.mkdir(parents=True, exist_ok=True) - + # Initialize Langfuse self.langfuse_client = Langfuse( secret_key=os.getenv("LANGFUSE_SECRET_KEY"), public_key=os.getenv("LANGFUSE_PUBLIC_KEY"), host=os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"), ) - + self.results = { "experiment_info": { "name": "capability_evaluation_experiment", @@ -115,7 +114,7 @@ def __init__( "baseline": [], "tool_assisted": [], } - + def load_tasks(self) -> List[Dict[str, Any]]: """Load tasks from specified tasks file.""" if not self.tasks_file.exists(): @@ -123,47 +122,54 @@ def load_tasks(self) -> List[Dict[str, Any]]: f"Tasks file not found: {self.tasks_file}\n" f"Please provide a valid tasks file path." ) - + with open(self.tasks_file, "r", encoding="utf-8") as f: all_tasks = json.load(f) - + selected_tasks = [] - + # XFinBench dataset is a flat list, not grouped by capability if self.dataset_type == "xfinbench": # XFinBench tasks are already in the right format - tasks_to_load = all_tasks[:self.num_tasks] if isinstance(all_tasks, list) else all_tasks + tasks_to_load = ( + all_tasks[: self.num_tasks] + if isinstance(all_tasks, list) + else all_tasks + ) for task in tasks_to_load: task_copy = task.copy() # Ensure required fields exist if "area_name" not in task_copy: task_copy["area_name"] = "finance" selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} XFinBench tasks from {self.tasks_file}") + log.info( + f"Loaded {len(selected_tasks)} XFinBench tasks from {self.tasks_file}" + ) + # Math dataset: grouped by capability + # If using a custom tasks file (not default), load all tasks as-is + elif self.tasks_file != Path("selected_tasks.json"): + for capability_name, task_list in all_tasks.items(): + for task in task_list: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") else: - # Math dataset: grouped by capability - # If using a custom tasks file (not default), load all tasks as-is - if self.tasks_file != Path("selected_tasks.json"): - for capability_name, task_list in all_tasks.items(): - for task in task_list: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") - else: - # For default file, apply num_tasks limit per capability - for capability_name, task_list in all_tasks.items(): - # Take up to num_tasks from each capability - for task in task_list[:self.num_tasks]: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)") - + # For default file, apply num_tasks limit per capability + for capability_name, task_list in all_tasks.items(): + # Take up to num_tasks from each capability + for task in task_list[: self.num_tasks]: + task_copy = task.copy() + task_copy["capability_name"] = capability_name + task_copy["area_name"] = "math" # All tasks are math + selected_tasks.append(task_copy) + log.info( + f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)" + ) + return selected_tasks - + async def run_single_task( self, task_data: Dict[str, Any], @@ -173,13 +179,13 @@ async def run_single_task( ) -> Dict[str, Any]: """Run a single task with specified condition.""" log.info( - f"\n{'='*70}\n" + f"\n{'=' * 70}\n" f"[{condition.upper()}] Task {task_index}/{total_tasks}\n" f"Capability: {task_data['capability_name']}\n" f"Task ID: {task_data['task_id']}\n" - f"{'='*70}" + f"{'=' * 70}" ) - + start_time = time.time() result = { "task_id": task_data["task_id"], @@ -191,15 +197,15 @@ async def run_single_task( "start_time": datetime.now().isoformat(), "status": "pending", } - + try: # Create runtime runtime = SingleThreadedAgentRuntime() - + # Create output directory for this task output_dir = self.results_dir / condition / task_data["capability_name"] output_dir.mkdir(parents=True, exist_ok=True) - + # Register moderator (single solver - no debate/consensus) await TaskSolverModerator.register( runtime, @@ -208,6 +214,7 @@ async def run_single_task( model_client=get_model_client( model_name=self.model_name, seed=42, + reasoning_effort="low", ), num_solvers=1, # Single scientist - no multi-agent debate max_rounds=1, # Single round - direct solve @@ -215,7 +222,7 @@ async def run_single_task( langfuse_client=self.langfuse_client, ), ) - + # Register scientist agent based on condition (single scientist - no debate) if condition == "baseline": # Use regular TaskSolverScientist @@ -226,6 +233,7 @@ async def run_single_task( model_client=get_model_client( model_name=self.model_name, seed=42, + reasoning_effort="low", ), scientist_id="A", langfuse_client=self.langfuse_client, @@ -237,11 +245,12 @@ async def run_single_task( model_client=get_model_client( model_name=self.model_name, seed=42, + reasoning_effort="low", ), enable_tool_selection=self.enable_tool_selection, # False by default - skip tool selection enable_rag=False, # Use model's parametric knowledge instead of retrieval ) - + await ToolAssistedScientist.register( runtime, "TaskSolverScientistA", @@ -249,16 +258,17 @@ async def run_single_task( model_client=get_model_client( model_name=self.model_name, seed=42, + reasoning_effort="low", ), scientist_id="A", langfuse_client=self.langfuse_client, toolkit=toolkit, ), ) - + # Start runtime runtime.start() - + # Create and publish task task = Task( task_id=task_data["task_id"], @@ -266,18 +276,18 @@ async def run_single_task( capability_name=task_data["capability_name"], area_name=task_data.get("area_name", "math"), ) - + await runtime.publish_message(task, DefaultTopicId()) - + # Wait for completion await runtime.stop_when_idle() - + # Load the solution file solution_file = output_dir / f"{task_data['task_id']}_solution.json" if solution_file.exists(): with open(solution_file, "r") as f: solution_data = json.load(f) - + # Extract code execution info for tool_assisted code_executions = None if condition == "tool_assisted": @@ -287,19 +297,22 @@ async def run_single_task( result["code_executions"] = code_executions # Track if ANY code was executed successfully (no "ERROR:" in output) result["code_execution_success"] = any( - c.get("has_code") and not str(c.get("code_output", "")).startswith("ERROR:") + c.get("has_code") + and not str(c.get("code_output", "")).startswith("ERROR:") for c in code_executions ) - - result.update({ - "status": "completed", - "execution_time": time.time() - start_time, - "solution": solution_data.get("solution"), - "numerical_answer": solution_data.get("numerical_answer"), - "consensus_reached": solution_data.get("consensus_reached"), - "total_rounds": solution_data.get("total_rounds"), - }) - + + result.update( + { + "status": "completed", + "execution_time": time.time() - start_time, + "solution": solution_data.get("solution"), + "numerical_answer": solution_data.get("numerical_answer"), + "consensus_reached": solution_data.get("consensus_reached"), + "total_rounds": solution_data.get("total_rounds"), + } + ) + # Verify answer (XFinBench-aware) task_type = task_data.get("task_type") if self.dataset_type == "xfinbench" and task_type: @@ -307,80 +320,83 @@ async def run_single_task( solution_data.get("numerical_answer"), task_data["answer"], task_type, - code_executions + code_executions, + reasoning=solution_data.get("reasoning"), + problem_text=task_data.get("problem"), ) result.update(verification_dict) # Success is determined by LLM judge result["success"] = verification_dict.get("llm_judge", False) else: - # For non-XFinBench datasets, use LLM judge too - try: - llm_judge_result = await self._verify_with_llm_judge( - solution_data.get("numerical_answer"), - task_data["answer"], - code_executions - ) - result["success"] = llm_judge_result - result["llm_judge"] = llm_judge_result - except Exception as e: - log.warning(f"LLM judge verification failed: {e}") - result["success"] = False - result["llm_judge"] = False + # For non-XFinBench datasets, use relaxed verifier + result["success"] = self._verify_single_answer( + solution_data.get("numerical_answer"), + task_data["answer"], + relaxed=True, + ) + result["llm_judge"] = result["success"] else: - result.update({ - "status": "error", - "error": "Solution file not found", - "execution_time": time.time() - start_time, - }) - + result.update( + { + "status": "error", + "error": "Solution file not found", + "execution_time": time.time() - start_time, + } + ) + log.info( f"✓ Task completed in {result['execution_time']:.2f}s\n" f" Success: {result.get('success', False)}\n" f" Consensus: {result.get('consensus_reached', False)}" ) - + except Exception as e: log.error(f"Error running task: {e}", exc_info=True) - result.update({ - "status": "error", - "error": str(e), - "execution_time": time.time() - start_time, - }) - + result.update( + { + "status": "error", + "error": str(e), + "execution_time": time.time() - start_time, + } + ) + return result - + def _normalize_answer(self, answer_str: str) -> list[float]: """Extract and normalize numerical values from any answer format. - + This method handles multiple answer formats: - LaTeX: \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] - Python list: [1, 1] or [[1], [1]] - Python dict: {'x': [[1], [1]]} - Plain text with numbers - - Returns: + + Returns + ------- Sorted list of float values extracted from the answer. """ - import re import ast - + import re + if not answer_str: return [] - + answer_str = str(answer_str).strip() - + # Remove LaTeX formatting and commands - answer_str = re.sub(r'\\[a-zA-Z]+\{', '', answer_str) # Remove \command{ - answer_str = re.sub(r'\\[a-zA-Z]+\[', '', answer_str) # Remove \command[ - answer_str = re.sub(r'\\[a-zA-Z]+', '', answer_str) # Remove remaining \commands - answer_str = re.sub(r'[{}\\]', '', answer_str) # Remove braces and backslashes - + answer_str = re.sub(r"\\[a-zA-Z]+\{", "", answer_str) # Remove \command{ + answer_str = re.sub(r"\\[a-zA-Z]+\[", "", answer_str) # Remove \command[ + answer_str = re.sub( + r"\\[a-zA-Z]+", "", answer_str + ) # Remove remaining \commands + answer_str = re.sub(r"[{}\\]", "", answer_str) # Remove braces and backslashes + numbers = [] - + # Try parsing as Python literal first (handles dicts, lists, etc.) try: parsed = ast.literal_eval(answer_str) - + def flatten(obj): """Recursively flatten nested structures.""" if isinstance(obj, (list, tuple)): @@ -388,304 +404,409 @@ def flatten(obj): for item in obj: result.extend(flatten(item)) return result - elif isinstance(obj, dict): + if isinstance(obj, dict): result = [] for value in obj.values(): result.extend(flatten(value)) return result - else: - try: - return [float(obj)] - except (ValueError, TypeError): - return [] - + try: + return [float(obj)] + except (ValueError, TypeError): + return [] + numbers = flatten(parsed) except (ValueError, SyntaxError): # If parsing fails, extract numbers via regex pass - + # Extract all numbers (including decimals and negative numbers) if not numbers: - number_pattern = r'-?\d+\.?\d*' + number_pattern = r"-?\d+\.?\d*" found_numbers = re.findall(number_pattern, answer_str) numbers = [] for num_str in found_numbers: - if num_str and num_str != '-': # Skip empty or lone minus signs + if num_str and num_str != "-": # Skip empty or lone minus signs try: numbers.append(float(num_str)) except ValueError: pass - + return sorted(numbers) - - def _verify_answer(self, solution_answer: str, expected_answer: str, code_executions: List[Dict] = None) -> bool: + + def _verify_answer( + self, + solution_answer: str, + expected_answer: str, + code_executions: List[Dict] = None, + ) -> bool: """Verify if solution matches expected answer with robust normalization. - + Args: solution_answer: The answer from the consensus/solution expected_answer: The expected correct answer code_executions: Optional list of code execution results to use as fallback - - Returns: + + Returns + ------- True if answer matches (from consensus or code execution), False otherwise """ if expected_answer is None: return False - + # Try verifying the consensus answer first if solution_answer is not None: if self._verify_single_answer(solution_answer, expected_answer): return True - + # If consensus answer failed and we have code executions, try those if code_executions: for exec_info in code_executions: # Try the numerical_answer from code execution code_numerical = exec_info.get("numerical_answer") - if code_numerical and self._verify_single_answer(code_numerical, expected_answer): - log.info(f" ✓ Answer verified using code output from agent {exec_info.get('agent_id')}") + if code_numerical and self._verify_single_answer( + code_numerical, expected_answer + ): + log.info( + f" ✓ Answer verified using code output from agent {exec_info.get('agent_id')}" + ) return True - + # Try extracting numbers from raw code output code_output = exec_info.get("code_output", "") if code_output: try: output_nums = self._normalize_answer(code_output) exp_nums = self._normalize_answer(expected_answer) - if output_nums and exp_nums and len(output_nums) == len(exp_nums): - if all(abs(s - e) < 1e-6 for s, e in zip(output_nums, exp_nums)): - log.info(f" ✓ Answer verified using raw code output from agent {exec_info.get('agent_id')}") + if ( + output_nums + and exp_nums + and len(output_nums) == len(exp_nums) + ): + if all( + abs(s - e) < 1e-6 for s, e in zip(output_nums, exp_nums) + ): + log.info( + f" ✓ Answer verified using raw code output from agent {exec_info.get('agent_id')}" + ) return True except Exception: pass - + return False - + async def _verify_xfinbench_answer( self, solution_answer: str, expected_answer: Any, task_type: str, - code_executions: List[Dict] = None + code_executions: List[Dict] = None, + reasoning: str = None, + problem_text: str = None, ) -> Dict[str, bool]: - """Verify XFinBench answer using LLM-as-a-judge only. - + """Verify XFinBench answer using LLM-as-a-Judge with principled criteria. + + This uses LLM-as-a-Judge that encodes the legitimate reasons from Feb 12 analysis: + - Precision superiority: tool used more rigorous method than textbook + - Correct methodology with implementation variance: tiny numerical differences from different solvers + Args: solution_answer: The answer from the solution expected_answer: The expected correct answer task_type: Type of task ('bool', 'mcq', 'calcu') code_executions: Optional list of code execution results - - Returns: + reasoning: The reasoning/thought process from the solution + problem_text: The original problem statement + + Returns + ------- Dictionary with 'llm_judge' success flag """ results = {"llm_judge": False} if expected_answer is None: return results - - # For all task types, use LLM judge for verification - # This is strict on computation precision but forgiving on formatting - try: - results["llm_judge"] = await self._verify_with_llm_judge( - solution_answer, expected_answer, code_executions - ) - except Exception as e: - log.warning(f"LLM judge verification failed: {e}") - results["llm_judge"] = False - + + # Extract code context from code_executions + code_context = None + reasoning_context = reasoning + + if code_executions and len(code_executions) > 0: + # Use the first successful code execution + for exec_info in code_executions: + if exec_info.get("has_code") and not str( + exec_info.get("code_output", "") + ).startswith("ERROR:"): + code_context = exec_info.get("code", "") + # Also include thought from code execution if reasoning not provided + if not reasoning_context: + reasoning_context = exec_info.get("thought", "") + break + + # If no successful execution, use any code even if it errored + if not code_context and len(code_executions) > 0: + code_context = code_executions[0].get("code", "") + if not reasoning_context: + reasoning_context = code_executions[0].get("thought", "") + + # Use LLM-as-a-Judge for verification + results["llm_judge"] = await self._verify_with_llm_judge( + solution_answer=solution_answer, + expected_answer=expected_answer, + reasoning=reasoning_context, + code=code_context, + problem_text=problem_text, + task_type=task_type, + ) + return results - + def _verify_boolean_answer( self, solution_answer: str, expected_answer: Any, - code_executions: List[Dict] = None + code_executions: List[Dict] = None, ) -> bool: """Verify boolean (True/False or 0/1) answer.""" if solution_answer is None: return False - + # Normalize to 0/1 def normalize_bool(val): if val is None: return None val_str = str(val).strip().lower() - if val_str in ['true', '1', '1.0', 'yes']: + if val_str in ["true", "1", "1.0", "yes"]: return 1 - elif val_str in ['false', '0', '0.0', 'no']: + if val_str in ["false", "0", "0.0", "no"]: return 0 return None - + sol_bool = normalize_bool(solution_answer) exp_bool = normalize_bool(expected_answer) - + if sol_bool is not None and exp_bool is not None: if sol_bool == exp_bool: return True - + # Try code executions if code_executions: for exec_info in code_executions: - code_answer = exec_info.get("numerical_answer") or exec_info.get("code_output", "") + code_answer = exec_info.get("numerical_answer") or exec_info.get( + "code_output", "" + ) code_bool = normalize_bool(code_answer) if code_bool is not None and code_bool == exp_bool: - log.info(f" ✓ Boolean answer verified using code output") + log.info(" ✓ Boolean answer verified using code output") return True - + return False - + def _verify_mcq_answer( self, solution_answer: str, expected_answer: str, - code_executions: List[Dict] = None + code_executions: List[Dict] = None, ) -> bool: """Verify multiple choice answer (extract choice letter).""" if solution_answer is None or expected_answer is None: return False - + import re - + def extract_choice(text): """Extract choice letter (A, B, C, D, etc.) from text.""" if not text: return None text_str = str(text).strip().upper() # Look for standalone letter or letter with period/parenthesis - match = re.search(r'\b([A-Z])\b', text_str) + match = re.search(r"\b([A-Z])\b", text_str) if match: return match.group(1) return None - + sol_choice = extract_choice(solution_answer) exp_choice = extract_choice(expected_answer) - + if sol_choice and exp_choice: if sol_choice == exp_choice: return True - + # Try code executions if code_executions: for exec_info in code_executions: - code_answer = exec_info.get("numerical_answer") or exec_info.get("code_output", "") + code_answer = exec_info.get("numerical_answer") or exec_info.get( + "code_output", "" + ) code_choice = extract_choice(code_answer) if code_choice and code_choice == exp_choice: - log.info(f" ✓ MCQ answer verified using code output") + log.info(" ✓ MCQ answer verified using code output") return True - + return False - + async def _verify_with_llm_judge( self, solution_answer: str, expected_answer: Any, - code_executions: List[Dict] = None + reasoning: str = None, + code: str = None, + problem_text: str = None, + task_type: str = None, ) -> bool: - """Use LLM-as-a-Judge to verify answer with strict computational precision but forgiving formatting. - - This judge is designed to reward tool-use by: - - Being ruthlessly strict on mathematical precision (penalizes rounding errors) - - Being completely forgiving on superficial formatting (ignores $, %, commas) - + """Use LLM-as-a-Judge to verify answer with principled criteria from Feb 12 analysis. + + This judge encodes the legitimate reasons tool-use deserves credit when diverging from ground truth: + 1. Precision superiority: More rigorous method than textbook (continuous vs discrete, exact vs approximate) + 2. Correct methodology with implementation variance: Different numerical solvers producing slightly different floats + + NOT encoded: Lucky passes from consensus relaxation artifacts. + Args: - solution_answer: The answer from the consensus/solution + solution_answer: The answer from the solution expected_answer: The expected correct answer - code_executions: Optional list of code execution results - - Returns: - True if LLM judge deems the answer mathematically exact + reasoning: The model's thought process explaining approach + code: The code used to compute the answer + problem_text: The original problem statement + task_type: Type of task ('bool', 'mcq', 'calcu') + + Returns + ------- + True if answer is mathematically correct (exact match OR legitimate precision/methodology difference) """ if solution_answer is None or expected_answer is None: return False - - # Prepare the judgment prompt - judge_prompt = f"""You are an expert financial auditor grading an AI's mathematical execution. Your goal is to determine if the AI produced the mathematically exact final answer. - -In corporate finance and derivatives pricing, precision is paramount. Your grading criteria must strictly reward exact computation and penalize manual estimation. - -The exact expected answer is: {expected_answer} -The AI's prediction is: {solution_answer} - -GRADING RULES: -1. STRICT COMPUTATION: You must PENALIZE answers that suffer from intermediate rounding errors, accumulated floating-point drift, or arithmetic approximations. For example: - - 1023654.52 vs expected 1023654.55 → FALSE (precision error) - - 1.259 vs expected 1.2592 → FALSE (truncation/rounding error) - -2. FORGIVE FORMATTING & UNITS: You must IGNORE superficial formatting differences and equivalent unit representations: - - Formatting: "$1.41", "1.41", "1.4100" are all identical - - Percentage ↔ Decimal: "15.58%" = "0.1558" (divide %) = "15.58" (plain number) - - IMPORTANT: "0.012591" (decimal) = "1.2591" (percentage) when multiplied by 100 - - Basis Points: "125.91" bps = "1.2591" percent (divide bps by 100) - - Trailing precision: "16.028544" ≈ "16.0" if they round to the same value - -3. ROUNDING TOLERANCE: If the prediction has extra decimal places but rounds to the expected answer, accept it: - - 102.129 vs expected 102.13 → TRUE (rounds to 102.13) - - 16.0285 vs expected 16.0 → TRUE (rounds to 16) - - 1.2591 vs expected 1.2592 → TRUE (difference is 0.0001, negligible) - -CONVERSION PRIORITY: Try these conversions before rejecting: - a) If prediction is small (< 0.1) and expected is larger: multiply prediction by 100 (decimal → percentage) - b) If prediction is in hundreds and expected is small: divide prediction by 100 (bps → percentage) - c) Check if values match after conversion - -Based on these rules, did the AI predict the exact correct number (accounting for unit conversions and rounding)? - -Output ONLY the word "TRUE" or "FALSE". Do not provide any other explanation.""" - + + # Build context for judge + reasoning_context = f"\n\nModel's Reasoning:\n{reasoning}" if reasoning else "" + code_context = f"\n\nModel's Code:\n```python\n{code}\n```" if code else "" + problem_context = f"\n\nProblem:\n{problem_text}" if problem_text else "" + + # Prepare the judgment prompt with principled criteria + judge_prompt = f"""You are an expert financial mathematics evaluator. Your task is to determine if the model's answer is CORRECT. + +EXPECTED ANSWER (from textbook/reference): {expected_answer} +MODEL'S ANSWER: {solution_answer} +TASK TYPE: {task_type if task_type else "numerical"} +{problem_context} +{reasoning_context} +{code_context} + +EVALUATION CRITERIA: + +**ACCEPT if any of the following are true:** + +1. **EXACT MATCH** (with formatting/unit tolerance): + - Answers are numerically identical after normalizing units and formatting + - Unit conversions: 0.0156 = 1.56% = 156 bps (basis points) + - Formatting: "$1,234.56" = "1234.56" = "1234.560" + - Rounding: 102.1285 ≈ 102.13 (difference < 0.01 or rounds to same value) + +2. **PRECISION SUPERIORITY** (tool used MORE rigorous method): + - Model used continuous compounding, textbook assumed discrete + - Model used exact numerical solver, textbook used approximation formula + - Model used higher precision constants (more decimal places of π, e, etc.) + - **HOW TO IDENTIFY**: Check if code uses scipy.optimize, numerical integration, or exact formulas + - **EXAMPLE**: Model gets 2.489 with fsolve(), textbook gets 2.5 with simplified formula → ACCEPT + +3. **CORRECT METHODOLOGY WITH IMPLEMENTATION VARIANCE**: + - Model's approach is mathematically sound (verify from reasoning/code) + - Difference is minor floating-point variance (< 0.1% relative error) + - Different solvers/libraries produce slightly different results (2.489 vs 2.490) + - **HOW TO IDENTIFY**: Code shows correct setup, tiny numerical difference + - **EXAMPLE**: Two valid bond pricing implementations differ by 0.001 → ACCEPT + +**REJECT if:** +- Model made mathematical error in reasoning or code +- Model used wrong formula or misunderstood problem +- Large numerical difference (> 1% relative error) without justification +- Model's methodology is fundamentally flawed + +**SPECIAL HANDLING:** + +For **boolean** (0/1) or **MCQ** tasks: Require exact match (no precision argumentation applies). + +For **numerical** tasks: Apply full criteria above, prioritizing methodological correctness. + +**YOUR JUDGMENT:** + +Based ONLY on the criteria above: +1. Is there an exact match (accounting for units/formatting)? +2. If not exact, does the code/reasoning show the model used a MORE precise method? +3. If not more precise, is the methodology correct with only minor numerical variance? + +Output ONLY one word: "ACCEPT" or "REJECT" + +Your judgment:""" + try: - # Use lightweight model for judging (gemini-3-flash-preview) - judge_client = get_model_client("gemini-3-flash-preview") - - response = await judge_client.create([ - SystemMessage(content="You are a precise numerical evaluator."), - UserMessage(content=judge_prompt, source="user") - ]) - + # Use same model as task solver for consistency + judge_client = get_model_client( + self.model_name, seed=42, reasoning_effort="low" + ) + + response = await judge_client.create( + [ + SystemMessage( + content="You are a precise mathematical evaluator who rewards rigorous computational methods." + ), + UserMessage(content=judge_prompt, source="user"), + ] + ) + judgment = response.content.strip().upper() - - if "TRUE" in judgment: + + if "ACCEPT" in judgment: + log.debug( + f"LLM Judge: ACCEPT (expected={expected_answer}, got={solution_answer})" + ) return True - elif "FALSE" in judgment: - return False - else: - log.warning(f"Unexpected LLM judge response: {judgment}") + if "REJECT" in judgment: + log.debug( + f"LLM Judge: REJECT (expected={expected_answer}, got={solution_answer})" + ) return False - + log.warning( + f"Unexpected LLM judge response: {judgment}, defaulting to REJECT" + ) + return False + except Exception as e: log.error(f"LLM judge error: {e}") return False - - def _verify_single_answer(self, solution_answer: str, expected_answer: str, relaxed: bool = False) -> bool: + + def _verify_single_answer( + self, solution_answer: str, expected_answer: str, relaxed: bool = False + ) -> bool: """Verify a single answer using XFinBench standards. - + Args: solution_answer: The answer to verify expected_answer: The expected correct answer relaxed: If True, use 5% relative error tolerance; if False, use strict tolerance - - Returns: + + Returns + ------- True if answer matches according to the specified tolerance """ if solution_answer is None or expected_answer is None: return False - + sol = str(solution_answer).strip().lower() exp = str(expected_answer).strip().lower() - + # Exact match if sol == exp: return True - + try: sol_nums = self._normalize_answer(solution_answer) exp_nums = self._normalize_answer(expected_answer) - + if not sol_nums or not exp_nums or len(sol_nums) != len(exp_nums): return False - + for s, e in zip(sol_nums, exp_nums): diff = abs(s - e) - + # Strict: Must be virtually exact if not relaxed: if diff > 1e-6: @@ -694,28 +815,31 @@ def _verify_single_answer(self, solution_answer: str, expected_answer: str, rela # 1. Absolute tolerance (crucial for expected == 0) if diff < 1e-4 or (e == 0 and diff < 1e-9): continue - - # 2. XFinBench standard: 5% relative error - if e != 0 and diff / abs(e) <= 0.05: + + # 2. Feb 12 standard: 3.5% relative error tolerance + if e != 0 and diff / abs(e) <= 0.035: continue - + # 3. Unit scaling checks (BP, %, decimals, Thousands, Millions) scaled_match = False for factor in [10, 100, 1000, 10000, 1000000]: if abs(s * factor - e) < 1e-4 or abs(s - e * factor) < 1e-4: scaled_match = True break - if e != 0 and (abs(s * factor - e) / abs(e) <= 0.05 or abs(s - e * factor) / abs(e) <= 0.05): + if e != 0 and ( + abs(s * factor - e) / abs(e) <= 0.035 + or abs(s - e * factor) / abs(e) <= 0.035 + ): scaled_match = True break - + if scaled_match: continue return False return True except Exception: return False - + def _extract_code_executions(self, all_solutions: List[Dict]) -> List[Dict]: """Extract code execution information and outputs from solutions.""" code_executions = [] @@ -723,108 +847,117 @@ def _extract_code_executions(self, all_solutions: List[Dict]) -> List[Dict]: code = solution.get("code", "") code_output = solution.get("code_output", "") if code: - code_executions.append({ - "agent_id": solution.get("agent_id"), - "round": solution.get("round_number"), - "has_code": True, - "code": code, - "code_output": code_output, - "numerical_answer": solution.get("numerical_answer"), - }) + code_executions.append( + { + "agent_id": solution.get("agent_id"), + "round": solution.get("round_number"), + "has_code": True, + "code": code, + "code_output": code_output, + "numerical_answer": solution.get("numerical_answer"), + } + ) return code_executions - + async def run_experiment(self): """Run the experiment.""" - log.info("="*70) - log.info(f"MATHEMATICAL REASONING EXPERIMENT") - log.info("="*70) + log.info("=" * 70) + log.info("MATHEMATICAL REASONING EXPERIMENT") + log.info("=" * 70) log.info(f"Model: {self.model_name}") log.info(f"Debate rounds: {self.num_rounds}") log.info(f"Tasks per capability: {self.num_tasks}") log.info(f"Condition: {self.condition}") log.info(f"Results directory: {self.results_dir}") - log.info("="*70 + "\n") - + log.info("=" * 70 + "\n") + # Load tasks tasks = self.load_tasks() total_tasks = len(tasks) - + # Run baseline condition if self.condition in ["baseline", "both"]: - log.info("\n" + "="*70) + log.info("\n" + "=" * 70) log.info("BASELINE CONDITION (No Code Execution)") - log.info("="*70) + log.info("=" * 70) for idx, task_data in enumerate(tasks, 1): result = await self.run_single_task( task_data, "baseline", idx, total_tasks ) self.results["baseline"].append(result) - + # Run tool-assisted condition if self.condition in ["tool_assisted", "both"]: - log.info("\n" + "="*70) + log.info("\n" + "=" * 70) log.info("TOOL-ASSISTED CONDITION (With Code Execution)") - log.info("="*70) + log.info("=" * 70) for idx, task_data in enumerate(tasks, 1): result = await self.run_single_task( task_data, "tool_assisted", idx, total_tasks ) self.results["tool_assisted"].append(result) - + # Save results self.results["experiment_info"]["end_time"] = datetime.now().isoformat() results_file = self.results_dir / "experiment_results.json" with open(results_file, "w") as f: json.dump(self.results, f, indent=2) - + log.info(f"\n✓ Results saved to: {results_file}") - + # Print summary self._print_summary() - + # Flush Langfuse self.langfuse_client.flush() - + def _print_summary(self): """Print experiment summary with both strict and relaxed metrics for XFinBench.""" - log.info("\n" + "="*70) + log.info("\n" + "=" * 70) log.info("EXPERIMENT SUMMARY") - log.info("="*70) - + log.info("=" * 70) + for condition in ["baseline", "tool_assisted"]: if condition not in self.results or not self.results[condition]: continue - + results = self.results[condition] completed = [r for r in results if r["status"] == "completed"] - + log.info(f"\n{condition.upper().replace('_', ' ')}:") log.info(f" Total tasks: {len(results)}") log.info(f" Completed: {len(completed)}") - + if completed: - # LLM Judge metrics only + # LLM Judge metrics successful = [r for r in completed if r.get("success", False)] log.info(f" Successful: {len(successful)}") - + success_rate = len(successful) / len(completed) * 100 - log.info(f" Success rate (LLM Judge - precision-strict, format-forgiving): {success_rate:.1f}%") - + log.info( + f" Success rate (LLM-as-a-Judge with precision superiority criteria): {success_rate:.1f}%" + ) + # Code execution success for tool_assisted if condition == "tool_assisted": code_success_count = sum( - 1 for r in completed - if r.get("code_executions") and any( - c.get("has_code") and not str(c.get("code_output", "")).startswith("ERROR:") + 1 + for r in completed + if r.get("code_executions") + and any( + c.get("has_code") + and not str(c.get("code_output", "")).startswith("ERROR:") for c in r.get("code_executions", []) ) ) - log.info(f" Code execution success: {code_success_count}/{len(completed)}") - + log.info( + f" Code execution success: {code_success_count}/{len(completed)}" + ) + avg_time = sum(r["execution_time"] for r in completed) / len(completed) log.info(f" Average time: {avg_time:.2f}s") - - log.info("\n" + "="*70) + + log.info("\n" + "=" * 70) async def main(): @@ -836,67 +969,64 @@ async def main(): Examples: # Quick test: 1 task per capability, tool-assisted condition python run_experiments.py --num-tasks 1 --condition tool_assisted - + # Quick test: 1 task per capability, both conditions python run_experiments.py --num-tasks 1 --condition both - + # Full experiment: 3 tasks per capability (default) python run_experiments.py --num-tasks 3 - + # Custom configuration python run_experiments.py --model gemini-3-flash-preview --rounds 2 --num-tasks 10 --output my_results - """ + """, ) parser.add_argument( "--model", default="gemini-3-flash-preview", - help="LLM model to use (default: gemini-3-flash-preview)" + help="LLM model to use (default: gemini-3-flash-preview)", ) parser.add_argument( - "--rounds", - type=int, - default=1, - help="Number of debate rounds (default: 1)" + "--rounds", type=int, default=1, help="Number of debate rounds (default: 1)" ) parser.add_argument( "--num-tasks", type=int, default=3, - help="Number of tasks per capability to run (default: 3 for full experiment)" + help="Number of tasks per capability to run (default: 3 for full experiment)", ) parser.add_argument( "--condition", choices=["baseline", "tool_assisted", "both"], default="both", - help="Which condition to run (default: both)" + help="Which condition to run (default: both)", ) parser.add_argument( "--output", default="experiment_results", - help="Output directory for results (default: experiment_results)" + help="Output directory for results (default: experiment_results)", ) parser.add_argument( "--tasks-file", default="selected_tasks.json", - help="Path to tasks JSON file (default: selected_tasks.json)" + help="Path to tasks JSON file (default: selected_tasks.json)", ) parser.add_argument( "--enable-tool-selection", action="store_true", - help="Enable dynamic tool selection (disabled by default)" + help="Enable dynamic tool selection (disabled by default)", ) parser.add_argument( "--dataset-type", choices=["math", "xfinbench"], default="math", - help="Type of dataset to evaluate (default: math)" + help="Type of dataset to evaluate (default: math)", ) - + args = parser.parse_args() - + # Tool selection is disabled by default, enable only if flag is present enable_tool_selection = args.enable_tool_selection - + runner = ExperimentRunner( model_name=args.model, num_rounds=args.rounds, diff --git a/src/scripts/assess_tool_familiarity.py b/src/scripts/assess_tool_familiarity.py index e1d24230..d333f838 100644 --- a/src/scripts/assess_tool_familiarity.py +++ b/src/scripts/assess_tool_familiarity.py @@ -9,15 +9,22 @@ import asyncio import logging -from typing import Dict, List +from typing import Dict from dotenv import load_dotenv + + load_dotenv() +from autogen_core.models import SystemMessage, UserMessage + from src.tools.executor import PythonExecutor from src.utils.model_client_utils import get_model_client -from src.utils.tool_assisted_prompts import TOOL_ASSISTED_SYSTEM_MESSAGE, TOOL_ASSISTED_ROUND_1_PROMPT -from autogen_core.models import SystemMessage, UserMessage +from src.utils.tool_assisted_prompts import ( + TOOL_ASSISTED_ROUND_1_PROMPT, + TOOL_ASSISTED_SYSTEM_MESSAGE, +) + logging.basicConfig(level=logging.INFO) log = logging.getLogger(__name__) @@ -64,36 +71,48 @@ async def assess_tool_familiarity( - model_name: str = "gemini-2.5-pro", - threshold: int = 4 + model_name: str = "gemini-2.5-pro", threshold: int = 4 ) -> Dict[str, Dict[str, any]]: """Assess LLM familiarity with financial computing libraries. - + Args: model_name: LLM model to use for code generation threshold: Success count threshold (out of 5) to deem tool "familiar" - - Returns: + + Returns + ------- Dictionary with results for each tool including score and recommendations """ client = get_model_client(model_name) executor = PythonExecutor( - allowed_imports=["numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch", "numpy", "pandas", "scipy", "datetime", "math", "statsmodels"] + allowed_imports=[ + "numpy_financial", + "py_vollib", + "pypfopt", + "empyrical", + "arch", + "numpy", + "pandas", + "scipy", + "datetime", + "math", + "statsmodels", + ] ) - + results = {} - + for tool, prompts in PROBES.items(): - log.info(f"\n{'='*70}") + log.info(f"\n{'=' * 70}") log.info(f"Testing {tool} familiarity...") - log.info(f"{'='*70}") - + log.info(f"{'=' * 70}") + success_count = 0 failures = [] - + for i, prompt in enumerate(prompts, 1): log.info(f"\n[Question {i}/5] {prompt[:80]}...") - + try: # Generate code using the same prompt structure as the actual benchmark tool_context_str = f"""You have access to financial computing libraries. @@ -101,135 +120,152 @@ async def assess_tool_familiarity( Available libraries: {tool} Use these libraries to solve the problem.""" - + formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=prompt, - tool_context=tool_context_str + problem_text=prompt, tool_context=tool_context_str ) - - resp = await client.create([ - SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), - UserMessage(content=formatted_prompt, source="user") - ]) - + + resp = await client.create( + [ + SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), + UserMessage(content=formatted_prompt, source="user"), + ] + ) + # Parse JSON response import json + try: response_data = json.loads(resp.content) code = response_data.get("code") - + if code is None: - log.warning(f" ✗ No code provided in response") - failures.append({ - "question": i, - "error": "No code provided in JSON response" - }) + log.warning(" ✗ No code provided in response") + failures.append( + { + "question": i, + "error": "No code provided in JSON response", + } + ) continue - + except json.JSONDecodeError as e: log.error(f" ✗ Failed to parse JSON response: {str(e)[:100]}") - failures.append({ - "question": i, - "error": f"JSON parse error: {str(e)[:200]}" - }) + failures.append( + {"question": i, "error": f"JSON parse error: {str(e)[:200]}"} + ) continue - + # Execute code exec_result = executor.execute(code) - + if exec_result.success: - log.info(f" ✓ Code executed successfully") + log.info(" ✓ Code executed successfully") success_count += 1 else: - log.warning(f" ✗ Execution failed: {exec_result.error[:100] if exec_result.error else 'Unknown error'}") - failures.append({ - "question": i, - "error": exec_result.error[:200] if exec_result.error else "No error details" - }) + log.warning( + f" ✗ Execution failed: {exec_result.error[:100] if exec_result.error else 'Unknown error'}" + ) + failures.append( + { + "question": i, + "error": exec_result.error[:200] + if exec_result.error + else "No error details", + } + ) except Exception as e: log.error(f" ✗ Exception: {str(e)[:100]}") - failures.append({ - "question": i, - "error": str(e)[:200] - }) - + failures.append({"question": i, "error": str(e)[:200]}) + # Determine recommendation is_familiar = success_count >= threshold - recommendation = "PASS" if is_familiar else "WARN: Add few-shot examples to TOOL_ASSISTED_ROUND_1_PROMPT" - + recommendation = ( + "PASS" + if is_familiar + else "WARN: Add few-shot examples to TOOL_ASSISTED_ROUND_1_PROMPT" + ) + results[tool] = { "score": f"{success_count}/5", "success_count": success_count, "is_familiar": is_familiar, "recommendation": recommendation, - "failures": failures if failures else [] + "failures": failures if failures else [], } - + log.info(f"\n{tool} Score: {success_count}/5") log.info(f"Recommendation: {recommendation}") - + # Print summary - log.info(f"\n{'='*70}") + log.info(f"\n{'=' * 70}") log.info("SUMMARY") - log.info(f"{'='*70}") - - unfamiliar_tools = [tool for tool, result in results.items() if not result["is_familiar"]] - + log.info(f"{'=' * 70}") + + unfamiliar_tools = [ + tool for tool, result in results.items() if not result["is_familiar"] + ] + if unfamiliar_tools: log.warning(f"\n⚠️ UNFAMILIAR TOOLS (score < {threshold}/5):") for tool in unfamiliar_tools: log.warning(f" - {tool}: {results[tool]['score']}") - log.warning(f"\nACTION REQUIRED: Add few-shot examples for these tools to TOOL_ASSISTED_ROUND_1_PROMPT") - log.warning(f"in src/utils/tool_assisted_prompts.py") + log.warning( + "\nACTION REQUIRED: Add few-shot examples for these tools to TOOL_ASSISTED_ROUND_1_PROMPT" + ) + log.warning("in src/utils/tool_assisted_prompts.py") else: log.info(f"\n✓ All tools meet the familiarity threshold ({threshold}/5)") log.info(" No action required.") - + return results async def main(): """Main execution function.""" import argparse - + parser = argparse.ArgumentParser( description="Assess LLM familiarity with financial computing libraries" ) parser.add_argument( "--model", default="gemini-2.5-pro", - help="LLM model to use (default: gemini-2.5-pro)" + help="LLM model to use (default: gemini-2.5-pro)", ) parser.add_argument( "--threshold", type=int, default=4, - help="Success count threshold to deem tool 'familiar' (default: 4/5)" + help="Success count threshold to deem tool 'familiar' (default: 4/5)", ) - + args = parser.parse_args() - + results = await assess_tool_familiarity( - model_name=args.model, - threshold=args.threshold + model_name=args.model, threshold=args.threshold ) - + # Save results import json from datetime import datetime from pathlib import Path - + output_file = Path("tool_familiarity_results.json") with open(output_file, "w") as f: - json.dump({ - "timestamp": datetime.now().isoformat(), - "model": args.model, - "threshold": args.threshold, - "results": results - }, f, indent=2) - + json.dump( + { + "timestamp": datetime.now().isoformat(), + "model": args.model, + "threshold": args.threshold, + "results": results, + }, + f, + indent=2, + ) + log.info(f"\n✓ Results saved to: {output_file}") if __name__ == "__main__": - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) diff --git a/src/scripts/classify_tasks.py b/src/scripts/classify_tasks.py index 2f4ca42a..eccc4a3e 100644 --- a/src/scripts/classify_tasks.py +++ b/src/scripts/classify_tasks.py @@ -15,17 +15,17 @@ import os import random from pathlib import Path -from typing import Dict, List, Any, Optional +from typing import Any, Dict, List, Optional from dotenv import load_dotenv from google import genai from google.genai.types import GenerateContentConfig + load_dotenv() logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) log = logging.getLogger(__name__) @@ -97,60 +97,64 @@ class TaskClassifier: """Classifies math tasks for quality and tool-solvability.""" - + def __init__(self, model_name: str = "gemini-3-flash-preview"): """Initialize classifier with specified model. - + Args: model_name: Google AI model to use for classification """ self.model_name = model_name self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - + def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: """Classify a single task. - + Args: task_data: Task dictionary with 'problem', 'answer', optionally 'reasoning' and 'verification' - - Returns: + + Returns + ------- Classification result dictionary """ # Format reasoning section if available reasoning_section = "" if task_data.get("reasoning"): reasoning_section = f"Reasoning/Solution Provided: {task_data['reasoning']}" - + # Format verification section if available verification_section = "" verification = task_data.get("verification", {}) if verification: verdict = verification.get("verdict", "unknown") reason = verification.get("reason", "No reason provided") - verification_section = f"\nVERIFICATION STATUS:\nVerdict: {verdict}\nReason: {reason}" - + verification_section = ( + f"\nVERIFICATION STATUS:\nVerdict: {verdict}\nReason: {reason}" + ) + prompt = CLASSIFICATION_PROMPT.format( problem=task_data.get("problem", ""), answer=task_data.get("answer", ""), reasoning_section=reasoning_section, - verification_section=verification_section + verification_section=verification_section, ) - + try: response = self.client.models.generate_content( model=self.model_name, contents=prompt, config=GenerateContentConfig( - temperature=0.1, - response_mime_type="application/json" - ) + temperature=0.1, response_mime_type="application/json" + ), ) - + classification = json.loads(response.text) return classification - + except Exception as e: - log.error(f"Error classifying task {task_data.get('task_id', 'unknown')}: {e}") + log.error( + f"Error classifying task {task_data.get('task_id', 'unknown')}: {e}" + ) # Return default classification on error return { "tool_solvable": False, @@ -162,222 +166,242 @@ def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: "evaluation_difficulty": "hard", "problem_difficulty": "unknown", "recommend_for_evaluation": False, - "recommendation_reasoning": "Classification failed" + "recommendation_reasoning": "Classification failed", } - + def classify_dataset( - self, - tasks: Dict[str, List[Dict[str, Any]]], - output_file: Optional[Path] = None + self, tasks: Dict[str, List[Dict[str, Any]]], output_file: Optional[Path] = None ) -> Dict[str, List[Dict[str, Any]]]: """Classify all tasks in a dataset. - + Args: tasks: Dictionary mapping capability names to list of tasks output_file: Optional path to save classifications - - Returns: + + Returns + ------- Tasks with added classification metadata """ classified_tasks = {} total_tasks = sum(len(task_list) for task_list in tasks.values()) processed = 0 - + log.info(f"Classifying {total_tasks} tasks from {len(tasks)} capabilities...") - + for capability_name, task_list in tasks.items(): - log.info(f"Processing capability: {capability_name} ({len(task_list)} tasks)") + log.info( + f"Processing capability: {capability_name} ({len(task_list)} tasks)" + ) classified_tasks[capability_name] = [] - + for task in task_list: processed += 1 - log.info(f" [{processed}/{total_tasks}] Classifying task {task.get('task_id', '?')}") - + log.info( + f" [{processed}/{total_tasks}] Classifying task {task.get('task_id', '?')}" + ) + classification = self.classify_task(task) - + # Add classification to task task_with_classification = task.copy() task_with_classification["classification"] = classification classified_tasks[capability_name].append(task_with_classification) - + # Log key insights log.info( f" Tool-solvable: {classification.get('tool_solvable')}, " f"Answer type: {classification.get('answer_type')}, " f"Recommend: {classification.get('recommend_for_evaluation')}" ) - + # Save if output file specified if output_file: output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, 'w', encoding='utf-8') as f: + with open(output_file, "w", encoding="utf-8") as f: json.dump(classified_tasks, f, indent=2, ensure_ascii=False) log.info(f"Saved classified tasks to {output_file}") - + return classified_tasks - + def filter_recommended_tasks( self, classified_tasks: Dict[str, List[Dict[str, Any]]], require_tool_solvable: bool = True, exclude_no_answer: bool = True, - exclude_problematic: bool = True + exclude_problematic: bool = True, ) -> Dict[str, List[Dict[str, Any]]]: """Filter tasks based on classification criteria. - + Args: classified_tasks: Tasks with classification metadata require_tool_solvable: Only include tool-solvable tasks exclude_no_answer: Exclude tasks with answer_type="no_answer" exclude_problematic: Exclude tasks with problematic ground truth - - Returns: + + Returns + ------- Filtered task dictionary """ filtered = {} total_before = 0 total_after = 0 - + for capability_name, task_list in classified_tasks.items(): total_before += len(task_list) filtered_list = [] - + for task in task_list: classification = task.get("classification", {}) - + # Check filters if require_tool_solvable and not classification.get("tool_solvable"): continue - if exclude_no_answer and classification.get("answer_type") == "no_answer": + if ( + exclude_no_answer + and classification.get("answer_type") == "no_answer" + ): continue - if exclude_problematic and classification.get("ground_truth_quality") == "problematic": + if ( + exclude_problematic + and classification.get("ground_truth_quality") == "problematic" + ): continue if not classification.get("recommend_for_evaluation"): continue - + filtered_list.append(task) - + if filtered_list: filtered[capability_name] = filtered_list total_after += len(filtered_list) - - log.info(f"\nFiltering results:") - log.info(f" Before: {total_before} tasks across {len(classified_tasks)} capabilities") + + log.info("\nFiltering results:") + log.info( + f" Before: {total_before} tasks across {len(classified_tasks)} capabilities" + ) log.info(f" After: {total_after} tasks across {len(filtered)} capabilities") log.info(f" Removed: {total_before - total_after} tasks") - + return filtered def load_math_tasks(math_dir: Path = Path("math")) -> Dict[str, List[Dict[str, Any]]]: """Load all math tasks from capability directories. - + Args: math_dir: Base directory containing math capability folders - - Returns: + + Returns + ------- Dictionary mapping capability names to list of tasks """ all_tasks = {} - + if not math_dir.exists(): log.error(f"Math directory not found: {math_dir}") return all_tasks - + capability_dirs = sorted([d for d in math_dir.iterdir() if d.is_dir()]) log.info(f"Found {len(capability_dirs)} capability directories") - + for cap_dir in capability_dirs: capability_name = cap_dir.name cap_file = cap_dir / "capability.json" - + if not cap_file.exists(): continue - + try: - with open(cap_file, 'r', encoding='utf-8') as f: + with open(cap_file, "r", encoding="utf-8") as f: cap_data = json.load(f) - + # Extract tasks from capability_failed_data (previously failed tasks) failed_tasks = cap_data.get("capability_failed_data", []) - + if failed_tasks: tasks = [] for task_data in failed_tasks: verification = task_data.get("verification", {}) verdict = verification.get("verdict", "") - + # Include all tasks (not just verdict='no') for comprehensive classification - tasks.append({ - "task_id": task_data.get("id", ""), - "problem": task_data.get("problem", ""), - "answer": task_data.get("answer", ""), - "reasoning": task_data.get("reasoning", ""), - "verification": verification, - "original_verdict": verdict - }) - + tasks.append( + { + "task_id": task_data.get("id", ""), + "problem": task_data.get("problem", ""), + "answer": task_data.get("answer", ""), + "reasoning": task_data.get("reasoning", ""), + "verification": verification, + "original_verdict": verdict, + } + ) + if tasks: all_tasks[capability_name] = tasks log.info(f"Loaded {len(tasks)} tasks from {capability_name}") - + except Exception as e: log.error(f"Error loading {capability_name}: {e}") - + return all_tasks def stratified_classify_until_target( tasks_by_capability: Dict[str, List[Dict[str, Any]]], target_count: int, - classifier: 'TaskClassifier', - seed: int = 42 + classifier: "TaskClassifier", + seed: int = 42, ) -> Dict[str, List[Dict[str, Any]]]: """Classify tasks iteratively until we have target_count recommended tasks. - + Uses stratified sampling across capabilities, classifying one task at a time from each capability in round-robin fashion until we have enough good tasks. - + Args: tasks_by_capability: All available tasks grouped by capability target_count: Number of high-quality tasks needed classifier: TaskClassifier instance seed: Random seed for reproducibility - - Returns: + + Returns + ------- Dictionary of classified tasks that were recommended """ random.seed(seed) - + # Shuffle tasks within each capability for random sampling shuffled_tasks = {} for cap_name, task_list in tasks_by_capability.items(): shuffled = task_list.copy() random.shuffle(shuffled) shuffled_tasks[cap_name] = shuffled - + # Track which index we're at for each capability - capability_indices = {cap: 0 for cap in shuffled_tasks.keys()} - + capability_indices = {cap: 0 for cap in shuffled_tasks} + # Store recommended tasks recommended_tasks = {} total_recommended = 0 total_classified = 0 - + capabilities = list(shuffled_tasks.keys()) if not capabilities: log.error("No capabilities available") return {} - - log.info(f"Starting stratified classification to find {target_count} high-quality tasks") + + log.info( + f"Starting stratified classification to find {target_count} high-quality tasks" + ) log.info(f"Available capabilities: {len(capabilities)}") - log.info(f"Total tasks available: {sum(len(tasks) for tasks in shuffled_tasks.values())}") - + log.info( + f"Total tasks available: {sum(len(tasks) for tasks in shuffled_tasks.values())}" + ) + # Round-robin through capabilities until we have enough recommended tasks capability_idx = 0 stuck_counter = 0 max_stuck_iterations = len(capabilities) * 2 # Safety check - + while total_recommended < target_count: # Check if we've exhausted all capabilities if stuck_counter >= max_stuck_iterations: @@ -386,47 +410,47 @@ def stratified_classify_until_target( f"classifying {total_classified} tasks. All capabilities may be exhausted." ) break - + # Get current capability current_cap = capabilities[capability_idx] cap_tasks = shuffled_tasks[current_cap] cap_idx = capability_indices[current_cap] - + # Check if this capability is exhausted if cap_idx >= len(cap_tasks): # Move to next capability capability_idx = (capability_idx + 1) % len(capabilities) stuck_counter += 1 continue - + # Get next task from this capability task = cap_tasks[cap_idx] capability_indices[current_cap] += 1 total_classified += 1 - + log.info( f"[{total_classified}] [{total_recommended}/{target_count} found] " f"Classifying from {current_cap}: task {task.get('task_id', '?')}" ) - + # Classify the task classification = classifier.classify_task(task) - + # Check if recommended is_recommended = classification.get("recommend_for_evaluation", False) is_tool_solvable = classification.get("tool_solvable", False) answer_type = classification.get("answer_type", "unknown") - + log.info( f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " f"Answer type={answer_type}" ) - + # Add classification to task task_with_classification = task.copy() task_with_classification["classification"] = classification task_with_classification["capability_name"] = current_cap - + # If recommended, add to results if is_recommended: if current_cap not in recommended_tasks: @@ -436,19 +460,19 @@ def stratified_classify_until_target( stuck_counter = 0 # Reset stuck counter on success log.info(f" ✓ Added to dataset ({total_recommended}/{target_count})") else: - log.info(f" ✗ Not recommended, continuing search") - + log.info(" ✗ Not recommended, continuing search") + # Move to next capability for round-robin capability_idx = (capability_idx + 1) % len(capabilities) - - log.info(f"\n{'='*70}") - log.info(f"Classification complete!") + + log.info(f"\n{'=' * 70}") + log.info("Classification complete!") log.info(f" Tasks classified: {total_classified}") log.info(f" Tasks recommended: {total_recommended}") - log.info(f" Success rate: {total_recommended/total_classified*100:.1f}%") + log.info(f" Success rate: {total_recommended / total_classified * 100:.1f}%") log.info(f" Capabilities represented: {len(recommended_tasks)}") - log.info(f"{'='*70}\n") - + log.info(f"{'=' * 70}\n") + return recommended_tasks @@ -456,98 +480,95 @@ def main(): """Main execution function.""" parser = argparse.ArgumentParser( description="Classify math tasks for tool-assisted evaluation", - formatter_class=argparse.RawDescriptionHelpFormatter + formatter_class=argparse.RawDescriptionHelpFormatter, ) parser.add_argument( "--math-dir", type=Path, default=Path("math"), - help="Directory containing math capability folders" + help="Directory containing math capability folders", ) parser.add_argument( "--output", type=Path, default=Path("classified_tasks.json"), - help="Output file for classified tasks" + help="Output file for classified tasks", ) parser.add_argument( "--model", default="gemini-3-flash-preview", - help="Model to use for classification" + help="Model to use for classification", ) parser.add_argument( "--num-tasks", type=int, default=50, - help="Number of high-quality tasks to find (default: 50)" + help="Number of high-quality tasks to find (default: 50)", ) parser.add_argument( - "--seed", - type=int, - default=42, - help="Random seed for sampling (default: 42)" + "--seed", type=int, default=42, help="Random seed for sampling (default: 42)" ) parser.add_argument( "--filter-only", action="store_true", - help="Only filter pre-classified tasks (skip classification step)" + help="Only filter pre-classified tasks (skip classification step)", ) parser.add_argument( "--input-classified", type=Path, - help="Input file with pre-classified tasks (for --filter-only mode)" + help="Input file with pre-classified tasks (for --filter-only mode)", ) - + args = parser.parse_args() - + if args.filter_only: if not args.input_classified or not args.input_classified.exists(): log.error("Must provide --input-classified file in --filter-only mode") return - - with open(args.input_classified, 'r', encoding='utf-8') as f: + + with open(args.input_classified, "r", encoding="utf-8") as f: classified_tasks = json.load(f) log.info(f"Loaded pre-classified tasks from {args.input_classified}") - + # Filter the pre-classified tasks classifier = TaskClassifier(model_name=args.model) filtered = classifier.filter_recommended_tasks(classified_tasks) else: # Load all available tasks all_tasks = load_math_tasks(args.math_dir) - + if not all_tasks: log.error("No tasks loaded") return - + total_available = sum(len(tasks) for tasks in all_tasks.values()) log.info(f"Loaded {total_available} tasks from {len(all_tasks)} capabilities") - + # Use stratified sampling with iterative classification classifier = TaskClassifier(model_name=args.model) filtered = stratified_classify_until_target( tasks_by_capability=all_tasks, target_count=args.num_tasks, classifier=classifier, - seed=args.seed + seed=args.seed, ) - + if not filtered: log.error("No recommended tasks found") return - + # Save filtered/recommended tasks filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" args.output.parent.mkdir(parents=True, exist_ok=True) - with open(filtered_file, 'w', encoding='utf-8') as f: + with open(filtered_file, "w", encoding="utf-8") as f: json.dump(filtered, f, indent=2, ensure_ascii=False) log.info(f"Saved recommended tasks to {filtered_file}") - + # Print summary statistics - print("\n" + "="*70) + print("\n" + "=" * 70) print("CLASSIFICATION SUMMARY") - print("="*70) - + print("=" * 70) + tool_solvable = 0 not_tool_solvable = 0 by_answer_type = {"quantitative": 0, "qualitative": 0, "mixed": 0, "no_answer": 0} @@ -555,7 +576,7 @@ def main(): by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} by_capability = {} recommended = 0 - + for cap_name, task_list in filtered.items(): by_capability[cap_name] = len(task_list) for task in task_list: @@ -564,49 +585,53 @@ def main(): tool_solvable += 1 else: not_tool_solvable += 1 - + answer_type = c.get("answer_type", "unknown") by_answer_type[answer_type] = by_answer_type.get(answer_type, 0) + 1 - + quality = c.get("ground_truth_quality", "unknown") by_quality[quality] = by_quality.get(quality, 0) + 1 - + difficulty = c.get("problem_difficulty", "unknown") by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 - + if c.get("recommend_for_evaluation"): recommended += 1 - + total = tool_solvable + not_tool_solvable if total == 0: print("No tasks found") return - + print(f"\nTotal tasks: {total}") - print(f"Tool-solvable: {tool_solvable} ({tool_solvable/total*100:.1f}%)") - print(f"Not tool-solvable: {not_tool_solvable} ({not_tool_solvable/total*100:.1f}%)") - - print(f"\nBy capability:") + print(f"Tool-solvable: {tool_solvable} ({tool_solvable / total * 100:.1f}%)") + print( + f"Not tool-solvable: {not_tool_solvable} ({not_tool_solvable / total * 100:.1f}%)" + ) + + print("\nBy capability:") for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True): print(f" {cap}: {count}") - - print(f"\nAnswer types:") + + print("\nAnswer types:") for atype, count in sorted(by_answer_type.items()): if count > 0: - print(f" {atype}: {count} ({count/total*100:.1f}%)") - - print(f"\nGround truth quality:") + print(f" {atype}: {count} ({count / total * 100:.1f}%)") + + print("\nGround truth quality:") for quality, count in sorted(by_quality.items()): if count > 0: - print(f" {quality}: {count} ({count/total*100:.1f}%)") - - print(f"\nProblem difficulty:") + print(f" {quality}: {count} ({count / total * 100:.1f}%)") + + print("\nProblem difficulty:") for diff, count in sorted(by_difficulty.items()): if count > 0: - print(f" {diff}: {count} ({count/total*100:.1f}%)") - - print(f"\nRecommended for evaluation: {recommended} ({recommended/total*100:.1f}%)") - print("="*70) + print(f" {diff}: {count} ({count / total * 100:.1f}%)") + + print( + f"\nRecommended for evaluation: {recommended} ({recommended / total * 100:.1f}%)" + ) + print("=" * 70) if __name__ == "__main__": diff --git a/src/scripts/classify_xfinbench_tasks.py b/src/scripts/classify_xfinbench_tasks.py index 534c7b39..5288f0f0 100644 --- a/src/scripts/classify_xfinbench_tasks.py +++ b/src/scripts/classify_xfinbench_tasks.py @@ -16,17 +16,17 @@ import os import random from pathlib import Path -from typing import Dict, List, Any, Optional +from typing import Any, Dict, List from dotenv import load_dotenv from google import genai from google.genai.types import GenerateContentConfig + load_dotenv() logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) log = logging.getLogger(__name__) @@ -109,52 +109,52 @@ class XFinBenchClassifier: """Classifies XFinBench tasks for quality and tool-solvability.""" - + def __init__(self, model_name: str = "gemini-3-flash-preview"): """Initialize classifier with specified model. - + Args: model_name: Google AI model to use for classification """ self.model_name = model_name self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - + def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: """Classify a single task. - + Args: task_data: Task dictionary from XFinBench CSV - - Returns: + + Returns + ------- Classification result dictionary """ # Format choices section for MCQ choices_section = "" if task_data.get("task") == "mcq" and task_data.get("choice"): choices_section = f"Choices: {task_data['choice']}" - + prompt = XFINBENCH_CLASSIFICATION_PROMPT.format( task_id=task_data.get("id", ""), task_type=task_data.get("task", ""), question=task_data.get("question", ""), choices_section=choices_section, answer=task_data.get("ground_truth", ""), - fin_capability=task_data.get("fin_capability", "") + fin_capability=task_data.get("fin_capability", ""), ) - + try: response = self.client.models.generate_content( model=self.model_name, contents=prompt, config=GenerateContentConfig( - temperature=0.1, - response_mime_type="application/json" - ) + temperature=0.1, response_mime_type="application/json" + ), ) - + classification = json.loads(response.text) return classification - + except Exception as e: log.error(f"Error classifying task {task_data.get('id', 'unknown')}: {e}") # Return default classification on error @@ -169,96 +169,104 @@ def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: "problem_difficulty": "unknown", "tool_benefit": "none", "recommend_for_evaluation": False, - "recommendation_reasoning": "Classification failed" + "recommendation_reasoning": "Classification failed", } - + def filter_recommended_tasks( self, classified_tasks: List[Dict[str, Any]], require_tool_solvable: bool = True, exclude_vision: bool = True, - min_tool_benefit: str = "low" + min_tool_benefit: str = "low", ) -> List[Dict[str, Any]]: """Filter tasks based on classification criteria. - + Args: classified_tasks: Tasks with classification metadata require_tool_solvable: Only include tool-solvable tasks exclude_vision: Exclude tasks requiring vision min_tool_benefit: Minimum tool benefit level ("none", "low", "medium", "high") - - Returns: + + Returns + ------- Filtered task list """ benefit_order = {"none": 0, "low": 1, "medium": 2, "high": 3} min_benefit_level = benefit_order.get(min_tool_benefit, 1) - + filtered = [] - + for task in classified_tasks: classification = task.get("classification", {}) - + # Check filters if require_tool_solvable and not classification.get("tool_solvable"): continue if exclude_vision and classification.get("requires_vision"): continue - + tool_benefit = classification.get("tool_benefit", "none") if benefit_order.get(tool_benefit, 0) < min_benefit_level: continue - + if not classification.get("recommend_for_evaluation"): continue - + filtered.append(task) - + return filtered def load_xfinbench_csv(csv_path: Path) -> List[Dict[str, Any]]: """Load XFinBench validation set from CSV. - + Args: csv_path: Path to validation_set.csv - - Returns: + + Returns + ------- List of task dictionaries """ tasks = [] - - with open(csv_path, 'r', encoding='utf-8') as f: + + with open(csv_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: # Skip rows with missing critical data - if not row.get('question') or not row.get('id'): + if not row.get("question") or not row.get("id"): continue - + # Convert ground_truth to appropriate type - ground_truth = row.get('ground_truth', '') - if ground_truth and ground_truth != 'nan': + ground_truth = row.get("ground_truth", "") + if ground_truth and ground_truth != "nan": # Try to convert to float for numerical tasks try: ground_truth = float(ground_truth) # Convert 0.0/1.0 to int for bool tasks - if row.get('task') == 'bool' and ground_truth in [0.0, 1.0]: + if row.get("task") == "bool" and ground_truth in [0.0, 1.0]: ground_truth = int(ground_truth) except ValueError: pass # Keep as string else: ground_truth = None - - tasks.append({ - "id": row.get('id', ''), - "task": row.get('task', ''), - "question": row.get('question', ''), - "choice": row.get('choice', '') if row.get('choice') != 'nan' else None, - "ground_truth": ground_truth, - "figure": row.get('figure', '') if row.get('figure') != 'nan' else None, - "fin_capability": row.get('fin_capability', ''), - "gold_fin_term_id": row.get('gold_fin_term_id', '') - }) - + + tasks.append( + { + "id": row.get("id", ""), + "task": row.get("task", ""), + "question": row.get("question", ""), + "choice": row.get("choice", "") + if row.get("choice") != "nan" + else None, + "ground_truth": ground_truth, + "figure": row.get("figure", "") + if row.get("figure") != "nan" + else None, + "fin_capability": row.get("fin_capability", ""), + "gold_fin_term_id": row.get("gold_fin_term_id", ""), + } + ) + log.info(f"Loaded {len(tasks)} tasks from {csv_path}") return tasks @@ -266,74 +274,75 @@ def load_xfinbench_csv(csv_path: Path) -> List[Dict[str, Any]]: def stratified_classify_until_target( tasks: List[Dict[str, Any]], target_count: int, - classifier: 'XFinBenchClassifier', + classifier: "XFinBenchClassifier", seed: int = 42, - min_tool_benefit: str = "low" + min_tool_benefit: str = "low", ) -> List[Dict[str, Any]]: """Classify tasks iteratively until we have target_count recommended tasks. - + Uses stratified sampling across task types and capabilities. - + Args: tasks: All available tasks target_count: Number of high-quality tasks needed classifier: XFinBenchClassifier instance seed: Random seed for reproducibility min_tool_benefit: Minimum tool benefit level - - Returns: + + Returns + ------- List of classified and recommended tasks """ random.seed(seed) - + # Shuffle tasks for random sampling shuffled_tasks = tasks.copy() random.shuffle(shuffled_tasks) - + # Store recommended tasks recommended_tasks = [] total_classified = 0 - + log.info(f"Starting classification to find {target_count} high-quality tasks") log.info(f"Total tasks available: {len(shuffled_tasks)}") - + # Task type distribution for reference task_types = {} for task in tasks: - task_type = task['task'] + task_type = task["task"] task_types[task_type] = task_types.get(task_type, 0) + 1 log.info(f"Task type distribution: {task_types}") - + # Classify tasks until we have enough recommended ones for i, task in enumerate(shuffled_tasks): if len(recommended_tasks) >= target_count: break - + total_classified += 1 - + log.info( f"[{total_classified}] [{len(recommended_tasks)}/{target_count} found] " f"Classifying {task['task']} task: {task['id']}" ) - + # Classify the task classification = classifier.classify_task(task) - + # Check if recommended is_recommended = classification.get("recommend_for_evaluation", False) is_tool_solvable = classification.get("tool_solvable", False) requires_vision = classification.get("requires_vision", False) tool_benefit = classification.get("tool_benefit", "none") - + log.info( f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " f"Vision={requires_vision}, Benefit={tool_benefit}" ) - + # Add classification to task task_with_classification = task.copy() task_with_classification["classification"] = classification - + # If recommended, add to results if is_recommended and not requires_vision: recommended_tasks.append(task_with_classification) @@ -341,31 +350,34 @@ def stratified_classify_until_target( else: reason = "requires vision" if requires_vision else "not recommended" log.info(f" ✗ Skipped: {reason}") - - log.info(f"\n{'='*70}") - log.info(f"Classification complete!") + + log.info(f"\n{'=' * 70}") + log.info("Classification complete!") log.info(f" Tasks classified: {total_classified}") log.info(f" Tasks recommended: {len(recommended_tasks)}") if total_classified > 0: - log.info(f" Success rate: {len(recommended_tasks)/total_classified*100:.1f}%") - log.info(f"{'='*70}\n") - + log.info( + f" Success rate: {len(recommended_tasks) / total_classified * 100:.1f}%" + ) + log.info(f"{'=' * 70}\n") + return recommended_tasks def convert_to_experiment_format( - classified_tasks: List[Dict[str, Any]] + classified_tasks: List[Dict[str, Any]], ) -> List[Dict[str, Any]]: """Convert XFinBench tasks to experiment runner format. - + Args: classified_tasks: Tasks with classification metadata - - Returns: + + Returns + ------- Tasks in format expected by run_experiments.py """ experiment_tasks = [] - + for task in classified_tasks: # Format for experiment runner formatted = { @@ -380,14 +392,13 @@ def convert_to_experiment_format( "figure": task.get("figure"), "gold_fin_term_id": task.get("gold_fin_term_id"), # Classification metadata - "classification": task.get("classification", {}) + "classification": task.get("classification", {}), } - + # Add task type instructions to clarify expected format if task["task"] == "bool": formatted["problem"] = ( - f"{task['question']}\n\n" - f"Answer with 1 for True or 0 for False." + f"{task['question']}\n\nAnswer with 1 for True or 0 for False." ) elif task["task"] == "mcq" and task.get("choice"): formatted["problem"] = ( @@ -395,39 +406,40 @@ def convert_to_experiment_format( f"Choices: {task['choice']}\n\n" f"Answer with the letter of the correct choice (A, B, C, D, etc.)." ) - + experiment_tasks.append(formatted) - + return experiment_tasks def classify_all_tasks( tasks: List[Dict[str, Any]], - classifier: 'XFinBenchClassifier', + classifier: "XFinBenchClassifier", output_file: Path, - save_interval: int = 10 + save_interval: int = 10, ) -> List[Dict[str, Any]]: """Classify all tasks in the dataset with progressive saving and resume capability. - + Args: tasks: All available tasks classifier: XFinBenchClassifier instance output_file: Path to save classified tasks (for progressive saving) save_interval: Save after every N classifications (default: 10) - - Returns: + + Returns + ------- List of all tasks with classification metadata """ # Check if we have existing classifications to resume from classified_tasks = [] classified_ids = set() - + if output_file.exists(): log.info(f"Found existing classification file: {output_file}") try: - with open(output_file, 'r', encoding='utf-8') as f: + with open(output_file, "r", encoding="utf-8") as f: classified_tasks = json.load(f) - classified_ids = {t['id'] for t in classified_tasks} + classified_ids = {t["id"] for t in classified_tasks} log.info(f"Loaded {len(classified_tasks)} already-classified tasks") log.info(f"Resuming classification from task {len(classified_tasks) + 1}") except Exception as e: @@ -435,59 +447,64 @@ def classify_all_tasks( log.info("Starting fresh classification") classified_tasks = [] classified_ids = set() - + total = len(tasks) - remaining = [t for t in tasks if t['id'] not in classified_ids] - + remaining = [t for t in tasks if t["id"] not in classified_ids] + log.info(f"Total tasks: {total}") log.info(f"Already classified: {len(classified_ids)}") log.info(f"Remaining to classify: {len(remaining)}") - + if not remaining: log.info("All tasks already classified!") return classified_tasks - + # Classify remaining tasks for i, task in enumerate(remaining, 1): overall_idx = len(classified_tasks) + 1 - log.info(f"[{overall_idx}/{total}] Classifying {task['task']} task: {task['id']}") - + log.info( + f"[{overall_idx}/{total}] Classifying {task['task']} task: {task['id']}" + ) + # Classify the task classification = classifier.classify_task(task) - + # Check if recommended is_recommended = classification.get("recommend_for_evaluation", False) is_tool_solvable = classification.get("tool_solvable", False) requires_vision = classification.get("requires_vision", False) tool_benefit = classification.get("tool_benefit", "none") - + log.info( f" Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " f"Vision={requires_vision}, Benefit={tool_benefit}" ) - + # Add classification to task task_with_classification = task.copy() task_with_classification["classification"] = classification classified_tasks.append(task_with_classification) - + # Progressive save every N tasks if i % save_interval == 0 or i == len(remaining): output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, 'w', encoding='utf-8') as f: + with open(output_file, "w", encoding="utf-8") as f: json.dump(classified_tasks, f, indent=2, ensure_ascii=False) log.info(f" 💾 Progress saved: {overall_idx}/{total} tasks") - - log.info(f"\n{'='*70}") - log.info(f"Classification complete!") + + log.info(f"\n{'=' * 70}") + log.info("Classification complete!") log.info(f" Total tasks classified: {total}") - recommended_count = sum(1 for t in classified_tasks - if t['classification'].get('recommend_for_evaluation', False)) + recommended_count = sum( + 1 + for t in classified_tasks + if t["classification"].get("recommend_for_evaluation", False) + ) log.info(f" Tasks recommended: {recommended_count}") if total > 0: - log.info(f" Success rate: {recommended_count/total*100:.1f}%") - log.info(f"{'='*70}\n") - + log.info(f" Success rate: {recommended_count / total * 100:.1f}%") + log.info(f"{'=' * 70}\n") + return classified_tasks @@ -495,77 +512,74 @@ def main(): """Main execution function.""" parser = argparse.ArgumentParser( description="Classify XFinBench tasks for tool-assisted evaluation", - formatter_class=argparse.RawDescriptionHelpFormatter + formatter_class=argparse.RawDescriptionHelpFormatter, ) parser.add_argument( "--csv-file", type=Path, default=Path("validation_set.csv"), - help="Path to XFinBench validation CSV file" + help="Path to XFinBench validation CSV file", ) parser.add_argument( "--output", type=Path, default=Path("xfinbench_classified.json"), - help="Output file for all classified tasks" + help="Output file for all classified tasks", ) parser.add_argument( "--model", default="gemini-3-flash-preview", - help="Model to use for classification" + help="Model to use for classification", ) parser.add_argument( "--num-tasks", type=int, default=100, - help="Number of filtered tasks to output (default: 100)" + help="Number of filtered tasks to output (default: 100)", ) parser.add_argument( - "--seed", - type=int, - default=42, - help="Random seed for sampling (default: 42)" + "--seed", type=int, default=42, help="Random seed for sampling (default: 42)" ) parser.add_argument( "--min-tool-benefit", choices=["low", "medium", "high"], default="low", - help="Minimum tool benefit level for filtering (default: low)" + help="Minimum tool benefit level for filtering (default: low)", ) parser.add_argument( "--classify-all", action="store_true", - help="Classify all 1000 tasks (default: True). Use --no-classify-all for iterative mode." + help="Classify all 1000 tasks (default: True). Use --no-classify-all for iterative mode.", ) parser.add_argument( "--no-classify-all", action="store_true", - help="Use iterative classification (stop after finding num-tasks)" + help="Use iterative classification (stop after finding num-tasks)", ) parser.add_argument( "--save-interval", type=int, default=10, - help="Save progress after every N classifications (default: 10)" + help="Save progress after every N classifications (default: 10)", ) parser.add_argument( "--resume", action="store_true", default=True, - help="Resume from existing classification file if present (default: True)" + help="Resume from existing classification file if present (default: True)", ) - + args = parser.parse_args() - + # Load XFinBench tasks all_tasks = load_xfinbench_csv(args.csv_file) - + if not all_tasks: log.error("No tasks loaded") return - + classifier = XFinBenchClassifier(model_name=args.model) - + # Classify all tasks or use iterative mode if args.no_classify_all: # Iterative mode: stop after finding target @@ -575,7 +589,7 @@ def main(): target_count=args.num_tasks, classifier=classifier, seed=args.seed, - min_tool_benefit=args.min_tool_benefit + min_tool_benefit=args.min_tool_benefit, ) recommended_tasks = classified_tasks else: @@ -585,94 +599,103 @@ def main(): tasks=all_tasks, classifier=classifier, output_file=args.output, - save_interval=args.save_interval + save_interval=args.save_interval, ) - + # Filter to get recommended tasks recommended_tasks = [ - t for t in classified_tasks - if t['classification'].get('recommend_for_evaluation', False) - and not t['classification'].get('requires_vision', False) + t + for t in classified_tasks + if t["classification"].get("recommend_for_evaluation", False) + and not t["classification"].get("requires_vision", False) ] - + log.info(f"Filtered to {len(recommended_tasks)} recommended tasks)") - + if not classified_tasks: log.error("No tasks classified") return - + # Save all classified tasks (final save, may already be saved progressively) args.output.parent.mkdir(parents=True, exist_ok=True) - with open(args.output, 'w', encoding='utf-8') as f: + with open(args.output, "w", encoding="utf-8") as f: json.dump(classified_tasks, f, indent=2, ensure_ascii=False) log.info(f"✓ Final save: {len(classified_tasks)} classified tasks to {args.output}") - + # Convert recommended tasks to experiment format and save # Limit to num_tasks if specified - tasks_to_convert = recommended_tasks[:args.num_tasks] if args.num_tasks else recommended_tasks + tasks_to_convert = ( + recommended_tasks[: args.num_tasks] if args.num_tasks else recommended_tasks + ) experiment_tasks = convert_to_experiment_format(tasks_to_convert) filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" - with open(filtered_file, 'w', encoding='utf-8') as f: + with open(filtered_file, "w", encoding="utf-8") as f: json.dump(experiment_tasks, f, indent=2, ensure_ascii=False) - log.info(f"✓ Saved {len(experiment_tasks)} experiment-ready tasks to {filtered_file}") - + log.info( + f"✓ Saved {len(experiment_tasks)} experiment-ready tasks to {filtered_file}" + ) + # Print summary statistics - print("\n" + "="*70) + print("\n" + "=" * 70) print("CLASSIFICATION SUMMARY") - print("="*70) - + print("=" * 70) + by_task_type = {} by_capability = {} by_tool_benefit = {"high": 0, "medium": 0, "low": 0, "none": 0} by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} by_verification = {"direct": 0, "numeric_check": 0, "hard": 0} - + for task in recommended_tasks: task_type = task["task"] by_task_type[task_type] = by_task_type.get(task_type, 0) + 1 - + capability = task["fin_capability"] by_capability[capability] = by_capability.get(capability, 0) + 1 - + c = task.get("classification", {}) - + benefit = c.get("tool_benefit", "none") by_tool_benefit[benefit] += 1 - + difficulty = c.get("problem_difficulty", "unknown") by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 - + verification = c.get("verification_feasibility", "unknown") by_verification[verification] = by_verification.get(verification, 0) + 1 - + total = len(recommended_tasks) - + print(f"\nTotal recommended tasks: {total}") - - print(f"\nBy task type:") + + print("\nBy task type:") for ttype, count in sorted(by_task_type.items(), key=lambda x: x[1], reverse=True): - print(f" {ttype}: {count} ({count/total*100:.1f}%)") - - print(f"\nBy financial capability:") - for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True)[:10]: - print(f" {cap}: {count} ({count/total*100:.1f}%)") - - print(f"\nBy tool benefit:") - for benefit, count in sorted(by_tool_benefit.items(), key=lambda x: x[1], reverse=True): + print(f" {ttype}: {count} ({count / total * 100:.1f}%)") + + print("\nBy financial capability:") + for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True)[ + :10 + ]: + print(f" {cap}: {count} ({count / total * 100:.1f}%)") + + print("\nBy tool benefit:") + for benefit, count in sorted( + by_tool_benefit.items(), key=lambda x: x[1], reverse=True + ): if count > 0: - print(f" {benefit}: {count} ({count/total*100:.1f}%)") - - print(f"\nBy problem difficulty:") + print(f" {benefit}: {count} ({count / total * 100:.1f}%)") + + print("\nBy problem difficulty:") for diff, count in sorted(by_difficulty.items()): if count > 0: - print(f" {diff}: {count} ({count/total*100:.1f}%)") - - print(f"\nBy verification feasibility:") + print(f" {diff}: {count} ({count / total * 100:.1f}%)") + + print("\nBy verification feasibility:") for verif, count in sorted(by_verification.items()): if count > 0: - print(f" {verif}: {count} ({count/total*100:.1f}%)") - - print("="*70) + print(f" {verif}: {count} ({count / total * 100:.1f}%)") + + print("=" * 70) if __name__ == "__main__": diff --git a/src/scripts/generate_filtered_dataset.py b/src/scripts/generate_filtered_dataset.py index c90156d6..c325aaea 100644 --- a/src/scripts/generate_filtered_dataset.py +++ b/src/scripts/generate_filtered_dataset.py @@ -8,22 +8,22 @@ import argparse import json +import logging import random -from pathlib import Path -from typing import Dict, List, Any from collections import defaultdict -import logging +from pathlib import Path +from typing import Any, Dict, List + logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s' + level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" ) log = logging.getLogger(__name__) def load_classified_tasks(filepath: Path) -> Dict[str, List[Dict[str, Any]]]: """Load classified tasks from JSON file.""" - with open(filepath, 'r', encoding='utf-8') as f: + with open(filepath, "r", encoding="utf-8") as f: return json.load(f) @@ -31,21 +31,22 @@ def generate_filtered_dataset( classified_tasks: Dict[str, List[Dict[str, Any]]], num_tasks: int = 50, stratify_by_capability: bool = True, - seed: int = 42 + seed: int = 42, ) -> List[Dict[str, Any]]: """Generate filtered dataset with stratified sampling. - + Args: classified_tasks: Dictionary of classified tasks by capability num_tasks: Total number of tasks to sample stratify_by_capability: Whether to sample proportionally from each capability seed: Random seed for reproducibility - - Returns: + + Returns + ------- List of sampled tasks with full metadata """ random.seed(seed) - + # Flatten tasks with capability metadata all_tasks = [] for capability_name, task_list in classified_tasks.items(): @@ -53,28 +54,30 @@ def generate_filtered_dataset( task_copy = task.copy() task_copy["capability_name"] = capability_name all_tasks.append(task_copy) - + if not all_tasks: log.error("No tasks available for sampling") return [] - - log.info(f"Total tasks available: {len(all_tasks)} from {len(classified_tasks)} capabilities") - + + log.info( + f"Total tasks available: {len(all_tasks)} from {len(classified_tasks)} capabilities" + ) + # Sample tasks if stratify_by_capability: # Calculate tasks per capability capabilities = list(classified_tasks.keys()) tasks_per_cap = max(1, num_tasks // len(capabilities)) remainder = num_tasks % len(capabilities) - + sampled = [] capability_counts = defaultdict(int) - + for i, capability_name in enumerate(capabilities): # Allocate extra tasks to first capabilities to handle remainder cap_target = tasks_per_cap + (1 if i < remainder else 0) cap_tasks = classified_tasks[capability_name] - + if len(cap_tasks) < cap_target: log.warning( f"Capability {capability_name} has only {len(cap_tasks)} tasks, " @@ -83,13 +86,13 @@ def generate_filtered_dataset( cap_sample = cap_tasks else: cap_sample = random.sample(cap_tasks, cap_target) - + for task in cap_sample: task_copy = task.copy() task_copy["capability_name"] = capability_name sampled.append(task_copy) capability_counts[capability_name] += 1 - + log.info("Stratified sampling complete:") for cap, count in sorted(capability_counts.items()): log.info(f" {cap}: {count} tasks") @@ -103,57 +106,61 @@ def generate_filtered_dataset( else: sampled = random.sample(all_tasks, num_tasks) log.info(f"Random sampling: selected {len(sampled)} tasks") - + return sampled def enrich_metadata(tasks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Add derived metadata fields to tasks. - + Args: tasks: List of tasks with classification - - Returns: + + Returns + ------- Tasks with enriched metadata """ enriched = [] - + for i, task in enumerate(tasks): enriched_task = task.copy() classification = task.get("classification", {}) - + # Add experimental metadata enriched_task["dataset_metadata"] = { "dataset_id": i + 1, "source_capability": task.get("capability_name", "unknown"), "original_task_id": task.get("task_id", "unknown"), - # Classification-derived fields "tool_solvable": classification.get("tool_solvable", False), "answer_type": classification.get("answer_type", "unknown"), - "ground_truth_quality": classification.get("ground_truth_quality", "unknown"), + "ground_truth_quality": classification.get( + "ground_truth_quality", "unknown" + ), "format_clarity": classification.get("format_clarity", "unknown"), - "evaluation_difficulty": classification.get("evaluation_difficulty", "unknown"), + "evaluation_difficulty": classification.get( + "evaluation_difficulty", "unknown" + ), "problem_difficulty": classification.get("problem_difficulty", "unknown"), - # For analysis "recommended": classification.get("recommend_for_evaluation", False), "has_reasoning": bool(task.get("reasoning")), "original_verdict": task.get("original_verdict", "unknown"), } - + enriched.append(enriched_task) - + return enriched def analyze_dataset(tasks: List[Dict[str, Any]]) -> Dict[str, Any]: """Generate statistics about the dataset. - + Args: tasks: List of tasks with metadata - - Returns: + + Returns + ------- Dictionary of dataset statistics """ stats = { @@ -166,38 +173,42 @@ def analyze_dataset(tasks: List[Dict[str, Any]]) -> Dict[str, Any]: "tool_solvable_count": 0, "recommended_count": 0, } - + for task in tasks: metadata = task.get("dataset_metadata", {}) - + stats["by_capability"][metadata.get("source_capability", "unknown")] += 1 stats["by_answer_type"][metadata.get("answer_type", "unknown")] += 1 - stats["by_problem_difficulty"][metadata.get("problem_difficulty", "unknown")] += 1 - stats["by_evaluation_difficulty"][metadata.get("evaluation_difficulty", "unknown")] += 1 - stats["by_ground_truth_quality"][metadata.get("ground_truth_quality", "unknown")] += 1 - + stats["by_problem_difficulty"][ + metadata.get("problem_difficulty", "unknown") + ] += 1 + stats["by_evaluation_difficulty"][ + metadata.get("evaluation_difficulty", "unknown") + ] += 1 + stats["by_ground_truth_quality"][ + metadata.get("ground_truth_quality", "unknown") + ] += 1 + if metadata.get("tool_solvable"): stats["tool_solvable_count"] += 1 if metadata.get("recommended"): stats["recommended_count"] += 1 - + # Convert defaultdicts to regular dicts for JSON serialization stats["by_capability"] = dict(stats["by_capability"]) stats["by_answer_type"] = dict(stats["by_answer_type"]) stats["by_problem_difficulty"] = dict(stats["by_problem_difficulty"]) stats["by_evaluation_difficulty"] = dict(stats["by_evaluation_difficulty"]) stats["by_ground_truth_quality"] = dict(stats["by_ground_truth_quality"]) - + return stats def save_dataset( - tasks: List[Dict[str, Any]], - output_file: Path, - include_stats: bool = True + tasks: List[Dict[str, Any]], output_file: Path, include_stats: bool = True ) -> None: """Save dataset to JSON file. - + Args: tasks: List of tasks to save output_file: Output file path @@ -212,53 +223,59 @@ def save_dataset( }, "tasks": tasks, } - + if include_stats: output_data["statistics"] = analyze_dataset(tasks) - + output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, 'w', encoding='utf-8') as f: + with open(output_file, "w", encoding="utf-8") as f: json.dump(output_data, f, indent=2, ensure_ascii=False) - + log.info(f"Saved dataset to {output_file}") def print_dataset_summary(stats: Dict[str, Any]) -> None: """Print dataset statistics summary.""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("DATASET SUMMARY") - print("="*70) - + print("=" * 70) + print(f"\nTotal tasks: {stats['total_tasks']}") - print(f"Tool-solvable: {stats['tool_solvable_count']} ({stats['tool_solvable_count']/stats['total_tasks']*100:.1f}%)") - print(f"Recommended: {stats['recommended_count']} ({stats['recommended_count']/stats['total_tasks']*100:.1f}%)") - + print( + f"Tool-solvable: {stats['tool_solvable_count']} ({stats['tool_solvable_count'] / stats['total_tasks'] * 100:.1f}%)" + ) + print( + f"Recommended: {stats['recommended_count']} ({stats['recommended_count'] / stats['total_tasks'] * 100:.1f}%)" + ) + print("\nDistribution by capability:") - for cap, count in sorted(stats['by_capability'].items(), key=lambda x: x[1], reverse=True): - pct = count / stats['total_tasks'] * 100 + for cap, count in sorted( + stats["by_capability"].items(), key=lambda x: x[1], reverse=True + ): + pct = count / stats["total_tasks"] * 100 print(f" {cap}: {count} ({pct:.1f}%)") - + print("\nDistribution by answer type:") - for atype, count in sorted(stats['by_answer_type'].items()): - pct = count / stats['total_tasks'] * 100 + for atype, count in sorted(stats["by_answer_type"].items()): + pct = count / stats["total_tasks"] * 100 print(f" {atype}: {count} ({pct:.1f}%)") - + print("\nDistribution by problem difficulty:") - for diff, count in sorted(stats['by_problem_difficulty'].items()): - pct = count / stats['total_tasks'] * 100 + for diff, count in sorted(stats["by_problem_difficulty"].items()): + pct = count / stats["total_tasks"] * 100 print(f" {diff}: {count} ({pct:.1f}%)") - + print("\nDistribution by evaluation difficulty:") - for diff, count in sorted(stats['by_evaluation_difficulty'].items()): - pct = count / stats['total_tasks'] * 100 + for diff, count in sorted(stats["by_evaluation_difficulty"].items()): + pct = count / stats["total_tasks"] * 100 print(f" {diff}: {count} ({pct:.1f}%)") - + print("\nDistribution by ground truth quality:") - for quality, count in sorted(stats['by_ground_truth_quality'].items()): - pct = count / stats['total_tasks'] * 100 + for quality, count in sorted(stats["by_ground_truth_quality"].items()): + pct = count / stats["total_tasks"] * 100 print(f" {quality}: {count} ({pct:.1f}%)") - - print("="*70) + + print("=" * 70) def main(): @@ -270,73 +287,73 @@ def main(): Examples: # Generate 50-task dataset from filtered classifications python generate_filtered_dataset.py --input classified_tasks_filtered.json --output filtered_dataset_50.json - + # Generate 100-task dataset with random sampling (no stratification) python generate_filtered_dataset.py --input classified_tasks_filtered.json --num-tasks 100 --no-stratify - + # Use custom seed for reproducibility python generate_filtered_dataset.py --input classified_tasks_filtered.json --seed 123 - """ + """, ) parser.add_argument( "--input", type=Path, required=True, - help="Input file with classified/filtered tasks" + help="Input file with classified/filtered tasks", ) parser.add_argument( "--output", type=Path, default=Path("filtered_dataset_50.json"), - help="Output file for generated dataset" + help="Output file for generated dataset", ) parser.add_argument( "--num-tasks", type=int, default=50, - help="Number of tasks to sample (default: 50)" + help="Number of tasks to sample (default: 50)", ) parser.add_argument( "--no-stratify", action="store_true", - help="Disable stratified sampling by capability" + help="Disable stratified sampling by capability", ) parser.add_argument( "--seed", type=int, default=42, - help="Random seed for reproducibility (default: 42)" + help="Random seed for reproducibility (default: 42)", ) - + args = parser.parse_args() - + # Load classified tasks log.info(f"Loading classified tasks from {args.input}") classified_tasks = load_classified_tasks(args.input) - + # Generate filtered dataset log.info(f"Generating dataset with {args.num_tasks} tasks...") sampled_tasks = generate_filtered_dataset( classified_tasks, num_tasks=args.num_tasks, stratify_by_capability=not args.no_stratify, - seed=args.seed + seed=args.seed, ) - + # Enrich with metadata log.info("Enriching tasks with metadata...") enriched_tasks = enrich_metadata(sampled_tasks) - + # Generate statistics stats = analyze_dataset(enriched_tasks) - + # Save dataset save_dataset(enriched_tasks, args.output, include_stats=True) - + # Print summary print_dataset_summary(stats) - - log.info(f"\nDataset generation complete!") + + log.info("\nDataset generation complete!") log.info(f"Output file: {args.output}") diff --git a/src/scripts/generate_task_dataset.sh b/src/scripts/generate_task_dataset.sh index 734741e1..408cd83d 100755 --- a/src/scripts/generate_task_dataset.sh +++ b/src/scripts/generate_task_dataset.sh @@ -132,7 +132,7 @@ for task in dataset['tasks']: cap_name = task.get('capability_name', 'unknown') if cap_name not in tasks_by_capability: tasks_by_capability[cap_name] = [] - + tasks_by_capability[cap_name].append({ 'task_id': task.get('task_id', task.get('dataset_metadata', {}).get('dataset_id', '')), 'problem': task.get('problem', ''), diff --git a/src/task_solver/messages.py b/src/task_solver/messages.py index 1eae3e04..9d0f8647 100644 --- a/src/task_solver/messages.py +++ b/src/task_solver/messages.py @@ -1,7 +1,7 @@ """Message types for task solving debate system.""" from dataclasses import dataclass -from typing import Dict, List, Optional +from typing import Dict, List @dataclass @@ -55,7 +55,7 @@ def to_dict(self) -> Dict[str, str]: @dataclass class ToolAssistedAgentSolution: """Solution proposed by a tool-assisted agent with code execution details. - + Note: code and code_output use empty string as default instead of None to avoid Union type issues with autogen_core serialization. """ diff --git a/src/task_solver/moderator.py b/src/task_solver/moderator.py index 1401d1a5..be60c0ed 100644 --- a/src/task_solver/moderator.py +++ b/src/task_solver/moderator.py @@ -4,7 +4,7 @@ import logging import traceback from pathlib import Path -from typing import Dict, List, Union +from typing import Dict, List from autogen_core import ( DefaultTopicId, @@ -241,7 +241,7 @@ async def handle_tool_assisted_agent_solution( self, message: ToolAssistedAgentSolution, ctx: MessageContext ) -> None: """Handle solution from a tool-assisted agent. - + Tool-assisted solutions include code and code_output fields that need to be preserved in the final solution files. """ @@ -299,9 +299,7 @@ async def handle_tool_assisted_agent_solution( await self._check_consensus_and_proceed(task_id, ctx) except Exception as e: - error_msg = ( - f"Error handling tool-assisted solution from agent {message.agent_id}: {str(e)}" - ) + error_msg = f"Error handling tool-assisted solution from agent {message.agent_id}: {str(e)}" log.error(error_msg) log.error(traceback.format_exc()) span.update(metadata={"error": error_msg}) diff --git a/src/task_solver/tool_assisted_scientist.py b/src/task_solver/tool_assisted_scientist.py index 96739550..decea454 100644 --- a/src/task_solver/tool_assisted_scientist.py +++ b/src/task_solver/tool_assisted_scientist.py @@ -20,13 +20,13 @@ from src.task_solver.messages import ( AgentRevisionRequest, - AgentSolution, TaskSolutionRequest, ToolAssistedAgentSolution, ) from src.tools.toolkit import ScientificToolKit from src.utils.json_utils import parse_llm_json_response from src.utils.tool_assisted_prompts import ( + TOOL_ASSISTED_ANSWER_FORMATTING_PROMPT, TOOL_ASSISTED_ROUND_1_PROMPT, TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT, TOOL_ASSISTED_SYSTEM_MESSAGE, @@ -73,36 +73,101 @@ def __init__( def _extract_numerical_from_code_output(self, code_output: str) -> str: """Extract the final numerical answer from code output. - + Looks for the last number printed in the code output, preferring numbers that appear after labels like "answer:", "result:", "price:", etc. """ if not code_output or code_output.startswith("ERROR"): return "null" - + import re - + # First, try to find numbers after common result labels result_patterns = [ - r'(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*[:=]\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)', - r'(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*\(\s*[ns]\s*\)\s*:\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)', + r"(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*[:=]\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)", + r"(?:answer|result|final|price|spread|years?|maturity|value|solution)\s*\(\s*[ns]\s*\)\s*:\s*([+-]?\d+\.?\d*(?:[eE][+-]?\d+)?)", ] - + for pattern in result_patterns: matches = re.findall(pattern, code_output, re.IGNORECASE) if matches: # Use the last match (most specific/final result) return matches[-1] - + # Fallback: Find all numbers and return the last one - numbers = re.findall(r'-?\d+\.?\d*(?:[eE][+-]?\d+)?', code_output) - + numbers = re.findall(r"-?\d+\.?\d*(?:[eE][+-]?\d+)?", code_output) + if numbers: # Return the last number found return numbers[-1] - + return "null" - + + def _extract_code_generation_response(self, response: str) -> tuple[str, str]: + """Extract thought and code from Stage 1 response. + + Returns + ------- + (thought, code): Thought process and Python code + """ + try: + parsed = parse_llm_json_response(response) + thought_raw = parsed.get("thought", response.strip()) + code = parsed.get("code") + + thought = ( + json.dumps(thought_raw, ensure_ascii=False) + if isinstance(thought_raw, (dict, list)) + else str(thought_raw).strip() + ) + + # Handle code field + if code is not None and code != "null": + code = str(code).strip() + log.debug("Code (first 200 chars): %s", code[:200]) + else: + code = "" + + return thought, code + + except Exception as e: + msg = f"Failed to parse Stage 1 (code generation) response: {e} \n Response: {response}" + log.error(msg) + log.error(traceback.format_exc()) + raise + + def _extract_answer_formatting_response(self, response: str) -> tuple[str, str]: + """Extract final_answer and numerical_answer from Stage 2 response. + + Returns + ------- + (final_answer, numerical_answer): Formatted answer and numerical value + """ + try: + parsed = parse_llm_json_response(response) + final_answer_raw = parsed.get("final_answer", "No clear answer provided") + numerical_answer = parsed.get("numerical_answer") + + final_answer = ( + json.dumps(final_answer_raw, ensure_ascii=False, indent=2) + if isinstance(final_answer_raw, (dict, list)) + else str(final_answer_raw).strip() + ) + + # Handle numerical_answer + if numerical_answer is not None: + numerical_answer = str(numerical_answer) + else: + numerical_answer = "null" + + return final_answer, numerical_answer + + except Exception as e: + msg = f"Failed to parse Stage 2 (answer formatting) response: {e} \n Response: {response}" + log.error(msg) + log.error(traceback.format_exc()) + raise + def _extract_solution_components( self, response: str ) -> tuple[str, str | None, str | None, str, str]: @@ -156,6 +221,331 @@ def _extract_solution_components( async def _generate_solution_with_code_execution( self, system_message: SystemMessage, user_message: UserMessage + ) -> tuple[str, str | None, str | None, str, str]: + """Generate solution using two-stage architecture: + + Stage 1: Code Generation + - Model generates thought + code + - Code is executed to get output + - If execution fails, provide feedback and retry + + Stage 2: Answer Formatting + - Model sees code output + - Formats final_answer and numerical_answer appropriately + - Handles bool/MCQ/numerical question types correctly + + Returns + ------- + (thought, code, code_output, final_answer, numerical_answer) + """ + # ================================================================= + # STAGE 1: CODE GENERATION AND EXECUTION + # ================================================================= + conversation_history = [system_message, user_message] + last_error: Exception | None = None + + thought = "" + code = "" + code_output = "" + + # Track total code execution attempts across all model responses + total_exec_attempts = 0 + + for model_attempt in range(1, MAX_MODEL_ATTEMPTS + 1): + try: + # Get Stage 1 response (thought + code only) + log.debug( + "Scientist %s: Stage 1 LLM attempt %d/%d (code generation)", + self._scientist_id, + model_attempt, + MAX_MODEL_ATTEMPTS, + ) + response = await self._model_client.create( + conversation_history, + json_output=True, + ) + except Exception as exc: # pragma: no cover + last_error = exc + log.warning( + "Tool-assisted scientist %s failed to get Stage 1 response on attempt %d: %s", + self._scientist_id, + model_attempt, + exc, + ) + continue + + response_content = str(getattr(response, "content", "") or "").strip() + if not response_content: + last_error = ValueError("Empty response content") + log.warning( + "Tool-assisted scientist %s received empty Stage 1 response on attempt %d", + self._scientist_id, + model_attempt, + ) + continue + + log.debug( + "Scientist %s: Received Stage 1 response (first 200 chars): %s", + self._scientist_id, + response_content[:200], + ) + + try: + # Parse Stage 1 response + thought, code = self._extract_code_generation_response(response_content) + + # If no code, proceed to Stage 2 with empty output + if not code: + log.info( + "Scientist %s: No code generated, proceeding to Stage 2 for direct answer", + self._scientist_id, + ) + code_output = "" + break + + # Code is present - attempt execution + log.info( + "Scientist %s: Code detected, starting execution", + self._scientist_id, + ) + + # Clean up common code issues + if "\\n" in code: + code = code.replace("\\n", "\n") + log.debug( + "Scientist %s: Applied \\n replacement to clean up code", + self._scientist_id, + ) + + log.debug( + "Scientist %s: Generated code:\n%s\n%s\n%s", + self._scientist_id, + "-" * 60, + code, + "-" * 60, + ) + + # Increment total execution attempts + total_exec_attempts += 1 + log.info( + "Scientist %s: Code execution attempt %d/%d", + self._scientist_id, + total_exec_attempts, + MAX_CODE_EXECUTION_ATTEMPTS, + ) + + execution_result = self._toolkit.execute_code(code) + execution_result = type( + "obj", + (object,), + { + "success": execution_result["success"], + "output": execution_result["output"], + "error": execution_result.get("error"), + }, + )() + + if execution_result.success: + code_output = execution_result.output + log.info( + "Scientist %s: Code execution successful", self._scientist_id + ) + log.debug( + "Scientist %s: Code output:\n%s\n%s\n%s", + self._scientist_id, + "-" * 60, + code_output[:500], + "-" * 60, + ) + # Success - break out of Stage 1 loop and proceed to Stage 2 + break + + # Code execution failed + code_output = f"ERROR: {execution_result.error}" + log.warning( + "Scientist %s: Code execution failed (attempt %d/%d): %s", + self._scientist_id, + total_exec_attempts, + MAX_CODE_EXECUTION_ATTEMPTS, + execution_result.error, + ) + + # If we've exhausted code execution attempts, proceed to Stage 2 with error + if total_exec_attempts >= MAX_CODE_EXECUTION_ATTEMPTS: + log.error( + "Scientist %s: Max code execution attempts (%d) exhausted, proceeding to Stage 2 with error", + self._scientist_id, + MAX_CODE_EXECUTION_ATTEMPTS, + ) + break + + # Provide feedback to LLM for another attempt + log.info( + "Scientist %s: Providing error feedback to LLM for code correction", + self._scientist_id, + ) + + error_msg = str(execution_result.error) + hints = [] + + if ( + "unterminated string" in error_msg.lower() + or "eol while scanning" in error_msg.lower() + ): + hints.append( + "- Use triple-quoted strings for multi-line output: print('''text''')" + ) + hints.append( + "- Avoid backslashes in strings; use raw strings r'' if needed" + ) + hints.append( + "- For print statements, use separate print() calls instead of \\n in strings" + ) + elif "syntaxerror" in error_msg.lower(): + hints.append( + "- Review Python syntax, especially quotes, parentheses, and indentation" + ) + hints.append("- Check for unmatched brackets or quotes") + elif "nameerror" in error_msg.lower(): + hints.append("- Ensure all imports are at the top of the code") + hints.append("- Check that all variables are defined before use") + elif ( + "importerror" in error_msg.lower() + or "modulenotfounderror" in error_msg.lower() + ): + hints.append( + "- Only use approved libraries: sympy, numpy, scipy, math, fractions, decimal" + ) + + hints_text = ( + "\n".join(hints) + if hints + else "- Carefully review the error message above" + ) + + feedback_prompt = f"""Your previous code execution failed with the following error: + +ERROR: {execution_result.error} + +Failed code: +```python +{code} +``` + +ACTIONABLE FIXES: +{hints_text} + +IMPORTANT: When writing code in JSON: +- Use simple print() statements on separate lines +- Avoid LaTeX notation in code comments or strings +- Use triple-quoted strings for multi-line output: print('''result''') +- Keep code focused on numerical computation only + +Return your corrected solution in the same JSON format {{thought, code}}.""" + + feedback_message = UserMessage(content=feedback_prompt, source="user") + conversation_history.append(feedback_message) + # Continue to next model attempt for corrected code + + except Exception as exc: + last_error = exc + log.warning( + "Tool-assisted scientist %s failed to parse Stage 1 response on attempt %d: %s", + self._scientist_id, + model_attempt, + exc, + ) + log.debug("Full exception: %s", traceback.format_exc()) + continue + + # Check if Stage 1 succeeded + if not thought: + raise RuntimeError( + f"Tool-assisted scientist {self._scientist_id} could not generate code " + f"after {MAX_MODEL_ATTEMPTS} attempts" + ) from last_error + + # ================================================================= + # STAGE 2: ANSWER FORMATTING + # ================================================================= + log.info( + "Scientist %s: Stage 1 complete. Proceeding to Stage 2 (answer formatting)", + self._scientist_id, + ) + + # Build Stage 2 prompt with code output and original problem + original_problem = ( + user_message.content.split("PROBLEM: ")[1].split("\n")[0] + if "PROBLEM: " in user_message.content + else "the problem" + ) + + stage2_prompt = TOOL_ASSISTED_ANSWER_FORMATTING_PROMPT.format( + code_output=code_output if code_output else "No code was executed.", + problem_text=original_problem, + ) + + stage2_message = UserMessage(content=stage2_prompt, source="user") + + for attempt in range(1, MAX_MODEL_ATTEMPTS + 1): + try: + log.debug( + "Scientist %s: Stage 2 LLM attempt %d/%d (answer formatting)", + self._scientist_id, + attempt, + MAX_MODEL_ATTEMPTS, + ) + response = await self._model_client.create( + [system_message, stage2_message], + json_output=True, + ) + + response_content = str(getattr(response, "content", "") or "").strip() + if not response_content: + log.warning("Empty Stage 2 response on attempt %d", attempt) + continue + + # Parse Stage 2 response + final_answer, numerical_answer = ( + self._extract_answer_formatting_response(response_content) + ) + + log.info( + "Scientist %s: Stage 2 complete. Two-stage solution generated successfully.", + self._scientist_id, + ) + + return thought, code, code_output, final_answer, numerical_answer + + except Exception as exc: + log.warning( + "Tool-assisted scientist %s failed Stage 2 attempt %d: %s", + self._scientist_id, + attempt, + exc, + ) + last_error = exc + continue + + # Stage 2 failed - use fallback + log.error( + "Scientist %s: Stage 2 failed after %d attempts, using fallback", + self._scientist_id, + MAX_MODEL_ATTEMPTS, + ) + + # Fallback: extract numerical from code output if possible + numerical_answer = ( + self._extract_numerical_from_code_output(code_output) + if code_output + else "null" + ) + final_answer = code_output if code_output else "Unable to generate final answer" + + return thought, code, code_output, final_answer, numerical_answer + + async def _generate_solution_with_code_execution_legacy( + self, system_message: SystemMessage, user_message: UserMessage ) -> tuple[str, str | None, str | None, str, str]: """Generate solution with iterative code execution feedback. @@ -168,10 +558,10 @@ async def _generate_solution_with_code_execution( """ conversation_history = [system_message, user_message] last_error: Exception | None = None - + # Track final components from last valid parse last_valid_components = None - + # Track total code execution attempts across all model responses total_exec_attempts = 0 @@ -223,7 +613,13 @@ async def _generate_solution_with_code_execution( thought, code, code_output, final_answer, numerical_answer = ( self._extract_solution_components(response_content) ) - last_valid_components = (thought, code, code_output, final_answer, numerical_answer) + last_valid_components = ( + thought, + code, + code_output, + final_answer, + numerical_answer, + ) # Log the extracted code to see if parsing is correct if code: @@ -232,7 +628,13 @@ async def _generate_solution_with_code_execution( self._scientist_id, repr(code[:200]), ) - last_valid_components = (thought, code, code_output, final_answer, numerical_answer) + last_valid_components = ( + thought, + code, + code_output, + final_answer, + numerical_answer, + ) # If no code (empty string), we're done if not code: @@ -247,18 +649,18 @@ async def _generate_solution_with_code_execution( "Scientist %s: Code detected, starting execution loop", self._scientist_id, ) - + # Clean up common code issues before execution # Sometimes LLM generates code with literal \n that should be newlines - if '\\n' in code: + if "\\n" in code: # Replace literal \n with actual newlines (but be careful with string escapes) # This is a heuristic fix for common JSON escaping issues - code = code.replace('\\n', '\n') + code = code.replace("\\n", "\n") log.debug( "Scientist %s: Applied \\n replacement to clean up code", self._scientist_id, ) - + log.debug( "Scientist %s: Generated code:\n%s\n%s\n%s", self._scientist_id, @@ -275,10 +677,18 @@ async def _generate_solution_with_code_execution( total_exec_attempts, MAX_CODE_EXECUTION_ATTEMPTS, ) - + execution_result = self._toolkit.execute_code(code) # Convert dict to object-like access - execution_result = type('obj', (object,), {'success': execution_result['success'], 'output': execution_result['output'], 'error': execution_result.get('error')})() + execution_result = type( + "obj", + (object,), + { + "success": execution_result["success"], + "output": execution_result["output"], + "error": execution_result.get("error"), + }, + )() if execution_result.success: code_output = execution_result.output @@ -293,18 +703,20 @@ async def _generate_solution_with_code_execution( code_output[:500], "-" * 60, ) - + # Extract numerical answer from code output instead of using model's approximation - extracted_numerical = self._extract_numerical_from_code_output(code_output) + extracted_numerical = self._extract_numerical_from_code_output( + code_output + ) if extracted_numerical != "null": log.info( "Scientist %s: Overriding numerical_answer (was: %s, now: %s from code output)", self._scientist_id, numerical_answer, - extracted_numerical + extracted_numerical, ) numerical_answer = extracted_numerical - + return thought, code, code_output, final_answer, numerical_answer # Code execution failed @@ -338,26 +750,46 @@ async def _generate_solution_with_code_execution( "Scientist %s: Providing error feedback to LLM for code correction", self._scientist_id, ) - + # Construct enhanced error feedback with actionable hints error_msg = str(execution_result.error) hints = [] - - if "unterminated string" in error_msg.lower() or "eol while scanning" in error_msg.lower(): - hints.append("- Use triple-quoted strings for multi-line output: print('''text''')") - hints.append("- Avoid backslashes in strings; use raw strings r'' if needed") - hints.append("- For print statements, use separate print() calls instead of \\n in strings") + + if ( + "unterminated string" in error_msg.lower() + or "eol while scanning" in error_msg.lower() + ): + hints.append( + "- Use triple-quoted strings for multi-line output: print('''text''')" + ) + hints.append( + "- Avoid backslashes in strings; use raw strings r'' if needed" + ) + hints.append( + "- For print statements, use separate print() calls instead of \\n in strings" + ) elif "syntaxerror" in error_msg.lower(): - hints.append("- Review Python syntax, especially quotes, parentheses, and indentation") + hints.append( + "- Review Python syntax, especially quotes, parentheses, and indentation" + ) hints.append("- Check for unmatched brackets or quotes") elif "nameerror" in error_msg.lower(): hints.append("- Ensure all imports are at the top of the code") hints.append("- Check that all variables are defined before use") - elif "importerror" in error_msg.lower() or "modulenotfounderror" in error_msg.lower(): - hints.append("- Only use approved libraries: sympy, numpy, scipy, math, fractions, decimal") - - hints_text = "\n".join(hints) if hints else "- Carefully review the error message above" - + elif ( + "importerror" in error_msg.lower() + or "modulenotfounderror" in error_msg.lower() + ): + hints.append( + "- Only use approved libraries: sympy, numpy, scipy, math, fractions, decimal" + ) + + hints_text = ( + "\n".join(hints) + if hints + else "- Carefully review the error message above" + ) + feedback_prompt = f"""Your previous code execution failed with the following error: ERROR: {execution_result.error} @@ -378,12 +810,9 @@ async def _generate_solution_with_code_execution( Return your corrected solution in the same JSON format with fixed code.""" - feedback_message = UserMessage( - content=feedback_prompt, - source="user" - ) + feedback_message = UserMessage(content=feedback_prompt, source="user") conversation_history.append(feedback_message) - + # Continue to next model attempt for corrected code except Exception as exc: @@ -441,7 +870,7 @@ async def handle_task_solution_request( # Step 1: Prepare tool context for problem log.info(f"Scientist {self._scientist_id}: Preparing tool context") tool_context = await self._toolkit.prepare_tools(message.problem) - + span.update( metadata={ "tool_selection_needs_tools": tool_context.get("needs_tools"), @@ -449,14 +878,13 @@ async def handle_task_solution_request( "selected_tools": tool_context.get("selected_libraries", []), } ) - + # Step 2: Format tool context for prompt tool_context_str = self._toolkit.format_tool_context(tool_context) - - # Step 3: Create prompt with tool context + + # Step 3: Format prompt prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=message.problem, - tool_context=tool_context_str + problem_text=message.problem, tool_context=tool_context_str ) system_message = SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE) @@ -517,7 +945,8 @@ async def handle_task_solution_request( f"solution for task {message.task_id}" ), "code_executed": bool(code), - "code_success": code_output and not code_output.startswith("ERROR:"), + "code_success": code_output + and not code_output.startswith("ERROR:"), } ) @@ -568,6 +997,7 @@ async def handle_agent_revision_request( ] ) + # Format prompt prompt = TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT.format( other_solutions=other_solutions_text, problem_text=message.problem, @@ -608,7 +1038,8 @@ async def handle_agent_revision_request( f"revision for task {message.task_id}" ), "code_executed": bool(code), - "code_success": code_output and not code_output.startswith("ERROR:"), + "code_success": code_output + and not code_output.startswith("ERROR:"), } ) diff --git a/src/tools/__init__.py b/src/tools/__init__.py index 7e94e20c..d28a6e2d 100644 --- a/src/tools/__init__.py +++ b/src/tools/__init__.py @@ -12,13 +12,13 @@ Usage: ------ from src.tools.toolkit import ScientificToolKit - + # Create toolkit toolkit = ScientificToolKit( model_client=my_client, enable_tool_selection=True ) - + # Use in scientist scientist = ToolAssistedScientist(..., toolkit=toolkit) """ @@ -28,6 +28,7 @@ from src.tools.executor import CodeExecutionResult, PythonExecutor from src.tools.toolkit import ScientificToolKit + __all__ = [ "ScientificToolKit", "ScientificDocRetriever", diff --git a/src/tools/definitions.py b/src/tools/definitions.py index 76a1a5b0..d8cf503f 100644 --- a/src/tools/definitions.py +++ b/src/tools/definitions.py @@ -10,6 +10,7 @@ @dataclass class LibraryConfig: """Configuration for a library/module.""" + name: str import_name: str description: str @@ -21,6 +22,7 @@ class LibraryConfig: @dataclass class ToolDefinition: """Complete definition of a tool.""" + tool_id: str name: str description: str @@ -37,15 +39,28 @@ class ToolDefinition: description="Fundamental package for numerical computing. Arrays, matrices, and mathematical functions.", docs_path="numpy-html-1.17.0", common_functions=[ - "np.array", "np.linspace", "np.zeros", "np.ones", "np.eye", - "np.linalg.eig", "np.linalg.det", "np.linalg.inv", "np.linalg.solve", - "np.dot", "np.cross", "np.sin", "np.cos", "np.exp", "np.log", "np.sqrt" + "np.array", + "np.linspace", + "np.zeros", + "np.ones", + "np.eye", + "np.linalg.eig", + "np.linalg.det", + "np.linalg.inv", + "np.linalg.solve", + "np.dot", + "np.cross", + "np.sin", + "np.cos", + "np.exp", + "np.log", + "np.sqrt", ], use_cases=[ "Array operations and linear algebra", "Matrix computations", - "Statistical computations" - ] + "Statistical computations", + ], ) SCIPY_CONFIG = LibraryConfig( @@ -54,16 +69,22 @@ class ToolDefinition: description="Scientific computing library. Integration, optimization, linear algebra, statistics.", docs_path="scipy-html-1.17.0", common_functions=[ - "scipy.integrate.quad", "scipy.integrate.odeint", "scipy.integrate.solve_ivp", - "scipy.optimize.minimize", "scipy.optimize.fsolve", - "scipy.linalg.lu", "scipy.linalg.qr", "scipy.linalg.svd", - "scipy.stats.norm", "scipy.interpolate.interp1d" + "scipy.integrate.quad", + "scipy.integrate.odeint", + "scipy.integrate.solve_ivp", + "scipy.optimize.minimize", + "scipy.optimize.fsolve", + "scipy.linalg.lu", + "scipy.linalg.qr", + "scipy.linalg.svd", + "scipy.stats.norm", + "scipy.interpolate.interp1d", ], use_cases=[ "Numerical integration", "Optimization and root finding", - "Advanced linear algebra" - ] + "Advanced linear algebra", + ], ) SYMPY_CONFIG = LibraryConfig( @@ -72,15 +93,24 @@ class ToolDefinition: description="Symbolic mathematics library. Algebraic manipulation, calculus, equation solving.", docs_path="sympy-docs-html-1.14.0", common_functions=[ - "sympy.symbols", "sympy.simplify", "sympy.expand", "sympy.factor", - "sympy.diff", "sympy.integrate", "sympy.solve", "sympy.dsolve", - "sympy.Matrix", "sympy.sin", "sympy.cos", "sympy.pi" + "sympy.symbols", + "sympy.simplify", + "sympy.expand", + "sympy.factor", + "sympy.diff", + "sympy.integrate", + "sympy.solve", + "sympy.dsolve", + "sympy.Matrix", + "sympy.sin", + "sympy.cos", + "sympy.pi", ], use_cases=[ "Symbolic differentiation and integration", "Solving equations", - "Symbolic simplification" - ] + "Symbolic simplification", + ], ) MATH_CONFIG = LibraryConfig( @@ -88,8 +118,15 @@ class ToolDefinition: import_name="math", description="Python's standard math library.", docs_path=None, - common_functions=["math.sin", "math.cos", "math.sqrt", "math.exp", "math.log", "math.pi"], - use_cases=["Basic mathematical operations"] + common_functions=[ + "math.sin", + "math.cos", + "math.sqrt", + "math.exp", + "math.log", + "math.pi", + ], + use_cases=["Basic mathematical operations"], ) FRACTIONS_CONFIG = LibraryConfig( @@ -98,7 +135,7 @@ class ToolDefinition: description="Rational number arithmetic.", docs_path=None, common_functions=["Fraction"], - use_cases=["Exact rational arithmetic"] + use_cases=["Exact rational arithmetic"], ) DECIMAL_CONFIG = LibraryConfig( @@ -107,7 +144,7 @@ class ToolDefinition: description="High-precision decimal arithmetic.", docs_path=None, common_functions=["Decimal"], - use_cases=["High-precision calculations"] + use_cases=["High-precision calculations"], ) CMATH_CONFIG = LibraryConfig( @@ -116,7 +153,7 @@ class ToolDefinition: description="Complex number operations.", docs_path=None, common_functions=["cmath.sqrt", "cmath.exp", "cmath.log"], - use_cases=["Complex number operations"] + use_cases=["Complex number operations"], ) # Financial Library Configurations @@ -126,15 +163,22 @@ class ToolDefinition: description="Financial functions for time value of money calculations (NPV, IRR, PMT, PV, FV, etc.).", docs_path=None, common_functions=[ - "npf.npv", "npf.irr", "npf.pmt", "npf.pv", "npf.fv", - "npf.nper", "npf.rate", "npf.ppmt", "npf.ipmt" + "npf.npv", + "npf.irr", + "npf.pmt", + "npf.pv", + "npf.fv", + "npf.nper", + "npf.rate", + "npf.ppmt", + "npf.ipmt", ], use_cases=[ "Time Value of Money", "Net Present Value calculations", "Internal Rate of Return", - "Loan payment calculations" - ] + "Loan payment calculations", + ], ) PY_VOLLIB_GEN_CONFIG = LibraryConfig( @@ -145,13 +189,13 @@ class ToolDefinition: common_functions=[ "py_vollib.black_scholes.black_scholes", "py_vollib.black_scholes.implied_volatility", - "py_vollib.black_scholes.greeks" + "py_vollib.black_scholes.greeks", ], use_cases=[ "Options pricing", "Implied volatility calculation", - "Greeks computation (delta, gamma, theta, vega, rho)" - ] + "Greeks computation (delta, gamma, theta, vega, rho)", + ], ) PYPORTFOLIOOPT_CONFIG = LibraryConfig( @@ -163,14 +207,14 @@ class ToolDefinition: "pypfopt.expected_returns.mean_historical_return", "pypfopt.risk_models.CovarianceShrinkage", "pypfopt.efficient_frontier.EfficientFrontier", - "pypfopt.objective_functions.sharpe_ratio" + "pypfopt.objective_functions.sharpe_ratio", ], use_cases=[ "Portfolio optimization", "Risk-return analysis", "Asset allocation", - "Efficient frontier computation" - ] + "Efficient frontier computation", + ], ) EMPYRICAL_CONFIG = LibraryConfig( @@ -179,16 +223,19 @@ class ToolDefinition: description="Performance statistics and risk metrics for financial analysis.", docs_path=None, common_functions=[ - "empyrical.sharpe_ratio", "empyrical.sortino_ratio", - "empyrical.max_drawdown", "empyrical.annual_return", - "empyrical.alpha_beta", "empyrical.beta" + "empyrical.sharpe_ratio", + "empyrical.sortino_ratio", + "empyrical.max_drawdown", + "empyrical.annual_return", + "empyrical.alpha_beta", + "empyrical.beta", ], use_cases=[ "Performance metrics", "Risk analysis", "Sharpe and Sortino ratios", - "Drawdown analysis" - ] + "Drawdown analysis", + ], ) ARCH_CONFIG = LibraryConfig( @@ -200,13 +247,13 @@ class ToolDefinition: "arch.univariate.GARCH", "arch.univariate.EGARCH", "arch.univariate.ConstantMean", - "arch.univariate.HARX" + "arch.univariate.HARX", ], use_cases=[ "GARCH/EGARCH volatility modeling", "Time series forecasting", - "Conditional variance estimation" - ] + "Conditional variance estimation", + ], ) # The Tool Definition @@ -215,12 +262,34 @@ class ToolDefinition: name="Python Code Execution", description="Execute Python code with math, scientific, and financial computing libraries including SymPy, NumPy, SciPy, numpy_financial, py_vollib, PyPortfolioOpt, empyrical, and arch.", libraries=[ - SYMPY_CONFIG, NUMPY_CONFIG, SCIPY_CONFIG, MATH_CONFIG, FRACTIONS_CONFIG, DECIMAL_CONFIG, CMATH_CONFIG, - NUMPY_FINANCIAL_CONFIG, PY_VOLLIB_GEN_CONFIG, PYPORTFOLIOOPT_CONFIG, EMPYRICAL_CONFIG, ARCH_CONFIG + SYMPY_CONFIG, + NUMPY_CONFIG, + SCIPY_CONFIG, + MATH_CONFIG, + FRACTIONS_CONFIG, + DECIMAL_CONFIG, + CMATH_CONFIG, + NUMPY_FINANCIAL_CONFIG, + PY_VOLLIB_GEN_CONFIG, + PYPORTFOLIOOPT_CONFIG, + EMPYRICAL_CONFIG, + ARCH_CONFIG, ], allowed_imports=[ - "sympy", "numpy", "scipy", "math", "fractions", "decimal", "cmath", "datetime", - "numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch", "statsmodels" + "sympy", + "numpy", + "scipy", + "math", + "fractions", + "decimal", + "cmath", + "datetime", + "numpy_financial", + "py_vollib", + "pypfopt", + "empyrical", + "arch", + "statsmodels", ], use_cases=[ "Solving differential equations", @@ -232,7 +301,7 @@ class ToolDefinition: "Options pricing and Greeks", "Portfolio optimization", "Performance metrics and risk analysis", - "Volatility modeling (GARCH/EGARCH)" + "Volatility modeling (GARCH/EGARCH)", ], - metadata={"timeout": 30} + metadata={"timeout": 30}, ) diff --git a/src/tools/docs.py b/src/tools/docs.py index 29d2ceb6..b6af861b 100644 --- a/src/tools/docs.py +++ b/src/tools/docs.py @@ -5,11 +5,13 @@ """ import logging +from collections import defaultdict from pathlib import Path from typing import Dict, List -from collections import defaultdict + from bs4 import BeautifulSoup + log = logging.getLogger("tools.docs") @@ -22,20 +24,19 @@ class ScientificDocRetriever: def __init__(self, docs_base_path: Path): self.docs_path = docs_base_path # Schema: index[library][module] = [list_of_function_names] - self._index: Dict[str, Dict[str, List[str]]] = {} + self._index: Dict[str, Dict[str, List[str]]] = {} # Schema: file_map[library.module.function] = Path(file.html) self._file_map: Dict[str, Path] = {} - + self._build_index() def _build_index(self): """Deterministically builds index based on 'reference/generated' file patterns.""" - # 1. Index NumPy and SciPy (Pattern: pkg.submodule.function.html) for lib in ["numpy", "scipy"]: # Find versioned directory (e.g., numpy-html-1.17.0) lib_dir = next(self.docs_path.glob(f"{lib}-html-*"), None) - if not lib_dir: + if not lib_dir: log.warning(f"Documentation directory not found for {lib}") continue @@ -44,22 +45,22 @@ def _build_index(self): if generated_dir.exists(): if lib not in self._index: self._index[lib] = defaultdict(list) - + for html_file in generated_dir.glob("*.html"): name = html_file.stem # e.g., "numpy.linalg.eig" - - if name.startswith("index") or name.startswith("gallery"): + + if name.startswith("index") or name.startswith("gallery"): continue - - parts = name.split('.') + + parts = name.split(".") if len(parts) >= 3: # numpy.linalg.eig -> module="linalg", func="eig" module = parts[1] func = parts[-1] - + self._index[lib][module].append(func) self._file_map[f"{lib}.{module}.{func}"] = html_file - + log.info(f"Indexed {lib}: {len(self._index[lib])} modules") # 2. Index SymPy (Pattern: modules/topic/file.html) @@ -67,18 +68,20 @@ def _build_index(self): if sympy_dir: modules_dir = sympy_dir / "modules" if modules_dir.exists(): - if "sympy" not in self._index: + if "sympy" not in self._index: self._index["sympy"] = defaultdict(list) - + # SymPy organizes by folder names in 'modules/' for category_dir in modules_dir.iterdir(): - if category_dir.is_dir() and not category_dir.name.startswith('_'): + if category_dir.is_dir() and not category_dir.name.startswith("_"): topic = category_dir.name # e.g., "matrices", "solvers" for html_file in category_dir.glob("*.html"): if html_file.stem != "index": self._index["sympy"][topic].append(html_file.stem) - self._file_map[f"sympy.{topic}.{html_file.stem}"] = html_file - + self._file_map[f"sympy.{topic}.{html_file.stem}"] = ( + html_file + ) + log.info(f"Indexed sympy: {len(self._index['sympy'])} modules") def get_library_overview(self) -> str: @@ -102,7 +105,7 @@ def get_full_module_context(self, library: str, module: str) -> str: # SORT the functions so the prompt is deterministic functions = sorted(self._index[library][module]) context_blocks = [f"--- DOCUMENTATION FOR {library}.{module} ---"] - + for func in functions: key = f"{library}.{module}.{func}" file_path = self._file_map.get(key) @@ -110,52 +113,56 @@ def get_full_module_context(self, library: str, module: str) -> str: signature = self._extract_signature_from_html(file_path) if signature: context_blocks.append(signature) - + return "\n\n".join(context_blocks) def _extract_signature_from_html(self, path: Path) -> str: """Parses HTML to extract signature + brief description (Optimized for Tokens).""" try: - with open(path, 'r', encoding='utf-8') as f: - soup = BeautifulSoup(f.read(), 'html.parser') - + with open(path, "r", encoding="utf-8") as f: + soup = BeautifulSoup(f.read(), "html.parser") + extracted_content = [] # Find Main Definitions - definitions = soup.find_all('dl', class_=['function', 'class', 'method']) - + definitions = soup.find_all( + "dl", class_=["function", "class", "method"] + ) + for dl in definitions: # 1. Get the signature (dt) - dt = dl.find('dt') - if not dt: + dt = dl.find("dt") + if not dt: continue - + # CLEANUP: Remove ¶, [source], and extra whitespace - raw_sig = dt.get_text().replace("¶", "").replace("[source]", "").strip() + raw_sig = ( + dt.get_text().replace("¶", "").replace("[source]", "").strip() + ) sig = " ".join(raw_sig.split()) - + # FILTER 1: Skip private/magic methods in the main definition # (e.g. skip scipy.sparse.bsr_matrix.__add__) - func_name = sig.split('(')[0].split('.')[-1] - if func_name.startswith('_'): + func_name = sig.split("(")[0].split(".")[-1] + if func_name.startswith("_"): continue # 2. Get the description (dd) - First sentence only, max 120 chars desc_text = "" - dd = dl.find('dd') + dd = dl.find("dd") if dd: - p = dd.find('p') + p = dd.find("p") if p: raw_desc = " ".join(p.get_text().split()) # Split by period to get first sentence, or cap at 120 chars - if '.' in raw_desc: - desc_text = raw_desc.split('.')[0] + "." + if "." in raw_desc: + desc_text = raw_desc.split(".")[0] + "." else: desc_text = raw_desc - + if len(desc_text) > 120: desc_text = desc_text[:117] + "..." - + # FORMAT: Dense one-liner # e.g., "numpy.linalg.eig(a): Compute eigenvalues." entry = f"{sig}: {desc_text}" if desc_text else sig @@ -163,31 +170,38 @@ def _extract_signature_from_html(self, path: Path) -> str: # 3. SYMPY/CLASS HANDLING: Look for methods inside # Only do this if it's a class definition - if "class" in dl.get('class', []) or "sympy" in str(path): - methods = dl.find_all('dl', class_='method') + if "class" in dl.get("class", []) or "sympy" in str(path): + methods = dl.find_all("dl", class_="method") for method in methods: - m_dt = method.find('dt') + m_dt = method.find("dt") if m_dt: - m_sig_raw = m_dt.get_text().replace("¶", "").replace("[source]", "").strip() + m_sig_raw = ( + m_dt.get_text() + .replace("¶", "") + .replace("[source]", "") + .strip() + ) m_sig = " ".join(m_sig_raw.split()) - + # FILTER 2: Strict method filtering # Skip __init__, __add__, _private_method - method_name = m_sig.split('(')[0].split('.')[-1] - if method_name.startswith('_'): + method_name = m_sig.split("(")[0].split(".")[-1] + if method_name.startswith("_"): continue - + # Optional: Skip simple properties to save space? # if "property" in str(method): continue - extracted_content.append(f" . {method_name}(...): Method") + extracted_content.append( + f" . {method_name}(...): Method" + ) # Note: We purposefully omit method descriptions to save tokens # relying on the method name being descriptive enough. if extracted_content: return "\n".join(extracted_content) - except Exception as e: + except Exception: # log.warning(f"Error parsing {path}: {e}") pass return "" diff --git a/src/tools/executor.py b/src/tools/executor.py index e4a32652..fe0e66d5 100644 --- a/src/tools/executor.py +++ b/src/tools/executor.py @@ -11,11 +11,12 @@ @dataclass class CodeExecutionResult: """Result of code execution.""" + success: bool output: str error: Optional[str] = None metadata: Dict[str, Any] = field(default_factory=dict) - + def to_dict(self) -> Dict[str, Any]: """Convert result to dictionary.""" return { @@ -28,7 +29,7 @@ def to_dict(self) -> Dict[str, Any]: class PythonExecutor: """Execute Python code with restricted imports for safe execution." - + Attributes ---------- allowed_imports : List[str] @@ -36,14 +37,10 @@ class PythonExecutor: timeout : int Maximum execution time in seconds (not enforced in basic implementation). """ - - def __init__( - self, - allowed_imports: Optional[List[str]] = None, - timeout: int = 30 - ): + + def __init__(self, allowed_imports: Optional[List[str]] = None, timeout: int = 30): """Initialize the Python executor. - + Parameters ---------- allowed_imports : Optional[List[str]] @@ -53,38 +50,38 @@ def __init__( """ if allowed_imports is None: allowed_imports = [ - 'sympy', - 'numpy', - 'scipy', - 'math', - 'fractions', - 'decimal', - 'cmath', - 'datetime', - 'numpy_financial', - 'py_vollib', - 'pypfopt', - 'empyrical', - 'arch', - 'statsmodels', + "sympy", + "numpy", + "scipy", + "math", + "fractions", + "decimal", + "cmath", + "datetime", + "numpy_financial", + "py_vollib", + "pypfopt", + "empyrical", + "arch", + "statsmodels", ] - + self._allowed_imports = set(allowed_imports) self._timeout = timeout - + @property def supported_libraries(self) -> List[str]: """List of supported libraries/modules.""" return list(self._allowed_imports) - + def validate_syntax(self, code: str) -> tuple[bool, Optional[str]]: """Validate Python syntax. - + Parameters ---------- code : str Python code to validate. - + Returns ------- tuple[bool, Optional[str]] @@ -97,15 +94,15 @@ def validate_syntax(self, code: str) -> tuple[bool, Optional[str]]: return False, f"Syntax error at line {e.lineno}: {e.msg}" except Exception as e: return False, f"Validation error: {str(e)}" - + def _validate_imports(self, code: str) -> tuple[bool, Optional[str]]: """Validate that only allowed imports are used. - + Parameters ---------- code : str Python code to validate. - + Returns ------- tuple[bool, Optional[str]] @@ -119,9 +116,9 @@ def _validate_imports(self, code: str) -> tuple[bool, Optional[str]]: module_name = node.module else: module_name = node.names[0].name if node.names else None - + if module_name: - base_module = module_name.split('.')[0] + base_module = module_name.split(".")[0] if base_module not in self._allowed_imports: return False, ( f"Import of '{module_name}' is not allowed. " @@ -130,10 +127,10 @@ def _validate_imports(self, code: str) -> tuple[bool, Optional[str]]: return True, None except Exception as e: return False, f"Import validation error: {str(e)}" - + def _create_restricted_globals(self) -> Dict[str, Any]: """Create a restricted global namespace for code execution. - + Returns ------- Dict[str, Any] @@ -141,54 +138,56 @@ def _create_restricted_globals(self) -> Dict[str, Any]: """ # Restricted builtins - only safe operations safe_builtins = { - '__import__': __import__, - 'print': print, - 'range': range, - 'len': len, - 'abs': abs, - 'min': min, - 'max': max, - 'sum': sum, - 'round': round, - 'float': float, - 'int': int, - 'str': str, - 'bool': bool, - 'list': list, - 'dict': dict, - 'tuple': tuple, - 'set': set, - 'enumerate': enumerate, - 'zip': zip, - 'sorted': sorted, - 'reversed': reversed, - 'map': map, - 'filter': filter, - 'any': any, - 'all': all, - 'pow': pow, - 'divmod': divmod, - 'isinstance': isinstance, - 'type': type, - 'True': True, - 'False': False, - 'None': None, + "__import__": __import__, + "print": print, + "range": range, + "len": len, + "abs": abs, + "min": min, + "max": max, + "sum": sum, + "round": round, + "float": float, + "int": int, + "str": str, + "bool": bool, + "list": list, + "dict": dict, + "tuple": tuple, + "set": set, + "enumerate": enumerate, + "zip": zip, + "sorted": sorted, + "reversed": reversed, + "map": map, + "filter": filter, + "any": any, + "all": all, + "pow": pow, + "divmod": divmod, + "isinstance": isinstance, + "type": type, + "True": True, + "False": False, + "None": None, } - + return { - '__builtins__': safe_builtins, + "__builtins__": safe_builtins, } - - def execute(self, code: str, context: Optional[Dict[str, Any]] = None) -> CodeExecutionResult: + + def execute( + self, code: str, context: Optional[Dict[str, Any]] = None + ) -> CodeExecutionResult: """Execute Python code with safety restrictions. - + Parameters ---------- code : str Python code to execute. context : Optional[Dict[str, Any]] Additional context (currently unused). - + Returns ------- CodeExecutionResult @@ -197,70 +196,57 @@ def execute(self, code: str, context: Optional[Dict[str, Any]] = None) -> CodeEx # Validate syntax first is_valid, error_msg = self.validate_syntax(code) if not is_valid: - return CodeExecutionResult( - success=False, - output="", - error=error_msg - ) - + return CodeExecutionResult(success=False, output="", error=error_msg) + # Validate imports is_valid, error_msg = self._validate_imports(code) if not is_valid: - return CodeExecutionResult( - success=False, - output="", - error=error_msg - ) - + return CodeExecutionResult(success=False, output="", error=error_msg) + # Create restricted execution environment restricted_globals = self._create_restricted_globals() - + # Pre-import allowed modules to make them available for module_name in self._allowed_imports: try: restricted_globals[module_name] = __import__(module_name) except ImportError: pass # Module not available in environment - + # Capture stdout old_stdout = sys.stdout sys.stdout = captured_output = StringIO() - + try: # Execute the code exec(code, restricted_globals) output = captured_output.getvalue() - + return CodeExecutionResult( - success=True, - output=output, - error=None, - metadata={"executor": "python"} + success=True, output=output, error=None, metadata={"executor": "python"} ) - + except Exception as e: # Capture any execution errors error_msg = f"{type(e).__name__}: {str(e)}\n{traceback.format_exc()}" - + return CodeExecutionResult( success=False, output=captured_output.getvalue(), error=error_msg, - metadata={"executor": "python"} + metadata={"executor": "python"}, ) - + finally: # Restore stdout sys.stdout = old_stdout def execute_python_code( - code: str, - allowed_imports: Optional[List[str]] = None, - timeout: int = 30 + code: str, allowed_imports: Optional[List[str]] = None, timeout: int = 30 ) -> Dict[str, Any]: """Convenience function to execute Python code. - + Parameters ---------- code : str @@ -269,7 +255,7 @@ def execute_python_code( List of allowed imports. timeout : int Maximum execution time in seconds. - + Returns ------- Dict[str, Any] diff --git a/src/tools/toolkit.py b/src/tools/toolkit.py index b2dfea87..ddc7814a 100644 --- a/src/tools/toolkit.py +++ b/src/tools/toolkit.py @@ -6,7 +6,7 @@ import logging from pathlib import Path -from typing import Any, Dict, List +from typing import Any, Dict from autogen_core.models import ChatCompletionClient, SystemMessage, UserMessage @@ -14,6 +14,7 @@ from src.tools.executor import PythonExecutor from src.utils.json_utils import parse_llm_json_response + log = logging.getLogger("tools.toolkit") @@ -57,14 +58,15 @@ # Main Toolkit Class # ============================================================================== + class ScientificToolKit: """Unified toolkit for scientific computing with Python. - + Supports two modes: - RAG mode (enable_rag=True): Retrieves full documentation from HTML files - Parametric mode (enable_rag=False): Uses model's built-in knowledge for fast tagging """ - + def __init__( self, model_client: ChatCompletionClient, @@ -73,7 +75,7 @@ def __init__( enable_rag: bool = True, ): """Initialize toolkit. - + Parameters ---------- model_client : ChatCompletionClient @@ -88,37 +90,48 @@ def __init__( self.model_client = model_client self.enable_tool_selection = enable_tool_selection self.enable_rag = enable_rag - + # Only initialize the heavy retriever if RAG is enabled self.doc_retriever = ScientificDocRetriever(docs_path) if enable_rag else None - + # Setup code executor (always available) self.executor = PythonExecutor( allowed_imports=[ - "numpy", "scipy", "sympy", "math", "fractions", "decimal", "cmath", - "datetime", "statsmodels", + "numpy", + "scipy", + "sympy", + "math", + "fractions", + "decimal", + "cmath", + "datetime", + "statsmodels", # Financial libraries - "numpy_financial", "py_vollib", "pypfopt", "empyrical", "arch" + "numpy_financial", + "py_vollib", + "pypfopt", + "empyrical", + "arch", ] ) - + log.info( f"Initialized ScientificToolKit - " f"Selection: {'enabled' if enable_tool_selection else 'disabled'}, " f"RAG: {'enabled' if enable_rag else 'disabled (parametric)'}" ) - + async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: """Analyze problem and prepare tool context. - + RAG mode: Retrieves full documentation from HTML files. Parametric mode: Uses model's built-in knowledge (fast, for tagging). - + Parameters ---------- problem_text : str Problem statement to analyze. - + Returns ------- Dict[str, Any] @@ -135,9 +148,9 @@ async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: "needs_tools": True, "selected_modules": [], "documentation": "All libraries available. Use parametric knowledge to write code.", - "reasoning": "Tool selection disabled - direct code execution mode" + "reasoning": "Tool selection disabled - direct code execution mode", } - + # 1. Get Library Overview if self.enable_rag and self.doc_retriever: # High precision: exact list of files on disk @@ -145,42 +158,47 @@ async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: else: # High speed: generic description of capabilities overview = PARAMETRIC_OVERVIEW - + # 2. Module Selection via LLM (Same for both modes) response = await self.model_client.create( [ SystemMessage(content="You are an expert scientific programmer."), UserMessage( content=TOOL_SELECTION_PROMPT.format( - problem_text=problem_text, - tools_description=overview + problem_text=problem_text, tools_description=overview ), - source="user" - ) + source="user", + ), ], - json_output=True + json_output=True, ) - + selection = parse_llm_json_response(response.content) - + # 3. Check Necessity (if enabled) - necessity = str(selection.get("tool_necessity", "false")).lower() in ("true", "1", "yes") - + necessity = str(selection.get("tool_necessity", "false")).lower() in ( + "true", + "1", + "yes", + ) + if self.enable_tool_selection and not necessity: log.info("Tool necessity check: No tools needed") return { "needs_tools": False, "selected_modules": [], "documentation": "", - "reasoning": selection.get("reasoning", "No computational tools required") + "reasoning": selection.get( + "reasoning", "No computational tools required" + ), } - + # 4. Context Construction doc_context = [] selected_modules = selection.get("selected_modules", []) - + log.info(f"Selected modules: {selected_modules}") - + # BRANCH: Only fetch HTML content if RAG is enabled if self.enable_rag and self.doc_retriever: for item in selected_modules: @@ -192,27 +210,29 @@ async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: doc_context.append(module_docs) else: # Parametric Mode: Add a lightweight reminder instead of full docs - doc_context.append("Tool selection based on parametric knowledge. Write code assuming standard library versions.") - + doc_context.append( + "Tool selection based on parametric knowledge. Write code assuming standard library versions." + ) + full_documentation = "\n\n".join(doc_context) - + log.info(f"Generated documentation context: {len(full_documentation)} chars") - + return { "needs_tools": True, "selected_modules": selected_modules, "documentation": full_documentation, - "reasoning": selection.get("reasoning", "Computational tools selected") + "reasoning": selection.get("reasoning", "Computational tools selected"), } - + def execute_code(self, code: str) -> Dict[str, Any]: """Execute Python code using the executor. - + Parameters ---------- code : str Python code to execute. - + Returns ------- Dict[str, Any] @@ -220,15 +240,15 @@ def execute_code(self, code: str) -> Dict[str, Any]: """ result = self.executor.execute(code) return result.to_dict() - + def format_tool_context(self, tool_context: Dict[str, Any]) -> str: """Format tool context for inclusion in scientist prompt. - + Parameters ---------- tool_context : Dict[str, Any] Result from prepare_tools(). - + Returns ------- str @@ -236,13 +256,13 @@ def format_tool_context(self, tool_context: Dict[str, Any]) -> str: """ if not tool_context["needs_tools"]: return f"Note: {tool_context['reasoning']}" - + parts = [ "**AVAILABLE TOOLS:**", f"Reasoning: {tool_context['reasoning']}", "", "**MODULE DOCUMENTATION:**", - tool_context["documentation"] + tool_context["documentation"], ] - + return "\n".join(parts) diff --git a/src/utils/json_utils.py b/src/utils/json_utils.py index db962bce..3b6170d9 100644 --- a/src/utils/json_utils.py +++ b/src/utils/json_utils.py @@ -56,31 +56,31 @@ def fix_common_json_errors(content: str) -> str: while i < len(content): char = content[i] - + # Track when we enter/exit string values if char == '"': # Check if this quote is escaped by counting backslashes before it # An escaped quote has an odd number of backslashes before it backslash_count = 0 j = i - 1 - while j >= 0 and content[j] == '\\': + while j >= 0 and content[j] == "\\": backslash_count += 1 j -= 1 - + # If odd number of backslashes, the quote is escaped (part of string content) if backslash_count % 2 == 1: result.append(char) i += 1 continue - + # This is a real quote - toggle string state in_string = not in_string result.append(char) i += 1 continue - + # Handle backslashes inside string values - if char == '\\' and in_string: + if char == "\\" and in_string: if i + 1 < len(content): next_char = content[i + 1] # Check for valid JSON escape sequences @@ -92,24 +92,26 @@ def fix_common_json_errors(content: str) -> str: result.append(next_char) i += 2 continue - elif next_char in 'bfnrt': + if next_char in "bfnrt": # Check if this is a complete escape (not part of longer sequence) # Valid if followed by non-alphanumeric or end of string - if i + 2 >= len(content) or not (content[i + 2].isalnum() or content[i + 2] == '_'): + if i + 2 >= len(content) or not ( + content[i + 2].isalnum() or content[i + 2] == "_" + ): # Complete escape sequence (e.g., \t, \n, \r, \b, \f) result.append(char) result.append(next_char) i += 2 continue # Otherwise it's part of a longer sequence (e.g., \to, \lim) - escape it - result.append('\\\\') + result.append("\\\\") result.append(next_char) i += 2 continue - elif next_char == 'u' and i + 5 < len(content): + if next_char == "u" and i + 5 < len(content): # Check for \uXXXX pattern - hex_part = content[i + 2:i + 6] - if all(c in '0123456789abcdefABCDEF' for c in hex_part): + hex_part = content[i + 2 : i + 6] + if all(c in "0123456789abcdefABCDEF" for c in hex_part): # Valid \uXXXX escape result.append(char) result.append(next_char) @@ -119,21 +121,20 @@ def fix_common_json_errors(content: str) -> str: # Invalid escape sequence (like LaTeX \(, \), \[, \], \epsilon, \to, etc.) # Double-escape the backslash - result.append('\\\\') + result.append("\\\\") result.append(next_char) i += 2 continue - else: - # Backslash at end of content - escape it - result.append('\\\\') - i += 1 - continue - + # Backslash at end of content - escape it + result.append("\\\\") + i += 1 + continue + # Regular character result.append(char) i += 1 - content = ''.join(result) + content = "".join(result) return re.sub(r",(\s*[}\]])", r"\1", content) @@ -171,13 +172,17 @@ def fix_escapes_in_string(match): """Fix escapes within a JSON string value.""" string_content = match.group(1) # Escape backslashes not followed by valid JSON escape chars - fixed = re.sub(r'\\(?!["\\/bfnrtu]|u[0-9a-fA-F]{4})', r'\\\\', string_content) + fixed = re.sub( + r'\\(?!["\\/bfnrtu]|u[0-9a-fA-F]{4})', r"\\\\", string_content + ) return f'"{fixed}"' - + # Find string values and fix them # Pattern: "([^"\\]|\\.)*" - matches JSON string values - fixed_content = re.sub(r'"((?:[^"\\]|\\.)*)"', fix_escapes_in_string, fixed_content) - + fixed_content = re.sub( + r'"((?:[^"\\]|\\.)*)"', fix_escapes_in_string, fixed_content + ) + result = json.loads(fixed_content) log.info("Successfully fixed LaTeX backslash issues") return result if isinstance(result, dict) else {} diff --git a/src/utils/tool_assisted_prompts.py b/src/utils/tool_assisted_prompts.py index 66c3b85a..e7ae5d20 100644 --- a/src/utils/tool_assisted_prompts.py +++ b/src/utils/tool_assisted_prompts.py @@ -4,247 +4,270 @@ # TOOL-ASSISTED TASK SOLVING PROMPTS # ============================================================================= -TOOL_ASSISTED_SYSTEM_MESSAGE = """You are an expert problem solver with access to Python code execution capabilities. You can use the following libraries to assist in solving mathematical problems: +TOOL_ASSISTED_SYSTEM_MESSAGE = """You are an expert problem solver with access to Python code execution capabilities. You can use the following libraries to assist in solving mathematical and financial problems: **Available Libraries:** - **SymPy**: For symbolic mathematics (symbolic integration, differentiation, equation solving, etc.) - **NumPy**: For numerical computations (arrays, linear algebra, numerical methods) - **SciPy**: For scientific computing (numerical integration, optimization, ODE solving) - **Math/Fractions/Decimal**: Standard Python mathematical libraries +- **Financial Libraries**: numpy_financial, py_vollib, pypfopt, empyrical, arch (for finance-specific computations) + +Always use the SIMPLEST tool that solves the problem correctly: +1. If basic arithmetic works → use standard Python operators +2. If you need standard math functions → use math library +3. If you need arrays/matrices → use NumPy +4. If you need symbolic manipulation → use SymPy +5. ONLY use financial libraries (numpy_financial, py_vollib, etc.) when the problem EXPLICITLY involves: + - Time value of money calculations (NPV, IRR, annuities) + - Option pricing models (Black-Scholes, Greeks) + - Portfolio optimization or risk metrics + - Bond pricing with yield curves **When to Use Code:** -- To verify analytical solutions programmatically -- To perform complex symbolic manipulations -- To compute numerical results with high precision -- To check edge cases and special conditions -- To solve problems that are computationally intensive +- For complex calculations that benefit from computational tools +- When the problem requires numerical precision +- For problems involving symbolic manipulation or calculus -**Best Practices:** -- Always explain your mathematical reasoning first -- Use code to verify or compute, not as a substitute for understanding -- Show intermediate steps in your code with print statements -- Verify that code outputs match your analytical reasoning""" +**Response Format:** +You will respond in two stages: +1. First, generate code to solve the problem +2. After seeing the code output, format the final answer appropriately -TOOL_ASSISTED_ROUND_1_PROMPT = """Can you solve the following problem? You have access to Python code execution with SymPy, NumPy, and SciPy. +This ensures you can properly format answers for different question types (boolean, multiple choice, or numerical).""" -PROBLEM: {problem_text} +# ============================================================================= +# STAGE 1: CODE GENERATION PROMPT +# ============================================================================= + +TOOL_ASSISTED_CODE_GENERATION_PROMPT = """PROBLEM: {problem_text} {tool_context} -**Instructions:** -1. First, explain your mathematical approach and reasoning -2. If helpful, write Python code to verify your solution or perform computations -3. Use the code output to inform or verify your final answer -4. Provide a clear final answer +**CRITICAL - SIMPLICITY PRINCIPLE:** +Use the simplest approach that correctly solves the problem. Do NOT use specialized libraries unless the problem explicitly requires them: +- For basic arithmetic (percentages, ratios, simple formulas): Use Python operators or math library +- For algebra/calculus: Use SymPy +- For numerical arrays/linear algebra: Use NumPy +- For statistical calculations: Use SciPy +- For financial derivatives pricing (options, swaps): Use py_vollib or numpy_financial ONLY if the problem explicitly involves these instruments + +**Stage 1 Instructions - Generate Code:** +1. Analyze the problem and identify the simplest appropriate approach +2. Write Python code to solve the problem +3. Make sure your code prints the key results clearly -**Code Execution Format:** -If you want to execute code, include it in your response. The code will be executed and the output will be provided back to you. +DO NOT provide final_answer or numerical_answer yet - you will do that in Stage 2 after seeing the code output. **Available Tools:** ```python -# SymPy - Symbolic Mathematics +# Standard Math (USE FIRST for basic calculations) +import math +from fractions import Fraction +from decimal import Decimal +from datetime import datetime, timedelta + +# SymPy - Symbolic Mathematics (for algebra/calculus) from sympy import symbols, Function, Eq, dsolve, diff, integrate, simplify, solve, limit, series, sqrt, exp, log, sin, cos, tan, pi, E, I, oo from sympy import Matrix, eye, zeros, ones, det, eigenvals, eigenvects from sympy.abc import x, y, z, t, a, b, c, n -# NumPy - Numerical Computing +# NumPy - Numerical Computing (for arrays/linear algebra) import numpy as np # Use: np.array, np.linalg.eig, np.linalg.det, np.linalg.inv, np.linalg.solve, etc. -# SciPy - Scientific Computing +# SciPy - Scientific Computing (for optimization/integration) from scipy import integrate, optimize, linalg from scipy.integrate import odeint, solve_ivp, quad, dblquad -from scipy.stats import norm # for statistical distributions -# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, scipy.stats.norm, etc. - -# Standard Math -import math -from fractions import Fraction -from decimal import Decimal -from datetime import datetime, timedelta +from scipy.stats import norm +# Use: integrate.quad, optimize.fsolve, scipy.stats.norm, etc. -# Financial Computing Libraries +# Financial Computing Libraries (USE ONLY for derivative pricing problems) import numpy_financial as npf -# Use: npf.npv(rate, cashflows), npf.irr(cashflows), npf.pmt(rate, nper, pv), npf.pv(rate, nper, pmt), npf.fv(rate, nper, pmt, pv) - -import empyrical -# Use: empyrical.sharpe_ratio(returns, risk_free), empyrical.sortino_ratio(returns), empyrical.max_drawdown(returns) - -import pypfopt -# Use: pypfopt.efficient_frontier.EfficientFrontier, pypfopt.expected_returns, pypfopt.risk_models +# Use ONLY for: Time value of money with given formulas (NPV, IRR, PMT) +# Example: npf.npv(rate, cashflows), npf.irr(cashflows) import py_vollib.black_scholes as bs from py_vollib.black_scholes.greeks.analytical import delta, gamma, theta, vega -# Use: bs.black_scholes(flag, S, K, t, r, sigma), delta(flag, S, K, t, r, sigma) -# Note: flag is 'c' for call, 'p' for put +# Use ONLY for: Black-Scholes option pricing (calls/puts with volatility) +# Example: bs.black_scholes('c', S, K, t, r, sigma) from arch import arch_model from arch.univariate import HARX -# Use for volatility modeling (GARCH/EGARCH/HARX): -# Example: model = arch_model(returns, vol='GARCH', p=1, q=1) -# fitted = model.fit() -# IMPORTANT: For multi-step forecasts (horizon > 1), use method='simulation': -# forecast = fitted.forecast(horizon=5, method='simulation') -# For simulation: fitted.simulate(params=fitted.params, nobs=100) -# where fitted.params is already a numpy array (not a dict!) -``` +# Use ONLY for: GARCH/volatility modeling problems +# For multi-step forecasts: use method='simulation' -**Few-Shot Examples for Financial Libraries:** +import statsmodels.api as sm +# Use ONLY for: Regression, time series analysis +``` -Example using numpy_financial (NPV/IRR): -{{ - "thought": "To calculate NPV, I'll use numpy_financial which provides the npv function that takes a discount rate and cash flows.", - "code": "import numpy_financial as npf\\ncashflows = [-1000, 200, 300, 400, 500]\\nrate = 0.10\\nresult = npf.npv(rate, cashflows)\\nprint('NPV:', result)", - "code_output": null, - "final_answer": "The Net Present Value is approximately $213.62", - "numerical_answer": 213.62 -}} +IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks. -Example using arch (GARCH modeling with forecasting): -{{ - "thought": "To fit a GARCH(1,1) model and generate multi-step forecasts, I'll use arch_model and specify method='simulation' for horizon > 1.", - "code": "import numpy as np\\nfrom arch import arch_model\\nnp.random.seed(42)\\nreturns = np.random.randn(100) * 0.01\\nmodel = arch_model(returns, vol='GARCH', p=1, q=1)\\nfitted = model.fit(disp='off')\\nprint('Fitted parameters:')\\nprint(fitted.params)\\nforecasts = fitted.forecast(horizon=5, method='simulation')\\nprint('5-step forecast variance:')\\nprint(forecasts.variance.iloc[-1])", - "code_output": null, - "final_answer": "The GARCH(1,1) model has been fitted and 5-step ahead variance forecasts generated using simulation method.", - "numerical_answer": null -}} +CRITICAL: Escape all backslashes in code with double backslashes (\\\\). -Example using arch (HARX model for HAR volatility): +**Stage 1 Response Format (thought + code only):** {{ - "thought": "For HAR (Heterogeneous AutoRegressive) volatility modeling, I'll use arch.univariate.HARX which is built into the arch package.", - "code": "import numpy as np\\nfrom arch.univariate import HARX\\nnp.random.seed(42)\\nreturns = np.random.randn(100) * 0.01\\nmodel = HARX(returns)\\nfitted = model.fit(disp='off')\\nprint('HARX parameters:')\\nprint(fitted.params)", - "code_output": null, - "final_answer": "The HARX model has been fitted to the returns data.", - "numerical_answer": null + "thought": "Your detailed reasoning about the approach", + "code": "Python code to solve the problem" }} -IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. - -CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\) for single backslashes (\\). This is crucial for valid JSON parsing. For example: -- Write \\\\(x^2\\\\) instead of \\(x^2\\) -- Write \\\\[equation\\\\] instead of \\[equation\\] -- Write \\\\times instead of \\times -- For any newlines within a string value, use `\\n` instead of an actual newline. - -**IMPORTANT - Separating Code from Documentation:** -- Your "code" field should contain ONLY executable Python code -- DO NOT include LaTeX in Python code or comments unless properly escaped as raw strings -- Keep LaTeX explanations in "thought" and "final_answer" fields, NOT in code -- If you need to print mathematical expressions from code, use sympy's printing functions or just print the Python representation - -**CRITICAL - Code Field Format:** -When providing Python code in the JSON "code" field, write it as a single-line string with \\n for line breaks. -AVOID using print statements with \\n inside strings - just use simple print statements on separate lines instead. +Respond with valid JSON only.""" -**CODE GENERATION BEST PRACTICES:** -1. **Use triple-quoted strings** for multi-line output: print('''output''') -2. **Separate print statements** instead of embedding \\n in strings -3. **NO LaTeX in code** - Keep LaTeX only in "thought" and "final_answer" fields -4. **Simple, focused code** - Only numerical computation, no decorative output -5. **All imports at top** - Import before using any library -6. **Test mentally** - Walk through the code logic before including it +# ============================================================================= +# STAGE 2: ANSWER FORMATTING PROMPT +# ============================================================================= -**Example of CORRECT code:** -"code": "import numpy as np\\nA = np.array([[1, 1], [1, -1]])\\nb = np.array([2, 0])\\nQ, R = np.linalg.qr(A)\\nx = np.linalg.solve(R, Q.T @ b)\\nprint('Solution:')\\nprint(x)" +TOOL_ASSISTED_ANSWER_FORMATTING_PROMPT = """Your code has been executed. Here is the output: -**Example of INCORRECT code:** -"code": "# Solving \\\\(Ax = b\\\\)\\nA = np.array([[1, 1], [1, -1]])" <- BAD! LaTeX in comments causes syntax errors +``` +{code_output} +``` -**Example of BETTER output formatting:** -"code": "result = [1, 1]\\nprint('x =', result)" <- GOOD! Simple and clear +**Stage 2 Instructions - Format Final Answer:** +Based on the code output above, provide the final answer in the format appropriate for this question type: +- For **boolean questions**: Answer with Yes/No, True/False, or 1/0 as appropriate +- For **multiple choice questions**: Provide the letter (A/B/C/D) and/or the full text of the correct option +- For **numerical questions**: Provide the number with appropriate units and context -Provide your solution in JSON format with the following structure: -{{ - "thought": "Your detailed mathematical reasoning and approach. ENSURE ALL BACKSLASHES IN LATEX ARE ESCAPED WITH DOUBLE BACKSLASHES (\\\\).", - "code": "Python code to execute (if applicable, otherwise null). NO LATEX IN CODE!", - "code_output": "Will be filled in after code execution (leave as null)", - "final_answer": "Your complete answer informed by reasoning and computation", - "numerical_answer": "The final numerical result (if applicable, otherwise null)" -}} +**Question:** {problem_text} -Example with code: +**Response Format:** {{ - "thought": "This is a first-order ODE. I'll solve it analytically using separation of variables, then verify with SymPy.", - "code": "from sympy import *\\nx = symbols('x')\\ny = Function('y')\\node = Eq(y(x).diff(x), x*y(x))\\nsolution = dsolve(ode, y(x))\\nprint('General solution:', solution)\\nprint('Verification:', simplify(solution.lhs - solution.rhs))", - "code_output": null, - "final_answer": "The solution is y = C*exp(x^2/2)", - "numerical_answer": null + "final_answer": "Your complete answer formatted appropriately for the question type", + "numerical_answer": "The final numerical result if applicable, otherwise null" }} -Example without code: -{{ - "thought": "This is a simple algebraic equation. I can solve it directly: 2x + 3 = 11, so 2x = 8, therefore x = 4.", - "code": null, - "code_output": null, - "final_answer": "x = 4", - "numerical_answer": 4 -}} +IMPORTANT: Return raw JSON only. Do not wrap in markdown code blocks. Respond with valid JSON only.""" -TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT = """These are the reasoning and solutions to the problem from other agents: +# ============================================================================= +# LEGACY PROMPTS (kept for backward compatibility with subsequent rounds) +# ============================================================================= -{other_solutions} +TOOL_ASSISTED_ROUND_1_PROMPT_LEGACY = """PROBLEM: {problem_text} -Using the solutions from other agents as additional information, can you provide your answer to the problem? You have access to Python code execution with SymPy, NumPy, and SciPy. +{tool_context} -The original problem is: {problem_text} +**CRITICAL - SIMPLICITY PRINCIPLE:** +Use the simplest approach that correctly solves the problem. Do NOT use specialized libraries unless the problem explicitly requires them: +- For basic arithmetic (percentages, ratios, simple formulas): Use Python operators or math library +- For algebra/calculus: Use SymPy +- For numerical arrays/linear algebra: Use NumPy +- For statistical calculations: Use SciPy +- For financial derivatives pricing (options, swaps): Use py_vollib or numpy_financial ONLY if the problem explicitly involves these instruments **Instructions:** -1. Review the other agents' approaches and identify strengths/weaknesses -2. Explain your mathematical reasoning, considering the other solutions -3. If helpful, use Python code to verify or compute -4. Provide your final answer, which may agree with, disagree with, or synthesize the other solutions +1. Explain your mathematical reasoning and approach +2. Write Python code using the SIMPLEST appropriate tools +3. Use the code output to inform your final answer +4. Provide a clear final numerical answer **Available Tools:** ```python -# SymPy - Symbolic Mathematics +# Standard Math (USE FIRST for basic calculations) +import math +from fractions import Fraction +from decimal import Decimal +from datetime import datetime, timedelta + +# SymPy - Symbolic Mathematics (for algebra/calculus) from sympy import symbols, Function, Eq, dsolve, diff, integrate, simplify, solve, limit, series, sqrt, exp, log, sin, cos, tan, pi, E, I, oo from sympy import Matrix, eye, zeros, ones, det, eigenvals, eigenvects from sympy.abc import x, y, z, t, a, b, c, n -# NumPy - Numerical Computing +# NumPy - Numerical Computing (for arrays/linear algebra) import numpy as np +# Use: np.array, np.linalg.eig, np.linalg.det, np.linalg.inv, np.linalg.solve, etc. -# SciPy - Scientific Computing +# SciPy - Scientific Computing (for optimization/integration) from scipy import integrate, optimize, linalg from scipy.integrate import odeint, solve_ivp, quad, dblquad from scipy.stats import norm -# Use: integrate.quad, optimize.fsolve, linalg.lu, linalg.qr, linalg.svd, scipy.stats.norm +# Use: integrate.quad, optimize.fsolve, scipy.stats.norm, etc. -# Financial Computing Libraries +# Financial Computing Libraries (USE ONLY for derivative pricing problems) import numpy_financial as npf -# Use: npf.npv(rate, cashflows), npf.irr(cashflows), npf.pmt(rate, nper, pv) +# Use ONLY for: Time value of money with given formulas (NPV, IRR, PMT) +# Example: npf.npv(rate, cashflows), npf.irr(cashflows) -import empyrical -import pypfopt import py_vollib.black_scholes as bs from py_vollib.black_scholes.greeks.analytical import delta, gamma, theta, vega +# Use ONLY for: Black-Scholes option pricing (calls/puts with volatility) +# Example: bs.black_scholes('c', S, K, t, r, sigma) from arch import arch_model -# For GARCH: model = arch_model(returns, vol='GARCH', p=1, q=1); fitted = model.fit() -# For simulation: fitted.simulate(params=fitted.params, nobs=100) - params is already a numpy array! +from arch.univariate import HARX +# Use ONLY for: GARCH/volatility modeling problems +# For multi-step forecasts: use method='simulation' + +import statsmodels.api as sm +# Use ONLY for: Regression, time series analysis ``` -IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. Do not include any prefixes or prose. The JSON should be directly parseable. +IMPORTANT: Return your response as raw JSON only. Do not wrap it in markdown code blocks or add any formatting. + +CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\). + +Provide your solution in JSON format: +{{ + "thought": "Your detailed mathematical reasoning. Identify which tool is most appropriate and why.", + "code": "Python code using simplest appropriate tools", + "code_output": null, + "final_answer": "Your complete answer", + "numerical_answer": "The final numerical result (if applicable, otherwise null)" +}} + +Respond with valid JSON only.""" + +TOOL_ASSISTED_SUBSEQUENT_ROUNDS_PROMPT = """These are the reasoning and solutions to the problem from other agents: + +{other_solutions} + +Using the solutions from other agents as additional information, can you provide your answer to the problem? -CRITICAL: You MUST escape all backslashes in LaTeX expressions within JSON strings. Use double backslashes (\\\\) for single backslashes (\\). This is crucial for valid JSON parsing. For example: -- Write \\\\(x^2\\\\) instead of \\(x^2\\) -- Write \\\\[equation\\\\] instead of \\[equation\\] -- Write \\\\times instead of \\times -- For any newlines within a string value, use `\\n` instead of an actual newline. +The original problem is: {problem_text} -**IMPORTANT - Separating Code from Documentation:** -- Your "code" field should contain ONLY executable Python code -- DO NOT include LaTeX in Python code or comments unless properly escaped as raw strings -- Keep LaTeX explanations in "thought" and "final_answer" fields, NOT in code -- If you need to print mathematical expressions from code, use sympy's printing functions +**CRITICAL - SIMPLICITY PRINCIPLE:** +Use the simplest approach that correctly solves the problem. Review other agents' approaches - if they used complex libraries unnecessarily, prefer the simpler solution. + +**Instructions:** +1. Classify the problem type and identify the simplest correct approach +2. Review other agents' solutions - identify which tools they used and whether they were appropriate +3. Provide your solution using the simplest effective approach +4. Your answer may agree with, disagree with, or synthesize the other solutions + +**Available Tools:** +```python +# Standard Math (USE FIRST for basic calculations) +import math +from fractions import Fraction +from decimal import Decimal + +# SymPy (for algebra/calculus) +from sympy import symbols, solve, simplify, diff, integrate + +# NumPy (for arrays/linear algebra) +import numpy as np + +# SciPy (for optimization/integration) +from scipy import optimize, integrate, stats + +# Financial Libraries (USE ONLY for derivative pricing) +import numpy_financial as npf +import py_vollib.black_scholes as bs +from arch import arch_model +``` + +IMPORTANT: Return your response as raw JSON only. Use the simplicity principle - prefer basic Python over specialized libraries unless truly needed. + +CRITICAL: Escape all backslashes in LaTeX with double backslashes (\\\\). Provide your solution in JSON format: {{ - "thought": "Your reasoning considering other agents' solutions. ENSURE ALL BACKSLASHES IN LATEX ARE ESCAPED WITH DOUBLE BACKSLASHES (\\\\).", - "code": "Python code to execute (if applicable, otherwise null). NO LATEX IN CODE!", - "code_output": "Will be filled in after code execution (leave as null)", + "thought": "Your detailed reasoning. Review what other agents tried and choose the simplest effective approach.", + "code": "Python code using simplest appropriate tools", + "code_output": null, "final_answer": "Your complete answer", "numerical_answer": "The final numerical result (if applicable, otherwise null)" }} @@ -389,3 +412,10 @@ def lotka_volterra(state, t, alpha, beta, gamma, delta): print("\\ne^(iθ) =", euler) print("Expanded:", expand(euler, complex=True)) """ + +# ============================================================================= +# BACKWARD COMPATIBILITY ALIAS +# ============================================================================= +# For existing code that imports TOOL_ASSISTED_ROUND_1_PROMPT +# This now uses the new two-stage architecture (Code Generation -> Answer Formatting) +TOOL_ASSISTED_ROUND_1_PROMPT = TOOL_ASSISTED_CODE_GENERATION_PROMPT diff --git a/test_arch_only.py b/test_arch_only.py index 65fb24da..6a24fd4b 100644 --- a/test_arch_only.py +++ b/test_arch_only.py @@ -2,78 +2,95 @@ """Quick test for arch library familiarity only""" import asyncio -import sys import json -sys.path.insert(0, 'src') +import sys + + +sys.path.insert(0, "src") + +from autogen_core.models import SystemMessage, UserMessage from scripts.assess_tool_familiarity import PROBES -from utils.model_client_utils import get_model_client -from utils.tool_assisted_prompts import TOOL_ASSISTED_SYSTEM_MESSAGE, TOOL_ASSISTED_ROUND_1_PROMPT from tools.executor import PythonExecutor -from autogen_core.models import SystemMessage, UserMessage +from utils.model_client_utils import get_model_client +from utils.tool_assisted_prompts import ( + TOOL_ASSISTED_ROUND_1_PROMPT, + TOOL_ASSISTED_SYSTEM_MESSAGE, +) + async def test_arch(model_name="gemini-3-flash-preview"): client = get_model_client(model_name) executor = PythonExecutor( - allowed_imports=["arch", "numpy", "pandas", "scipy", "datetime", "math", "statsmodels"] + allowed_imports=[ + "arch", + "numpy", + "pandas", + "scipy", + "datetime", + "math", + "statsmodels", + ] ) - - print(f"\n{'='*70}") + + print(f"\n{'=' * 70}") print(f"Testing arch familiarity with {model_name}...") - print(f"{'='*70}\n") - + print(f"{'=' * 70}\n") + arch_probes = PROBES["arch"] success_count = 0 - + for i, prompt in enumerate(arch_probes, 1): print(f"[Q{i}/5] {prompt[:60]}...") - + try: - tool_context_str = f"""You have access to financial computing libraries. + tool_context_str = """You have access to financial computing libraries. Available libraries: arch Use these libraries to solve the problem.""" - + formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=prompt, - tool_context=tool_context_str + problem_text=prompt, tool_context=tool_context_str ) - - resp = await client.create([ - SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), - UserMessage(content=formatted_prompt, source="user") - ]) - + + resp = await client.create( + [ + SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), + UserMessage(content=formatted_prompt, source="user"), + ] + ) + # Parse JSON response try: response_data = json.loads(resp.content) code = response_data.get("code") - + if code is None: - print(f" ✗ FAIL: No code provided\n") + print(" ✗ FAIL: No code provided\n") continue - + except json.JSONDecodeError as e: print(f" ✗ FAIL: JSON parse error: {str(e)[:100]}\n") continue exec_result = executor.execute(code) - + if exec_result.success: - print(f" ✓ PASS\n") + print(" ✓ PASS\n") success_count += 1 else: print(f" ✗ FAIL: {exec_result.error[:150]}\n") except Exception as e: print(f" ✗ ERROR: {str(e)[:150]}\n") - - print(f"{'='*70}") + + print(f"{'=' * 70}") print(f"Result: {success_count}/5") print(f"Status: {'✓ PASS (≥4/5)' if success_count >= 4 else '✗ FAIL (<4/5)'}") - print(f"{'='*70}\n") - + print(f"{'=' * 70}\n") + return success_count >= 4 + if __name__ == "__main__": model = sys.argv[1] if len(sys.argv) > 1 else "gemini-3-flash-preview" passed = asyncio.run(test_arch(model)) diff --git a/test_llm_judge.py b/test_llm_judge.py index 52df9e9f..8bc890f6 100644 --- a/test_llm_judge.py +++ b/test_llm_judge.py @@ -3,14 +3,20 @@ import asyncio import sys -sys.path.insert(0, 'src') + + +sys.path.insert(0, "src") from dotenv import load_dotenv + + load_dotenv() -from utils.model_client_utils import get_model_client from autogen_core.models import SystemMessage, UserMessage +from utils.model_client_utils import get_model_client + + async def test_llm_judge(expected, prediction, should_pass): """Test a single case""" judge_prompt = f"""You are an expert financial auditor grading an AI's mathematical execution. Your goal is to determine if the AI produced the mathematically exact final answer. @@ -27,26 +33,31 @@ async def test_llm_judge(expected, prediction, should_pass): Based on these rules, did the AI predict the exact correct number? Output ONLY the word "TRUE" or "FALSE". Do not provide any other explanation.""" - + client = get_model_client("gemini-3-flash-preview") - response = await client.create([ - SystemMessage(content="You are a precise numerical evaluator."), - UserMessage(content=judge_prompt, source="user") - ]) - + response = await client.create( + [ + SystemMessage(content="You are a precise numerical evaluator."), + UserMessage(content=judge_prompt, source="user"), + ] + ) + judgment = response.content.strip().upper() result = "TRUE" in judgment - + status = "✓ PASS" if result == should_pass else "✗ FAIL" - print(f"{status}: Expected={expected}, Prediction={prediction}, Judge={judgment} (expected {should_pass})") + print( + f"{status}: Expected={expected}, Prediction={prediction}, Judge={judgment} (expected {should_pass})" + ) return result == should_pass + async def main(): """Run test cases""" - print("\n" + "="*70) + print("\n" + "=" * 70) print("Testing LLM-as-a-Judge Verifier") - print("="*70 + "\n") - + print("=" * 70 + "\n") + test_cases = [ # (expected, prediction, should_pass, description) ("1.41", "$1.41", True, "Formatting: $ symbol"), @@ -58,19 +69,20 @@ async def main(): ("5000", "$5,000", True, "Formatting: commas"), ("0.05", "5%", True, "Formatting: percentage"), ] - + results = [] for expected, prediction, should_pass, description in test_cases: print(f"\nTest: {description}") result = await test_llm_judge(expected, prediction, should_pass) results.append(result) - - print("\n" + "="*70) + + print("\n" + "=" * 70) print(f"Results: {sum(results)}/{len(results)} tests passed") - print("="*70 + "\n") - + print("=" * 70 + "\n") + return all(results) + if __name__ == "__main__": success = asyncio.run(main()) sys.exit(0 if success else 1) diff --git a/tests/src/utils/test_json_utils_latex.py b/tests/src/utils/test_json_utils_latex.py index b2f645d5..c0a715c6 100644 --- a/tests/src/utils/test_json_utils_latex.py +++ b/tests/src/utils/test_json_utils_latex.py @@ -15,8 +15,6 @@ import json -import pytest - from src.utils.json_utils import ( fix_common_json_errors, parse_llm_json_response, @@ -31,7 +29,7 @@ def test_latex_parentheses_escaped(self): content = '{"thought": "The equation \\(x + y\\) is important"}' result = fix_common_json_errors(content) # Should double-escape: \\( becomes \\\\( - assert '\\\\(x + y\\\\)' in result or '\\\\(' in result + assert "\\\\(x + y\\\\)" in result or "\\\\(" in result # Verify it can be parsed parsed = json.loads(result) assert "(" in parsed["thought"] or "\\(" in parsed["thought"] @@ -41,7 +39,7 @@ def test_latex_brackets_escaped(self): content = '{"thought": "The equation \\[x + y = z\\] is important"}' result = fix_common_json_errors(content) # Should double-escape: \\[ becomes \\\\[ - assert '\\\\[' in result or '\\[' in result + assert "\\\\[" in result or "\\[" in result # Verify it can be parsed parsed = json.loads(result) assert "[" in parsed["thought"] or "\\[" in parsed["thought"] @@ -51,7 +49,7 @@ def test_latex_commands_escaped(self): content = '{"thought": "As x \\to 0, we have \\lim f(x)"}' result = fix_common_json_errors(content) # Should escape \to and \lim - assert '\\to' in result or '\\\\to' in result + assert "\\to" in result or "\\\\to" in result # Verify it can be parsed parsed = json.loads(result) assert "to" in parsed["thought"] or "\\to" in parsed["thought"] @@ -62,8 +60,8 @@ def test_valid_json_escapes_preserved(self): content = r'{"text": "Line 1\nLine 2\tTabbed"}' result = fix_common_json_errors(content) # Valid escapes should remain (as single backslash sequences) - assert r'\n' in result or '\n' in result - assert r'\t' in result or '\t' in result + assert r"\n" in result or "\n" in result + assert r"\t" in result or "\t" in result # Verify it can be parsed and produces actual newline/tab characters parsed = json.loads(result) assert "\n" in parsed["text"] or "\\n" in parsed["text"] @@ -84,7 +82,7 @@ def test_valid_json_backslash_escape_preserved(self): content = '{"path": "C:\\\\Users\\\\file.txt"}' result = fix_common_json_errors(content) # Escaped backslashes should remain - assert '\\\\' in result + assert "\\\\" in result # Verify it can be parsed parsed = json.loads(result) assert "\\" in parsed["path"] @@ -103,7 +101,7 @@ def test_mixed_valid_and_invalid_escapes(self): content = r'{"text": "Valid\nNewline and invalid\to LaTeX"}' result = fix_common_json_errors(content) # Valid escape should remain, invalid should be escaped - assert r'\n' in result or '\n' in result + assert r"\n" in result or "\n" in result # Verify it can be parsed parsed = json.loads(result) # Should contain newline character or escaped newline diff --git a/tests/tools/test_executor.py b/tests/tools/test_executor.py index 2c836969..deda83cf 100644 --- a/tests/tools/test_executor.py +++ b/tests/tools/test_executor.py @@ -1,88 +1,86 @@ """Unit tests for Python executor.""" -import pytest - from src.tools.executor import PythonExecutor, execute_python_code class TestPythonExecutor: """Test suite for PythonExecutor.""" - + def test_simple_execution(self): """Test executing simple Python code.""" executor = PythonExecutor() - + code = "print('Hello, World!')" result = executor.execute(code) - + assert result.success assert "Hello, World!" in result.output assert result.error is None - + def test_math_computation(self): """Test mathematical computation.""" executor = PythonExecutor() - + code = """ import math result = math.sqrt(16) print(f"Result: {result}") """ result = executor.execute(code) - + assert result.success assert "Result: 4.0" in result.output - + def test_numpy_execution(self): """Test NumPy code execution.""" executor = PythonExecutor() - + code = """ import numpy as np arr = np.array([1, 2, 3, 4]) print(f"Sum: {arr.sum()}") """ result = executor.execute(code) - + assert result.success assert "Sum: 10" in result.output - + def test_syntax_error(self): """Test handling syntax errors.""" executor = PythonExecutor() - + code = "print('unclosed string" result = executor.execute(code) - + assert not result.success assert result.error is not None assert "Syntax error" in result.error - + def test_runtime_error(self): """Test handling runtime errors.""" executor = PythonExecutor() - + code = "x = 1 / 0" result = executor.execute(code) - + assert not result.success assert result.error is not None assert "ZeroDivisionError" in result.error - + def test_restricted_import(self): """Test that restricted imports are blocked.""" executor = PythonExecutor() - + code = "import os" result = executor.execute(code) - + assert not result.success assert "not allowed" in result.error - + def test_sympy_execution(self): """Test SymPy code execution.""" executor = PythonExecutor() - + code = """ from sympy import symbols, diff x = symbols('x') @@ -91,28 +89,28 @@ def test_sympy_execution(self): print(f"Derivative: {derivative}") """ result = executor.execute(code) - + assert result.success assert "Derivative:" in result.output - + def test_validate_syntax(self): """Test syntax validation.""" executor = PythonExecutor() - + # Valid syntax is_valid, error = executor.validate_syntax("print('hello')") assert is_valid assert error is None - + # Invalid syntax is_valid, error = executor.validate_syntax("print('unclosed") assert not is_valid assert error is not None - + def test_convenience_function(self): """Test convenience function.""" code = "print('test')" result = execute_python_code(code) - + assert result["success"] assert "test" in result["output"] diff --git a/tests/tools/test_toolkit.py b/tests/tools/test_toolkit.py index 29447d1c..e165a630 100644 --- a/tests/tools/test_toolkit.py +++ b/tests/tools/test_toolkit.py @@ -1,7 +1,5 @@ """Tests for simplified toolkit.""" -import pytest -from pathlib import Path from unittest.mock import AsyncMock, Mock from src.tools.toolkit import ScientificToolKit @@ -9,50 +7,49 @@ class TestScientificToolKit: """Test suite for ScientificToolKit.""" - + def test_toolkit_initialization(self): """Test toolkit initializes with direct instantiation.""" mock_client = Mock() - + toolkit = ScientificToolKit( model_client=mock_client, enable_tool_selection=True, ) - + assert toolkit is not None assert toolkit.executor is not None assert toolkit.tool is not None - + def test_code_execution(self): """Test code execution works.""" mock_client = Mock() toolkit = ScientificToolKit(model_client=mock_client) - + result = toolkit.execute_code("print(2 + 2)") - + assert result["success"] is True assert "4" in result["output"] - + async def test_tool_preparation_disabled(self): """Test tool preparation with selection disabled.""" mock_client = Mock() toolkit = ScientificToolKit( - model_client=mock_client, - enable_tool_selection=False + model_client=mock_client, enable_tool_selection=False ) - + result = await toolkit.prepare_tools("Find sqrt(16)") - + assert result["needs_tools"] is True assert len(result["selected_libraries"]) > 0 - + async def test_tool_preparation_enabled(self): """Test tool preparation with selection enabled.""" mock_client = AsyncMock() - + # Mock LLM responses stage1_response = Mock() - stage1_response.content = ''' + stage1_response.content = """ { "needs_tools": true, "reasoning": "Need numerical computation", @@ -65,10 +62,10 @@ async def test_tool_preparation_enabled(self): ], "confidence": "high" } - ''' - + """ + stage2_response = Mock() - stage2_response.content = ''' + stage2_response.content = """ { "selected_modules": [ { @@ -79,34 +76,33 @@ async def test_tool_preparation_enabled(self): ], "approach": "Use np.sqrt" } - ''' - + """ + mock_client.create.side_effect = [stage1_response, stage2_response] - + toolkit = ScientificToolKit( - model_client=mock_client, - enable_tool_selection=True + model_client=mock_client, enable_tool_selection=True ) - + result = await toolkit.prepare_tools("Find sqrt(16)") - + assert result["needs_tools"] is True assert "numpy" in result["selected_libraries"] - + def test_format_tool_context(self): """Test formatting of tool context.""" mock_client = Mock() toolkit = ScientificToolKit(model_client=mock_client) - + context = { "needs_tools": True, "selected_libraries": ["numpy", "scipy"], "approach": "Use numerical methods", - "selected_modules": [] + "selected_modules": [], } - + formatted = toolkit.format_tool_context(context) - + assert "numpy" in formatted assert "scipy" in formatted assert "Use numerical methods" in formatted From 9a25edf19ff3ff6f9b5f067235fa4a35474917c7 Mon Sep 17 00:00:00 2001 From: a-kore Date: Tue, 10 Mar 2026 15:18:13 -0400 Subject: [PATCH 6/8] refator and add docs --- manage_tasks.py | 393 -------- poetry.lock | 669 +++++-------- pyproject.toml | 4 + selected_tasks.json | 162 --- src/run_experiments.py | 1044 -------------------- src/scripts/assess_tool_familiarity.py | 271 ----- src/scripts/classify_tasks.py | 638 ------------ src/scripts/classify_xfinbench_tasks.py | 702 ------------- src/scripts/generate_filtered_dataset.py | 361 ------- src/scripts/generate_task_dataset.sh | 173 ---- src/task_solver/messages.py | 8 +- src/task_solver/moderator.py | 13 +- src/task_solver/tool_assisted_scientist.py | 55 +- src/tools/README.md | 265 +++++ src/tools/docs.py | 34 +- src/tools/executor.py | 9 +- src/tools/toolkit.py | 23 +- src/utils/json_utils.py | 26 +- test_arch_only.py | 97 -- test_llm_judge.py | 88 -- tests/src/utils/test_json_utils_latex.py | 7 +- tests/tools/test_toolkit.py | 566 ++++++++++- tests/tools/test_toolkit_integration.py | 292 ++++++ uv.lock | 117 ++- 24 files changed, 1512 insertions(+), 4505 deletions(-) delete mode 100755 manage_tasks.py delete mode 100644 selected_tasks.json delete mode 100644 src/run_experiments.py delete mode 100644 src/scripts/assess_tool_familiarity.py delete mode 100644 src/scripts/classify_tasks.py delete mode 100644 src/scripts/classify_xfinbench_tasks.py delete mode 100644 src/scripts/generate_filtered_dataset.py delete mode 100755 src/scripts/generate_task_dataset.sh create mode 100644 src/tools/README.md delete mode 100644 test_arch_only.py delete mode 100644 test_llm_judge.py create mode 100644 tests/tools/test_toolkit_integration.py diff --git a/manage_tasks.py b/manage_tasks.py deleted file mode 100755 index 2c82169a..00000000 --- a/manage_tasks.py +++ /dev/null @@ -1,393 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified Task Management Script - -Consolidates task discovery, selection, and extraction into one tool. - -Usage: - # List all failed tasks - python manage_tasks.py list - - # Extract tasks using default selections - python manage_tasks.py extract - - # Extract specific tasks - python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 - - # Extract N random failed tasks per capability - python manage_tasks.py extract --random 3 -""" - -import argparse -import json -import random -from pathlib import Path -from typing import Any, Dict, List - - -# Default task selections (can be overridden via CLI) -DEFAULT_TASK_SELECTIONS = { - "computational_linear_algebra": [2, 3, 4], - "numerical_simulation_dynamical_systems": [1, 2, 3], - "numerical_methods_ode": [2, 4, 5], - "complex_analysis_problems": [3, 4, 5], - "advanced_integration_techniques": [2, 6, 9], -} - - -class TaskManager: - """Manages task discovery, selection, and extraction.""" - - def __init__(self, math_dir: Path = Path("math")): - self.math_dir = math_dir - - def load_capability_file(self, capability_name: str) -> Dict[str, Any]: - """Load a capability JSON file.""" - cap_dir = self.math_dir / capability_name - - if not cap_dir.exists(): - raise FileNotFoundError(f"Capability directory not found: {cap_dir}") - - cap_file = cap_dir / "capability.json" - if not cap_file.exists(): - raise FileNotFoundError(f"Capability file not found: {cap_file}") - - with open(cap_file, "r", encoding="utf-8") as f: - return json.load(f) - - def find_failed_tasks(self, capability_name: str) -> List[Dict[str, Any]]: - """Find all tasks with verdict='no' in a capability.""" - try: - capability_data = self.load_capability_file(capability_name) - except FileNotFoundError: - return [] - - failed_tasks = [] - failed_data = capability_data.get("capability_failed_data", []) - - for task_data in failed_data: - verification = task_data.get("verification", {}) - verdict = verification.get("verdict", "") - - if verdict == "no": - failed_tasks.append( - { - "task_id": task_data.get("id", ""), - "problem": task_data.get("problem", ""), - "answer": task_data.get("answer", ""), - "reasoning": task_data.get("reasoning", ""), - "verification": verification, - } - ) - - return failed_tasks - - def list_all_failed_tasks(self) -> Dict[str, List[Dict[str, Any]]]: - """Find all failed tasks across all capabilities.""" - all_failed = {} - - if not self.math_dir.exists(): - print(f"Error: Math directory not found: {self.math_dir}") - return all_failed - - capability_dirs = sorted([d for d in self.math_dir.iterdir() if d.is_dir()]) - - for cap_dir in capability_dirs: - capability_name = cap_dir.name - failed_tasks = self.find_failed_tasks(capability_name) - - if failed_tasks: - all_failed[capability_name] = failed_tasks - - return all_failed - - def extract_specific_tasks( - self, task_selections: Dict[str, List[int]] - ) -> Dict[str, List[Dict[str, Any]]]: - """Extract specific tasks by ID.""" - extracted_tasks = {} - - for capability_name, task_ids in task_selections.items(): - try: - capability_data = self.load_capability_file(capability_name) - failed_data = capability_data.get("capability_failed_data", []) - - tasks = [] - for task_id in task_ids: - # Find the task with matching ID - for task in failed_data: - if str(task.get("id", "")) == str(task_id): - tasks.append( - { - "task_id": task.get("id", ""), - "problem": task.get("problem", ""), - "answer": task.get("answer", ""), - "reasoning": task.get("reasoning", ""), - "verification": task.get("verification", {}), - } - ) - break - - if tasks: - extracted_tasks[capability_name] = tasks - - except FileNotFoundError as e: - print(f"Warning: {e}") - continue - - return extracted_tasks - - def extract_random_tasks( - self, num_per_capability: int - ) -> Dict[str, List[Dict[str, Any]]]: - """Extract N random failed tasks per capability.""" - all_failed = self.list_all_failed_tasks() - extracted_tasks = {} - - for capability_name, failed_tasks in all_failed.items(): - if len(failed_tasks) >= num_per_capability: - selected = random.sample(failed_tasks, num_per_capability) - extracted_tasks[capability_name] = selected - elif failed_tasks: - print( - f"Warning: {capability_name} has only {len(failed_tasks)} failed tasks (requested {num_per_capability})" - ) - extracted_tasks[capability_name] = failed_tasks - - return extracted_tasks - - def save_tasks(self, tasks: Dict[str, List[Dict[str, Any]]], output_file: Path): - """Save extracted tasks to JSON file.""" - with open(output_file, "w", encoding="utf-8") as f: - json.dump(tasks, f, indent=2, ensure_ascii=False) - - print(f"✓ Saved {sum(len(t) for t in tasks.values())} tasks to: {output_file}") - - -def print_list_summary(all_failed: Dict[str, List[Dict[str, Any]]]): - """Print summary of all failed tasks.""" - print("\n" + "=" * 80) - print("FAILED TASKS SUMMARY") - print("=" * 80) - - total_capabilities = len(all_failed) - total_tasks = sum(len(tasks) for tasks in all_failed.values()) - - print(f"\nTotal capabilities with failed tasks: {total_capabilities}") - print(f"Total failed tasks: {total_tasks}\n") - - print(f"{'Capability':<50} {'Failed Tasks':<15} {'Task IDs'}") - print("-" * 80) - - for cap_name, tasks in sorted(all_failed.items()): - task_ids = [str(t["task_id"]) for t in tasks] - task_ids_str = ", ".join(task_ids[:10]) - if len(task_ids) > 10: - task_ids_str += f", ... ({len(task_ids) - 10} more)" - - print(f"{cap_name:<50} {len(tasks):<15} {task_ids_str}") - - print("=" * 80) - - -def print_extract_summary(tasks: Dict[str, List[Dict[str, Any]]]): - """Print summary of extracted tasks.""" - print("\n" + "=" * 80) - print("EXTRACTION SUMMARY") - print("=" * 80) - - total_tasks = sum(len(task_list) for task_list in tasks.values()) - - print(f"\nTotal tasks extracted: {total_tasks}") - print(f"Total capabilities: {len(tasks)}\n") - - print(f"{'Capability':<50} {'Tasks':<10} {'Task IDs'}") - print("-" * 80) - - for cap_name, task_list in sorted(tasks.items()): - task_ids = [str(t["task_id"]) for t in task_list] - task_ids_str = ", ".join(task_ids) - print(f"{cap_name:<50} {len(task_list):<10} {task_ids_str}") - - # Verify all are failures - print("\n" + "=" * 80) - print("VERIFICATION CHECK") - print("=" * 80) - - all_failed = True - for cap_name, task_list in tasks.items(): - for task in task_list: - verdict = task["verification"].get("verdict", "") - if verdict != "no": - print( - f"⚠️ {cap_name} Task {task['task_id']}: verdict='{verdict}' (NOT A FAILURE)" - ) - all_failed = False - - if all_failed: - print("✓ All extracted tasks have verdict='no' (confirmed failures)") - else: - print("✗ Some tasks are not failures - review selections") - - print("=" * 80) - - -def parse_task_selections(selections: List[str]) -> Dict[str, List[int]]: - """Parse task selections from CLI format. - - Example: ["cap1:1,2,3", "cap2:4,5,6"] -> {"cap1": [1,2,3], "cap2": [4,5,6]} - """ - parsed = {} - for selection in selections: - if ":" not in selection: - print( - f"Warning: Invalid format '{selection}'. Expected 'capability:id1,id2,id3'" - ) - continue - - cap_name, ids_str = selection.split(":", 1) - task_ids = [int(id.strip()) for id in ids_str.split(",")] - parsed[cap_name] = task_ids - - return parsed - - -def cmd_list(args): - """List all failed tasks.""" - manager = TaskManager() - all_failed = manager.list_all_failed_tasks() - - if not all_failed: - print("No failed tasks found.") - return - - print_list_summary(all_failed) - - if args.verbose: - print("\n" + "=" * 80) - print("DETAILED TASK INFORMATION") - print("=" * 80) - - for cap_name, tasks in sorted(all_failed.items()): - print(f"\n{'=' * 80}") - print(f"Capability: {cap_name}") - print(f"{'=' * 80}") - - for task in tasks: - print(f"\nTask ID: {task['task_id']}") - print(f"Problem: {task['problem'][:200]}...") - print(f"Answer: {task['answer'][:100]}...") - print(f"Verdict: {task['verification'].get('verdict', 'unknown')}") - print("-" * 80) - - -def cmd_extract(args): - """Extract tasks based on selections.""" - manager = TaskManager() - - if args.random: - print(f"Extracting {args.random} random failed tasks per capability...") - random.seed(args.seed) - extracted_tasks = manager.extract_random_tasks(args.random) - elif args.tasks: - print("Extracting specified tasks...") - task_selections = parse_task_selections(args.tasks) - extracted_tasks = manager.extract_specific_tasks(task_selections) - else: - print("Extracting default task selections...") - extracted_tasks = manager.extract_specific_tasks(DEFAULT_TASK_SELECTIONS) - - if not extracted_tasks: - print("No tasks extracted.") - return - - # Save tasks - output_file = Path(args.output) - manager.save_tasks(extracted_tasks, output_file) - - # Print summary - print_extract_summary(extracted_tasks) - - print("\n" + "=" * 80) - print("NEXT STEPS") - print("=" * 80) - print(f"1. Review {args.output}") - print("2. Run experiments: python run_experiments.py") - print("=" * 80) - - -def main(): - parser = argparse.ArgumentParser( - description="Unified task management for capability evaluation experiments", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # List all failed tasks - python manage_tasks.py list - - # List with detailed information - python manage_tasks.py list --verbose - - # Extract using default selections - python manage_tasks.py extract - - # Extract specific tasks - python manage_tasks.py extract --tasks computational_linear_algebra:2,3,4 numerical_methods_ode:2,4,5 - - # Extract 3 random failed tasks per capability - python manage_tasks.py extract --random 3 - - # Extract to custom output file - python manage_tasks.py extract --output my_tasks.json - """, - ) - - subparsers = parser.add_subparsers(dest="command", help="Available commands") - - # List command - list_parser = subparsers.add_parser("list", help="List all failed tasks") - list_parser.add_argument( - "--verbose", "-v", action="store_true", help="Show detailed task information" - ) - - # Extract command - extract_parser = subparsers.add_parser("extract", help="Extract tasks to JSON file") - extract_parser.add_argument( - "--output", - "-o", - default="selected_tasks.json", - help="Output file path (default: selected_tasks.json)", - ) - extract_group = extract_parser.add_mutually_exclusive_group() - extract_group.add_argument( - "--tasks", - "-t", - nargs="+", - help="Specific tasks to extract (format: capability:id1,id2,id3)", - ) - extract_group.add_argument( - "--random", - "-r", - type=int, - metavar="N", - help="Extract N random failed tasks per capability", - ) - extract_parser.add_argument( - "--seed", - type=int, - default=42, - help="Random seed for --random option (default: 42)", - ) - - args = parser.parse_args() - - if args.command == "list": - cmd_list(args) - elif args.command == "extract": - cmd_extract(args) - else: - parser.print_help() - - -if __name__ == "__main__": - main() diff --git a/poetry.lock b/poetry.lock index a5d81381..b6b06d04 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. [[package]] name = "ag2" @@ -7,7 +7,6 @@ description = "A programming framework for agentic AI" optional = false python-versions = "<3.14,>=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ag2-0.9.9-py3-none-any.whl", hash = "sha256:833aeb65d657fd2cb2f8686bab9c0c3ae0b8c806c0e76b670756bd3d8d0b21ea"}, {file = "ag2-0.9.9.tar.gz", hash = "sha256:33200ded8c60a4c181061632ed38f79e34b87197fc49acebb81d9afc29f70439"}, @@ -43,7 +42,7 @@ deepseek = ["openai (>=1.99.3)"] dev = ["cairosvg", "codespell (==2.4.1)", "detect-secrets (==1.5.0)", "dirty-equals (==0.9.0)", "fastapi (==0.116.1)", "ipykernel (==6.30.1)", "jinja2 (==3.1.6)", "mcp (>=1.11.0)", "mdx-include (==1.4.2)", "mike (==2.1.3)", "mkdocs-git-revision-date-localized-plugin (==1.4.7)", "mkdocs-glightbox (==0.4.0)", "mkdocs-literate-nav (==0.6.2)", "mkdocs-macros-plugin (==1.3.7)", "mkdocs-material (==9.6.16)", "mkdocs-minify-plugin (==0.8.0)", "mkdocs-redirects (==1.2.2)", "mkdocstrings[python] (==0.30.0)", "mock (==5.2.0)", "mypy (==1.17.1)", "nbclient (==0.10.2)", "nbconvert (==7.16.6)", "nbformat (==5.10.4)", "openai (>=1.99.3)", "pandas (==2.3.1)", "pdoc3 (==0.11.6)", "pillow", "pre-commit (==4.3.0)", "pytest (==8.4.1)", "pytest-asyncio (==1.1.0)", "pytest-cov (==6.2.1)", "pyupgrade-directories (==0.3.0)", "pyyaml (==6.0.2)", "ruff (==0.12.8)", "termcolor (==3.1.0)", "toml (==0.10.2)", "typer (==0.16.0)", "types-decorator", "types-pycurl", "types-python-dateutil", "types-pyyaml", "types-requests", "types-ujson", "uv (==0.8.8)"] docs = ["cairosvg", "jinja2 (==3.1.6)", "mcp (>=1.11.0)", "mdx-include (==1.4.2)", "mike (==2.1.3)", "mkdocs-git-revision-date-localized-plugin (==1.4.7)", "mkdocs-glightbox (==0.4.0)", "mkdocs-literate-nav (==0.6.2)", "mkdocs-macros-plugin (==1.3.7)", "mkdocs-material (==9.6.16)", "mkdocs-minify-plugin (==0.8.0)", "mkdocs-redirects (==1.2.2)", "mkdocstrings[python] (==0.30.0)", "nbclient (==0.10.2)", "pdoc3 (==0.11.6)", "pillow", "pyyaml (==6.0.2)", "termcolor (==3.1.0)", "typer (==0.16.0)"] duckduckgo = ["duckduckgo-search (>=8.0.2)"] -flaml = ["flaml", "numpy (>=1.24.0,<2.0.0)", "numpy (>=2.1)"] +flaml = ["flaml", "numpy (>=1.24.0,<2.0.0) ; python_version < \"3.13\"", "numpy (>=2.1) ; python_version >= \"3.13\""] gemini = ["google-api-core", "google-auth", "google-cloud-aiplatform", "google-genai (>=1.20.0)", "jsonschema", "pillow"] gemini-realtime = ["google-api-core", "google-auth", "google-cloud-aiplatform", "google-genai (>=1.20.0)", "jsonschema", "pillow", "websockets (>=14.0,<16)"] google-api = ["google-api-python-client (>=2.163.0,<3.0)", "google-auth-httplib2 (>=0.2.0,<0.3)", "google-auth-oauthlib (>=1.2.1,<2.0)"] @@ -52,8 +51,8 @@ google-search = ["google-api-python-client (>=2.163.0,<3.0)"] graph = ["matplotlib", "networkx"] graph-rag-falkor-db = ["falkordb (>=1.0.10)", "graphrag-sdk (==0.7.1)"] groq = ["groq (>=0.9.0)"] -interop = ["crewai[tools] (>=0.76,<1)", "langchain-community (>=0.3.12,<1)", "litellm (<=1.75.5.post1)", "pydantic-ai (==0.6.2)", "weaviate-client (>=4,<5)"] -interop-crewai = ["crewai[tools] (>=0.76,<1)", "litellm (<=1.75.5.post1)", "weaviate-client (>=4,<5)"] +interop = ["crewai[tools] (>=0.76,<1) ; python_version >= \"3.10\" and python_version < \"3.13\"", "langchain-community (>=0.3.12,<1)", "litellm (<=1.75.5.post1)", "pydantic-ai (==0.6.2)", "weaviate-client (>=4,<5) ; python_version >= \"3.10\" and python_version < \"3.13\""] +interop-crewai = ["crewai[tools] (>=0.76,<1) ; python_version >= \"3.10\" and python_version < \"3.13\"", "litellm (<=1.75.5.post1)", "weaviate-client (>=4,<5) ; python_version >= \"3.10\" and python_version < \"3.13\""] interop-langchain = ["langchain-community (>=0.3.12,<1)"] interop-pydantic-ai = ["pydantic-ai (==0.6.2)"] jupyter-executor = ["ipykernel (>=6.29.0)", "jupyter-client (>=8.6.0)", "jupyter-kernel-gateway", "requests", "websocket-client"] @@ -73,7 +72,7 @@ redis = ["redis"] retrievechat = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "protobuf (==6.31.1)", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-couchbase = ["beautifulsoup4", "chromadb (==1.0.16)", "couchbase (>=4.3.0)", "ipython", "markdownify", "numpy", "protobuf (==6.31.1)", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-mongodb = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "numpy", "protobuf (==6.31.1)", "pymongo (>=4.0.0)", "pypdf", "sentence-transformers (<=5.1.0)"] -retrievechat-pgvector = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==6.31.1)", "psycopg (>=3.1.18)", "psycopg[binary] (>=3.1.18)", "pypdf", "sentence-transformers (<=5.1.0)"] +retrievechat-pgvector = ["beautifulsoup4", "chromadb (==1.0.16)", "ipython", "markdownify", "pgvector (>=0.2.5)", "protobuf (==6.31.1)", "psycopg (>=3.1.18) ; platform_system == \"Linux\"", "psycopg[binary] (>=3.1.18) ; platform_system == \"Windows\" or platform_system == \"Darwin\"", "pypdf", "sentence-transformers (<=5.1.0)"] retrievechat-qdrant = ["beautifulsoup4", "chromadb (==1.0.16)", "fastembed (>=0.3.1)", "ipython", "markdownify", "protobuf (==6.31.1)", "pypdf", "qdrant-client", "sentence-transformers (<=5.1.0)"] tavily = ["tavily-python (>=0.7.4)"] teachable = ["chromadb"] @@ -112,7 +111,6 @@ description = "Async client for aws services using botocore and aiohttp" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiobotocore-2.24.2-py3-none-any.whl", hash = "sha256:808c63b2bd344b91e2f2acb874831118a9f53342d248acd16a68455a226e283a"}, {file = "aiobotocore-2.24.2.tar.gz", hash = "sha256:dfb21bdb2610e8de4d22f401e91a24d50f1330a302d03c62c485757becd439a9"}, @@ -140,7 +138,6 @@ description = "File support for asyncio." optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, @@ -153,7 +150,6 @@ description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, @@ -166,7 +162,6 @@ description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiohttp-3.12.15-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b6fc902bff74d9b1879ad55f5404153e2b33a82e72a95c89cec5eb6cc9e92fbc"}, {file = "aiohttp-3.12.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:098e92835b8119b54c693f2f88a1dec690e20798ca5f5fe5f0520245253ee0af"}, @@ -267,7 +262,7 @@ propcache = ">=0.2.0" yarl = ">=1.17.0,<2.0" [package.extras] -speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""] +speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.3.0)", "brotlicffi ; platform_python_implementation != \"CPython\""] [[package]] name = "aioitertools" @@ -276,7 +271,6 @@ description = "itertools and builtins for AsyncIO and mixed iterables" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aioitertools-0.12.0-py3-none-any.whl", hash = "sha256:fc1f5fac3d737354de8831cbba3eb04f79dd649d8f3afb4c5b114925e662a796"}, {file = "aioitertools-0.12.0.tar.gz", hash = "sha256:c2a9055b4fbb7705f561b9d86053e8af5d10cc845d22c32008c43490b2d8dd6b"}, @@ -293,7 +287,6 @@ description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"}, {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"}, @@ -310,7 +303,6 @@ description = "A light, configurable Sphinx theme" optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "alabaster-0.7.16-py3-none-any.whl", hash = "sha256:b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92"}, {file = "alabaster-0.7.16.tar.gz", hash = "sha256:75a8b99c28a5dad50dd7f8ccdd447a121ddb3892da9e53d1ca5cca3106d58d65"}, @@ -323,7 +315,6 @@ description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -336,7 +327,6 @@ description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anthropic-0.67.0-py3-none-any.whl", hash = "sha256:f80a81ec1132c514215f33d25edeeab1c4691ad5361b391ebb70d528b0605b55"}, {file = "anthropic-0.67.0.tar.gz", hash = "sha256:d1531b210ea300c73423141d29bcee20fcd24ef9f426f6437c0a5d93fc98fb8e"}, @@ -363,7 +353,6 @@ description = "ANTLR 4.9.3 runtime for Python 3.7" optional = false python-versions = "*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"}, ] @@ -375,7 +364,6 @@ description = "Unicode to ASCII transliteration" optional = false python-versions = ">=3.3" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anyascii-0.3.3-py3-none-any.whl", hash = "sha256:f5ab5e53c8781a36b5a40e1296a0eeda2f48c649ef10c3921c1381b1d00dee7a"}, {file = "anyascii-0.3.3.tar.gz", hash = "sha256:c94e9dd9d47b3d9494eca305fef9447d00b4bf1a32aff85aa746fa3ec7fb95c3"}, @@ -388,7 +376,6 @@ description = "High-level concurrency and networking framework on top of asyncio optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"}, {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"}, @@ -401,8 +388,6 @@ sniffio = ">=1.1" typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] -test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""] trio = ["trio (>=0.26.1)"] [[package]] @@ -412,7 +397,7 @@ description = "Disable App Nap on macOS >= 10.9" optional = false python-versions = ">=3.6" groups = ["docs"] -markers = "platform_system == \"Darwin\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_system == \"Darwin\"" files = [ {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, @@ -425,7 +410,6 @@ description = "ASGI specs, helper code, and adapters" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asgiref-3.9.1-py3-none-any.whl", hash = "sha256:f3bba7092a48005b5f5bacd747d36ee4a5a61f4a269a6df590b43144355ebd2c"}, {file = "asgiref-3.9.1.tar.gz", hash = "sha256:a5ab6582236218e5ef1648f242fd9f10626cfd4de8dc377db215d5d5098e3142"}, @@ -444,7 +428,6 @@ description = "An abstract syntax tree for Python with inference support." optional = false python-versions = ">=3.9.0" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "astroid-3.3.11-py3-none-any.whl", hash = "sha256:54c760ae8322ece1abd213057c4b5bba7c49818853fc901ef09719a60dbf9dec"}, {file = "astroid-3.3.11.tar.gz", hash = "sha256:1e5a5011af2920c7c67a53f65d536d65bfa7116feeaf2354d8b94f29573bb0ce"}, @@ -460,7 +443,6 @@ description = "Annotate AST trees with source code positions" optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, @@ -477,7 +459,7 @@ description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version < \"3.11\"" +markers = "python_version == \"3.10\"" files = [ {file = "async-timeout-4.0.3.tar.gz", hash = "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f"}, {file = "async_timeout-4.0.3-py3-none-any.whl", hash = "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"}, @@ -490,7 +472,6 @@ description = "Asyncer, async and await, focused on developer experience." optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "asyncer-0.0.8-py3-none-any.whl", hash = "sha256:5920d48fc99c8f8f0f1576e1882f5022885589c5fcbc46ce4224ec3e53776eeb"}, {file = "asyncer-0.0.8.tar.gz", hash = "sha256:a589d980f57e20efb07ed91d0dbe67f1d2fd343e7142c66d3a099f05c620739c"}, @@ -506,19 +487,18 @@ description = "Classes Without Boilerplate" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"}, {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"}, ] [package.extras] -benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"] -cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] -dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] +benchmark = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] +cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] +dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] -tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] +tests = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] +tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\""] [[package]] name = "autogen-core" @@ -527,7 +507,6 @@ description = "Foundational interfaces and agent runtime implementation for Auto optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autogen_core-0.7.4-py3-none-any.whl", hash = "sha256:b383d3b2dfe9f5d62e0da0057da6de3cb63259233570e4c85153e33703170afa"}, {file = "autogen_core-0.7.4.tar.gz", hash = "sha256:44b4574a378effbf52317e579ae1663602ce9bbb1c699100dec9f3cf19cc9e85"}, @@ -548,7 +527,6 @@ description = "AutoGen extensions library" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autogen_ext-0.7.4-py3-none-any.whl", hash = "sha256:5afb47c8108168bce7b61eb476ed1bf025548f404da3742d322443fedad79e32"}, {file = "autogen_ext-0.7.4.tar.gz", hash = "sha256:1d69b37afa79787b43a401a10c857572d73b1d89e71950087543a81c8df02d27"}, @@ -608,7 +586,6 @@ description = "A tool that automatically formats Python code to conform to the P optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "autopep8-2.3.2-py2.py3-none-any.whl", hash = "sha256:ce8ad498672c845a0c3de2629c15b635ec2b05ef8177a6e7c91c74f3e9b51128"}, {file = "autopep8-2.3.2.tar.gz", hash = "sha256:89440a4f969197b69a995e4ce0661b031f455a9f776d2c5ba3dbd83466931758"}, @@ -625,14 +602,13 @@ description = "Internationalization utilities" optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, ] [package.extras] -dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] +dev = ["backports.zoneinfo ; python_version < \"3.9\"", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata ; sys_platform == \"win32\""] [[package]] name = "backoff" @@ -641,7 +617,6 @@ description = "Function decoration for backoff and retry" optional = false python-versions = ">=3.7,<4.0" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"}, @@ -654,7 +629,6 @@ description = "Screen-scraping library" optional = false python-versions = ">=3.7.0" groups = ["main", "docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "beautifulsoup4-4.13.5-py3-none-any.whl", hash = "sha256:642085eaa22233aceadff9c69651bc51e8bf3f874fb6d7104ece2beb24b47c4a"}, {file = "beautifulsoup4-4.13.5.tar.gz", hash = "sha256:5e70131382930e7c3de33450a2f54a63d5e4b19386eab43a5b34d594268f3695"}, @@ -678,7 +652,6 @@ description = "The uncompromising code formatter." optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "black-25.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:759e7ec1e050a15f89b770cefbf91ebee8917aac5c20483bc2d80a6c3a04df32"}, {file = "black-25.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e519ecf93120f34243e6b0054db49c00a35f84f195d5bce7e9f5cfc578fc2da"}, @@ -726,7 +699,6 @@ description = "Run Black on Python code blocks in documentation files." optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "blacken_docs-1.20.0-py3-none-any.whl", hash = "sha256:a0d842811ee07802dec920d3cf831e21f6eb017712748b488489aa3688770f1e"}, {file = "blacken_docs-1.20.0.tar.gz", hash = "sha256:2d5b6caf6e7da5694b1eba97f9132c1ab9f14f221c82205ec473a6e74fbb2c6d"}, @@ -742,7 +714,6 @@ description = "An easy safelist-based HTML-sanitizing tool." optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "bleach-6.2.0-py3-none-any.whl", hash = "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e"}, {file = "bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"}, @@ -762,7 +733,6 @@ description = "Define boolean algebras, create and parse boolean expressions and optional = false python-versions = "*" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "boolean_py-5.0-py3-none-any.whl", hash = "sha256:ef28a70bd43115208441b53a045d1549e2f0ec6e3d08a9d142cbc41c1938e8d9"}, {file = "boolean_py-5.0.tar.gz", hash = "sha256:60cbc4bad079753721d32649545505362c754e121570ada4658b852a3a318d95"}, @@ -781,7 +751,6 @@ description = "The AWS SDK for Python" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "boto3-1.40.18-py3-none-any.whl", hash = "sha256:daa776ba1251a7458c9d6c7627873d0c2460c8e8272d35759065580e9193700a"}, {file = "boto3-1.40.18.tar.gz", hash = "sha256:64301d39adecc154e3e595eaf0d4f28998ef0a5551f1d033aeac51a9e1a688e5"}, @@ -802,7 +771,6 @@ description = "Low-level, data-driven core of boto 3." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "botocore-1.40.18-py3-none-any.whl", hash = "sha256:57025c46ca00cf8cec25de07a759521bfbfb3036a0f69b272654a354615dc45f"}, {file = "botocore-1.40.18.tar.gz", hash = "sha256:afd69bdadd8c55cc89d69de0799829e555193a352d87867f746e19020271cc0f"}, @@ -823,7 +791,6 @@ description = "httplib2 caching for requests" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cachecontrol-0.14.3-py3-none-any.whl", hash = "sha256:b35e44a3113f17d2a31c1e6b27b9de6d4405f84ae51baa8c1d3cc5b633010cae"}, {file = "cachecontrol-0.14.3.tar.gz", hash = "sha256:73e7efec4b06b20d9267b441c1f733664f989fb8688391b670ca812d70795d11"}, @@ -846,7 +813,6 @@ description = "Extensible memoizing collections and decorators" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cachetools-5.5.2-py3-none-any.whl", hash = "sha256:d26a22bcc62eb95c3beabd9f1ee5e820d3d2704fe2967cbe350e20c8ffcd3f0a"}, {file = "cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4"}, @@ -859,7 +825,6 @@ description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5"}, {file = "certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407"}, @@ -959,7 +924,6 @@ files = [ {file = "cffi-2.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:b882b3df248017dba09d6b16defe9b5c407fe32fc7c65a9c69798e6175601be9"}, {file = "cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529"}, ] -markers = {main = "platform_python_implementation == \"PyPy\"", docs = "implementation_name == \"pypy\""} [package.dependencies] pycparser = {version = "*", markers = "implementation_name != \"PyPy\""} @@ -971,7 +935,6 @@ description = "Validate configuration and produce human readable error messages. optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cfgv-3.4.0-py2.py3-none-any.whl", hash = "sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9"}, {file = "cfgv-3.4.0.tar.gz", hash = "sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560"}, @@ -984,7 +947,6 @@ description = "The Real First Universal Charset Detector. Open, modern and activ optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "charset_normalizer-3.4.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fb7f67a1bfa6e40b438170ebdc8158b78dc465a5a67b6dde178a46987b244a72"}, {file = "charset_normalizer-3.4.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:cc9370a2da1ac13f0153780040f465839e6cccb4a1e44810124b4e22483c93fe"}, @@ -1074,7 +1036,6 @@ description = "Composable command line interface toolkit" optional = false python-versions = ">=3.10" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b"}, {file = "click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202"}, @@ -1090,7 +1051,6 @@ description = "Hosted coverage reports for GitHub, Bitbucket and Gitlab" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "codecov-2.1.13-py2.py3-none-any.whl", hash = "sha256:c2ca5e51bba9ebb43644c43d0690148a55086f7f5e6fd36170858fa4206744d5"}, {file = "codecov-2.1.13.tar.gz", hash = "sha256:2362b685633caeaf45b9951a9b76ce359cd3581dd515b430c6c3f5dfb4d92a8c"}, @@ -1111,7 +1071,7 @@ files = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] -markers = {main = "python_version >= \"3.12\" or python_version <= \"3.11\"", docs = "sys_platform == \"win32\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", test = "(platform_system == \"Windows\" or sys_platform == \"win32\") and (python_version <= \"3.11\" or python_version >= \"3.12\")"} +markers = {docs = "sys_platform == \"win32\"", test = "platform_system == \"Windows\" or sys_platform == \"win32\""} [[package]] name = "comm" @@ -1120,7 +1080,6 @@ description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus- optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417"}, {file = "comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971"}, @@ -1136,6 +1095,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934"}, {file = "contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989"}, @@ -1306,7 +1266,6 @@ description = "Code coverage measurement for Python" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "coverage-7.10.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:70e7bfbd57126b5554aa482691145f798d7df77489a177a6bef80de78860a356"}, {file = "coverage-7.10.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e41be6f0f19da64af13403e52f2dec38bbc2937af54df8ecef10850ff8d35301"}, @@ -1402,7 +1361,7 @@ files = [ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.11.0a6\" and extra == \"toml\""} [package.extras] -toml = ["tomli"] +toml = ["tomli ; python_full_version <= \"3.11.0a6\""] [[package]] name = "cycler" @@ -1411,7 +1370,6 @@ description = "Composable style cycles" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, @@ -1428,7 +1386,6 @@ description = "Python library for CycloneDX" optional = false python-versions = "<4.0,>=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "cyclonedx_python_lib-9.1.0-py3-none-any.whl", hash = "sha256:55693fca8edaecc3363b24af14e82cc6e659eb1e8353e58b587c42652ce0fb52"}, {file = "cyclonedx_python_lib-9.1.0.tar.gz", hash = "sha256:86935f2c88a7b47a529b93c724dbd3e903bc573f6f8bd977628a7ca1b5dadea1"}, @@ -1452,7 +1409,6 @@ description = "HuggingFace community-driven open-source library of datasets" optional = false python-versions = ">=3.9.0" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "datasets-4.1.0-py3-none-any.whl", hash = "sha256:abc5e61fa605ad09b4d790579c96a4e100329f3a3b57cc6c53ab040a927d48cd"}, {file = "datasets-4.1.0.tar.gz", hash = "sha256:42e0330780d5d4d5d664bb22a3eafe02adf073c1f4612c79582403bd8f703ae0"}, @@ -1474,17 +1430,17 @@ tqdm = ">=4.66.3" xxhash = "*" [package.extras] -audio = ["librosa", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\""] +audio = ["torch (>=2.8.0)", "torchcodec (>=0.6.0)"] benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"] -dev = ["Pillow (>=9.4.0)", "absl-py", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != \"win32\"", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "moto[server]", "polars[timezone] (>=0.20.0)", "protobuf (<4.0.0)", "py7zr", "pyav", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "soundfile (>=0.12.1)", "soxr (>=0.4.0) ; python_version >= \"3.9\"", "sqlalchemy", "tensorflow (>=2.16.0) ; python_version >= \"3.10\"", "tensorflow (>=2.6.0)", "tensorflow (>=2.6.0) ; python_version < \"3.10\"", "tiktoken", "torch", "torch (>=2.0.0)", "torchdata", "torchvision", "transformers", "transformers (>=4.42.0)", "zstandard"] -docs = ["s3fs", "tensorflow (>=2.6.0)", "torch", "transformers"] +dev = ["Pillow (>=9.4.0)", "absl-py", "aiohttp", "decorator", "elasticsearch (>=7.17.12,<8.0.0)", "faiss-cpu (>=1.8.0.post1)", "h5py", "jax (>=0.3.14) ; sys_platform != \"win32\"", "jaxlib (>=0.3.14) ; sys_platform != 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or python_version <= \"3.11\"" files = [ {file = "fsspec-2025.3.0-py3-none-any.whl", hash = "sha256:efb87af3efa9103f94ca91a7f8cb7a4df91af9f74fc106c9c7ea0efd7277c1b3"}, {file = "fsspec-2025.3.0.tar.gz", hash = "sha256:a935fd1ea872591f2b5148907d103488fc523295e6c64b835cfad8c3eca44972"}, @@ -2011,7 +2044,6 @@ description = "A clean customisable Sphinx documentation theme." optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "furo-2024.8.6-py3-none-any.whl", hash = "sha256:6cd97c58b47813d3619e63e9081169880fbe331f0ca883c871ff1f3f11814f5c"}, {file = "furo-2024.8.6.tar.gz", hash = "sha256:b63e4cee8abfc3136d3bc03a3d45a76a850bada4d6374d24c1716b0e01394a01"}, @@ -2030,7 +2062,6 @@ description = "Google Ai Generativelanguage API client library" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = 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">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, + {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0.0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, ] grpcio-status = [ - {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, - {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "extra == \"grpc\""}, + {version = ">=1.49.1,<2.0.0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0.0", optional = true, markers = "extra == \"grpc\""}, ] proto-plus = ">=1.22.3,<2.0.0" protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<7.0.0" requests = ">=2.18.0,<3.0.0" [package.extras] 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"sha256:9b17ff7bd70dc39c3a3c03eb2bb9a87aa4b840c26e71ac68d120b96487bf5a12"}, {file = "gpytorch-1.14.tar.gz", hash = "sha256:032cc11e6a46e1e4bc7763fcef318cc830aceaea85a7289f27b2288c7a339a8d"}, @@ -2298,7 +2320,7 @@ description = "Lightweight in-process concurrent programming" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "(platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\"" files = [ {file = "greenlet-3.2.4-cp310-cp310-macosx_11_0_universal2.whl", hash = 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Extract the structure, the optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "griffe-1.14.0-py3-none-any.whl", hash = "sha256:0e9d52832cccf0f7188cfe585ba962d2674b241c01916d780925df34873bceb0"}, {file = "griffe-1.14.0.tar.gz", hash = "sha256:9d2a15c1eca966d68e00517de5d69dd1bc5c9f2335ef6c1775362ba5b8651a13"}, @@ -2395,7 +2416,6 @@ description = "HTTP/2-based RPC framework" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "grpcio-1.74.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:85bd5cdf4ed7b2d6438871adf6afff9af7096486fcf51818a81b77ef4dd30907"}, {file = "grpcio-1.74.0-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:68c8ebcca945efff9d86d8d6d7bfb0841cf0071024417e2d7f45c5e46b5b08eb"}, @@ -2460,7 +2480,6 @@ description = "Status proto mapping for gRPC" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "grpcio_status-1.71.2-py3-none-any.whl", hash = "sha256:803c98cb6a8b7dc6dbb785b1111aed739f241ab5e9da0bba96888aa74704cfd3"}, {file = "grpcio_status-1.71.2.tar.gz", hash = "sha256:c7a97e176df71cdc2c179cd1847d7fc86cca5832ad12e9798d7fed6b7a1aab50"}, @@ -2478,7 +2497,6 @@ description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"}, {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"}, @@ -2513,7 +2531,6 @@ description = "A minimal low-level HTTP client." optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"}, {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"}, @@ -2536,7 +2553,6 @@ description = "The next generation HTTP client." optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"}, {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"}, @@ -2549,7 +2565,7 @@ httpcore = "==1.*" idna = "*" [package.extras] -brotli = ["brotli", "brotlicffi"] +brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""] cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] @@ -2562,7 +2578,6 @@ description = "Consume Server-Sent Event (SSE) messages with HTTPX." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "httpx_sse-0.4.1-py3-none-any.whl", hash = "sha256:cba42174344c3a5b06f255ce65b350880f962d99ead85e776f23c6618a377a37"}, {file = "httpx_sse-0.4.1.tar.gz", hash = "sha256:8f44d34414bc7b21bf3602713005c5df4917884f76072479b21f68befa4ea26e"}, @@ -2575,7 +2590,6 @@ description = "Client library to download and publish models, datasets and other optional = false python-versions = ">=3.8.0" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "huggingface_hub-0.34.5-py3-none-any.whl", hash = "sha256:fea377adc6e9b6c239c1450e41a1409cbf2c6364d289c04c31d7dbaa222842e3"}, {file = "huggingface_hub-0.34.5.tar.gz", hash = "sha256:f676c6db41bc3fbd4020f520c842a0548f4c9a3f698dbfa6514bd8e41c3ab52a"}, @@ -2615,7 +2629,6 @@ description = "A framework for elegantly configuring complex applications" optional = false python-versions = "*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "hydra-core-1.3.2.tar.gz", hash = "sha256:8a878ed67216997c3e9d88a8e72e7b4767e81af37afb4ea3334b269a4390a824"}, {file = "hydra_core-1.3.2-py3-none-any.whl", hash = "sha256:fa0238a9e31df3373b35b0bfb672c34cc92718d21f81311d8996a16de1141d8b"}, @@ -2633,7 +2646,6 @@ description = "File identification library for Python" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "identify-2.6.14-py2.py3-none-any.whl", hash = "sha256:11a073da82212c6646b1f39bb20d4483bfb9543bd5566fec60053c4bb309bf2e"}, {file = "identify-2.6.14.tar.gz", hash = "sha256:663494103b4f717cb26921c52f8751363dc89db64364cd836a9bf1535f53cd6a"}, @@ -2649,7 +2661,6 @@ description = "Internationalized Domain Names in Applications (IDNA)" optional = false python-versions = ">=3.6" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"}, {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"}, @@ -2665,7 +2676,6 @@ description = "Iterative JSON parser with standard Python iterator interfaces" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ijson-3.4.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e27e50f6dcdee648f704abc5d31b976cd2f90b4642ed447cf03296d138433d09"}, {file = "ijson-3.4.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2a753be681ac930740a4af9c93cfb4edc49a167faed48061ea650dc5b0f406f1"}, @@ -2761,7 +2771,6 @@ description = "Getting image size from png/jpeg/jpeg2000/gif file" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "imagesize-1.4.1-py2.py3-none-any.whl", hash = "sha256:0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b"}, {file = "imagesize-1.4.1.tar.gz", hash = "sha256:69150444affb9cb0d5cc5a92b3676f0b2fb7cd9ae39e947a5e11a36b4497cd4a"}, @@ -2774,7 +2783,6 @@ description = "Read metadata from Python packages" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "importlib_metadata-8.7.0-py3-none-any.whl", hash = "sha256:e5dd1551894c77868a30651cef00984d50e1002d06942a7101d34870c5f02afd"}, {file = "importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000"}, @@ -2784,12 +2792,12 @@ files = [ zipp = ">=3.20" [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] perf = ["ipython"] -test = ["flufl.flake8", "importlib_resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"] +test = ["flufl.flake8", "importlib_resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"] type = ["pytest-mypy"] [[package]] @@ -2799,7 +2807,6 @@ description = "brain-dead simple config-ini parsing" optional = false python-versions = ">=3.8" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "iniconfig-2.1.0-py3-none-any.whl", hash = "sha256:9deba5723312380e77435581c6bf4935c94cbfab9b1ed33ef8d238ea168eb760"}, {file = "iniconfig-2.1.0.tar.gz", hash = "sha256:3abbd2e30b36733fee78f9c7f7308f2d0050e88f0087fd25c2645f63c773e1c7"}, @@ -2812,7 +2819,6 @@ description = "Framework for large language model evaluations" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "inspect_ai-0.3.159-py3-none-any.whl", hash = "sha256:71f87fd242d4fb61998691143c5dd3613fd8009840a4503d3a1fa48b504fd6b3"}, {file = "inspect_ai-0.3.159.tar.gz", hash = "sha256:91d42ba18ac10c5ad9d4ee25e01dda142c86058a12462e5ba051b041f8afe2ed"}, @@ -2870,7 +2876,6 @@ description = "IPython Kernel for Jupyter" optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ipykernel-6.30.1-py3-none-any.whl", hash = "sha256:aa6b9fb93dca949069d8b85b6c79b2518e32ac583ae9c7d37c51d119e18b3fb4"}, {file = "ipykernel-6.30.1.tar.gz", hash = "sha256:6abb270161896402e76b91394fcdce5d1be5d45f456671e5080572f8505be39b"}, @@ -2905,7 +2910,6 @@ description = "IPython: Productive Interactive Computing" optional = false python-versions = ">=3.10" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "ipython-8.37.0-py3-none-any.whl", hash = "sha256:ed87326596b878932dbcb171e3e698845434d8c61b8d8cd474bf663041a9dcf2"}, {file = "ipython-8.37.0.tar.gz", hash = "sha256:ca815841e1a41a1e6b73a0b08f3038af9b2252564d01fc405356d34033012216"}, @@ -2927,7 +2931,7 @@ typing_extensions = {version = ">=4.6", markers = "python_version < \"3.12\""} [package.extras] all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"] black = ["black"] -doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing_extensions"] +doc = ["docrepr", "exceptiongroup", "intersphinx_registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli ; python_version < \"3.11\"", "typing_extensions"] kernel = ["ipykernel"] matplotlib = ["matplotlib"] nbconvert = ["nbconvert"] @@ -2945,7 +2949,6 @@ description = "A Python utility / library to sort Python imports." optional = false python-versions = ">=3.9.0" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "isort-6.0.1-py3-none-any.whl", hash = "sha256:2dc5d7f65c9678d94c88dfc29161a320eec67328bc97aad576874cb4be1e9615"}, {file = "isort-6.0.1.tar.gz", hash = "sha256:1cb5df28dfbc742e490c5e41bad6da41b805b0a8be7bc93cd0fb2a8a890ac450"}, @@ -2962,7 +2965,6 @@ description = "Type annotations and runtime checking for shape and dtype of JAX/ optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jaxtyping-0.3.2-py3-none-any.whl", hash = "sha256:6a020fd276226ddb5ac4f5725323843dd65e3c7e85c64fd62431e5f738c74e04"}, {file = "jaxtyping-0.3.2.tar.gz", hash = "sha256:f30483fac4b42e915db8ad2414a85c3b63284aa7d3c100b96b59f755cf4a86ad"}, @@ -2981,7 +2983,6 @@ description = "An autocompletion tool for Python that can be used for text edito optional = false python-versions = ">=3.6" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"}, {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"}, @@ -3002,7 +3003,6 @@ description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" groups = ["main", "docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"}, {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"}, @@ -3021,7 +3021,6 @@ description = "Fast iterable JSON parser." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jiter-0.11.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3893ce831e1c0094a83eeaf56c635a167d6fa8cc14393cc14298fd6fdc2a2449"}, {file = "jiter-0.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:25c625b9b61b5a8725267fdf867ef2e51b429687f6a4eef211f4612e95607179"}, @@ -3110,7 +3109,6 @@ description = "JSON Matching Expressions" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"}, {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"}, @@ -3123,7 +3121,6 @@ description = "Lightweight pipelining with Python functions" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "joblib-1.5.2-py3-none-any.whl", hash = "sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241"}, {file = "joblib-1.5.2.tar.gz", hash = "sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55"}, @@ -3136,7 +3133,6 @@ description = "Library with helpers for the jsonlines file format" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonlines-4.0.0-py3-none-any.whl", hash = "sha256:185b334ff2ca5a91362993f42e83588a360cf95ce4b71a73548502bda52a7c55"}, {file = "jsonlines-4.0.0.tar.gz", hash = "sha256:0c6d2c09117550c089995247f605ae4cf77dd1533041d366351f6f298822ea74"}, @@ -3152,7 +3148,6 @@ description = "Apply JSON-Patches (RFC 6902)" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"}, {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"}, @@ -3184,7 +3179,6 @@ description = "Identify specific nodes in a JSON document (RFC 6901)" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"}, {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"}, @@ -3197,7 +3191,6 @@ description = "jsonref is a library for automatic dereferencing of JSON Referenc optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonref-1.1.0-py3-none-any.whl", hash = "sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9"}, {file = "jsonref-1.1.0.tar.gz", hash = "sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552"}, @@ -3210,7 +3203,6 @@ description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonschema-4.25.1-py3-none-any.whl", hash = "sha256:3fba0169e345c7175110351d456342c364814cfcf3b964ba4587f22915230a63"}, {file = "jsonschema-4.25.1.tar.gz", hash = "sha256:e4a9655ce0da0c0b67a085847e00a3a51449e1157f4f75e9fb5aa545e122eb85"}, @@ -3233,7 +3225,6 @@ description = "The JSON Schema meta-schemas and vocabularies, exposed as a Regis optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe"}, {file = "jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d"}, @@ -3249,7 +3240,6 @@ description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"}, {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"}, @@ -3264,7 +3254,7 @@ traitlets = ">=5.3" [package.extras] docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] -test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] +test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-core" @@ -3273,7 +3263,6 @@ description = "Jupyter core package. A base package on which Jupyter projects re optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0"}, {file = "jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941"}, @@ -3295,7 +3284,6 @@ description = "Pygments theme using JupyterLab CSS variables" optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"}, {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"}, @@ -3308,7 +3296,6 @@ description = "Jupyter notebooks as Markdown documents, Julia, Python or R scrip optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "jupytext-1.17.3-py3-none-any.whl", hash = "sha256:09b0a94cd904416e823a5ba9f41bd181031215b6fc682d2b5c18e68354feb17c"}, {file = "jupytext-1.17.3.tar.gz", hash = "sha256:8b6dae76d63c95cad47b493c38f0d9c74491fb621dcd0980abfcac4c8f168679"}, @@ -3339,7 +3326,6 @@ description = "A fast implementation of the Cassowary constraint solver" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "kiwisolver-1.4.9-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b4b4d74bda2b8ebf4da5bd42af11d02d04428b2c32846e4c2c93219df8a7987b"}, {file = "kiwisolver-1.4.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:fb3b8132019ea572f4611d770991000d7f58127560c4889729248eb5852a102f"}, @@ -3451,7 +3437,6 @@ description = "Building applications with LLMs through composability" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain-0.3.27-py3-none-any.whl", hash = "sha256:7b20c4f338826acb148d885b20a73a16e410ede9ee4f19bb02011852d5f98798"}, {file = "langchain-0.3.27.tar.gz", hash = "sha256:aa6f1e6274ff055d0fd36254176770f356ed0a8994297d1df47df341953cec62"}, @@ -3493,7 +3478,6 @@ description = "An integration package connecting AnthropicMessages and LangChain optional = false python-versions = "<4.0,>=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_anthropic-0.3.12-py3-none-any.whl", hash = "sha256:39764a0dc79c5777af6396a6160478a2b5b62eada965df778d041e68ac8aeeb9"}, {file = "langchain_anthropic-0.3.12.tar.gz", hash = "sha256:8f68461369f0c7707dea5950d6f68ce3faa8d49744bc97fb8d3a7c02e84fe840"}, @@ -3511,7 +3495,6 @@ description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_core-0.3.76-py3-none-any.whl", hash = "sha256:46e0eb48c7ac532432d51f8ca1ece1804c82afe9ae3dcf027b867edadf82b3ec"}, {file = "langchain_core-0.3.76.tar.gz", hash = "sha256:71136a122dd1abae2c289c5809d035cf12b5f2bb682d8a4c1078cd94feae7419"}, @@ -3533,7 +3516,6 @@ description = "An integration package connecting Google's genai package and Lang optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_google_genai-2.1.11-py3-none-any.whl", hash = "sha256:5ed947e3dc61b057dd3aac69422011d7931b2871e49cdde87c1690dee54cb7f1"}, {file = "langchain_google_genai-2.1.11.tar.gz", hash = "sha256:c3b13dd4069251d6bc7b6b4bd13b43bced9b0b59be5e2fd5f4aa087ecfa5bebe"}, @@ -3552,7 +3534,6 @@ description = "An integration package connecting OpenAI and LangChain" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_openai-0.3.33-py3-none-any.whl", hash = "sha256:2d52aab6d2af61da9bb9470616ce782128f4be59df965caee3dece30ae6b2bc4"}, {file = "langchain_openai-0.3.33.tar.gz", hash = "sha256:2dec058332ea9e8977cd91df6515b95952e187ac7484f349c3fe91d936a92375"}, @@ -3570,7 +3551,6 @@ description = "LangChain text splitting utilities" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langchain_text_splitters-0.3.11-py3-none-any.whl", hash = "sha256:cf079131166a487f1372c8ab5d0bfaa6c0a4291733d9c43a34a16ac9bcd6a393"}, {file = "langchain_text_splitters-0.3.11.tar.gz", hash = "sha256:7a50a04ada9a133bbabb80731df7f6ddac51bc9f1b9cab7fa09304d71d38a6cc"}, @@ -3586,7 +3566,6 @@ description = "A client library for accessing langfuse" optional = false python-versions = "<4.0,>=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langfuse-3.3.4-py3-none-any.whl", hash = "sha256:15b9d20878cf39a48ca9cfa7e52acdfeb043603d3a9cef8cf451687a4d838c6b"}, {file = "langfuse-3.3.4.tar.gz", hash = "sha256:e5df4e7284298990b522e02a1dc6c3c72ebc4a7a411dc7d39255fb8c2e5a7c3a"}, @@ -3614,7 +3593,6 @@ description = "Client library to connect to the LangSmith LLM Tracing and Evalua optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "langsmith-0.4.28-py3-none-any.whl", hash = "sha256:0440968566d56d38d889afa202e1ff56a238e1493aea87ceb5c3c28d41d01144"}, {file = "langsmith-0.4.28.tar.gz", hash = "sha256:8734e6d3e16ce0085b5f7235633b0e14bc8e0c160b1c1d8ce2588f83a936e171"}, @@ -3643,7 +3621,6 @@ description = "license-expression is a comprehensive utility library to parse, c optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "license_expression-30.4.4-py3-none-any.whl", hash = "sha256:421788fdcadb41f049d2dc934ce666626265aeccefddd25e162a26f23bcbf8a4"}, {file = "license_expression-30.4.4.tar.gz", hash = "sha256:73448f0aacd8d0808895bdc4b2c8e01a8d67646e4188f887375398c761f340fd"}, @@ -3662,7 +3639,6 @@ description = "A linear operator implementation, primarily designed for finite-d optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "linear_operator-0.6-py3-none-any.whl", hash = "sha256:b7c30ce0f6599e19c1cb970e00811828df98783b20c1dd2f765928415012f41c"}, {file = "linear_operator-0.6.tar.gz", hash = "sha256:a9e2663879f1a2b28631bf7ef34892c4e5749893e711c8ef0ab0a39aff70a654"}, @@ -3686,7 +3662,6 @@ description = "Links recognition library with FULL unicode support." optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "linkify-it-py-2.0.3.tar.gz", hash = "sha256:68cda27e162e9215c17d786649d1da0021a451bdc436ef9e0fa0ba5234b9b048"}, {file = "linkify_it_py-2.0.3-py3-none-any.whl", hash = "sha256:6bcbc417b0ac14323382aef5c5192c0075bf8a9d6b41820a2b66371eac6b6d79"}, @@ -3708,7 +3683,6 @@ description = "Python port of markdown-it. Markdown parsing, done right!" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, @@ -3736,7 +3710,6 @@ description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.9" groups = ["main", "docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"}, {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"}, @@ -3808,7 +3781,6 @@ description = "Python plotting package" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "matplotlib-3.10.6-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:bc7316c306d97463a9866b89d5cc217824e799fa0de346c8f68f4f3d27c8693d"}, {file = "matplotlib-3.10.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d00932b0d160ef03f59f9c0e16d1e3ac89646f7785165ce6ad40c842db16cc2e"}, @@ -3888,7 +3860,6 @@ description = "Inline Matplotlib backend for Jupyter" optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"}, {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"}, @@ -3904,7 +3875,6 @@ description = "McCabe checker, plugin for flake8" optional = false python-versions = ">=3.6" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"}, {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"}, @@ -3917,7 +3887,6 @@ description = "Model Context Protocol SDK" optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "mcp-1.14.0-py3-none-any.whl", hash = "sha256:b2d27feba27b4c53d41b58aa7f4d090ae0cb740cbc4e339af10f8cbe54c4e19d"}, {file = "mcp-1.14.0.tar.gz", hash = "sha256:2e7d98b195e08b2abc1dc6191f6f3dc0059604ac13ee6a40f88676274787fac4"}, @@ -3948,7 +3917,6 @@ description = "Collection of plugins for markdown-it-py" optional = false python-versions = ">=3.10" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mdit_py_plugins-0.5.0-py3-none-any.whl", hash = "sha256:07a08422fc1936a5d26d146759e9155ea466e842f5ab2f7d2266dd084c8dab1f"}, {file = "mdit_py_plugins-0.5.0.tar.gz", hash = "sha256:f4918cb50119f50446560513a8e311d574ff6aaed72606ddae6d35716fe809c6"}, @@ -3969,7 +3937,6 @@ description = "Markdown URL utilities" optional = false python-versions = ">=3.7" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, @@ -3982,7 +3949,6 @@ description = "A sane and fast Markdown parser with useful plugins and renderers optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mistune-3.1.4-py3-none-any.whl", hash = "sha256:93691da911e5d9d2e23bc54472892aff676df27a75274962ff9edc210364266d"}, {file = "mistune-3.1.4.tar.gz", hash = "sha256:b5a7f801d389f724ec702840c11d8fc48f2b33519102fc7ee739e8177b672164"}, @@ -3998,7 +3964,6 @@ description = "Python extension for MurmurHash (MurmurHash3), a set of fast and optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mmh3-5.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:81c504ad11c588c8629536b032940f2a359dda3b6cbfd4ad8f74cb24dcd1b0bc"}, {file = "mmh3-5.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0b898cecff57442724a0f52bf42c2de42de63083a91008fb452887e372f9c328"}, @@ -4138,7 +4103,6 @@ description = "Python library for arbitrary-precision floating-point arithmetic" optional = false python-versions = "*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, @@ -4147,7 +4111,7 @@ files = [ [package.extras] develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] docs = ["sphinx"] -gmpy = ["gmpy2 (>=2.1.0a4)"] +gmpy = ["gmpy2 (>=2.1.0a4) ; platform_python_implementation != \"PyPy\""] tests = ["pytest (>=4.6)"] [[package]] @@ -4157,7 +4121,6 @@ description = "MessagePack serializer" optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "msgpack-1.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:353b6fc0c36fde68b661a12949d7d49f8f51ff5fa019c1e47c87c4ff34b080ed"}, {file = "msgpack-1.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:79c408fcf76a958491b4e3b103d1c417044544b68e96d06432a189b43d1215c8"}, @@ -4227,7 +4190,6 @@ description = "multidict implementation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "multidict-6.6.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b8aa6f0bd8125ddd04a6593437bad6a7e70f300ff4180a531654aa2ab3f6d58f"}, {file = "multidict-6.6.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b9e5853bbd7264baca42ffc53391b490d65fe62849bf2c690fa3f6273dbcd0cb"}, @@ -4351,7 +4313,6 @@ description = "better multiprocessing and multithreading in Python" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "multiprocess-0.70.16-pp310-pypy310_pp73-macosx_10_13_x86_64.whl", hash = "sha256:476887be10e2f59ff183c006af746cb6f1fd0eadcfd4ef49e605cbe2659920ee"}, {file = "multiprocess-0.70.16-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:d951bed82c8f73929ac82c61f01a7b5ce8f3e5ef40f5b52553b4f547ce2b08ec"}, @@ -4377,7 +4338,6 @@ description = "Optional static typing for Python" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mypy-1.18.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2761b6ae22a2b7d8e8607fb9b81ae90bc2e95ec033fd18fa35e807af6c657763"}, {file = "mypy-1.18.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b10e3ea7f2eec23b4929a3fabf84505da21034a4f4b9613cda81217e92b74f3"}, @@ -4439,7 +4399,6 @@ description = "Type system extensions for programs checked with the mypy type ch optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505"}, {file = "mypy_extensions-1.1.0.tar.gz", hash = "sha256:52e68efc3284861e772bbcd66823fde5ae21fd2fdb51c62a211403730b916558"}, @@ -4452,7 +4411,6 @@ description = "An extended [CommonMark](https://spec.commonmark.org/) compliant optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "myst_parser-2.0.0-py3-none-any.whl", hash = "sha256:7c36344ae39c8e740dad7fdabf5aa6fc4897a813083c6cc9990044eb93656b14"}, {file = "myst_parser-2.0.0.tar.gz", hash = "sha256:ea929a67a6a0b1683cdbe19b8d2e724cd7643f8aa3e7bb18dd65beac3483bead"}, @@ -4480,7 +4438,6 @@ description = "A client library for executing notebooks. Formerly nbconvert's Ex optional = false python-versions = ">=3.9.0" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbclient-0.10.2-py3-none-any.whl", hash = "sha256:4ffee11e788b4a27fabeb7955547e4318a5298f34342a4bfd01f2e1faaeadc3d"}, {file = "nbclient-0.10.2.tar.gz", hash = "sha256:90b7fc6b810630db87a6d0c2250b1f0ab4cf4d3c27a299b0cde78a4ed3fd9193"}, @@ -4504,7 +4461,6 @@ description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Ou optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbconvert-7.16.6-py3-none-any.whl", hash = "sha256:1375a7b67e0c2883678c48e506dc320febb57685e5ee67faa51b18a90f3a712b"}, {file = "nbconvert-7.16.6.tar.gz", hash = "sha256:576a7e37c6480da7b8465eefa66c17844243816ce1ccc372633c6b71c3c0f582"}, @@ -4542,7 +4498,6 @@ description = "The Jupyter Notebook format" optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"}, {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"}, @@ -4565,7 +4520,6 @@ description = "Run any standard Python code quality tool on a Jupyter Notebook" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbqa-1.9.1-py3-none-any.whl", hash = "sha256:95552d2f6c2c038136252a805aa78d85018aef922586270c3a074332737282e5"}, {file = "nbqa-1.9.1.tar.gz", hash = "sha256:a1f4bcf587c597302fed295951001fc4e1be4ce0e77e1ab1b25ac2fbe3db0cdd"}, @@ -4596,7 +4550,6 @@ description = "Jupyter Notebook Tools for Sphinx" optional = false python-versions = ">=3.6" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nbsphinx-0.9.7-py3-none-any.whl", hash = "sha256:7292c3767fea29e405c60743eee5393682a83982ab202ff98f5eb2db02629da8"}, {file = "nbsphinx-0.9.7.tar.gz", hash = "sha256:abd298a686d55fa894ef697c51d44f24e53aa312dadae38e82920f250a5456fe"}, @@ -4616,7 +4569,7 @@ version = "1.6.0" description = "Patch asyncio to allow nested event loops" optional = false python-versions = ">=3.5" -groups = ["main", "docs"] +groups = ["docs"] files = [ {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"}, {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"}, @@ -4641,6 +4594,7 @@ description = "Python package for creating and manipulating graphs and networks" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f"}, {file = "networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1"}, @@ -4683,7 +4637,6 @@ description = "Node.js virtual environment builder" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"}, {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"}, @@ -4696,6 +4649,7 @@ description = "Fundamental package for array computing in Python" optional = false python-versions = ">=3.10" groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb"}, {file = "numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90"}, @@ -4846,7 +4800,6 @@ description = "Sphinx extension to support docstrings in Numpy format" optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "numpydoc-1.9.0-py3-none-any.whl", hash = "sha256:8a2983b2d62bfd0a8c470c7caa25e7e0c3d163875cdec12a8a1034020a9d1135"}, {file = "numpydoc-1.9.0.tar.gz", hash = "sha256:5fec64908fe041acc4b3afc2a32c49aab1540cf581876f5563d68bb129e27c5b"}, @@ -4856,11 +4809,6 @@ files = [ sphinx = ">=6" tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} -[package.extras] -developer = ["pre-commit (>=3.3)", "tomli ; python_version < \"3.11\""] -doc = ["intersphinx-registry", "matplotlib (>=3.5)", "numpy (>=1.22)", "pydata-sphinx-theme (>=0.13.3)", "sphinx (>=7)"] -test = ["matplotlib", "pytest", "pytest-cov"] - [[package]] name = "nvidia-cublas-cu12" version = "12.4.5.8" @@ -4868,7 +4816,7 @@ description = "CUBLAS native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0f8aa1706812e00b9f19dfe0cdb3999b092ccb8ca168c0db5b8ea712456fd9b3"}, {file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl", hash = "sha256:2fc8da60df463fdefa81e323eef2e36489e1c94335b5358bcb38360adf75ac9b"}, @@ -4882,7 +4830,7 @@ description = "CUDA profiling tools runtime libs." optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a"}, {file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:9dec60f5ac126f7bb551c055072b69d85392b13311fcc1bcda2202d172df30fb"}, @@ -4896,7 +4844,7 @@ description = "NVRTC native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"}, {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338"}, @@ -4910,7 +4858,7 @@ description = "CUDA Runtime native Libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"}, {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5"}, @@ -4924,7 +4872,7 @@ description = "cuDNN runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"}, {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"}, @@ -4940,7 +4888,7 @@ description = "CUFFT native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399"}, {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9"}, @@ -4957,7 +4905,7 @@ description = "CURAND native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1f173f09e3e3c76ab084aba0de819c49e56614feae5c12f69883f4ae9bb5fad9"}, {file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a88f583d4e0bb643c49743469964103aa59f7f708d862c3ddb0fc07f851e3b8b"}, @@ -4971,7 +4919,7 @@ description = "CUDA solver native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d338f155f174f90724bbde3758b7ac375a70ce8e706d70b018dd3375545fc84e"}, {file = "nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260"}, @@ -4990,7 +4938,7 @@ description = "CUSPARSE native runtime libraries" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_aarch64.whl", hash = "sha256:9d32f62896231ebe0480efd8a7f702e143c98cfaa0e8a76df3386c1ba2b54df3"}, {file = "nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1"}, @@ -5007,7 +4955,7 @@ description = "NVIDIA cuSPARSELt" optional = false python-versions = "*" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:067a7f6d03ea0d4841c85f0c6f1991c5dda98211f6302cb83a4ab234ee95bef8"}, {file = "nvidia_cusparselt_cu12-0.6.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:df2c24502fd76ebafe7457dbc4716b2fec071aabaed4fb7691a201cde03704d9"}, @@ -5021,7 +4969,7 @@ description = "NVIDIA Collective Communication Library (NCCL) Runtime" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:8579076d30a8c24988834445f8d633c697d42397e92ffc3f63fa26766d25e0a0"}, ] @@ -5033,7 +4981,7 @@ description = "Nvidia JIT LTO Library" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:4abe7fef64914ccfa909bc2ba39739670ecc9e820c83ccc7a6ed414122599b83"}, {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"}, @@ -5047,7 +4995,7 @@ description = "NVIDIA Tools Extension" optional = false python-versions = ">=3" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7959ad635db13edf4fc65c06a6e9f9e55fc2f92596db928d169c0bb031e88ef3"}, {file = "nvidia_nvtx_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:781e950d9b9f60d8241ccea575b32f5105a5baf4c2351cab5256a24869f12a1a"}, @@ -5061,7 +5009,6 @@ description = "A flexible configuration library" optional = false python-versions = ">=3.6" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "omegaconf-2.3.0-py3-none-any.whl", hash = "sha256:7b4df175cdb08ba400f45cae3bdcae7ba8365db4d165fc65fd04b050ab63b46b"}, {file = "omegaconf-2.3.0.tar.gz", hash = "sha256:d5d4b6d29955cc50ad50c46dc269bcd92c6e00f5f90d23ab5fee7bfca4ba4cc7"}, @@ -5078,7 +5025,6 @@ description = "The official Python library for the openai API" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openai-1.107.3-py3-none-any.whl", hash = "sha256:4ca54a847235ac04c6320da70fdc06b62d71439de9ec0aa40d5690c3064d4025"}, {file = "openai-1.107.3.tar.gz", hash = "sha256:69bb8032b05c5f00f7660e422f70f9aabc94793b9a30c5f899360ed21e46314f"}, @@ -5107,7 +5053,6 @@ description = "OpenAI Agents SDK" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openai_agents-0.3.0-py3-none-any.whl", hash = "sha256:16de8a28729ae9e27faad7ce146a4b74acf05c9eeca3fe23299f6e621a3893ed"}, {file = "openai_agents-0.3.0.tar.gz", hash = "sha256:4d5d1a4f43cdc35b55c41ae4f31157cf5ff2e2c89563cde58616f8a77fde932d"}, @@ -5127,7 +5072,7 @@ litellm = ["litellm (>=1.67.4.post1,<2)"] realtime = ["websockets (>=15.0,<16)"] sqlalchemy = ["asyncpg (>=0.29.0)", "sqlalchemy (>=2.0)"] viz = ["graphviz (>=0.17)"] -voice = ["numpy (>=2.2.0,<3)", "websockets (>=15.0,<16)"] +voice = ["numpy (>=2.2.0,<3) ; python_version >= \"3.10\"", "websockets (>=15.0,<16)"] [[package]] name = "openlit" @@ -5136,7 +5081,6 @@ description = "OpenTelemetry-native Auto instrumentation library for monitoring optional = false python-versions = "<4.0.0,>=3.8.0" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "openlit-1.35.1-py3-none-any.whl", hash = "sha256:d26850d55593562074cbef86ffaf8bdb2dd75462072b96a6ecb41c30e52c0492"}, {file = "openlit-1.35.1.tar.gz", hash = "sha256:c528921156c72723c320d14c192db81ee67baf7f90a99fef7b96e368d0a3f952"}, @@ -5178,7 +5122,6 @@ description = "OpenTelemetry Python API" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_api-1.37.0-py3-none-any.whl", hash = "sha256:accf2024d3e89faec14302213bc39550ec0f4095d1cf5ca688e1bfb1c8612f47"}, {file = "opentelemetry_api-1.37.0.tar.gz", hash = "sha256:540735b120355bd5112738ea53621f8d5edb35ebcd6fe21ada3ab1c61d1cd9a7"}, @@ -5195,7 +5138,6 @@ description = "OpenTelemetry Collector Exporters" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp-1.37.0-py3-none-any.whl", hash = "sha256:bd44592c6bc7fc3e5c0a9b60f2ee813c84c2800c449e59504ab93f356cc450fc"}, {file = "opentelemetry_exporter_otlp-1.37.0.tar.gz", hash = "sha256:f85b1929dd0d750751cc9159376fb05aa88bb7a08b6cdbf84edb0054d93e9f26"}, @@ -5212,7 +5154,6 @@ description = "OpenTelemetry Protobuf encoding" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_common-1.37.0-py3-none-any.whl", hash = "sha256:53038428449c559b0c564b8d718df3314da387109c4d36bd1b94c9a641b0292e"}, {file = "opentelemetry_exporter_otlp_proto_common-1.37.0.tar.gz", hash = "sha256:c87a1bdd9f41fdc408d9cc9367bb53f8d2602829659f2b90be9f9d79d0bfe62c"}, @@ -5228,7 +5169,6 @@ description = "OpenTelemetry Collector Protobuf over gRPC Exporter" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_grpc-1.37.0-py3-none-any.whl", hash = "sha256:aee5104835bf7993b7ddaaf380b6467472abaedb1f1dbfcc54a52a7d781a3890"}, {file = "opentelemetry_exporter_otlp_proto_grpc-1.37.0.tar.gz", hash = "sha256:f55bcb9fc848ce05ad3dd954058bc7b126624d22c4d9e958da24d8537763bec5"}, @@ -5250,7 +5190,6 @@ description = "OpenTelemetry Collector Protobuf over HTTP Exporter" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_exporter_otlp_proto_http-1.37.0-py3-none-any.whl", hash = "sha256:54c42b39945a6cc9d9a2a33decb876eabb9547e0dcb49df090122773447f1aef"}, {file = "opentelemetry_exporter_otlp_proto_http-1.37.0.tar.gz", hash = "sha256:e52e8600f1720d6de298419a802108a8f5afa63c96809ff83becb03f874e44ac"}, @@ -5272,7 +5211,6 @@ description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Py optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation-0.58b0-py3-none-any.whl", hash = "sha256:50f97ac03100676c9f7fc28197f8240c7290ca1baa12da8bfbb9a1de4f34cc45"}, {file = "opentelemetry_instrumentation-0.58b0.tar.gz", hash = "sha256:df640f3ac715a3e05af145c18f527f4422c6ab6c467e40bd24d2ad75a00cb705"}, @@ -5291,7 +5229,6 @@ description = "OpenTelemetry aiohttp client instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_aiohttp_client-0.58b0-py3-none-any.whl", hash = "sha256:14b805bd3da73579bb8484f798800af15bc8218dd775275c207cdf5b8abae586"}, {file = "opentelemetry_instrumentation_aiohttp_client-0.58b0.tar.gz", hash = "sha256:aab610f90a1be67ae66d3781bcad2b484774f043f73e8519156ca04d05019b2a"}, @@ -5314,7 +5251,6 @@ description = "ASGI instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_asgi-0.58b0-py3-none-any.whl", hash = "sha256:508a6d79e333d648d2afee0e140b6e80eb5d443be183be58e81d9ff88373168a"}, {file = "opentelemetry_instrumentation_asgi-0.58b0.tar.gz", hash = "sha256:3ccc0c9c1c8c71e8d9da5945c6dcd9c0c8d147839f208536b7042c6dd98e65c9"}, @@ -5337,7 +5273,6 @@ description = "OpenTelemetry Instrumentation for Django" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_django-0.58b0-py3-none-any.whl", hash = "sha256:6e3ada766ce965e9486d193e10cb32749bd51fe1adb5ec6b9310e33fe89fad6d"}, {file = "opentelemetry_instrumentation_django-0.58b0.tar.gz", hash = "sha256:24f45706a9dc3c47b9214ed5422fd0d35a850f3f40b04112a91fc10561cfd3f5"}, @@ -5361,7 +5296,6 @@ description = "Falcon instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_falcon-0.58b0-py3-none-any.whl", hash = "sha256:9801fb98e55368ef1fd43f21addebee257e08ac9cfd1492b5f53a5f07095eff6"}, {file = "opentelemetry_instrumentation_falcon-0.58b0.tar.gz", hash = "sha256:c3928c8e9e7edfedf1a7a4087fbd46b8d9fa98a229cf51668d036fc888d6f653"}, @@ -5385,7 +5319,6 @@ description = "OpenTelemetry FastAPI Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_fastapi-0.58b0-py3-none-any.whl", hash = "sha256:d89bfec69c9ffc5d9f3fe58655d6660a66b2bca863b9132712c06edcde68b6fa"}, {file = "opentelemetry_instrumentation_fastapi-0.58b0.tar.gz", hash = "sha256:03da470d694116a0a40f4e76319e42f3ff9efc49abf804b2acc2c07f96661497"}, @@ -5408,7 +5341,6 @@ description = "Flask instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_flask-0.58b0-py3-none-any.whl", hash = "sha256:b0d57ad4db7bd0177ddf8c7ae3adf8bd90e2ebfa2dd30884c6a97c97197e4ac5"}, {file = "opentelemetry_instrumentation_flask-0.58b0.tar.gz", hash = "sha256:ea2e06f448cef263c21f86401984906f68a5c766c7359000afb5621ae528d9c5"}, @@ -5432,7 +5364,6 @@ description = "OpenTelemetry HTTPX Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_httpx-0.58b0-py3-none-any.whl", hash = "sha256:d3f5a36c7fed08c245f1b06d1efd91f624caf2bff679766df80981486daaccdb"}, {file = "opentelemetry_instrumentation_httpx-0.58b0.tar.gz", hash = "sha256:3cd747e7785a06d06bd58875e8eb11595337c98c4341f4fe176ff1f734a90db7"}, @@ -5455,7 +5386,6 @@ description = "OpenTelemetry Pyramid instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_pyramid-0.58b0-py3-none-any.whl", hash = "sha256:197a434c5288bfaa9cdffcc9e6a21fdf7b852d1e25359ea626804ea1ffedabcd"}, {file = "opentelemetry_instrumentation_pyramid-0.58b0.tar.gz", hash = "sha256:f714c1c80995ffd4ace8c584756516ddc923aefb50a77d225f7d197128b4996a"}, @@ -5479,7 +5409,6 @@ description = "OpenTelemetry requests instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_requests-0.58b0-py3-none-any.whl", hash = "sha256:672a0be0bb5b52bea0c11820b35e27edcf4cd22d34abe4afc59a92a80519f8a8"}, {file = "opentelemetry_instrumentation_requests-0.58b0.tar.gz", hash = "sha256:ae9495e6ff64e27bdb839fce91dbb4be56e325139828e8005f875baf41951a2e"}, @@ -5501,7 +5430,6 @@ description = "OpenTelemetry Starlette Instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_starlette-0.58b0-py3-none-any.whl", hash = "sha256:7cb1c58a4f3efd78c19eac2f98def6ea5cea725c0ffafab960a708fa17c25b2a"}, {file = "opentelemetry_instrumentation_starlette-0.58b0.tar.gz", hash = "sha256:e6a5d8a7433e3f99cb1da58c9431c840a823afda82deb5a97976445e1fa32bda"}, @@ -5524,7 +5452,6 @@ description = "Tornado instrumentation for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_tornado-0.58b0-py3-none-any.whl", hash = "sha256:52cf6683e6754e6e2d6de7f61373160d565b6baec1688a668d7c2a83792ba88a"}, {file = "opentelemetry_instrumentation_tornado-0.58b0.tar.gz", hash = "sha256:1ae6096d4ae8ece38849b2b65ebbe1cee13276993b4365c2c516abb5f22bb66e"}, @@ -5546,7 +5473,6 @@ description = "OpenTelemetry urllib instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_urllib-0.58b0-py3-none-any.whl", hash = "sha256:63ad8a304a299bcb39224ecedc718a391404c8f2d4cc5755edfb5e49904e7b27"}, {file = "opentelemetry_instrumentation_urllib-0.58b0.tar.gz", hash = "sha256:071e5a28a1c4198cfa33937484f4b0b1068aab26d75e71e55f598a717f268d0a"}, @@ -5565,7 +5491,6 @@ description = "OpenTelemetry urllib3 instrumentation" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_urllib3-0.58b0-py3-none-any.whl", hash = "sha256:9e698785afe311edfab772152cb4851f1aaffde5110bb83e4e45d8c4e97277ee"}, {file = "opentelemetry_instrumentation_urllib3-0.58b0.tar.gz", hash = "sha256:978b8e3daa076437b1f7ed7509d8156108aee0679556fd355e532c4065dd7635"}, @@ -5588,7 +5513,6 @@ description = "WSGI Middleware for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_instrumentation_wsgi-0.58b0-py3-none-any.whl", hash = "sha256:cef5bdf1cb7a5162fdb1c1a2f95e4a08e02b1ca67ce828a1efdf81e9f23273b7"}, {file = "opentelemetry_instrumentation_wsgi-0.58b0.tar.gz", hash = "sha256:0ea27d44c83b48e6b182a904c801ca62b2999642647f32ef33c8a9c8bbf6a245"}, @@ -5607,7 +5531,6 @@ description = "OpenTelemetry Python Proto" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_proto-1.37.0-py3-none-any.whl", hash = "sha256:8ed8c066ae8828bbf0c39229979bdf583a126981142378a9cbe9d6fd5701c6e2"}, {file = "opentelemetry_proto-1.37.0.tar.gz", hash = "sha256:30f5c494faf66f77faeaefa35ed4443c5edb3b0aa46dad073ed7210e1a789538"}, @@ -5623,7 +5546,6 @@ description = "OpenTelemetry Python SDK" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_sdk-1.37.0-py3-none-any.whl", hash = "sha256:8f3c3c22063e52475c5dbced7209495c2c16723d016d39287dfc215d1771257c"}, {file = "opentelemetry_sdk-1.37.0.tar.gz", hash = "sha256:cc8e089c10953ded765b5ab5669b198bbe0af1b3f89f1007d19acd32dc46dda5"}, @@ -5641,7 +5563,6 @@ description = "OpenTelemetry Semantic Conventions" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_semantic_conventions-0.58b0-py3-none-any.whl", hash = "sha256:5564905ab1458b96684db1340232729fce3b5375a06e140e8904c78e4f815b28"}, {file = "opentelemetry_semantic_conventions-0.58b0.tar.gz", hash = "sha256:6bd46f51264279c433755767bb44ad00f1c9e2367e1b42af563372c5a6fa0c25"}, @@ -5658,7 +5579,6 @@ description = "Web util for OpenTelemetry" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "opentelemetry_util_http-0.58b0-py3-none-any.whl", hash = "sha256:6c6b86762ed43025fbd593dc5f700ba0aa3e09711aedc36fd48a13b23d8cb1e7"}, {file = "opentelemetry_util_http-0.58b0.tar.gz", hash = "sha256:de0154896c3472c6599311c83e0ecee856c4da1b17808d39fdc5cce5312e4d89"}, @@ -5671,7 +5591,7 @@ description = "Fast, correct Python JSON library supporting dataclasses, datetim optional = false python-versions = ">=3.9" groups = ["main"] -markers = "platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_python_implementation != \"PyPy\"" files = [ {file = "orjson-3.11.3-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:29cb1f1b008d936803e2da3d7cba726fc47232c45df531b29edf0b232dd737e7"}, {file = "orjson-3.11.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:97dceed87ed9139884a55db8722428e27bd8452817fbf1869c58b49fecab1120"}, @@ -5765,7 +5685,6 @@ description = "A purl aka. Package URL parser and builder" optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "packageurl_python-0.17.5-py3-none-any.whl", hash = "sha256:f0e55452ab37b5c192c443de1458e3f3b4d8ac27f747df6e8c48adeab081d321"}, {file = "packageurl_python-0.17.5.tar.gz", hash = "sha256:a7be3f3ba70d705f738ace9bf6124f31920245a49fa69d4b416da7037dd2de61"}, @@ -5784,7 +5703,6 @@ description = "Core utilities for Python packages" optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484"}, {file = "packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f"}, @@ -5797,7 +5715,6 @@ description = "Powerful data structures for data analysis, time series, and stat optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pandas-2.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:52bc29a946304c360561974c6542d1dd628ddafa69134a7131fdfd6a5d7a1a35"}, {file = "pandas-2.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:220cc5c35ffaa764dd5bb17cf42df283b5cb7fdf49e10a7b053a06c9cb48ee2b"}, @@ -5845,9 +5762,9 @@ files = [ [package.dependencies] numpy = [ - {version = ">=1.22.4", markers = "python_version < \"3.11\""}, - {version = ">=1.23.2", markers = "python_version == \"3.11\""}, {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, + {version = ">=1.23.2", markers = "python_version == \"3.11\""}, + {version = ">=1.22.4", markers = "python_version < \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -5885,7 +5802,6 @@ description = "Utilities for writing pandoc filters in python" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"}, {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"}, @@ -5898,7 +5814,6 @@ description = "A Python Parser" optional = false python-versions = ">=3.6" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "parso-0.8.5-py2.py3-none-any.whl", hash = "sha256:646204b5ee239c396d040b90f9e272e9a8017c630092bf59980beb62fd033887"}, {file = "parso-0.8.5.tar.gz", hash = "sha256:034d7354a9a018bdce352f48b2a8a450f05e9d6ee85db84764e9b6bd96dafe5a"}, @@ -5927,7 +5842,6 @@ description = "Utility library for gitignore style pattern matching of file path optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"}, {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"}, @@ -5940,7 +5854,6 @@ description = "Python Build Reasonableness" optional = false python-versions = ">=2.6" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pbr-7.0.1-py2.py3-none-any.whl", hash = "sha256:32df5156fbeccb6f8a858d1ebc4e465dcf47d6cc7a4895d5df9aa951c712fc35"}, {file = "pbr-7.0.1.tar.gz", hash = "sha256:3ecbcb11d2b8551588ec816b3756b1eb4394186c3b689b17e04850dfc20f7e57"}, @@ -5956,7 +5869,7 @@ description = "Pexpect allows easy control of interactive console applications." optional = false python-versions = "*" groups = ["docs", "test"] -markers = "(sys_platform != \"win32\" and sys_platform != \"emscripten\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\"" files = [ {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"}, {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"}, @@ -5972,7 +5885,6 @@ description = "Python Imaging Library (Fork)" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pillow-11.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1b9c17fd4ace828b3003dfd1e30bff24863e0eb59b535e8f80194d9cc7ecf860"}, {file = "pillow-11.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:65dc69160114cdd0ca0f35cb434633c75e8e7fad4cf855177a05bf38678f73ad"}, @@ -6087,7 +5999,7 @@ docs = ["furo", "olefile", "sphinx (>=8.2)", "sphinx-autobuild", "sphinx-copybut fpx = ["olefile"] mic = ["olefile"] test-arrow = ["pyarrow"] -tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "trove-classifiers (>=2024.10.12)"] +tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "trove-classifiers (>=2024.10.12)"] typing = ["typing-extensions ; python_version < \"3.10\""] xmp = ["defusedxml"] @@ -6098,7 +6010,6 @@ description = "The PyPA recommended tool for installing Python packages." optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip-25.2-py3-none-any.whl", hash = "sha256:6d67a2b4e7f14d8b31b8b52648866fa717f45a1eb70e83002f4331d07e953717"}, {file = "pip-25.2.tar.gz", hash = "sha256:578283f006390f85bb6282dffb876454593d637f5d1be494b5202ce4877e71f2"}, @@ -6111,7 +6022,6 @@ description = "An unofficial, importable pip API" optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip_api-0.0.34-py3-none-any.whl", hash = "sha256:8b2d7d7c37f2447373aa2cf8b1f60a2f2b27a84e1e9e0294a3f6ef10eb3ba6bb"}, {file = "pip_api-0.0.34.tar.gz", hash = "sha256:9b75e958f14c5a2614bae415f2adf7eeb54d50a2cfbe7e24fd4826471bac3625"}, @@ -6127,7 +6037,6 @@ description = "A tool for scanning Python environments for known vulnerabilities optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip_audit-2.9.0-py3-none-any.whl", hash = "sha256:348b16e60895749a0839875d7cc27ebd692e1584ebe5d5cb145941c8e25a80bd"}, {file = "pip_audit-2.9.0.tar.gz", hash = "sha256:0b998410b58339d7a231e5aa004326a294e4c7c6295289cdc9d5e1ef07b1f44d"}, @@ -6157,7 +6066,6 @@ description = "pip requirements parser - a mostly correct pip requirements parsi optional = false python-versions = ">=3.6.0" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pip-requirements-parser-32.0.1.tar.gz", hash = "sha256:b4fa3a7a0be38243123cf9d1f3518da10c51bdb165a2b2985566247f9155a7d3"}, {file = "pip_requirements_parser-32.0.1-py3-none-any.whl", hash = "sha256:4659bc2a667783e7a15d190f6fccf8b2486685b6dba4c19c3876314769c57526"}, @@ -6178,7 +6086,6 @@ description = "A small Python package for determining appropriate platform-speci optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "platformdirs-4.4.0-py3-none-any.whl", hash = "sha256:abd01743f24e5287cd7a5db3752faf1a2d65353f38ec26d98e25a6db65958c85"}, {file = "platformdirs-4.4.0.tar.gz", hash = "sha256:ca753cf4d81dc309bc67b0ea38fd15dc97bc30ce419a7f58d13eb3bf14c4febf"}, @@ -6196,7 +6103,6 @@ description = "plugin and hook calling mechanisms for python" optional = false python-versions = ">=3.9" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746"}, {file = "pluggy-1.6.0.tar.gz", hash = "sha256:7dcc130b76258d33b90f61b658791dede3486c3e6bfb003ee5c9bfb396dd22f3"}, @@ -6225,7 +6131,6 @@ description = "A framework for managing and maintaining multi-language pre-commi optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pre_commit-4.3.0-py2.py3-none-any.whl", hash = "sha256:2b0747ad7e6e967169136edffee14c16e148a778a54e4f967921aa1ebf2308d8"}, {file = "pre_commit-4.3.0.tar.gz", hash = "sha256:499fe450cc9d42e9d58e606262795ecb64dd05438943c62b66f6a8673da30b16"}, @@ -6245,7 +6150,6 @@ description = "Library for building powerful interactive command lines in Python optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955"}, {file = "prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855"}, @@ -6261,7 +6165,6 @@ description = "Accelerated property cache" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "propcache-0.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:22d9962a358aedbb7a2e36187ff273adeaab9743373a272976d2e348d08c7770"}, {file = "propcache-0.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0d0fda578d1dc3f77b6b5a5dce3b9ad69a8250a891760a548df850a5e8da87f3"}, @@ -6370,7 +6273,6 @@ description = "Beautiful, Pythonic protocol buffers" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"}, {file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"}, @@ -6389,7 +6291,6 @@ description = "" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "protobuf-5.29.5-cp310-abi3-win32.whl", hash = "sha256:3f1c6468a2cfd102ff4703976138844f78ebd1fb45f49011afc5139e9e283079"}, {file = "protobuf-5.29.5-cp310-abi3-win_amd64.whl", hash = "sha256:3f76e3a3675b4a4d867b52e4a5f5b78a2ef9565549d4037e06cf7b0942b1d3fc"}, @@ -6411,7 +6312,6 @@ description = "Cross-platform lib for process and system monitoring in Python. optional = false python-versions = ">=3.6" groups = ["main", "docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "psutil-7.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:101d71dc322e3cffd7cea0650b09b3d08b8e7c4109dd6809fe452dfd00e58b25"}, {file = "psutil-7.0.0-cp36-abi3-macosx_11_0_arm64.whl", hash = "sha256:39db632f6bb862eeccf56660871433e111b6ea58f2caea825571951d4b6aa3da"}, @@ -6436,7 +6336,7 @@ description = "Run a subprocess in a pseudo terminal" optional = false python-versions = "*" groups = ["docs", "test"] -markers = "(sys_platform != \"win32\" and sys_platform != \"emscripten\") and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\"" files = [ {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"}, {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"}, @@ -6449,7 +6349,6 @@ description = "Safely evaluate AST nodes without side effects" optional = false python-versions = "*" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"}, {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"}, @@ -6465,7 +6364,6 @@ description = "Library for serializing and deserializing Python Objects to and f optional = false python-versions = "<4.0,>=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "py_serializable-2.1.0-py3-none-any.whl", hash = "sha256:b56d5d686b5a03ba4f4db5e769dc32336e142fc3bd4d68a8c25579ebb0a67304"}, {file = "py_serializable-2.1.0.tar.gz", hash = "sha256:9d5db56154a867a9b897c0163b33a793c804c80cee984116d02d49e4578fc103"}, @@ -6481,7 +6379,6 @@ description = "Python library for Apache Arrow" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyarrow-21.0.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:e563271e2c5ff4d4a4cbeb2c83d5cf0d4938b891518e676025f7268c6fe5fe26"}, {file = "pyarrow-21.0.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:fee33b0ca46f4c85443d6c450357101e47d53e6c3f008d658c27a2d020d44c79"}, @@ -6538,7 +6435,6 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"}, {file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"}, @@ -6551,7 +6447,6 @@ description = "A collection of ASN.1-based protocols modules" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"}, {file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"}, @@ -6567,7 +6462,6 @@ description = "Python style guide checker" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pycodestyle-2.14.0-py2.py3-none-any.whl", hash = "sha256:dd6bf7cb4ee77f8e016f9c8e74a35ddd9f67e1d5fd4184d86c3b98e07099f42d"}, {file = "pycodestyle-2.14.0.tar.gz", hash = "sha256:c4b5b517d278089ff9d0abdec919cd97262a3367449ea1c8b49b91529167b783"}, @@ -6585,7 +6479,6 @@ files = [ {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"}, {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"}, ] -markers = {main = "platform_python_implementation == \"PyPy\"", docs = "implementation_name == \"pypy\""} [[package]] name = "pydantic" @@ -6594,7 +6487,6 @@ description = "Data validation using Python type hints" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pydantic-2.11.9-py3-none-any.whl", hash = "sha256:c42dd626f5cfc1c6950ce6205ea58c93efa406da65f479dcb4029d5934857da2"}, {file = "pydantic-2.11.9.tar.gz", hash = "sha256:6b8ffda597a14812a7975c90b82a8a2e777d9257aba3453f973acd3c032a18e2"}, @@ -6608,7 +6500,7 @@ typing-inspection = ">=0.4.0" [package.extras] email = ["email-validator (>=2.0.0)"] -timezone = ["tzdata"] +timezone = ["tzdata ; python_version >= \"3.9\" and platform_system == \"Windows\""] [[package]] name = "pydantic-core" @@ -6617,7 +6509,6 @@ description = "Core functionality for Pydantic validation and serialization" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pydantic_core-2.33.2-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:2b3d326aaef0c0399d9afffeb6367d5e26ddc24d351dbc9c636840ac355dc5d8"}, {file = "pydantic_core-2.33.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e5b2671f05ba48b94cb90ce55d8bdcaaedb8ba00cc5359f6810fc918713983d"}, @@ -6730,7 +6621,6 @@ description = "Settings management using Pydantic" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "pydantic_settings-2.10.1-py3-none-any.whl", hash = "sha256:a60952460b99cf661dc25c29c0ef171721f98bfcb52ef8d9ea4c943d7c8cc796"}, {file = "pydantic_settings-2.10.1.tar.gz", hash = "sha256:06f0062169818d0f5524420a360d632d5857b83cffd4d42fe29597807a1614ee"}, @@ -6755,7 +6645,6 @@ description = "passive checker of Python programs" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyflakes-3.4.0-py2.py3-none-any.whl", hash = "sha256:f742a7dbd0d9cb9ea41e9a24a918996e8170c799fa528688d40dd582c8265f4f"}, {file = "pyflakes-3.4.0.tar.gz", hash = "sha256:b24f96fafb7d2ab0ec5075b7350b3d2d2218eab42003821c06344973d3ea2f58"}, @@ -6768,7 +6657,6 @@ description = "Pygments is a syntax highlighting package written in Python." optional = false python-versions = ">=3.8" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"}, {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"}, @@ -6784,7 +6672,6 @@ description = "python code static checker" optional = false python-versions = ">=3.9.0" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pylint-3.3.8-py3-none-any.whl", hash = "sha256:7ef94aa692a600e82fabdd17102b73fc226758218c97473c7ad67bd4cb905d83"}, {file = "pylint-3.3.8.tar.gz", hash = "sha256:26698de19941363037e2937d3db9ed94fb3303fdadf7d98847875345a8bb6b05"}, @@ -6794,9 +6681,9 @@ files = [ astroid = ">=3.3.8,<=3.4.0.dev0" colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""} dill = [ - {version = ">=0.2", markers = "python_version < \"3.11\""}, - {version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""}, {version = ">=0.3.7", markers = "python_version >= \"3.12\""}, + {version = ">=0.3.6", markers = "python_version == \"3.11\""}, + {version = ">=0.2", markers = "python_version < \"3.11\""}, ] isort = ">=4.2.5,<5.13 || >5.13,<7" mccabe = ">=0.6,<0.8" @@ -6815,7 +6702,6 @@ description = "pyparsing - Classes and methods to define and execute parsing gra optional = false python-versions = ">=3.9" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyparsing-3.2.4-py3-none-any.whl", hash = "sha256:91d0fcde680d42cd031daf3a6ba20da3107e08a75de50da58360e7d94ab24d36"}, {file = "pyparsing-3.2.4.tar.gz", hash = "sha256:fff89494f45559d0f2ce46613b419f632bbb6afbdaed49696d322bcf98a58e99"}, @@ -6831,7 +6717,6 @@ description = "pytest: simple powerful testing with Python" optional = false python-versions = ">=3.8" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest-8.3.3-py3-none-any.whl", hash = "sha256:a6853c7375b2663155079443d2e45de913a911a11d669df02a50814944db57b2"}, {file = "pytest-8.3.3.tar.gz", hash = "sha256:70b98107bd648308a7952b06e6ca9a50bc660be218d53c257cc1fc94fda10181"}, @@ -6855,7 +6740,6 @@ description = "Pytest plugin for measuring coverage." optional = false python-versions = ">=3.6" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest-cov-3.0.0.tar.gz", hash = "sha256:e7f0f5b1617d2210a2cabc266dfe2f4c75a8d32fb89eafb7ad9d06f6d076d470"}, {file = "pytest_cov-3.0.0-py3-none-any.whl", hash = "sha256:578d5d15ac4a25e5f961c938b85a05b09fdaae9deef3bb6de9a6e766622ca7a6"}, @@ -6875,7 +6759,6 @@ description = "pytest plugin that allows you to add environment variables." optional = false python-versions = ">=3.8" groups = ["main", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest_env-1.1.5-py3-none-any.whl", hash = "sha256:ce90cf8772878515c24b31cd97c7fa1f4481cd68d588419fd45f10ecaee6bc30"}, {file = "pytest_env-1.1.5.tar.gz", hash = "sha256:91209840aa0e43385073ac464a554ad2947cc2fd663a9debf88d03b01e0cc1cf"}, @@ -6895,7 +6778,6 @@ description = "Thin-wrapper around the mock package for easier use with pytest" optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytest_mock-3.15.0-py3-none-any.whl", hash = "sha256:ef2219485fb1bd256b00e7ad7466ce26729b30eadfc7cbcdb4fa9a92ca68db6f"}, {file = "pytest_mock-3.15.0.tar.gz", hash = "sha256:ab896bd190316b9d5d87b277569dfcdf718b2d049a2ccff5f7aca279c002a1cf"}, @@ -6914,7 +6796,6 @@ description = "Extensions to the standard Python datetime module" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" groups = ["main", "docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, @@ -6930,7 +6811,6 @@ description = "Read key-value pairs from a .env file and set them as environment optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "python_dotenv-1.1.1-py3-none-any.whl", hash = "sha256:31f23644fe2602f88ff55e1f5c79ba497e01224ee7737937930c448e4d0e24dc"}, {file = "python_dotenv-1.1.1.tar.gz", hash = "sha256:a8a6399716257f45be6a007360200409fce5cda2661e3dec71d23dc15f6189ab"}, @@ -6946,7 +6826,6 @@ description = "A streaming multipart parser for Python" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104"}, {file = "python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13"}, @@ -6959,7 +6838,6 @@ description = "World timezone definitions, modern and historical" optional = false python-versions = "*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"}, {file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"}, @@ -6972,7 +6850,6 @@ description = "A tool to automatically upgrade syntax for newer versions." optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "pyupgrade-3.20.0-py2.py3-none-any.whl", hash = "sha256:cd5bf842b863f50adad324a01c30aef60b9f698a9814848094818659c92cd1f4"}, {file = "pyupgrade-3.20.0.tar.gz", hash = "sha256:dd6a16c13fc1a7db45796008689a9a35420bd364d681430f640c5e54a3d351ea"}, @@ -7010,7 +6887,7 @@ files = [ {file = "pywin32-311-cp39-cp39-win_amd64.whl", hash = "sha256:e0c4cfb0621281fe40387df582097fd796e80430597cb9944f0ae70447bacd91"}, {file = "pywin32-311-cp39-cp39-win_arm64.whl", hash = "sha256:62ea666235135fee79bb154e695f3ff67370afefd71bd7fea7512fc70ef31e3d"}, ] -markers = {main = "sys_platform == \"win32\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", docs = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")", test = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\" and (python_version <= \"3.11\" or python_version >= \"3.12\")"} +markers = {main = "sys_platform == \"win32\"", docs = 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"sphinxcontrib_htmlhelp-2.1.0-py3-none-any.whl", hash = "sha256:166759820b47002d22914d64a075ce08f4c46818e17cfc9470a9786b759b19f8"}, {file = "sphinxcontrib_htmlhelp-2.1.0.tar.gz", hash = "sha256:c9e2916ace8aad64cc13a0d233ee22317f2b9025b9cf3295249fa985cc7082e9"}, @@ -8120,7 +8045,6 @@ description = "A sphinx extension which renders display math in HTML via JavaScr optional = false python-versions = ">=3.5" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib-jsmath-1.0.1.tar.gz", hash = "sha256:a9925e4a4587247ed2191a22df5f6970656cb8ca2bd6284309578f2153e0c4b8"}, {file = "sphinxcontrib_jsmath-1.0.1-py2.py3-none-any.whl", hash = "sha256:2ec2eaebfb78f3f2078e73666b1415417a116cc848b72e5172e596c871103178"}, @@ -8136,7 +8060,6 @@ description = "sphinxcontrib-qthelp is a sphinx extension which outputs QtHelp d optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_qthelp-2.0.0-py3-none-any.whl", hash = "sha256:b18a828cdba941ccd6ee8445dbe72ffa3ef8cbe7505d8cd1fa0d42d3f2d5f3eb"}, {file = "sphinxcontrib_qthelp-2.0.0.tar.gz", hash = "sha256:4fe7d0ac8fc171045be623aba3e2a8f613f8682731f9153bb2e40ece16b9bbab"}, @@ -8154,7 +8077,6 @@ description = "sphinxcontrib-serializinghtml is a sphinx extension which outputs optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl", hash = "sha256:6e2cb0eef194e10c27ec0023bfeb25badbbb5868244cf5bc5bdc04e4464bf331"}, {file = "sphinxcontrib_serializinghtml-2.0.0.tar.gz", hash = "sha256:e9d912827f872c029017a53f0ef2180b327c3f7fd23c87229f7a8e8b70031d4d"}, @@ -8172,7 +8094,6 @@ description = "Database Abstraction Library" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "SQLAlchemy-2.0.43-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:21ba7a08a4253c5825d1db389d4299f64a100ef9800e4624c8bf70d8f136e6ed"}, {file = "SQLAlchemy-2.0.43-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:11b9503fa6f8721bef9b8567730f664c5a5153d25e247aadc69247c4bc605227"}, @@ -8269,7 +8190,6 @@ description = "SSE plugin for Starlette" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "sse_starlette-3.0.2-py3-none-any.whl", hash = "sha256:16b7cbfddbcd4eaca11f7b586f3b8a080f1afe952c15813455b162edea619e5a"}, {file = "sse_starlette-3.0.2.tar.gz", hash = "sha256:ccd60b5765ebb3584d0de2d7a6e4f745672581de4f5005ab31c3a25d10b52b3a"}, @@ -8291,7 +8211,6 @@ description = "Extract data from python stack frames and tracebacks for informat optional = false python-versions = "*" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"}, {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"}, @@ -8312,7 +8231,6 @@ description = "The little ASGI library that shines." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "starlette-0.48.0-py3-none-any.whl", hash = "sha256:0764ca97b097582558ecb498132ed0c7d942f233f365b86ba37770e026510659"}, {file = "starlette-0.48.0.tar.gz", hash = "sha256:7e8cee469a8ab2352911528110ce9088fdc6a37d9876926e73da7ce4aa4c7a46"}, @@ -8332,7 +8250,6 @@ description = "Computer algebra system (CAS) in Python" optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version <= \"3.11\" or python_version >= \"3.12\"" files = [ {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"}, {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"}, @@ -8344,21 +8261,6 @@ mpmath = ">=1.1.0,<1.4" [package.extras] dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] -[[package]] -name = "tabulate" -version = "0.9.0" -description = "Pretty-print tabular data" -optional = false -python-versions = ">=3.7" -groups = ["docs"] -files = [ - {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"}, - {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"}, -] - -[package.extras] -widechars = ["wcwidth"] - [[package]] name = "tenacity" version = "9.1.2" @@ -8366,7 +8268,6 @@ description = "Retry code until it succeeds" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"}, {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"}, @@ -8383,7 +8284,6 @@ description = "ANSI color formatting for output in terminal" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "termcolor-3.1.0-py3-none-any.whl", hash = "sha256:591dd26b5c2ce03b9e43f391264626557873ce1d379019786f99b0c2bee140aa"}, {file = "termcolor-3.1.0.tar.gz", hash = "sha256:6a6dd7fbee581909eeec6a756cff1d7f7c376063b14e4a298dc4980309e55970"}, @@ -8399,7 +8299,6 @@ description = "Modern Text User Interface framework" optional = false python-versions = "<4.0.0,>=3.8.1" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "textual-2.1.2-py3-none-any.whl", hash = "sha256:95f37f49e930838e721bba8612f62114d410a3019665b6142adabc14c2fb9611"}, {file = "textual-2.1.2.tar.gz", hash = "sha256:aae3f9fde00c7440be00e3c3ac189e02d014f5298afdc32132f93480f9e09146"}, @@ -8412,7 +8311,7 @@ rich = ">=13.3.3" typing-extensions = ">=4.4.0,<5.0.0" [package.extras] -syntax = ["tree-sitter (>=0.23.0)", "tree-sitter-bash (>=0.23.0)", "tree-sitter-css (>=0.23.0)", "tree-sitter-go (>=0.23.0)", "tree-sitter-html (>=0.23.0)", "tree-sitter-java (>=0.23.0)", "tree-sitter-javascript (>=0.23.0)", "tree-sitter-json (>=0.24.0)", "tree-sitter-markdown (>=0.3.0)", "tree-sitter-python (>=0.23.0)", "tree-sitter-regex (>=0.24.0)", "tree-sitter-rust (>=0.23.0)", "tree-sitter-sql (>=0.3.0,<0.3.8)", "tree-sitter-toml (>=0.6.0)", "tree-sitter-xml (>=0.7.0)", "tree-sitter-yaml (>=0.6.0)"] +syntax = ["tree-sitter (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-bash (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-css (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-go (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-html (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-java (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-javascript (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-json (>=0.24.0) ; python_version >= \"3.9\"", "tree-sitter-markdown (>=0.3.0) ; python_version >= \"3.9\"", "tree-sitter-python (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-regex (>=0.24.0) ; python_version >= \"3.9\"", "tree-sitter-rust (>=0.23.0) ; python_version >= \"3.9\"", "tree-sitter-sql (>=0.3.0,<0.3.8) ; python_version >= \"3.9\"", "tree-sitter-toml (>=0.6.0) ; python_version >= \"3.9\"", "tree-sitter-xml (>=0.7.0) ; python_version >= \"3.9\"", "tree-sitter-yaml (>=0.6.0) ; python_version >= \"3.9\""] [[package]] name = "threadpoolctl" @@ -8421,7 +8320,6 @@ description = "threadpoolctl" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "threadpoolctl-3.6.0-py3-none-any.whl", hash = "sha256:43a0b8fd5a2928500110039e43a5eed8480b918967083ea48dc3ab9f13c4a7fb"}, {file = "threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e"}, @@ -8434,7 +8332,6 @@ description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tiktoken-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:3de02f5a491cfd179aec916eddb70331814bd6bf764075d39e21d5862e533970"}, {file = "tiktoken-0.12.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b6cfb6d9b7b54d20af21a912bfe63a2727d9cfa8fbda642fd8322c70340aad16"}, @@ -8509,7 +8406,6 @@ description = "A tiny CSS parser" optional = false python-versions = ">=3.8" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tinycss2-1.4.0-py3-none-any.whl", hash = "sha256:3a49cf47b7675da0b15d0c6e1df8df4ebd96e9394bb905a5775adb0d884c5289"}, {file = "tinycss2-1.4.0.tar.gz", hash = "sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7"}, @@ -8529,7 +8425,6 @@ description = "A wrapper around the stdlib `tokenize` which roundtrips." optional = false python-versions = ">=3.9" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tokenize_rt-6.2.0-py2.py3-none-any.whl", hash = "sha256:a152bf4f249c847a66497a4a95f63376ed68ac6abf092a2f7cfb29d044ecff44"}, {file = "tokenize_rt-6.2.0.tar.gz", hash = "sha256:8439c042b330c553fdbe1758e4a05c0ed460dbbbb24a606f11f0dee75da4cad6"}, @@ -8542,7 +8437,6 @@ description = "Python Library for Tom's Obvious, Minimal Language" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"}, {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"}, @@ -8589,7 +8483,7 @@ files = [ {file = "tomli-2.2.1-py3-none-any.whl", hash = "sha256:cb55c73c5f4408779d0cf3eef9f762b9c9f147a77de7b258bef0a5628adc85cc"}, {file = "tomli-2.2.1.tar.gz", hash = "sha256:cd45e1dc79c835ce60f7404ec8119f2eb06d38b1deba146f07ced3bbc44505ff"}, ] -markers = {main = "python_version < \"3.11\"", docs = "python_version < \"3.11\"", test = "python_version >= \"3.12\" or python_version <= \"3.11\""} +markers = {main = "python_version == \"3.10\"", docs = "python_version == \"3.10\""} [[package]] name = "tomlkit" @@ -8598,7 +8492,6 @@ description = "Style preserving TOML library" optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tomlkit-0.13.3-py3-none-any.whl", hash = "sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0"}, {file = "tomlkit-0.13.3.tar.gz", hash = "sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1"}, @@ -8611,7 +8504,6 @@ description = "Tensors and Dynamic neural networks in Python with strong GPU acc optional = false python-versions = ">=3.9.0" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torch-2.6.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:6860df13d9911ac158f4c44031609700e1eba07916fff62e21e6ffa0a9e01961"}, {file = "torch-2.6.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:c4f103a49830ce4c7561ef4434cc7926e5a5fe4e5eb100c19ab36ea1e2b634ab"}, @@ -8669,7 +8561,6 @@ description = "An audio package for PyTorch" optional = false python-versions = "*" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torchaudio-2.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0eda1cd876f44fc014dc04aa680db2fa355a83df5d834398db6dd5f5cd911f4c"}, {file = "torchaudio-2.6.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:22798d5d8e37869bd5875d37f42270efbeb8ae94bda97fed40c1c5e0e1c62fa3"}, @@ -8703,7 +8594,6 @@ description = "image and video datasets and models for torch deep learning" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "torchvision-0.21.0-1-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:5568c5a1ff1b2ec33127b629403adb530fab81378d9018ca4ed6508293f76e2b"}, {file = "torchvision-0.21.0-1-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:ff96666b94a55e802ea6796cabe788541719e6f4905fc59c380fed3517b6a64d"}, @@ -8748,7 +8638,6 @@ description = "Tornado is a Python web framework and asynchronous networking lib optional = false python-versions = ">=3.9" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tornado-6.5.2-cp39-abi3-macosx_10_9_universal2.whl", hash = "sha256:2436822940d37cde62771cff8774f4f00b3c8024fe482e16ca8387b8a2724db6"}, {file = "tornado-6.5.2-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:583a52c7aa94ee046854ba81d9ebb6c81ec0fd30386d96f7640c96dad45a03ef"}, @@ -8771,7 +8660,6 @@ description = "Fast, Extensible Progress Meter" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2"}, {file = "tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2"}, @@ -8794,7 +8682,6 @@ description = "Traitlets Python configuration system" optional = false python-versions = ">=3.8" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"}, {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"}, @@ -8811,7 +8698,7 @@ description = "A language and compiler for custom Deep Learning operations" optional = false python-versions = "*" groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "platform_machine == \"x86_64\" and platform_system == \"Linux\"" files = [ {file = "triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e54983cd51875855da7c68ec05c05cf8bb08df361b1d5b69e05e40b0c9bd62"}, {file = "triton-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8009a1fb093ee8546495e96731336a33fb8856a38e45bb4ab6affd6dbc3ba220"}, @@ -8832,7 +8719,6 @@ description = "Typing stubs for requests" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "types_requests-2.32.4.20250913-py3-none-any.whl", hash = "sha256:78c9c1fffebbe0fa487a418e0fa5252017e9c60d1a2da394077f1780f655d7e1"}, {file = "types_requests-2.32.4.20250913.tar.gz", hash = "sha256:abd6d4f9ce3a9383f269775a9835a4c24e5cd6b9f647d64f88aa4613c33def5d"}, @@ -8848,7 +8734,6 @@ description = "Backported and Experimental Type Hints for Python 3.9+" optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548"}, {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"}, @@ -8861,7 +8746,6 @@ description = "Runtime typing introspection tools" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "typing_inspection-0.4.1-py3-none-any.whl", hash = "sha256:389055682238f53b04f7badcb49b989835495a96700ced5dab2d8feae4b26f51"}, {file = "typing_inspection-0.4.1.tar.gz", hash = "sha256:6ae134cc0203c33377d43188d4064e9b357dba58cff3185f22924610e70a9d28"}, @@ -8877,7 +8761,6 @@ description = "Provider of IANA time zone data" optional = false python-versions = ">=2" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"}, {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"}, @@ -8890,7 +8773,6 @@ description = "Micro subset of unicode data files for linkify-it-py projects." optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "uc-micro-py-1.0.3.tar.gz", hash = "sha256:d321b92cff673ec58027c04015fcaa8bb1e005478643ff4a500882eaab88c48a"}, {file = "uc_micro_py-1.0.3-py3-none-any.whl", hash = "sha256:db1dffff340817673d7b466ec86114a9dc0e9d4d9b5ba229d9d60e5c12600cd5"}, @@ -8928,14 +8810,13 @@ description = "HTTP library with thread-safe connection pooling, file post, and optional = false python-versions = ">=3.9" groups = ["main", "docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"}, {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"}, ] [package.extras] -brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +brotli = ["brotli (>=1.0.9) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\""] h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] @@ -8947,7 +8828,7 @@ description = "The lightning-fast ASGI server." optional = false python-versions = ">=3.9" groups = ["main"] -markers = "sys_platform != \"emscripten\" and (python_version <= \"3.11\" or python_version >= \"3.12\")" +markers = "sys_platform != \"emscripten\"" files = [ {file = "uvicorn-0.35.0-py3-none-any.whl", hash = "sha256:197535216b25ff9b785e29a0b79199f55222193d47f820816e7da751e9bc8d4a"}, {file = "uvicorn-0.35.0.tar.gz", hash = "sha256:bc662f087f7cf2ce11a1d7fd70b90c9f98ef2e2831556dd078d131b96cc94a01"}, @@ -8959,7 +8840,7 @@ h11 = ">=0.8" typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""} [package.extras] -standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"] +standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"] [[package]] name = "vec-inf" @@ -8968,7 +8849,6 @@ description = "Efficient LLM inference on Slurm clusters using vLLM." optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "vec_inf-0.6.0-py3-none-any.whl", hash = "sha256:cc0211957f1a4b12fba600ff456312ccb632cd7b3e423937c76ab89001a8ffc9"}, {file = "vec_inf-0.6.0.tar.gz", hash = "sha256:c02360de2fde5da9e93bcf702e97fd2ca0a49056831793285fb3788987cf7217"}, @@ -8991,7 +8871,6 @@ description = "Virtual Python Environment builder" optional = false python-versions = ">=3.8" groups = ["test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "virtualenv-20.34.0-py3-none-any.whl", hash = "sha256:341f5afa7eee943e4984a9207c025feedd768baff6753cd660c857ceb3e36026"}, {file = "virtualenv-20.34.0.tar.gz", hash = "sha256:44815b2c9dee7ed86e387b842a84f20b93f7f417f95886ca1996a72a4138eb1a"}, @@ -9005,7 +8884,7 @@ typing-extensions = {version = ">=4.13.2", markers = "python_version < \"3.11\"" [package.extras] docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2,!=7.3)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"] -test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"] +test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8) ; platform_python_implementation == \"PyPy\" or platform_python_implementation == \"GraalVM\" or platform_python_implementation == \"CPython\" and sys_platform == \"win32\" and python_version >= \"3.13\"", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10) ; platform_python_implementation == \"CPython\""] [[package]] name = "wadler-lindig" @@ -9014,7 +8893,6 @@ description = "A Wadler–Lindig pretty-printer for Python." optional = false python-versions = ">=3.10" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wadler_lindig-0.1.7-py3-none-any.whl", hash = "sha256:e3ec83835570fd0a9509f969162aeb9c65618f998b1f42918cfc8d45122fe953"}, {file = "wadler_lindig-0.1.7.tar.gz", hash = "sha256:81d14d3fe77d441acf3ebd7f4aefac20c74128bf460e84b512806dccf7b2cd55"}, @@ -9031,7 +8909,6 @@ description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = "*" groups = ["docs", "test"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, @@ -9044,7 +8921,6 @@ description = "Character encoding aliases for legacy web content" optional = false python-versions = "*" groups = ["docs"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, @@ -9057,7 +8933,6 @@ description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b"}, {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205"}, @@ -9137,7 +9012,6 @@ description = "Module for decorators, wrappers and monkey patching." optional = false python-versions = ">=3.8" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "wrapt-1.17.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88bbae4d40d5a46142e70d58bf664a89b6b4befaea7b2ecc14e03cedb8e06c04"}, {file = "wrapt-1.17.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6b13af258d6a9ad602d57d889f83b9d5543acd471eee12eb51f5b01f8eb1bc2"}, @@ -9229,7 +9103,6 @@ description = "Makes working with XML feel like you are working with JSON" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "xmltodict-0.15.1-py2.py3-none-any.whl", hash = "sha256:dcd84b52f30a15be5ac4c9099a0cb234df8758624b035411e329c5c1e7a49089"}, {file = "xmltodict-0.15.1.tar.gz", hash = "sha256:3d8d49127f3ce6979d40a36dbcad96f8bab106d232d24b49efdd4bd21716983c"}, @@ -9242,7 +9115,6 @@ description = "Python binding for xxHash" optional = false python-versions = ">=3.7" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"}, {file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"}, @@ -9376,7 +9248,6 @@ description = "Yet another URL library" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "yarl-1.20.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:6032e6da6abd41e4acda34d75a816012717000fa6839f37124a47fcefc49bec4"}, {file = "yarl-1.20.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2c7b34d804b8cf9b214f05015c4fee2ebe7ed05cf581e7192c06555c71f4446a"}, @@ -9496,18 +9367,17 @@ description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}, {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [[package]] @@ -9517,7 +9387,6 @@ description = "Zstandard bindings for Python" optional = false python-versions = ">=3.9" groups = ["main"] -markers = "python_version >= \"3.12\" or python_version <= \"3.11\"" files = [ {file = "zstandard-0.25.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e59fdc271772f6686e01e1b3b74537259800f57e24280be3f29c8a0deb1904dd"}, {file = "zstandard-0.25.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4d441506e9b372386a5271c64125f72d5df6d2a8e8a2a45a0ae09b03cb781ef7"}, @@ -9626,4 +9495,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt [metadata] lock-version = "2.1" python-versions = ">=3.10, <3.13" -content-hash = "2dbaf7bb11506a213ffe1235df6de7ad4a1943ff5a1ad29e98c9c108d3210230" +content-hash = "2ca08429df55e63e3001da780f9032e2da40b906d8092b2bb6e97e5e44b09d34" diff --git a/pyproject.toml b/pyproject.toml index 339f4f5b..979451e9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,7 @@ dependencies = [ "PyPortfolioOpt>=1.5.0", "empyrical-reloaded>=0.5.9", "arch>=6.0.0", + "numpy-financial>=1.0.0", ] [project.urls] @@ -62,6 +63,7 @@ test = [ "pytest-cov>=3.0.0", "pytest-env>=1.1.5", "pytest-mock>=3.10.0", + "pytest-asyncio>=0.23.0", "codecov>=2.1.13", "mypy>=1.15.0", "ruff>=0.11.4,<0.12.0", @@ -180,6 +182,8 @@ filterwarnings = [ ] # Exclude legacy tests (imports are broken after code was moved) norecursedirs = ["legacy"] +asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "function" [tool.coverage] [tool.coverage.run] diff --git a/selected_tasks.json b/selected_tasks.json deleted file mode 100644 index d1f8bd70..00000000 --- a/selected_tasks.json +++ /dev/null @@ -1,162 +0,0 @@ -{ - "computational_linear_algebra": [ - { - "task_id": "2", - "problem": "Use QR factorization to solve the system Ax = b for A = \\[\begin{pmatrix} 1 & 1 \\ 1 & -1 \\end{pmatrix}\\] and b = \\[\begin{pmatrix} 2 \\ 0 \\end{pmatrix}\\].", - "answer": "\\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]", - "reasoning": "I'll solve this step by step using QR factorization.\n\n1) First, let's find the QR factorization of A where:\nA = \\[begin{pmatrix} 1 & 1 \\\\ 1 & -1 \\end{pmatrix}\\]\n\n2) For QR factorization, we need to find Q (orthogonal) and R (upper triangular).\n\n3) To find Q, we'll use the Gram-Schmidt process:\n - v₁ = \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]\n - q₁ = v₁/‖v₁‖ = \\[begin{pmatrix} 1/√2 \\\\ 1/√2 \\end{pmatrix}\\]\n - v₂ = \\[begin{pmatrix} 1 \\\\ -1 \\end{pmatrix}\\]\n - q₂ = v₂/‖v₂‖ = \\[begin{pmatrix} 1/√2 \\\\ -1/√2 \\end{pmatrix}\\]\n\n4) Therefore, Q = \\[begin{pmatrix} 1/√2 & 1/√2 \\\\ 1/√2 & -1/√2 \\end{pmatrix}\\]\n\n5) R = Q^T A = \\[begin{pmatrix} √2 & 0 \\\\ 0 & √2 \\end{pmatrix}\\]\n\n6) Now we solve Rx = Q^T b:\n - Q^T b = \\[begin{pmatrix} 1/√2 & 1/√2 \\\\ 1/√2 & -1/√2 \\end{pmatrix}\\] \\[begin{pmatrix} 2 \\\\ 0 \\end{pmatrix}\\]\n - Q^T b = \\[begin{pmatrix} √2 \\\\ √2 \\end{pmatrix}\\]\n\n7) This gives us:\n √2x₁ = √2\n √2x₂ = √2\n\n8) Solving:\n x₁ = 1\n x₂ = 1\n\nTherefore, x = \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]\n\nANSWER: \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\]", - "verification": { - "verdict": "no", - "reason": "This answer only provides the final solution vector x without showing the QR factorization process that was specifically requested in the problem. While the solution vector \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] appears to be correct (we can verify that A\\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] = \\[begin{pmatrix} 2 \\\\ 0 \\end{pmatrix}\\]), the problem explicitly asks to use QR factorization to solve the system. The answer should include:\n1. The calculation of Q and R matrices\n2. Using these matrices to solve the system (Rx = Q^T b)\n3. The final solution" - } - }, - { - "task_id": "3", - "problem": "Explain how computational linear algebra techniques can be implemented in Python using libraries such as NumPy and SciPy.", - "answer": "NO_ANSWER", - "reasoning": "I'll explain how to implement computational linear algebra techniques using Python libraries, focusing on the most common operations and their implementations.\n\n1. Basic Matrix Operations with NumPy\n```python\nimport numpy as np\n\n# Creating matrices\nA = np.array([[1, 2], [3, 4]])\nB = np.array([[5, 6], [7, 8]])\n\n# Matrix addition\naddition = A + B\n\n# Matrix multiplication\nmultiplication = np.dot(A, B) # or A @ B\n\n# Transpose\ntranspose = A.T\n\n# Matrix inverse\ninverse = np.linalg.inv(A)\n```\n\n2. Solving Linear Systems using NumPy\n```python\n# Solve Ax = b\nA = np.array([[2, 1], [1, 3]])\nb = np.array([4, 5])\nx = np.linalg.solve(A, b)\n```\n\n3. Eigenvalues and Eigenvectors\n```python\n# Calculate eigenvalues and eigenvectors\neigenvalues, eigenvectors = np.linalg.eig(A)\n```\n\n4. Matrix Decompositions using SciPy\n```python\nfrom scipy import linalg\n\n# LU Decomposition\nP, L, U = linalg.lu(A)\n\n# QR Decomposition\nQ, R = linalg.qr(A)\n\n# Singular Value Decomposition (SVD)\nU, s, Vh = linalg.svd(A)\n```\n\n5. Sparse Matrix Operations with SciPy\n```python\nfrom scipy import sparse\n\n# Create sparse matrix\nsparse_matrix = sparse.csr_matrix([[1, 0, 0], [0, 2, 0], [0, 0, 3]])\n\n# Sparse matrix operations\nresult = sparse.linalg.spsolve(sparse_matrix, b)\n```\n\nKey Features and Benefits:\n\n1. Efficiency:\n- NumPy and SciPy use optimized C implementations\n- Sparse matrix support for large, sparse systems\n- Vectorized operations avoid explicit loops\n\n2. Accuracy:\n- Built-in numerical stability considerations\n- Multiple precision options\n- Various algorithms for different problem types\n\n3. Convenience:\n- Simple syntax for complex operations\n- Comprehensive documentation\n- Wide range of pre-implemented methods\n\n4. Common Applications:\n- Scientific computing\n- Machine learning\n- Data analysis\n- Engineering simulations\n\nBest Practices:\n\n1. Choose appropriate data types:\n- Use np.float64 for standard precision\n- Consider np.float32 for memory-intensive applications\n\n2. Error handling:\n- Check matrix conditions (singular, ill-conditioned)\n- Validate input dimensions\n- Handle numerical errors appropriately\n\n3. Performance optimization:\n- Use sparse matrices for sparse problems\n- Vectorize operations when possible\n- Consider memory constraints for large systems\n\n4. Algorithm selection:\n- Choose decomposition methods based on matrix properties\n- Consider numerical stability requirements\n- Balance accuracy vs. computational cost\n\nThese implementations provide a robust foundation for computational linear algebra tasks, offering both performance and ease of use.", - "verification": { - "verdict": "no", - "reason": "unsolved" - } - }, - { - "task_id": "4", - "problem": "Implement the Gaussian elimination algorithm to solve a system of linear equations without using any built-in linear algebra functions. Test your implementation on the system: $\\begin{cases} 2x + y + z = 8 \\\\ 4x - 2y + 3z = 1 \\\\ -2x + 3y - z = 5 \\end{cases}$", - "answer": "(3, 3.25, -2)", - "reasoning": "I'll implement Gaussian elimination to solve this system of linear equations step by step.\n\n```python\ndef gaussian_elimination(A, b):\n n = len(A)\n # Augment the matrix A with the vector b\n Ab = [[A[i][j] for j in range(n)] + [b[i]] for i in range(n)]\n \n # Forward elimination\n for i in range(n):\n # Find pivot\n pivot = Ab[i][i]\n \n # Eliminate column i\n for j in range(i + 1, n):\n factor = Ab[j][i] / pivot\n for k in range(i, n + 1):\n Ab[j][k] -= factor * Ab[i][k]\n \n # Back substitution\n x = [0] * n\n for i in range(n-1, -1, -1):\n x[i] = Ab[i][n]\n for j in range(i+1, n):\n x[i] -= Ab[i][j] * x[j]\n x[i] /= Ab[i][i]\n \n return x\n\n# Define the system\nA = [[2, 1, 1],\n [4, -2, 3],\n [-2, 3, -1]]\nb = [8, 1, 5]\n\n# Solve the system\nsolution = gaussian_elimination(A, b)\n\n# Round to 4 decimal places for clarity\nsolution = [round(x, 4) for x in solution]\n```\n\nLet's solve this step by step:\n\n1) First, we create the augmented matrix:\n [2 1 1 | 8]\n [4 -2 3 | 1]\n [-2 3 -1 | 5]\n\n2) Forward elimination:\n R2 = R2 - 2R1\n R3 = R3 + R1\n \n [2 1 1 | 8]\n [0 -4 1 |-15]\n [0 4 0 | 13]\n\n3) Continue elimination:\n R3 = R3 + R2\n \n [2 1 1 | 8]\n [0 -4 1 |-15]\n [0 0 1 | -2]\n\n4) Back substitution:\n z = -2\n y = (-15 - z)/(-4) = (-15 + 2)/(-4) = 13/4\n x = (8 - y - z)/2 = (8 - 13/4 + 2)/2 = 3\n\nThe solution is:\nx = 3\ny = 3.25\nz = -2\n\nANSWER: (3, 3.25, -2)\n\nTo verify this is correct, we can substitute these values back into the original equations:\n2(3) + 3.25 + (-2) = 8 ✓\n4(3) - 2(3.25) + 3(-2) = 1 ✓\n-2(3) + 3(3.25) - (-2) = 5 ✓", - "verification": { - "verdict": "no", - "reason": "The problem asks for two things: 1) Implementation of Gaussian elimination algorithm and 2) Solution to the specific system of equations. The answer only provides the final solution (3, 3.25, -2) without showing the implementation or the steps of Gaussian elimination. While I can verify that these values do satisfy the system of equations:\n\n2(3) + (3.25) + (-2) = 8\n4(3) - 2(3.25) + 3(-2) = 1\n-2(3) + 3(3.25) + (-1)(-2) = 5\n\nThe answer fails to address the main requirement of implementing the Gaussian elimination algorithm. The implementation part was a crucial component of the problem." - } - } - ], - "numerical_simulation_dynamical_systems": [ - { - "task_id": "1", - "problem": "Use Euler's method to approximate the solution of dy/dt = y, y(0) = 1, with a step size of 0.1 over the interval [0, 1].", - "answer": "2.593743", - "reasoning": "I'll solve this step by step using Euler's method.\n\nEuler's method formula: y(t + h) = y(t) + h * f(t,y(t))\nwhere h = 0.1 (step size)\nf(t,y) = y (given in the differential equation)\nInitial condition: y(0) = 1\n\nLet's calculate each step:\n\nt = 0.0: y(0) = 1.000000\nt = 0.1: y(0.1) = 1 + 0.1(1) = 1.100000\nt = 0.2: y(0.2) = 1.1 + 0.1(1.1) = 1.210000\nt = 0.3: y(0.3) = 1.21 + 0.1(1.21) = 1.331000\nt = 0.4: y(0.4) = 1.331 + 0.1(1.331) = 1.464100\nt = 0.5: y(0.5) = 1.4641 + 0.1(1.4641) = 1.610510\nt = 0.6: y(0.6) = 1.61051 + 0.1(1.61051) = 1.771561\nt = 0.7: y(0.7) = 1.771561 + 0.1(1.771561) = 1.948717\nt = 0.8: y(0.8) = 1.948717 + 0.1(1.948717) = 2.143589\nt = 0.9: y(0.9) = 2.143589 + 0.1(2.143589) = 2.357948\nt = 1.0: y(1.0) = 2.357948 + 0.1(2.357948) = 2.593743\n\nANSWER: 2.593743", - "verification": { - "verdict": "no", - "reason": "The problem asks for an approximation using Euler's method, which requires showing the iterative steps and calculations. While 2.593743 is close to the actual final value at t=1 (which should be e ≈ 2.718282), just providing a single numerical value is insufficient. A complete solution should show:\n1. The formula used (yn+1 = yn + h*f(yn))\n2. The iterative calculations for each step from t=0 to t=1 with h=0.1\n3. A table or sequence of approximations" - } - }, - { - "task_id": "2", - "problem": "Apply the fourth-order Runge-Kutta method to solve the system: dx/dt = y, dy/dt = -x, with initial conditions x(0)=1, y(0)=0, step size 0.2, over [0, 2].", - "answer": "-0.416,-0.909", - "reasoning": "I'll solve this using the fourth-order Runge-Kutta (RK4) method. Let's break this down step by step.\n\nThe system is:\nf₁(t,x,y) = y\nf₂(t,x,y) = -x\n\nRK4 formulas for each step:\nk₁ᵡ = h·f₁(t,x,y)\nk₁ʸ = h·f₂(t,x,y)\n\nk₂ᵡ = h·f₁(t+h/2, x+k₁ᵡ/2, y+k₁ʸ/2)\nk₂ʸ = h·f₂(t+h/2, x+k₁ᵡ/2, y+k₁ʸ/2)\n\nk₃ᵡ = h·f₁(t+h/2, x+k₂ᵡ/2, y+k₂ʸ/2)\nk₃ʸ = h·f₂(t+h/2, x+k₂ᵡ/2, y+k₂ʸ/2)\n\nk₄ᵡ = h·f₁(t+h, x+k₃ᵡ, y+k₃ʸ)\nk₄ʸ = h·f₂(t+h, x+k₃ᵡ, y+k₃ʸ)\n\nWith h = 0.2, t₀ = 0, x₀ = 1, y₀ = 0\n\nLet's calculate the first few points:\n\nt=0:\nx₀=1.000000, y₀=0.000000\n\nt=0.2:\nk₁ᵡ = 0.2(0) = 0\nk₁ʸ = 0.2(-1) = -0.2\nk₂ᵡ = 0.2(-0.1) = -0.02\nk₂ʸ = 0.2(-0.99) = -0.198\nk₃ᵡ = 0.2(-0.099) = -0.0198\nk₃ʸ = 0.2(-0.99) = -0.198\nk₄ᵡ = 0.2(-0.198) = -0.0396\nk₄ʸ = 0.2(-0.98) = -0.196\n\nx₁ = 1 + (1/6)(0 - 0.02 - 0.0198 - 0.0396) = 0.987\ny₁ = 0 + (1/6)(-0.2 - 0.198 - 0.198 - 0.196) = -0.198\n\nContinuing this process for the remaining points up to t=2...\n\nThe final values at t=2 are approximately:\nx(2) = -0.416\ny(2) = -0.909\n\nANSWER: -0.416,-0.909", - "verification": { - "verdict": "no", - "reason": "The problem asks for a numerical solution using RK4 method for a coupled system of ODEs over the interval [0,2]. A complete solution should include:\n1. Multiple points along the solution trajectory (not just one point)\n2. Both x and y values at each time step\n3. Values computed using the proper RK4 formulas\n4. A sequence starting from t=0 to t=2 with step size 0.2\n\nThe given answer only provides two numbers (-0.416,-0.909) without context or the full solution trajectory. This is insufficient as it doesn't show the evolution of the system over the specified time interval and doesn't demonstrate the application of the RK4 method." - } - }, - { - "task_id": "3", - "problem": "Discuss the importance of numerical stability when simulating the Lorenz system with sensitive initial conditions.", - "answer": "Numerical stability is essential for Lorenz system simulation because the chaotic nature amplifies small errors exponentially; proper choice of integration method and step size is crucial to maintain accuracy and preserve the system's qualitative behavior while controlling error growth.", - "reasoning": "Let me discuss the importance of numerical stability when simulating the Lorenz system.\n\nThe Lorenz system is a classic example of a chaotic dynamical system, described by three coupled differential equations:\n\ndx/dt = σ(y - x)\ndy/dt = x(ρ - z) - y\ndz/dt = xy - βz\n\nNumerical stability is crucial when simulating this system for several reasons:\n\n1. Sensitive Dependence on Initial Conditions:\n- The Lorenz system exhibits chaos, meaning tiny differences in initial conditions lead to vastly different trajectories\n- Numerical errors can rapidly compound and lead to completely different solutions\n- This makes stability of the numerical method critical\n\n2. Choice of Time Step:\n- Too large a time step can lead to numerical instability\n- Too small a time step increases computational cost and round-off errors\n- Finding the right balance is essential for accurate simulation\n\n3. Method Selection:\n- Explicit methods (like RK4) need smaller time steps for stability\n- Implicit methods offer better stability but are computationally expensive\n- The choice of numerical method affects the accuracy of the strange attractor\n\n4. Error Propagation:\n- Local truncation errors can grow exponentially due to the system's chaotic nature\n- Maintaining numerical stability helps control error growth\n- Stable methods help preserve the qualitative behavior of the system\n\n5. Conservation Properties:\n- Some numerical methods better preserve the system's invariants\n- Stability helps maintain the bounded nature of solutions\n- Proper simulation should show the butterfly-shaped attractor\n\nPractical Recommendations:\n- Use adaptive step-size methods\n- Implement symplectic integrators when possible\n- Validate results with different numerical schemes\n- Monitor conservation laws and system invariants\n\nANSWER: Numerical stability is essential for Lorenz system simulation because the chaotic nature amplifies small errors exponentially; proper choice of integration method and step size is crucial to maintain accuracy and preserve the system's qualitative behavior while controlling error growth.", - "verification": { - "verdict": "no", - "reason": "Failed to verify task: AssertionError(\"Invalid response: no\\n\\nwhile the answer provides correct basic information about numerical stability in lorenz system simulation, it lacks sufficient depth for a complete response. a more comprehensive answer should include: examples of appropriate numerical schemes (like runge-kutta methods), explanation of how lyapunov exponents relate to numerical stability, discussion of local versus global stability considerations, and specific strategies for handling the butterfly effect in numerical implementations. the current answer gives only a high-level overview without the technical detail needed for this advanced topic.. Expected 'yes' or 'no'.\")" - } - } - ], - "numerical_methods_ode": [ - { - "task_id": "2", - "problem": "Apply the 4th order Runge-Kutta method to solve dy/dx = -2x + y, y(0) = 1 with step size h = 0.2, find y(0.4).", - "answer": "1.3089", - "reasoning": "Let me solve this step by step using the 4th order Runge-Kutta method (RK4).\n\nThe RK4 method for dy/dx = f(x,y) uses:\nk1 = h*f(x_n, y_n)\nk2 = h*f(x_n + h/2, y_n + k1/2)\nk3 = h*f(x_n + h/2, y_n + k2/2)\nk4 = h*f(x_n + h, y_n + k3)\ny_(n+1) = y_n + (1/6)(k1 + 2k2 + 2k3 + k4)\n\nWhere f(x,y) = -2x + y\n\nGiven:\n- h = 0.2\n- y(0) = 1\n- We need to find y(0.4), which means we need 2 steps\n\nStep 1: From x = 0 to x = 0.2\nx_0 = 0, y_0 = 1\nk1 = 0.2*(-2(0) + 1) = 0.2\nk2 = 0.2*(-2(0.1) + (1 + 0.1)) = 0.2*(-0.2 + 1.1) = 0.18\nk3 = 0.2*(-2(0.1) + (1 + 0.09)) = 0.2*(-0.2 + 1.09) = 0.178\nk4 = 0.2*(-2(0.2) + (1 + 0.178)) = 0.2*(-0.4 + 1.178) = 0.1556\n\ny_1 = 1 + (1/6)(0.2 + 2(0.18) + 2(0.178) + 0.1556)\ny_1 = 1 + 0.1789 = 1.1789\n\nStep 2: From x = 0.2 to x = 0.4\nx_1 = 0.2, y_1 = 1.1789\nk1 = 0.2*(-2(0.2) + 1.1789) = 0.2*(0.7789) = 0.15578\nk2 = 0.2*(-2(0.3) + (1.1789 + 0.07789)) = 0.2*(-0.6 + 1.25679) = 0.13136\nk3 = 0.2*(-2(0.3) + (1.1789 + 0.06568)) = 0.2*(-0.6 + 1.24458) = 0.12892\nk4 = 0.2*(-2(0.4) + (1.1789 + 0.12892)) = 0.2*(-0.8 + 1.30782) = 0.10156\n\ny_2 = 1.1789 + (1/6)(0.15578 + 2(0.13136) + 2(0.12892) + 0.10156)\ny_2 = 1.1789 + 0.13 = 1.3089\n\nANSWER: 1.3089", - "verification": { - "verdict": "no", - "reason": "Failed to verify task: AssertionError(\"Invalid response: no\\n\\nthe answer only provides the final numerical result without showing the necessary intermediate calculations and steps of the rk4 method. for numerical methods problems, demonstrating the proper application of the method through intermediate steps is essential for a complete solution. a complete solution should show the calculation of k1, k2, k3, k4 for each step and how the final value was obtained.. Expected 'yes' or 'no'.\")" - } - }, - { - "task_id": "4", - "problem": "Using Euler's method with step size h = 0.1, compute the first three approximations for the solution of $\\frac{dy}{dx} = y - x^2$ with initial condition y(0) = 1 on the interval [0, 0.3].", - "answer": "1.3259", - "reasoning": "Let me solve this step by step using Euler's method.\n\nEuler's method formula: y_{n+1} = y_n + h*f(x_n, y_n)\nwhere f(x, y) = y - x²\n\nGiven:\n- h = 0.1\n- x₀ = 0\n- y₀ = 1\n- f(x, y) = y - x²\n\nStep 1: Find y₁ at x₁ = 0.1\nf(x₀, y₀) = f(0, 1) = 1 - 0² = 1\ny₁ = y₀ + h*f(x₀, y₀)\ny₁ = 1 + 0.1(1)\ny₁ = 1.1\n\nStep 2: Find y₂ at x₂ = 0.2\nf(x₁, y₁) = f(0.1, 1.1) = 1.1 - (0.1)² = 1.1 - 0.01 = 1.09\ny₂ = y₁ + h*f(x₁, y₁)\ny₂ = 1.1 + 0.1(1.09)\ny₂ = 1.209\n\nStep 3: Find y₃ at x₃ = 0.3\nf(x₂, y₂) = f(0.2, 1.209) = 1.209 - (0.2)² = 1.209 - 0.04 = 1.169\ny₃ = y₂ + h*f(x₂, y₂)\ny₃ = 1.209 + 0.1(1.169)\ny₃ = 1.3259\n\nThe three approximations are:\ny₁ = 1.1\ny₂ = 1.209\ny₃ = 1.3259\n\nANSWER: 1.3259", - "verification": { - "verdict": "no", - "reason": "The problem asks for the first three approximations using Euler's method, which means we need values at x = 0, 0.1, 0.2, and 0.3. The answer only provides a single number (1.3259) without showing the step-by-step calculations or the other approximations. Euler's method requires calculating each point using the formula y_{n+1} = y_n + h*f(x_n, y_n), where f(x,y) = y - x². The answer is incomplete as it doesn't show all three approximations requested." - } - }, - { - "task_id": "5", - "problem": "Compare the local truncation error between Euler's method and the fourth-order Runge-Kutta method for solving $\\frac{dy}{dx} = -2y$ with y(0) = 1 using a step size of h = 0.2.", - "answer": "0.07027", - "reasoning": "Let me solve this step by step.\n\n1) First, let's recall the true solution to this ODE:\n y(x) = e^(-2x)\n\n2) For the local truncation error, we'll look at just one step from x₀ = 0 to x₁ = 0.2\n\n3) True value at x₁ = 0.2:\n y(0.2) = e^(-2×0.2) = e^(-0.4) = 0.67032\n\n4) Euler's Method:\n y₁ = y₀ + h×f(x₀,y₀)\n f(x,y) = -2y\n y₁ = 1 + 0.2×(-2×1)\n y₁ = 1 - 0.4 = 0.6\n\n5) Fourth-order Runge-Kutta Method:\n k₁ = h×f(x₀,y₀) = 0.2×(-2×1) = -0.4\n k₂ = h×f(x₀+h/2, y₀+k₁/2) = 0.2×(-2×(1-0.2)) = -0.32\n k₃ = h×f(x₀+h/2, y₀+k₂/2) = 0.2×(-2×(1-0.16)) = -0.336\n k₄ = h×f(x₀+h, y₀+k₃) = 0.2×(-2×(1-0.336)) = -0.2656\n\n y₁ = y₀ + (k₁ + 2k₂ + 2k₃ + k₄)/6\n y₁ = 1 + (-0.4 - 0.64 - 0.672 - 0.2656)/6\n y₁ = 0.67027\n\n6) Local Truncation Errors:\n Euler: |0.67032 - 0.6| = 0.07032\n RK4: |0.67032 - 0.67027| = 0.00005\n\nThe difference in local truncation errors is 0.07032 - 0.00005 = 0.07027\n\nANSWER: 0.07027", - "verification": { - "verdict": "no", - "reason": "This problem requires comparing the local truncation errors between two numerical methods (Euler and RK4) for a specific ODE. The answer only provides a single number (0.07027) without any context, comparison, or explanation. A complete answer should:\n1. Calculate the local truncation error for both methods\n2. Show the comparison between them\n3. Provide clear context for what the number represents\n4. Include appropriate units and reference points" - } - } - ], - "complex_analysis_problems": [ - { - "task_id": "3", - "problem": "Is the function $f(z) = z^2$ conformal at $z = 2$?", - "answer": "Yes", - "reasoning": "Let me solve this step by step.\n\n1) For a function to be conformal at a point, it must be:\n * Analytic at that point\n * Have a non-zero derivative at that point\n\n2) First, let's check if $f(z) = z^2$ is analytic at $z = 2$\n * $z^2$ is a polynomial function\n * All polynomial functions are analytic everywhere in the complex plane\n * Therefore, $f(z)$ is analytic at $z = 2$\n\n3) Next, let's find the derivative of $f(z)$\n * $f'(z) = 2z$\n * At $z = 2$: $f'(2) = 4$\n\n4) Since $f'(2) = 4 \\neq 0$, and $f(z)$ is analytic at $z = 2$, the function is conformal at $z = 2$.\n\nANSWER: Yes", - "verification": { - "verdict": "no", - "reason": "To determine if a function is conformal at a point, we need to verify that it's both analytic at that point and has a non-zero derivative there. The answer simply states \"Yes\" without showing any verification of these conditions. While the answer happens to be correct (since f(z)=z² is analytic everywhere and f'(z)=2z which is non-zero at z=2), the lack of justification means the answer is incomplete. A complete answer should demonstrate why these conditions are satisfied." - } - }, - { - "task_id": "4", - "problem": "Evaluate the contour integral $\\oint_{|z|=2} \\frac{z^2+1}{z^3-8} dz$ using the residue theorem.", - "answer": "5πi/6", - "reasoning": "Let me solve this step by step.\n\n1) First, let's find the poles inside the contour |z|=2.\n - The denominator z³-8 = 0 when z = 2 (since 8 = 2³)\n - The cube root of 8 has three values: 2, 2e^(2πi/3), and 2e^(4πi/3)\n - Since |z|=2, only z=2 lies on our contour\n\n2) Since z=2 lies on the contour, we need to use a slightly larger contour.\n Let's use |z|=2+ε where ε is small and positive.\n Now only z=2 lies inside our contour.\n\n3) Let's find the residue at z=2.\n - This is a simple pole\n - For a simple pole at a point a, the residue is:\n Res(f,a) = lim_{z→a} (z-a)f(z)\n\n4) At z=2:\n Res(f,2) = lim_{z→2} (z-2)(z²+1)/(z³-8)\n = lim_{z→2} (z²+1)/(z²+2z+4)\n = (4+1)/(4+4+4)\n = 5/12\n\n5) By the Residue Theorem:\n $\\oint_{|z|=2} \\frac{z^2+1}{z^3-8} dz = 2πi \\sum Res$\n = 2πi(5/12)\n = 5πi/6\n\nANSWER: 5πi/6", - "verification": { - "verdict": "no", - "reason": "Failed to verify task: AssertionError('Invalid response: no\\n\\nthe answer \"5πi/6\" might be numerically correct, but it does not demonstrate the solution process required for this complex analysis problem. a complete answer should:\\n1. identify the poles and their nature\\n2. show the residue calculations\\n3. apply the residue theorem correctly\\n4. handle the fact that z=2 lies on the contour\\n5. show the final computation leading to 5πi/6. Expected \\'yes\\' or \\'no\\'.')" - } - }, - { - "task_id": "5", - "problem": "Find the conformal mapping that transforms the unit disk onto the upper half-plane and maps the points -1, i, and 1 to -1, i, and 1 respectively.", - "answer": "f(z) = (z + 1)/(z - 1)", - "reasoning": "Let me solve this step by step.\n\n1) We know that a Möbius transformation will be the conformal mapping we're looking for, as it preserves circles and lines.\n\n2) The general form of a Möbius transformation is:\n f(z) = (az + b)/(cz + d)\n where a, b, c, d are complex numbers and ad - bc ≠ 0\n\n3) We have three conditions:\n f(-1) = -1\n f(i) = i\n f(1) = 1\n\n4) Since we're mapping the unit disk to the upper half-plane, a well-known transformation is:\n f(z) = i(1 + z)/(1 - z)\n\n5) However, this maps:\n z = 1 → ∞\n z = -1 → 0\n z = i → 1\n\n6) We need to modify this to match our conditions. Let's compose it with another Möbius transformation.\n\n7) After composing with appropriate transformations and simplifying, the mapping that satisfies all conditions is:\n f(z) = (z + 1)/(z - 1)\n\n8) Let's verify:\n - When z = -1: f(-1) = (-1 + 1)/(-1 - 1) = 0/-2 = -1\n - When z = i: f(i) = (i + 1)/(i - 1) = i\n - When z = 1: f(1) = (1 + 1)/(1 - 1) = ∞/0 = 1 (in the limit)\n\n9) This mapping takes the unit disk to the upper half-plane and satisfies all the required point mappings.\n\nANSWER: f(z) = (z + 1)/(z - 1)", - "verification": { - "verdict": "no", - "reason": "Let me analyze this systematically:\n1. First, the Möbius transformation f(z) = (z+1)/(z-1) is indeed a conformal mapping.\n2. The unit disk to upper half-plane mapping typically uses the Cayley transform f(z) = i(1+z)/(1-z).\n3. The given function f(z) = (z+1)/(z-1) does NOT map the unit disk to the upper half-plane. In fact, it maps the unit disk to the left half-plane.\n4. While the function does map -1 to -1 and 1 to 1, it does NOT map i to i. We can verify this by substituting z = i:\n f(i) = (i+1)/(i-1) = (i+1)(-i-1)/(i-1)(-i-1) = (-i-i+1)/(2) = -i+1/2 ≠ i" - } - } - ], - "advanced_integration_techniques": [ - { - "task_id": "2", - "problem": "Calculate \\( \\int \frac{1}{x^2 + 4x + 5} \\, dx \\).", - "answer": "$\\arctan(x + 2) + C$", - "reasoning": "Let me solve this integral using partial fractions and arctangent integration.\n\n1) First, let's complete the square in the denominator:\n \\( x^2 + 4x + 5 = (x^2 + 4x + 4) + 1 = (x + 2)^2 + 1 \\)\n\n2) Now our integral is:\n \\( \\int \\frac{1}{(x + 2)^2 + 1} \\, dx \\)\n\n3) This is a standard form integral that results in arctangent:\n The general form is: \\( \\int \\frac{1}{a^2 + u^2} \\, du = \\frac{1}{a}\\arctan(\\frac{u}{a}) + C \\)\n where in our case, u = (x + 2) and a = 1\n\n4) Therefore:\n \\( \\int \\frac{1}{(x + 2)^2 + 1} \\, dx = \\arctan(x + 2) + C \\)\n\nANSWER: $\\arctan(x + 2) + C$", - "verification": { - "verdict": "no", - "reason": "Failed to verify task: AssertionError(\"Invalid response: yes\\n\\nthe answer $\\\\arctan(x + 2) + c$ is correct because:\\n- the denominator x² + 4x + 5 can be rewritten as (x + 2)² + 1\\n- the integral follows the standard form ∫ 1/(u² + 1) du = arctan(u) + c\\n- the substitution u = x + 2 leads directly to the given answer\\n- the inclusion of the constant of integration c makes the answer complete. Expected 'yes' or 'no'.\")" - } - }, - { - "task_id": "6", - "problem": "Evaluate $\\int \\frac{x^2+3x+2}{(x+1)(x+2)}dx$ using partial fraction decomposition.", - "answer": "$\\ln|\\frac{(x+2)^4}{x+1}| + C$", - "reasoning": "Let me solve this step by step.\n\n1) First, let's set up partial fraction decomposition:\n $\\frac{x^2+3x+2}{(x+1)(x+2)} = \\frac{A}{x+1} + \\frac{B}{x+2}$\n\n2) Multiply both sides by (x+1)(x+2):\n $x^2+3x+2 = A(x+2) + B(x+1)$\n $x^2+3x+2 = (A+B)x + (2A+B)$\n\n3) Compare coefficients:\n $x^2$ term: 1 = 0 \n $x^1$ term: 3 = A+B\n $x^0$ term: 2 = 2A+B\n\n4) Solve the system of equations:\n A+B = 3\n 2A+B = 2\n \n Subtracting equations:\n -A = 1\n A = -1\n B = 4\n\n5) Therefore:\n $\\frac{x^2+3x+2}{(x+1)(x+2)} = -\\frac{1}{x+1} + \\frac{4}{x+2}$\n\n6) Now integrate:\n $\\int \\frac{x^2+3x+2}{(x+1)(x+2)}dx = -\\int\\frac{1}{x+1}dx + 4\\int\\frac{1}{x+2}dx$\n \n7) These are basic logarithmic integrals:\n $= -\\ln|x+1| + 4\\ln|x+2| + C$\n \n8) This can be written as:\n $= \\ln|\\frac{(x+2)^4}{x+1}| + C$\n\nANSWER: $\\ln|\\frac{(x+2)^4}{x+1}| + C$", - "verification": { - "verdict": "no", - "reason": "Let me analyze this step by step:\n1) For partial fraction decomposition, we should get terms of the form A/(x+1) + B/(x+2)\n2) The numerator x^2+3x+2 and denominator (x+1)(x+2) suggest this is possible\n3) The given answer $\\ln|\\frac{(x+2)^4}{x+1}| + C$ cannot be correct because:\n - It has (x+2)^4 in the numerator, which would give a much higher degree when differentiated\n - The original fraction doesn't suggest we should get a fourth power\n - When you differentiate this answer, it won't give back the original integrand" - } - }, - { - "task_id": "9", - "problem": "Evaluate $\\int \\frac{x^3+2}{x^4+1}dx$ using partial fraction decomposition with complex roots.", - "answer": "$\\frac{1}{2}\\ln(x^4+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$", - "reasoning": "Let me solve this step by step using partial fraction decomposition.\n\n1) First, let's factor the denominator x⁴+1\n The roots are: x = ±(1+i)/√2 and x = ±(-1+i)/√2\n So, x⁴+1 = (x² + √2x + 1)(x² - √2x + 1)\n\n2) Set up partial fraction decomposition:\n $\\frac{x^3+2}{x^4+1} = \\frac{Ax+B}{x^2+\\sqrt{2}x+1} + \\frac{Cx+D}{x^2-\\sqrt{2}x+1}$\n\n3) Multiply both sides by (x⁴+1):\n x³+2 = (Ax+B)(x² - √2x + 1) + (Cx+D)(x² + √2x + 1)\n\n4) Comparing coefficients:\n x³: A + C = 1\n x²: -A√2 + B + C√2 + D = 0\n x¹: A + B(-√2) + C + D√2 = 0\n x⁰: B + D = 2\n\n5) Solving this system of equations:\n A = 1/2\n B = 1\n C = 1/2\n D = 1\n\n6) Therefore:\n $\\int \\frac{x^3+2}{x^4+1}dx = \\int \\frac{\\frac{1}{2}x+1}{x^2+\\sqrt{2}x+1}dx + \\int \\frac{\\frac{1}{2}x+1}{x^2-\\sqrt{2}x+1}dx$\n\n7) Each of these integrals can be solved using the arctangent substitution:\n For the first integral: $\\frac{1}{2}\\ln(x^2+\\sqrt{2}x+1) + \\frac{1}{\\sqrt{2}}\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})$\n For the second integral: $\\frac{1}{2}\\ln(x^2-\\sqrt{2}x+1) - \\frac{1}{\\sqrt{2}}\\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})$\n\n8) Combining terms:\n $\\frac{1}{2}\\ln(x^2+\\sqrt{2}x+1) + \\frac{1}{2}\\ln(x^2-\\sqrt{2}x+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$\n\nANSWER: $\\frac{1}{2}\\ln(x^4+1) + \\frac{1}{\\sqrt{2}}[\\arctan(\\frac{x+\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}}) - \\arctan(\\frac{x-\\frac{\\sqrt{2}}{2}}{{\\frac{\\sqrt{2}}{2}}})] + C$", - "verification": { - "verdict": "no", - "reason": "Failed to verify task: AssertionError(\"Invalid response: yes\\n\\nthe answer is both correct and complete. it properly uses partial fraction decomposition with complex roots, includes all necessary terms with correct coefficients, and provides the constant of integration. the result is in its simplest form and would differentiate back to the original integrand.. Expected 'yes' or 'no'.\")" - } - } - ] -} diff --git a/src/run_experiments.py b/src/run_experiments.py deleted file mode 100644 index ac7b838d..00000000 --- a/src/run_experiments.py +++ /dev/null @@ -1,1044 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified Experiment Runner for Multi-Agent Mathematical Reasoning - -Supports experiments with: -- Configurable model (default: gemini-2.5-pro) -- Configurable debate rounds (default: 1) -- Scalable tasks per capability: 1-15 (default: 15) -- Both baseline and tool-assisted conditions - -Usage: - # Quick test: 1 task per capability - python run_experiments.py --num-tasks 1 --condition tool_assisted - - # Full experiment: 15 tasks per capability - python run_experiments.py --num-tasks 15 --condition both - - # Custom configuration - python run_experiments.py --model gemini-2.5-pro --rounds 2 --num-tasks 10 -""" - -import argparse -import asyncio -import json -import logging -import os -import time -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List - -from autogen_core import ( - DefaultTopicId, - SingleThreadedAgentRuntime, -) -from dotenv import load_dotenv -from langfuse import Langfuse - - -# Load environment variables from .env file -load_dotenv() - -from autogen_core.models import SystemMessage, UserMessage - -from src.task_solver.messages import Task -from src.task_solver.moderator import TaskSolverModerator -from src.task_solver.scientist import TaskSolverScientist -from src.task_solver.tool_assisted_scientist import ToolAssistedScientist -from src.tools.toolkit import ScientificToolKit -from src.utils.model_client_utils import get_model_client - - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - handlers=[logging.FileHandler("experiments.log"), logging.StreamHandler()], -) -log = logging.getLogger(__name__) - - -class ExperimentRunner: - """Runner for mathematical reasoning experiments.""" - - def __init__( - self, - model_name: str = "gemini-3-flash-preview", - num_rounds: int = 1, - num_tasks: int = 15, - condition: str = "both", - output_dir: str = "experiment_results", - enable_tool_selection: bool = False, - tasks_file: str = "selected_tasks.json", - dataset_type: str = "math", - ): - """Initialize experiment runner. - - Args: - model_name: LLM model to use - num_rounds: Number of debate rounds - num_tasks: Number of tasks per capability to run (1-15) - condition: Which condition to run - 'baseline', 'tool_assisted', or 'both' - output_dir: Directory for saving results - enable_tool_selection: Whether to enable dynamic tool selection (default: True) - tasks_file: Path to tasks JSON file (default: selected_tasks.json) - dataset_type: Type of dataset - 'math' or 'xfinbench' (default: math) - """ - self.model_name = model_name - self.num_rounds = num_rounds - self.num_tasks = num_tasks - self.condition = condition - self.enable_tool_selection = enable_tool_selection - self.tasks_file = Path(tasks_file) - self.dataset_type = dataset_type - self.results_dir = Path(output_dir) - self.results_dir.mkdir(parents=True, exist_ok=True) - - # Initialize Langfuse - self.langfuse_client = Langfuse( - secret_key=os.getenv("LANGFUSE_SECRET_KEY"), - public_key=os.getenv("LANGFUSE_PUBLIC_KEY"), - host=os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"), - ) - - self.results = { - "experiment_info": { - "name": "capability_evaluation_experiment", - "version": "1.0", - "start_time": datetime.now().isoformat(), - "model": self.model_name, - "num_rounds": self.num_rounds, - "tasks_per_capability": self.num_tasks, - }, - "baseline": [], - "tool_assisted": [], - } - - def load_tasks(self) -> List[Dict[str, Any]]: - """Load tasks from specified tasks file.""" - if not self.tasks_file.exists(): - raise FileNotFoundError( - f"Tasks file not found: {self.tasks_file}\n" - f"Please provide a valid tasks file path." - ) - - with open(self.tasks_file, "r", encoding="utf-8") as f: - all_tasks = json.load(f) - - selected_tasks = [] - - # XFinBench dataset is a flat list, not grouped by capability - if self.dataset_type == "xfinbench": - # XFinBench tasks are already in the right format - tasks_to_load = ( - all_tasks[: self.num_tasks] - if isinstance(all_tasks, list) - else all_tasks - ) - for task in tasks_to_load: - task_copy = task.copy() - # Ensure required fields exist - if "area_name" not in task_copy: - task_copy["area_name"] = "finance" - selected_tasks.append(task_copy) - log.info( - f"Loaded {len(selected_tasks)} XFinBench tasks from {self.tasks_file}" - ) - # Math dataset: grouped by capability - # If using a custom tasks file (not default), load all tasks as-is - elif self.tasks_file != Path("selected_tasks.json"): - for capability_name, task_list in all_tasks.items(): - for task in task_list: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info(f"Loaded {len(selected_tasks)} tasks from {self.tasks_file}") - else: - # For default file, apply num_tasks limit per capability - for capability_name, task_list in all_tasks.items(): - # Take up to num_tasks from each capability - for task in task_list[: self.num_tasks]: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - task_copy["area_name"] = "math" # All tasks are math - selected_tasks.append(task_copy) - log.info( - f"Loaded {len(selected_tasks)} tasks ({self.num_tasks} per capability)" - ) - - return selected_tasks - - async def run_single_task( - self, - task_data: Dict[str, Any], - condition: str, - task_index: int, - total_tasks: int, - ) -> Dict[str, Any]: - """Run a single task with specified condition.""" - log.info( - f"\n{'=' * 70}\n" - f"[{condition.upper()}] Task {task_index}/{total_tasks}\n" - f"Capability: {task_data['capability_name']}\n" - f"Task ID: {task_data['task_id']}\n" - f"{'=' * 70}" - ) - - start_time = time.time() - result = { - "task_id": task_data["task_id"], - "capability_name": task_data["capability_name"], - "condition": condition, - "problem": task_data["problem"], - "expected_answer": task_data["answer"], - "task_type": task_data.get("task_type"), # For XFinBench - "start_time": datetime.now().isoformat(), - "status": "pending", - } - - try: - # Create runtime - runtime = SingleThreadedAgentRuntime() - - # Create output directory for this task - output_dir = self.results_dir / condition / task_data["capability_name"] - output_dir.mkdir(parents=True, exist_ok=True) - - # Register moderator (single solver - no debate/consensus) - await TaskSolverModerator.register( - runtime, - "TaskSolverModerator", - lambda: TaskSolverModerator( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - reasoning_effort="low", - ), - num_solvers=1, # Single scientist - no multi-agent debate - max_rounds=1, # Single round - direct solve - output_dir=output_dir, - langfuse_client=self.langfuse_client, - ), - ) - - # Register scientist agent based on condition (single scientist - no debate) - if condition == "baseline": - # Use regular TaskSolverScientist - await TaskSolverScientist.register( - runtime, - "TaskSolverScientistA", - lambda: TaskSolverScientist( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - reasoning_effort="low", - ), - scientist_id="A", - langfuse_client=self.langfuse_client, - ), - ) - else: - # Use ToolAssistedScientist with toolkit (no tool selection - direct code execution) - toolkit = ScientificToolKit( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - reasoning_effort="low", - ), - enable_tool_selection=self.enable_tool_selection, # False by default - skip tool selection - enable_rag=False, # Use model's parametric knowledge instead of retrieval - ) - - await ToolAssistedScientist.register( - runtime, - "TaskSolverScientistA", - lambda: ToolAssistedScientist( - model_client=get_model_client( - model_name=self.model_name, - seed=42, - reasoning_effort="low", - ), - scientist_id="A", - langfuse_client=self.langfuse_client, - toolkit=toolkit, - ), - ) - - # Start runtime - runtime.start() - - # Create and publish task - task = Task( - task_id=task_data["task_id"], - problem=task_data["problem"], - capability_name=task_data["capability_name"], - area_name=task_data.get("area_name", "math"), - ) - - await runtime.publish_message(task, DefaultTopicId()) - - # Wait for completion - await runtime.stop_when_idle() - - # Load the solution file - solution_file = output_dir / f"{task_data['task_id']}_solution.json" - if solution_file.exists(): - with open(solution_file, "r") as f: - solution_data = json.load(f) - - # Extract code execution info for tool_assisted - code_executions = None - if condition == "tool_assisted": - code_executions = self._extract_code_executions( - solution_data.get("all_solutions", []) - ) - result["code_executions"] = code_executions - # Track if ANY code was executed successfully (no "ERROR:" in output) - result["code_execution_success"] = any( - c.get("has_code") - and not str(c.get("code_output", "")).startswith("ERROR:") - for c in code_executions - ) - - result.update( - { - "status": "completed", - "execution_time": time.time() - start_time, - "solution": solution_data.get("solution"), - "numerical_answer": solution_data.get("numerical_answer"), - "consensus_reached": solution_data.get("consensus_reached"), - "total_rounds": solution_data.get("total_rounds"), - } - ) - - # Verify answer (XFinBench-aware) - task_type = task_data.get("task_type") - if self.dataset_type == "xfinbench" and task_type: - verification_dict = await self._verify_xfinbench_answer( - solution_data.get("numerical_answer"), - task_data["answer"], - task_type, - code_executions, - reasoning=solution_data.get("reasoning"), - problem_text=task_data.get("problem"), - ) - result.update(verification_dict) - # Success is determined by LLM judge - result["success"] = verification_dict.get("llm_judge", False) - else: - # For non-XFinBench datasets, use relaxed verifier - result["success"] = self._verify_single_answer( - solution_data.get("numerical_answer"), - task_data["answer"], - relaxed=True, - ) - result["llm_judge"] = result["success"] - else: - result.update( - { - "status": "error", - "error": "Solution file not found", - "execution_time": time.time() - start_time, - } - ) - - log.info( - f"✓ Task completed in {result['execution_time']:.2f}s\n" - f" Success: {result.get('success', False)}\n" - f" Consensus: {result.get('consensus_reached', False)}" - ) - - except Exception as e: - log.error(f"Error running task: {e}", exc_info=True) - result.update( - { - "status": "error", - "error": str(e), - "execution_time": time.time() - start_time, - } - ) - - return result - - def _normalize_answer(self, answer_str: str) -> list[float]: - """Extract and normalize numerical values from any answer format. - - This method handles multiple answer formats: - - LaTeX: \\[begin{pmatrix} 1 \\\\ 1 \\end{pmatrix}\\] - - Python list: [1, 1] or [[1], [1]] - - Python dict: {'x': [[1], [1]]} - - Plain text with numbers - - Returns - ------- - Sorted list of float values extracted from the answer. - """ - import ast - import re - - if not answer_str: - return [] - - answer_str = str(answer_str).strip() - - # Remove LaTeX formatting and commands - answer_str = re.sub(r"\\[a-zA-Z]+\{", "", answer_str) # Remove \command{ - answer_str = re.sub(r"\\[a-zA-Z]+\[", "", answer_str) # Remove \command[ - answer_str = re.sub( - r"\\[a-zA-Z]+", "", answer_str - ) # Remove remaining \commands - answer_str = re.sub(r"[{}\\]", "", answer_str) # Remove braces and backslashes - - numbers = [] - - # Try parsing as Python literal first (handles dicts, lists, etc.) - try: - parsed = ast.literal_eval(answer_str) - - def flatten(obj): - """Recursively flatten nested structures.""" - if isinstance(obj, (list, tuple)): - result = [] - for item in obj: - result.extend(flatten(item)) - return result - if isinstance(obj, dict): - result = [] - for value in obj.values(): - result.extend(flatten(value)) - return result - try: - return [float(obj)] - except (ValueError, TypeError): - return [] - - numbers = flatten(parsed) - except (ValueError, SyntaxError): - # If parsing fails, extract numbers via regex - pass - - # Extract all numbers (including decimals and negative numbers) - if not numbers: - number_pattern = r"-?\d+\.?\d*" - found_numbers = re.findall(number_pattern, answer_str) - numbers = [] - for num_str in found_numbers: - if num_str and num_str != "-": # Skip empty or lone minus signs - try: - numbers.append(float(num_str)) - except ValueError: - pass - - return sorted(numbers) - - def _verify_answer( - self, - solution_answer: str, - expected_answer: str, - code_executions: List[Dict] = None, - ) -> bool: - """Verify if solution matches expected answer with robust normalization. - - Args: - solution_answer: The answer from the consensus/solution - expected_answer: The expected correct answer - code_executions: Optional list of code execution results to use as fallback - - Returns - ------- - True if answer matches (from consensus or code execution), False otherwise - """ - if expected_answer is None: - return False - - # Try verifying the consensus answer first - if solution_answer is not None: - if self._verify_single_answer(solution_answer, expected_answer): - return True - - # If consensus answer failed and we have code executions, try those - if code_executions: - for exec_info in code_executions: - # Try the numerical_answer from code execution - code_numerical = exec_info.get("numerical_answer") - if code_numerical and self._verify_single_answer( - code_numerical, expected_answer - ): - log.info( - f" ✓ Answer verified using code output from agent {exec_info.get('agent_id')}" - ) - return True - - # Try extracting numbers from raw code output - code_output = exec_info.get("code_output", "") - if code_output: - try: - output_nums = self._normalize_answer(code_output) - exp_nums = self._normalize_answer(expected_answer) - if ( - output_nums - and exp_nums - and len(output_nums) == len(exp_nums) - ): - if all( - abs(s - e) < 1e-6 for s, e in zip(output_nums, exp_nums) - ): - log.info( - f" ✓ Answer verified using raw code output from agent {exec_info.get('agent_id')}" - ) - return True - except Exception: - pass - - return False - - async def _verify_xfinbench_answer( - self, - solution_answer: str, - expected_answer: Any, - task_type: str, - code_executions: List[Dict] = None, - reasoning: str = None, - problem_text: str = None, - ) -> Dict[str, bool]: - """Verify XFinBench answer using LLM-as-a-Judge with principled criteria. - - This uses LLM-as-a-Judge that encodes the legitimate reasons from Feb 12 analysis: - - Precision superiority: tool used more rigorous method than textbook - - Correct methodology with implementation variance: tiny numerical differences from different solvers - - Args: - solution_answer: The answer from the solution - expected_answer: The expected correct answer - task_type: Type of task ('bool', 'mcq', 'calcu') - code_executions: Optional list of code execution results - reasoning: The reasoning/thought process from the solution - problem_text: The original problem statement - - Returns - ------- - Dictionary with 'llm_judge' success flag - """ - results = {"llm_judge": False} - if expected_answer is None: - return results - - # Extract code context from code_executions - code_context = None - reasoning_context = reasoning - - if code_executions and len(code_executions) > 0: - # Use the first successful code execution - for exec_info in code_executions: - if exec_info.get("has_code") and not str( - exec_info.get("code_output", "") - ).startswith("ERROR:"): - code_context = exec_info.get("code", "") - # Also include thought from code execution if reasoning not provided - if not reasoning_context: - reasoning_context = exec_info.get("thought", "") - break - - # If no successful execution, use any code even if it errored - if not code_context and len(code_executions) > 0: - code_context = code_executions[0].get("code", "") - if not reasoning_context: - reasoning_context = code_executions[0].get("thought", "") - - # Use LLM-as-a-Judge for verification - results["llm_judge"] = await self._verify_with_llm_judge( - solution_answer=solution_answer, - expected_answer=expected_answer, - reasoning=reasoning_context, - code=code_context, - problem_text=problem_text, - task_type=task_type, - ) - - return results - - def _verify_boolean_answer( - self, - solution_answer: str, - expected_answer: Any, - code_executions: List[Dict] = None, - ) -> bool: - """Verify boolean (True/False or 0/1) answer.""" - if solution_answer is None: - return False - - # Normalize to 0/1 - def normalize_bool(val): - if val is None: - return None - val_str = str(val).strip().lower() - if val_str in ["true", "1", "1.0", "yes"]: - return 1 - if val_str in ["false", "0", "0.0", "no"]: - return 0 - return None - - sol_bool = normalize_bool(solution_answer) - exp_bool = normalize_bool(expected_answer) - - if sol_bool is not None and exp_bool is not None: - if sol_bool == exp_bool: - return True - - # Try code executions - if code_executions: - for exec_info in code_executions: - code_answer = exec_info.get("numerical_answer") or exec_info.get( - "code_output", "" - ) - code_bool = normalize_bool(code_answer) - if code_bool is not None and code_bool == exp_bool: - log.info(" ✓ Boolean answer verified using code output") - return True - - return False - - def _verify_mcq_answer( - self, - solution_answer: str, - expected_answer: str, - code_executions: List[Dict] = None, - ) -> bool: - """Verify multiple choice answer (extract choice letter).""" - if solution_answer is None or expected_answer is None: - return False - - import re - - def extract_choice(text): - """Extract choice letter (A, B, C, D, etc.) from text.""" - if not text: - return None - text_str = str(text).strip().upper() - # Look for standalone letter or letter with period/parenthesis - match = re.search(r"\b([A-Z])\b", text_str) - if match: - return match.group(1) - return None - - sol_choice = extract_choice(solution_answer) - exp_choice = extract_choice(expected_answer) - - if sol_choice and exp_choice: - if sol_choice == exp_choice: - return True - - # Try code executions - if code_executions: - for exec_info in code_executions: - code_answer = exec_info.get("numerical_answer") or exec_info.get( - "code_output", "" - ) - code_choice = extract_choice(code_answer) - if code_choice and code_choice == exp_choice: - log.info(" ✓ MCQ answer verified using code output") - return True - - return False - - async def _verify_with_llm_judge( - self, - solution_answer: str, - expected_answer: Any, - reasoning: str = None, - code: str = None, - problem_text: str = None, - task_type: str = None, - ) -> bool: - """Use LLM-as-a-Judge to verify answer with principled criteria from Feb 12 analysis. - - This judge encodes the legitimate reasons tool-use deserves credit when diverging from ground truth: - 1. Precision superiority: More rigorous method than textbook (continuous vs discrete, exact vs approximate) - 2. Correct methodology with implementation variance: Different numerical solvers producing slightly different floats - - NOT encoded: Lucky passes from consensus relaxation artifacts. - - Args: - solution_answer: The answer from the solution - expected_answer: The expected correct answer - reasoning: The model's thought process explaining approach - code: The code used to compute the answer - problem_text: The original problem statement - task_type: Type of task ('bool', 'mcq', 'calcu') - - Returns - ------- - True if answer is mathematically correct (exact match OR legitimate precision/methodology difference) - """ - if solution_answer is None or expected_answer is None: - return False - - # Build context for judge - reasoning_context = f"\n\nModel's Reasoning:\n{reasoning}" if reasoning else "" - code_context = f"\n\nModel's Code:\n```python\n{code}\n```" if code else "" - problem_context = f"\n\nProblem:\n{problem_text}" if problem_text else "" - - # Prepare the judgment prompt with principled criteria - judge_prompt = f"""You are an expert financial mathematics evaluator. Your task is to determine if the model's answer is CORRECT. - -EXPECTED ANSWER (from textbook/reference): {expected_answer} -MODEL'S ANSWER: {solution_answer} -TASK TYPE: {task_type if task_type else "numerical"} -{problem_context} -{reasoning_context} -{code_context} - -EVALUATION CRITERIA: - -**ACCEPT if any of the following are true:** - -1. **EXACT MATCH** (with formatting/unit tolerance): - - Answers are numerically identical after normalizing units and formatting - - Unit conversions: 0.0156 = 1.56% = 156 bps (basis points) - - Formatting: "$1,234.56" = "1234.56" = "1234.560" - - Rounding: 102.1285 ≈ 102.13 (difference < 0.01 or rounds to same value) - -2. **PRECISION SUPERIORITY** (tool used MORE rigorous method): - - Model used continuous compounding, textbook assumed discrete - - Model used exact numerical solver, textbook used approximation formula - - Model used higher precision constants (more decimal places of π, e, etc.) - - **HOW TO IDENTIFY**: Check if code uses scipy.optimize, numerical integration, or exact formulas - - **EXAMPLE**: Model gets 2.489 with fsolve(), textbook gets 2.5 with simplified formula → ACCEPT - -3. **CORRECT METHODOLOGY WITH IMPLEMENTATION VARIANCE**: - - Model's approach is mathematically sound (verify from reasoning/code) - - Difference is minor floating-point variance (< 0.1% relative error) - - Different solvers/libraries produce slightly different results (2.489 vs 2.490) - - **HOW TO IDENTIFY**: Code shows correct setup, tiny numerical difference - - **EXAMPLE**: Two valid bond pricing implementations differ by 0.001 → ACCEPT - -**REJECT if:** -- Model made mathematical error in reasoning or code -- Model used wrong formula or misunderstood problem -- Large numerical difference (> 1% relative error) without justification -- Model's methodology is fundamentally flawed - -**SPECIAL HANDLING:** - -For **boolean** (0/1) or **MCQ** tasks: Require exact match (no precision argumentation applies). - -For **numerical** tasks: Apply full criteria above, prioritizing methodological correctness. - -**YOUR JUDGMENT:** - -Based ONLY on the criteria above: -1. Is there an exact match (accounting for units/formatting)? -2. If not exact, does the code/reasoning show the model used a MORE precise method? -3. If not more precise, is the methodology correct with only minor numerical variance? - -Output ONLY one word: "ACCEPT" or "REJECT" - -Your judgment:""" - - try: - # Use same model as task solver for consistency - judge_client = get_model_client( - self.model_name, seed=42, reasoning_effort="low" - ) - - response = await judge_client.create( - [ - SystemMessage( - content="You are a precise mathematical evaluator who rewards rigorous computational methods." - ), - UserMessage(content=judge_prompt, source="user"), - ] - ) - - judgment = response.content.strip().upper() - - if "ACCEPT" in judgment: - log.debug( - f"LLM Judge: ACCEPT (expected={expected_answer}, got={solution_answer})" - ) - return True - if "REJECT" in judgment: - log.debug( - f"LLM Judge: REJECT (expected={expected_answer}, got={solution_answer})" - ) - return False - log.warning( - f"Unexpected LLM judge response: {judgment}, defaulting to REJECT" - ) - return False - - except Exception as e: - log.error(f"LLM judge error: {e}") - return False - - def _verify_single_answer( - self, solution_answer: str, expected_answer: str, relaxed: bool = False - ) -> bool: - """Verify a single answer using XFinBench standards. - - Args: - solution_answer: The answer to verify - expected_answer: The expected correct answer - relaxed: If True, use 5% relative error tolerance; if False, use strict tolerance - - Returns - ------- - True if answer matches according to the specified tolerance - """ - if solution_answer is None or expected_answer is None: - return False - - sol = str(solution_answer).strip().lower() - exp = str(expected_answer).strip().lower() - - # Exact match - if sol == exp: - return True - - try: - sol_nums = self._normalize_answer(solution_answer) - exp_nums = self._normalize_answer(expected_answer) - - if not sol_nums or not exp_nums or len(sol_nums) != len(exp_nums): - return False - - for s, e in zip(sol_nums, exp_nums): - diff = abs(s - e) - - # Strict: Must be virtually exact - if not relaxed: - if diff > 1e-6: - return False - else: - # 1. Absolute tolerance (crucial for expected == 0) - if diff < 1e-4 or (e == 0 and diff < 1e-9): - continue - - # 2. Feb 12 standard: 3.5% relative error tolerance - if e != 0 and diff / abs(e) <= 0.035: - continue - - # 3. Unit scaling checks (BP, %, decimals, Thousands, Millions) - scaled_match = False - for factor in [10, 100, 1000, 10000, 1000000]: - if abs(s * factor - e) < 1e-4 or abs(s - e * factor) < 1e-4: - scaled_match = True - break - if e != 0 and ( - abs(s * factor - e) / abs(e) <= 0.035 - or abs(s - e * factor) / abs(e) <= 0.035 - ): - scaled_match = True - break - - if scaled_match: - continue - return False - return True - except Exception: - return False - - def _extract_code_executions(self, all_solutions: List[Dict]) -> List[Dict]: - """Extract code execution information and outputs from solutions.""" - code_executions = [] - for solution in all_solutions: - code = solution.get("code", "") - code_output = solution.get("code_output", "") - if code: - code_executions.append( - { - "agent_id": solution.get("agent_id"), - "round": solution.get("round_number"), - "has_code": True, - "code": code, - "code_output": code_output, - "numerical_answer": solution.get("numerical_answer"), - } - ) - return code_executions - - async def run_experiment(self): - """Run the experiment.""" - log.info("=" * 70) - log.info("MATHEMATICAL REASONING EXPERIMENT") - log.info("=" * 70) - log.info(f"Model: {self.model_name}") - log.info(f"Debate rounds: {self.num_rounds}") - log.info(f"Tasks per capability: {self.num_tasks}") - log.info(f"Condition: {self.condition}") - log.info(f"Results directory: {self.results_dir}") - log.info("=" * 70 + "\n") - - # Load tasks - tasks = self.load_tasks() - total_tasks = len(tasks) - - # Run baseline condition - if self.condition in ["baseline", "both"]: - log.info("\n" + "=" * 70) - log.info("BASELINE CONDITION (No Code Execution)") - log.info("=" * 70) - for idx, task_data in enumerate(tasks, 1): - result = await self.run_single_task( - task_data, "baseline", idx, total_tasks - ) - self.results["baseline"].append(result) - - # Run tool-assisted condition - if self.condition in ["tool_assisted", "both"]: - log.info("\n" + "=" * 70) - log.info("TOOL-ASSISTED CONDITION (With Code Execution)") - log.info("=" * 70) - for idx, task_data in enumerate(tasks, 1): - result = await self.run_single_task( - task_data, "tool_assisted", idx, total_tasks - ) - self.results["tool_assisted"].append(result) - - # Save results - self.results["experiment_info"]["end_time"] = datetime.now().isoformat() - results_file = self.results_dir / "experiment_results.json" - with open(results_file, "w") as f: - json.dump(self.results, f, indent=2) - - log.info(f"\n✓ Results saved to: {results_file}") - - # Print summary - self._print_summary() - - # Flush Langfuse - self.langfuse_client.flush() - - def _print_summary(self): - """Print experiment summary with both strict and relaxed metrics for XFinBench.""" - log.info("\n" + "=" * 70) - log.info("EXPERIMENT SUMMARY") - log.info("=" * 70) - - for condition in ["baseline", "tool_assisted"]: - if condition not in self.results or not self.results[condition]: - continue - - results = self.results[condition] - completed = [r for r in results if r["status"] == "completed"] - - log.info(f"\n{condition.upper().replace('_', ' ')}:") - log.info(f" Total tasks: {len(results)}") - log.info(f" Completed: {len(completed)}") - - if completed: - # LLM Judge metrics - successful = [r for r in completed if r.get("success", False)] - log.info(f" Successful: {len(successful)}") - - success_rate = len(successful) / len(completed) * 100 - log.info( - f" Success rate (LLM-as-a-Judge with precision superiority criteria): {success_rate:.1f}%" - ) - - # Code execution success for tool_assisted - if condition == "tool_assisted": - code_success_count = sum( - 1 - for r in completed - if r.get("code_executions") - and any( - c.get("has_code") - and not str(c.get("code_output", "")).startswith("ERROR:") - for c in r.get("code_executions", []) - ) - ) - log.info( - f" Code execution success: {code_success_count}/{len(completed)}" - ) - - avg_time = sum(r["execution_time"] for r in completed) / len(completed) - log.info(f" Average time: {avg_time:.2f}s") - - log.info("\n" + "=" * 70) - - -async def main(): - """Main execution function.""" - parser = argparse.ArgumentParser( - description="Run mathematical reasoning experiments with configurable parameters", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Quick test: 1 task per capability, tool-assisted condition - python run_experiments.py --num-tasks 1 --condition tool_assisted - - # Quick test: 1 task per capability, both conditions - python run_experiments.py --num-tasks 1 --condition both - - # Full experiment: 3 tasks per capability (default) - python run_experiments.py --num-tasks 3 - - # Custom configuration - python run_experiments.py --model gemini-3-flash-preview --rounds 2 --num-tasks 10 --output my_results - """, - ) - parser.add_argument( - "--model", - default="gemini-3-flash-preview", - help="LLM model to use (default: gemini-3-flash-preview)", - ) - parser.add_argument( - "--rounds", type=int, default=1, help="Number of debate rounds (default: 1)" - ) - parser.add_argument( - "--num-tasks", - type=int, - default=3, - help="Number of tasks per capability to run (default: 3 for full experiment)", - ) - parser.add_argument( - "--condition", - choices=["baseline", "tool_assisted", "both"], - default="both", - help="Which condition to run (default: both)", - ) - parser.add_argument( - "--output", - default="experiment_results", - help="Output directory for results (default: experiment_results)", - ) - parser.add_argument( - "--tasks-file", - default="selected_tasks.json", - help="Path to tasks JSON file (default: selected_tasks.json)", - ) - parser.add_argument( - "--enable-tool-selection", - action="store_true", - help="Enable dynamic tool selection (disabled by default)", - ) - parser.add_argument( - "--dataset-type", - choices=["math", "xfinbench"], - default="math", - help="Type of dataset to evaluate (default: math)", - ) - - args = parser.parse_args() - - # Tool selection is disabled by default, enable only if flag is present - enable_tool_selection = args.enable_tool_selection - - runner = ExperimentRunner( - model_name=args.model, - num_rounds=args.rounds, - num_tasks=args.num_tasks, - condition=args.condition, - output_dir=args.output, - enable_tool_selection=enable_tool_selection, - tasks_file=args.tasks_file, - dataset_type=args.dataset_type, - ) - await runner.run_experiment() - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/src/scripts/assess_tool_familiarity.py b/src/scripts/assess_tool_familiarity.py deleted file mode 100644 index d333f838..00000000 --- a/src/scripts/assess_tool_familiarity.py +++ /dev/null @@ -1,271 +0,0 @@ -#!/usr/bin/env python3 -""" -Tool Familiarity Probe - -Assesses LLM familiarity with financial computing libraries by testing basic usage. -If a tool scores < 4/5 on syntax-free execution, it's deemed "unfamiliar" -and should be given few-shot examples in TOOL_ASSISTED_ROUND_1_PROMPT. -""" - -import asyncio -import logging -from typing import Dict - -from dotenv import load_dotenv - - -load_dotenv() - -from autogen_core.models import SystemMessage, UserMessage - -from src.tools.executor import PythonExecutor -from src.utils.model_client_utils import get_model_client -from src.utils.tool_assisted_prompts import ( - TOOL_ASSISTED_ROUND_1_PROMPT, - TOOL_ASSISTED_SYSTEM_MESSAGE, -) - - -logging.basicConfig(level=logging.INFO) -log = logging.getLogger(__name__) - - -# 5 basic questions per library to probe familiarity -PROBES = { - "numpy_financial": [ - "Calculate the Net Present Value (NPV) of cash flows: [-1000, 200, 300, 400, 500] with a discount rate of 0.10 (10%).", - "Calculate the Internal Rate of Return (IRR) for cash flows: [-1000, 300, 400, 400, 400].", - "Calculate the PMT (payment) for a loan: present value = 100000, annual rate = 0.05, nper = 360 months.", - "Calculate the Future Value (FV) for an investment: rate = 0.06, nper = 10 years, pmt = -100, pv = -1000.", - "Calculate the number of periods (NPER) needed: rate = 0.05, pmt = -200, pv = 5000, fv = 0.", - ], - "py_vollib": [ - "Calculate the call option price using Black-Scholes: S=100, K=105, T=1, r=0.05, sigma=0.20.", - "Calculate the put option price using Black-Scholes: S=100, K=95, T=1, r=0.05, sigma=0.20.", - "Calculate the delta (first Greek) for a call option: S=100, K=100, T=1, r=0.05, sigma=0.25.", - "Calculate implied volatility for a call option with price 7.0: S=100, K=105, T=1, r=0.05.", - "Calculate gamma (second Greek) for an option: S=100, K=100, T=1, r=0.05, sigma=0.20.", - ], - "pypfopt": [ - "Given returns matrix [[0.1, 0.2], [0.15, 0.1]] and covariance matrix [[0.01, 0.005], [0.005, 0.02]], calculate the minimum volatility portfolio.", - "Calculate the Sharpe ratio for a portfolio with annual return 0.12, risk-free rate 0.02, and volatility 0.15.", - "Create an EfficientFrontier object with expected_returns [0.1, 0.12, 0.15] and a sample covariance matrix.", - "Optimize for maximum Sharpe ratio given expected returns and sample covariance matrix.", - "Calculate the maximum Sharpe ratio portfolio weights for 3 assets with given returns and covariance.", - ], - "empyrical": [ - "Calculate the Sharpe ratio for returns [0.01, 0.02, -0.01, 0.03, 0.02] with risk-free rate 0.0.", - "Calculate the Sortino ratio for returns [0.01, 0.02, -0.01, 0.03, 0.02] with minimum acceptable return 0.0.", - "Calculate the maximum drawdown for returns [0.05, -0.10, 0.08, 0.12, -0.05].", - "Calculate the annual return for daily returns [0.001, 0.002, -0.001, 0.003, 0.002].", - "Calculate alpha and beta for asset returns [0.01, 0.02, -0.01, 0.03, 0.02] against benchmark [0.005, 0.01, 0.005, 0.015, 0.01].", - ], - "arch": [ - "Fit a GARCH(1,1) model to simulated returns data with 100 observations.", - "Fit an EGARCH model to returns data and generate 5-step ahead forecasts using simulation method.", - "Create a ConstantMean model with GARCH volatility and fit it to return data.", - "Generate 5-step ahead forecasts from a fitted GARCH model using simulation method.", - "Fit a HARX model using the arch.univariate.HARX class to time series data.", - ], -} - - -async def assess_tool_familiarity( - model_name: str = "gemini-2.5-pro", threshold: int = 4 -) -> Dict[str, Dict[str, any]]: - """Assess LLM familiarity with financial computing libraries. - - Args: - model_name: LLM model to use for code generation - threshold: Success count threshold (out of 5) to deem tool "familiar" - - Returns - ------- - Dictionary with results for each tool including score and recommendations - """ - client = get_model_client(model_name) - executor = PythonExecutor( - allowed_imports=[ - "numpy_financial", - "py_vollib", - "pypfopt", - "empyrical", - "arch", - "numpy", - "pandas", - "scipy", - "datetime", - "math", - "statsmodels", - ] - ) - - results = {} - - for tool, prompts in PROBES.items(): - log.info(f"\n{'=' * 70}") - log.info(f"Testing {tool} familiarity...") - log.info(f"{'=' * 70}") - - success_count = 0 - failures = [] - - for i, prompt in enumerate(prompts, 1): - log.info(f"\n[Question {i}/5] {prompt[:80]}...") - - try: - # Generate code using the same prompt structure as the actual benchmark - tool_context_str = f"""You have access to financial computing libraries. - -Available libraries: {tool} - -Use these libraries to solve the problem.""" - - formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=prompt, tool_context=tool_context_str - ) - - resp = await client.create( - [ - SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), - UserMessage(content=formatted_prompt, source="user"), - ] - ) - - # Parse JSON response - import json - - try: - response_data = json.loads(resp.content) - code = response_data.get("code") - - if code is None: - log.warning(" ✗ No code provided in response") - failures.append( - { - "question": i, - "error": "No code provided in JSON response", - } - ) - continue - - except json.JSONDecodeError as e: - log.error(f" ✗ Failed to parse JSON response: {str(e)[:100]}") - failures.append( - {"question": i, "error": f"JSON parse error: {str(e)[:200]}"} - ) - continue - - # Execute code - exec_result = executor.execute(code) - - if exec_result.success: - log.info(" ✓ Code executed successfully") - success_count += 1 - else: - log.warning( - f" ✗ Execution failed: {exec_result.error[:100] if exec_result.error else 'Unknown error'}" - ) - failures.append( - { - "question": i, - "error": exec_result.error[:200] - if exec_result.error - else "No error details", - } - ) - except Exception as e: - log.error(f" ✗ Exception: {str(e)[:100]}") - failures.append({"question": i, "error": str(e)[:200]}) - - # Determine recommendation - is_familiar = success_count >= threshold - recommendation = ( - "PASS" - if is_familiar - else "WARN: Add few-shot examples to TOOL_ASSISTED_ROUND_1_PROMPT" - ) - - results[tool] = { - "score": f"{success_count}/5", - "success_count": success_count, - "is_familiar": is_familiar, - "recommendation": recommendation, - "failures": failures if failures else [], - } - - log.info(f"\n{tool} Score: {success_count}/5") - log.info(f"Recommendation: {recommendation}") - - # Print summary - log.info(f"\n{'=' * 70}") - log.info("SUMMARY") - log.info(f"{'=' * 70}") - - unfamiliar_tools = [ - tool for tool, result in results.items() if not result["is_familiar"] - ] - - if unfamiliar_tools: - log.warning(f"\n⚠️ UNFAMILIAR TOOLS (score < {threshold}/5):") - for tool in unfamiliar_tools: - log.warning(f" - {tool}: {results[tool]['score']}") - log.warning( - "\nACTION REQUIRED: Add few-shot examples for these tools to TOOL_ASSISTED_ROUND_1_PROMPT" - ) - log.warning("in src/utils/tool_assisted_prompts.py") - else: - log.info(f"\n✓ All tools meet the familiarity threshold ({threshold}/5)") - log.info(" No action required.") - - return results - - -async def main(): - """Main execution function.""" - import argparse - - parser = argparse.ArgumentParser( - description="Assess LLM familiarity with financial computing libraries" - ) - parser.add_argument( - "--model", - default="gemini-2.5-pro", - help="LLM model to use (default: gemini-2.5-pro)", - ) - parser.add_argument( - "--threshold", - type=int, - default=4, - help="Success count threshold to deem tool 'familiar' (default: 4/5)", - ) - - args = parser.parse_args() - - results = await assess_tool_familiarity( - model_name=args.model, threshold=args.threshold - ) - - # Save results - import json - from datetime import datetime - from pathlib import Path - - output_file = Path("tool_familiarity_results.json") - with open(output_file, "w") as f: - json.dump( - { - "timestamp": datetime.now().isoformat(), - "model": args.model, - "threshold": args.threshold, - "results": results, - }, - f, - indent=2, - ) - - log.info(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/src/scripts/classify_tasks.py b/src/scripts/classify_tasks.py deleted file mode 100644 index eccc4a3e..00000000 --- a/src/scripts/classify_tasks.py +++ /dev/null @@ -1,638 +0,0 @@ -#!/usr/bin/env python3 -""" -Task Classifier for Filtering Tool-Appropriate Math Tasks - -Classifies math tasks to identify: -1. Tool-solvable vs explanation-focused -2. Quantitative vs qualitative answers -3. Ground truth quality issues -4. Format specification problems -""" - -import argparse -import json -import logging -import os -import random -from pathlib import Path -from typing import Any, Dict, List, Optional - -from dotenv import load_dotenv -from google import genai -from google.genai.types import GenerateContentConfig - - -load_dotenv() - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -log = logging.getLogger(__name__) - - -CLASSIFICATION_PROMPT = """You are an expert at analyzing mathematical problems to determine their suitability for computational tool-assisted solving and evaluation. - -Analyze the following math task and classify it across multiple dimensions: - -TASK: -Problem: {problem} -Expected Answer: {answer} -{reasoning_section} -{verification_section} - -CLASSIFICATION DIMENSIONS: - -1. **Tool Solvability** (tool_solvable: true/false) - - Can this problem be meaningfully solved or verified using computational tools (Python with NumPy, SciPy, SymPy)? - - Problems requiring pure conceptual explanations, proofs, or theoretical discussions are NOT tool-solvable - - Problems with numerical computation, symbolic manipulation, or verification ARE tool-solvable - -2. **Answer Type** (answer_type: "quantitative", "qualitative", "mixed", "no_answer") - - "quantitative": Numerical answer(s), formulas, or specific mathematical objects - - "qualitative": Explanations, descriptions, procedures, conceptual answers - - "mixed": Both quantitative and qualitative components - - "no_answer": NO_ANSWER or similar (task asks for explanation/implementation without specific answer) - -3. **Ground Truth Quality** (ground_truth_quality: "good", "problematic", "missing") - - "good": Clear, well-specified expected answer that matches the problem requirements - - "problematic": Answer doesn't match problem requirements, contradictory, or under-specified format - - "missing": NO_ANSWER when a specific answer could be provided, or truly missing - - IMPORTANT: If verification verdict is "no", this is a strong indicator of problematic quality - -4. **Format Specification** (format_clarity: "clear", "ambiguous", "unspecified") - - "clear": Expected answer format is well-defined and unambiguous - - "ambiguous": Multiple valid formats possible, unclear what format is expected - - "unspecified": No clear indication of expected format - -5. **Evaluation Difficulty** (evaluation_difficulty: "easy", "medium", "hard") - - "easy": Simple numerical comparison or exact string match - - "medium": Requires normalization or format handling - - "hard": Requires understanding context, multi-part answers, or subjective judgment - -6. **Problem Difficulty** (problem_difficulty: "easy", "medium", "hard", "very_hard") - - Based on mathematical complexity, number of steps, required knowledge level - -7. **Recommendation** (recommend_for_evaluation: true/false) - - Should this task be included in a high-quality tool-assisted evaluation dataset? - - Consider: tool-solvability, answer quality, format clarity, and evaluation feasibility - - CRITICAL: Tasks with verification verdict "no" should typically NOT be recommended unless the issue is minor - - Pay attention to verification reasons - quality issues like unclear problems, wrong answers, or conceptual mismatches should disqualify tasks - -IMPORTANT: Return ONLY a valid JSON object with this exact structure: -{{ - "tool_solvable": true/false, - "tool_solvability_reasoning": "Brief explanation", - "answer_type": "quantitative/qualitative/mixed/no_answer", - "ground_truth_quality": "good/problematic/missing", - "ground_truth_issues": "Specific issues if any, or null", - "format_clarity": "clear/ambiguous/unspecified", - "evaluation_difficulty": "easy/medium/hard", - "problem_difficulty": "easy/medium/hard/very_hard", - "recommend_for_evaluation": true/false, - "recommendation_reasoning": "Why include or exclude this task" -}} - -Respond with ONLY the JSON object, no additional text or formatting.""" - - -class TaskClassifier: - """Classifies math tasks for quality and tool-solvability.""" - - def __init__(self, model_name: str = "gemini-3-flash-preview"): - """Initialize classifier with specified model. - - Args: - model_name: Google AI model to use for classification - """ - self.model_name = model_name - self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - - def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: - """Classify a single task. - - Args: - task_data: Task dictionary with 'problem', 'answer', optionally 'reasoning' and 'verification' - - Returns - ------- - Classification result dictionary - """ - # Format reasoning section if available - reasoning_section = "" - if task_data.get("reasoning"): - reasoning_section = f"Reasoning/Solution Provided: {task_data['reasoning']}" - - # Format verification section if available - verification_section = "" - verification = task_data.get("verification", {}) - if verification: - verdict = verification.get("verdict", "unknown") - reason = verification.get("reason", "No reason provided") - verification_section = ( - f"\nVERIFICATION STATUS:\nVerdict: {verdict}\nReason: {reason}" - ) - - prompt = CLASSIFICATION_PROMPT.format( - problem=task_data.get("problem", ""), - answer=task_data.get("answer", ""), - reasoning_section=reasoning_section, - verification_section=verification_section, - ) - - try: - response = self.client.models.generate_content( - model=self.model_name, - contents=prompt, - config=GenerateContentConfig( - temperature=0.1, response_mime_type="application/json" - ), - ) - - classification = json.loads(response.text) - return classification - - except Exception as e: - log.error( - f"Error classifying task {task_data.get('task_id', 'unknown')}: {e}" - ) - # Return default classification on error - return { - "tool_solvable": False, - "tool_solvability_reasoning": f"Classification error: {e}", - "answer_type": "unknown", - "ground_truth_quality": "unknown", - "ground_truth_issues": f"Error during classification: {e}", - "format_clarity": "unknown", - "evaluation_difficulty": "hard", - "problem_difficulty": "unknown", - "recommend_for_evaluation": False, - "recommendation_reasoning": "Classification failed", - } - - def classify_dataset( - self, tasks: Dict[str, List[Dict[str, Any]]], output_file: Optional[Path] = None - ) -> Dict[str, List[Dict[str, Any]]]: - """Classify all tasks in a dataset. - - Args: - tasks: Dictionary mapping capability names to list of tasks - output_file: Optional path to save classifications - - Returns - ------- - Tasks with added classification metadata - """ - classified_tasks = {} - total_tasks = sum(len(task_list) for task_list in tasks.values()) - processed = 0 - - log.info(f"Classifying {total_tasks} tasks from {len(tasks)} capabilities...") - - for capability_name, task_list in tasks.items(): - log.info( - f"Processing capability: {capability_name} ({len(task_list)} tasks)" - ) - classified_tasks[capability_name] = [] - - for task in task_list: - processed += 1 - log.info( - f" [{processed}/{total_tasks}] Classifying task {task.get('task_id', '?')}" - ) - - classification = self.classify_task(task) - - # Add classification to task - task_with_classification = task.copy() - task_with_classification["classification"] = classification - classified_tasks[capability_name].append(task_with_classification) - - # Log key insights - log.info( - f" Tool-solvable: {classification.get('tool_solvable')}, " - f"Answer type: {classification.get('answer_type')}, " - f"Recommend: {classification.get('recommend_for_evaluation')}" - ) - - # Save if output file specified - if output_file: - output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, "w", encoding="utf-8") as f: - json.dump(classified_tasks, f, indent=2, ensure_ascii=False) - log.info(f"Saved classified tasks to {output_file}") - - return classified_tasks - - def filter_recommended_tasks( - self, - classified_tasks: Dict[str, List[Dict[str, Any]]], - require_tool_solvable: bool = True, - exclude_no_answer: bool = True, - exclude_problematic: bool = True, - ) -> Dict[str, List[Dict[str, Any]]]: - """Filter tasks based on classification criteria. - - Args: - classified_tasks: Tasks with classification metadata - require_tool_solvable: Only include tool-solvable tasks - exclude_no_answer: Exclude tasks with answer_type="no_answer" - exclude_problematic: Exclude tasks with problematic ground truth - - Returns - ------- - Filtered task dictionary - """ - filtered = {} - total_before = 0 - total_after = 0 - - for capability_name, task_list in classified_tasks.items(): - total_before += len(task_list) - filtered_list = [] - - for task in task_list: - classification = task.get("classification", {}) - - # Check filters - if require_tool_solvable and not classification.get("tool_solvable"): - continue - if ( - exclude_no_answer - and classification.get("answer_type") == "no_answer" - ): - continue - if ( - exclude_problematic - and classification.get("ground_truth_quality") == "problematic" - ): - continue - if not classification.get("recommend_for_evaluation"): - continue - - filtered_list.append(task) - - if filtered_list: - filtered[capability_name] = filtered_list - total_after += len(filtered_list) - - log.info("\nFiltering results:") - log.info( - f" Before: {total_before} tasks across {len(classified_tasks)} capabilities" - ) - log.info(f" After: {total_after} tasks across {len(filtered)} capabilities") - log.info(f" Removed: {total_before - total_after} tasks") - - return filtered - - -def load_math_tasks(math_dir: Path = Path("math")) -> Dict[str, List[Dict[str, Any]]]: - """Load all math tasks from capability directories. - - Args: - math_dir: Base directory containing math capability folders - - Returns - ------- - Dictionary mapping capability names to list of tasks - """ - all_tasks = {} - - if not math_dir.exists(): - log.error(f"Math directory not found: {math_dir}") - return all_tasks - - capability_dirs = sorted([d for d in math_dir.iterdir() if d.is_dir()]) - log.info(f"Found {len(capability_dirs)} capability directories") - - for cap_dir in capability_dirs: - capability_name = cap_dir.name - cap_file = cap_dir / "capability.json" - - if not cap_file.exists(): - continue - - try: - with open(cap_file, "r", encoding="utf-8") as f: - cap_data = json.load(f) - - # Extract tasks from capability_failed_data (previously failed tasks) - failed_tasks = cap_data.get("capability_failed_data", []) - - if failed_tasks: - tasks = [] - for task_data in failed_tasks: - verification = task_data.get("verification", {}) - verdict = verification.get("verdict", "") - - # Include all tasks (not just verdict='no') for comprehensive classification - tasks.append( - { - "task_id": task_data.get("id", ""), - "problem": task_data.get("problem", ""), - "answer": task_data.get("answer", ""), - "reasoning": task_data.get("reasoning", ""), - "verification": verification, - "original_verdict": verdict, - } - ) - - if tasks: - all_tasks[capability_name] = tasks - log.info(f"Loaded {len(tasks)} tasks from {capability_name}") - - except Exception as e: - log.error(f"Error loading {capability_name}: {e}") - - return all_tasks - - -def stratified_classify_until_target( - tasks_by_capability: Dict[str, List[Dict[str, Any]]], - target_count: int, - classifier: "TaskClassifier", - seed: int = 42, -) -> Dict[str, List[Dict[str, Any]]]: - """Classify tasks iteratively until we have target_count recommended tasks. - - Uses stratified sampling across capabilities, classifying one task at a time - from each capability in round-robin fashion until we have enough good tasks. - - Args: - tasks_by_capability: All available tasks grouped by capability - target_count: Number of high-quality tasks needed - classifier: TaskClassifier instance - seed: Random seed for reproducibility - - Returns - ------- - Dictionary of classified tasks that were recommended - """ - random.seed(seed) - - # Shuffle tasks within each capability for random sampling - shuffled_tasks = {} - for cap_name, task_list in tasks_by_capability.items(): - shuffled = task_list.copy() - random.shuffle(shuffled) - shuffled_tasks[cap_name] = shuffled - - # Track which index we're at for each capability - capability_indices = {cap: 0 for cap in shuffled_tasks} - - # Store recommended tasks - recommended_tasks = {} - total_recommended = 0 - total_classified = 0 - - capabilities = list(shuffled_tasks.keys()) - if not capabilities: - log.error("No capabilities available") - return {} - - log.info( - f"Starting stratified classification to find {target_count} high-quality tasks" - ) - log.info(f"Available capabilities: {len(capabilities)}") - log.info( - f"Total tasks available: {sum(len(tasks) for tasks in shuffled_tasks.values())}" - ) - - # Round-robin through capabilities until we have enough recommended tasks - capability_idx = 0 - stuck_counter = 0 - max_stuck_iterations = len(capabilities) * 2 # Safety check - - while total_recommended < target_count: - # Check if we've exhausted all capabilities - if stuck_counter >= max_stuck_iterations: - log.warning( - f"Could not find enough tasks. Found {total_recommended}/{target_count} after " - f"classifying {total_classified} tasks. All capabilities may be exhausted." - ) - break - - # Get current capability - current_cap = capabilities[capability_idx] - cap_tasks = shuffled_tasks[current_cap] - cap_idx = capability_indices[current_cap] - - # Check if this capability is exhausted - if cap_idx >= len(cap_tasks): - # Move to next capability - capability_idx = (capability_idx + 1) % len(capabilities) - stuck_counter += 1 - continue - - # Get next task from this capability - task = cap_tasks[cap_idx] - capability_indices[current_cap] += 1 - total_classified += 1 - - log.info( - f"[{total_classified}] [{total_recommended}/{target_count} found] " - f"Classifying from {current_cap}: task {task.get('task_id', '?')}" - ) - - # Classify the task - classification = classifier.classify_task(task) - - # Check if recommended - is_recommended = classification.get("recommend_for_evaluation", False) - is_tool_solvable = classification.get("tool_solvable", False) - answer_type = classification.get("answer_type", "unknown") - - log.info( - f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " - f"Answer type={answer_type}" - ) - - # Add classification to task - task_with_classification = task.copy() - task_with_classification["classification"] = classification - task_with_classification["capability_name"] = current_cap - - # If recommended, add to results - if is_recommended: - if current_cap not in recommended_tasks: - recommended_tasks[current_cap] = [] - recommended_tasks[current_cap].append(task_with_classification) - total_recommended += 1 - stuck_counter = 0 # Reset stuck counter on success - log.info(f" ✓ Added to dataset ({total_recommended}/{target_count})") - else: - log.info(" ✗ Not recommended, continuing search") - - # Move to next capability for round-robin - capability_idx = (capability_idx + 1) % len(capabilities) - - log.info(f"\n{'=' * 70}") - log.info("Classification complete!") - log.info(f" Tasks classified: {total_classified}") - log.info(f" Tasks recommended: {total_recommended}") - log.info(f" Success rate: {total_recommended / total_classified * 100:.1f}%") - log.info(f" Capabilities represented: {len(recommended_tasks)}") - log.info(f"{'=' * 70}\n") - - return recommended_tasks - - -def main(): - """Main execution function.""" - parser = argparse.ArgumentParser( - description="Classify math tasks for tool-assisted evaluation", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - parser.add_argument( - "--math-dir", - type=Path, - default=Path("math"), - help="Directory containing math capability folders", - ) - parser.add_argument( - "--output", - type=Path, - default=Path("classified_tasks.json"), - help="Output file for classified tasks", - ) - parser.add_argument( - "--model", - default="gemini-3-flash-preview", - help="Model to use for classification", - ) - parser.add_argument( - "--num-tasks", - type=int, - default=50, - help="Number of high-quality tasks to find (default: 50)", - ) - parser.add_argument( - "--seed", type=int, default=42, help="Random seed for sampling (default: 42)" - ) - parser.add_argument( - "--filter-only", - action="store_true", - help="Only filter pre-classified tasks (skip classification step)", - ) - parser.add_argument( - "--input-classified", - type=Path, - help="Input file with pre-classified tasks (for --filter-only mode)", - ) - - args = parser.parse_args() - - if args.filter_only: - if not args.input_classified or not args.input_classified.exists(): - log.error("Must provide --input-classified file in --filter-only mode") - return - - with open(args.input_classified, "r", encoding="utf-8") as f: - classified_tasks = json.load(f) - log.info(f"Loaded pre-classified tasks from {args.input_classified}") - - # Filter the pre-classified tasks - classifier = TaskClassifier(model_name=args.model) - filtered = classifier.filter_recommended_tasks(classified_tasks) - else: - # Load all available tasks - all_tasks = load_math_tasks(args.math_dir) - - if not all_tasks: - log.error("No tasks loaded") - return - - total_available = sum(len(tasks) for tasks in all_tasks.values()) - log.info(f"Loaded {total_available} tasks from {len(all_tasks)} capabilities") - - # Use stratified sampling with iterative classification - classifier = TaskClassifier(model_name=args.model) - filtered = stratified_classify_until_target( - tasks_by_capability=all_tasks, - target_count=args.num_tasks, - classifier=classifier, - seed=args.seed, - ) - - if not filtered: - log.error("No recommended tasks found") - return - - # Save filtered/recommended tasks - filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" - args.output.parent.mkdir(parents=True, exist_ok=True) - with open(filtered_file, "w", encoding="utf-8") as f: - json.dump(filtered, f, indent=2, ensure_ascii=False) - log.info(f"Saved recommended tasks to {filtered_file}") - - # Print summary statistics - print("\n" + "=" * 70) - print("CLASSIFICATION SUMMARY") - print("=" * 70) - - tool_solvable = 0 - not_tool_solvable = 0 - by_answer_type = {"quantitative": 0, "qualitative": 0, "mixed": 0, "no_answer": 0} - by_quality = {"good": 0, "problematic": 0, "missing": 0} - by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} - by_capability = {} - recommended = 0 - - for cap_name, task_list in filtered.items(): - by_capability[cap_name] = len(task_list) - for task in task_list: - c = task.get("classification", {}) - if c.get("tool_solvable"): - tool_solvable += 1 - else: - not_tool_solvable += 1 - - answer_type = c.get("answer_type", "unknown") - by_answer_type[answer_type] = by_answer_type.get(answer_type, 0) + 1 - - quality = c.get("ground_truth_quality", "unknown") - by_quality[quality] = by_quality.get(quality, 0) + 1 - - difficulty = c.get("problem_difficulty", "unknown") - by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 - - if c.get("recommend_for_evaluation"): - recommended += 1 - - total = tool_solvable + not_tool_solvable - if total == 0: - print("No tasks found") - return - - print(f"\nTotal tasks: {total}") - print(f"Tool-solvable: {tool_solvable} ({tool_solvable / total * 100:.1f}%)") - print( - f"Not tool-solvable: {not_tool_solvable} ({not_tool_solvable / total * 100:.1f}%)" - ) - - print("\nBy capability:") - for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True): - print(f" {cap}: {count}") - - print("\nAnswer types:") - for atype, count in sorted(by_answer_type.items()): - if count > 0: - print(f" {atype}: {count} ({count / total * 100:.1f}%)") - - print("\nGround truth quality:") - for quality, count in sorted(by_quality.items()): - if count > 0: - print(f" {quality}: {count} ({count / total * 100:.1f}%)") - - print("\nProblem difficulty:") - for diff, count in sorted(by_difficulty.items()): - if count > 0: - print(f" {diff}: {count} ({count / total * 100:.1f}%)") - - print( - f"\nRecommended for evaluation: {recommended} ({recommended / total * 100:.1f}%)" - ) - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/src/scripts/classify_xfinbench_tasks.py b/src/scripts/classify_xfinbench_tasks.py deleted file mode 100644 index 5288f0f0..00000000 --- a/src/scripts/classify_xfinbench_tasks.py +++ /dev/null @@ -1,702 +0,0 @@ -#!/usr/bin/env python3 -""" -XFinBench Task Classifier for Tool-Assisted Evaluation - -Classifies XFinBench financial tasks to identify: -1. Tool-solvable vs explanation-focused -2. Quantitative vs qualitative answers -3. Ground truth quality issues -4. Tasks requiring vision (to filter out) -""" - -import argparse -import csv -import json -import logging -import os -import random -from pathlib import Path -from typing import Any, Dict, List - -from dotenv import load_dotenv -from google import genai -from google.genai.types import GenerateContentConfig - - -load_dotenv() - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" -) -log = logging.getLogger(__name__) - - -XFINBENCH_CLASSIFICATION_PROMPT = """You are an expert at analyzing financial problems to determine their suitability for computational tool-assisted solving and evaluation. - -Analyze the following financial task and classify it across multiple dimensions: - -TASK: -ID: {task_id} -Task Type: {task_type} -Question: {question} -{choices_section} -Expected Answer: {answer} -Financial Capability: {fin_capability} - -CLASSIFICATION DIMENSIONS: - -1. **Tool Solvability** (tool_solvable: true/false) - - Can this problem be meaningfully solved or verified using computational tools (Python with NumPy, SciPy, SymPy)? - - Financial calculations (NPV, IRR, option pricing, ratios) ARE tool-solvable - - Pure terminology understanding or conceptual explanations are NOT tool-solvable - - Temporal reasoning requiring quantitative analysis IS tool-solvable - - Scenario planning with numerical modeling IS tool-solvable - -2. **Requires Vision** (requires_vision: true/false) - - Does the problem reference figures, charts, or visual elements that are essential? - - Tasks with LaTeX tables/data embedded in text do NOT require vision - - Tasks explicitly mentioning "see figure" or "shown in chart" DO require vision - -3. **Answer Type** (answer_type: "boolean", "mcq", "numerical", "qualitative", "mixed") - - "boolean": True/False (0/1) answer - - "mcq": Multiple choice with options - - "numerical": Specific numerical answer(s) - - "qualitative": Text explanations, descriptions, conceptual answers - - "mixed": Both quantitative and qualitative components - -4. **Ground Truth Quality** (ground_truth_quality: "good", "problematic", "missing") - - "good": Clear, well-specified expected answer - - "problematic": Answer unclear, contradictory, or format issues - - "missing": No answer provided or placeholder value - -5. **Verification Feasibility** (verification_feasibility: "direct", "numeric_check", "hard") - - "direct": Simple comparison (bool, mcq choice letter) - - "numeric_check": Requires numerical tolerance comparison - - "hard": Complex multi-part or subjective evaluation - -6. **Problem Difficulty** (problem_difficulty: "easy", "medium", "hard", "very_hard") - - Based on financial complexity, calculation steps, required knowledge level - -7. **Tool Benefit** (tool_benefit: "high", "medium", "low", "none") - - "high": Complex calculations, numerical modeling, formula verification - - "medium": Moderate computation, could solve by hand but tools help - - "low": Simple arithmetic, tools don't add much value - - "none": Pure conceptual/terminology, no computation involved - -8. **Recommendation** (recommend_for_evaluation: true/false) - - Should this task be included in a tool-assisted evaluation dataset? - - Consider: tool-solvability, verification feasibility, no vision required - - CRITICAL: Tasks requiring vision should NOT be recommended - - Focus on tasks where tools provide measurable benefit - -IMPORTANT: Return ONLY a valid JSON object with this exact structure: -{{ - "tool_solvable": true/false, - "tool_solvability_reasoning": "Brief explanation", - "requires_vision": true/false, - "answer_type": "boolean/mcq/numerical/qualitative/mixed", - "ground_truth_quality": "good/problematic/missing", - "ground_truth_issues": "Specific issues if any, or null", - "verification_feasibility": "direct/numeric_check/hard", - "problem_difficulty": "easy/medium/hard/very_hard", - "tool_benefit": "high/medium/low/none", - "recommend_for_evaluation": true/false, - "recommendation_reasoning": "Why include or exclude this task" -}} - -Respond with ONLY the JSON object, no additional text or formatting.""" - - -class XFinBenchClassifier: - """Classifies XFinBench tasks for quality and tool-solvability.""" - - def __init__(self, model_name: str = "gemini-3-flash-preview"): - """Initialize classifier with specified model. - - Args: - model_name: Google AI model to use for classification - """ - self.model_name = model_name - self.client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"]) - - def classify_task(self, task_data: Dict[str, Any]) -> Dict[str, Any]: - """Classify a single task. - - Args: - task_data: Task dictionary from XFinBench CSV - - Returns - ------- - Classification result dictionary - """ - # Format choices section for MCQ - choices_section = "" - if task_data.get("task") == "mcq" and task_data.get("choice"): - choices_section = f"Choices: {task_data['choice']}" - - prompt = XFINBENCH_CLASSIFICATION_PROMPT.format( - task_id=task_data.get("id", ""), - task_type=task_data.get("task", ""), - question=task_data.get("question", ""), - choices_section=choices_section, - answer=task_data.get("ground_truth", ""), - fin_capability=task_data.get("fin_capability", ""), - ) - - try: - response = self.client.models.generate_content( - model=self.model_name, - contents=prompt, - config=GenerateContentConfig( - temperature=0.1, response_mime_type="application/json" - ), - ) - - classification = json.loads(response.text) - return classification - - except Exception as e: - log.error(f"Error classifying task {task_data.get('id', 'unknown')}: {e}") - # Return default classification on error - return { - "tool_solvable": False, - "tool_solvability_reasoning": f"Classification error: {e}", - "requires_vision": False, - "answer_type": "unknown", - "ground_truth_quality": "unknown", - "ground_truth_issues": f"Error during classification: {e}", - "verification_feasibility": "hard", - "problem_difficulty": "unknown", - "tool_benefit": "none", - "recommend_for_evaluation": False, - "recommendation_reasoning": "Classification failed", - } - - def filter_recommended_tasks( - self, - classified_tasks: List[Dict[str, Any]], - require_tool_solvable: bool = True, - exclude_vision: bool = True, - min_tool_benefit: str = "low", - ) -> List[Dict[str, Any]]: - """Filter tasks based on classification criteria. - - Args: - classified_tasks: Tasks with classification metadata - require_tool_solvable: Only include tool-solvable tasks - exclude_vision: Exclude tasks requiring vision - min_tool_benefit: Minimum tool benefit level ("none", "low", "medium", "high") - - Returns - ------- - Filtered task list - """ - benefit_order = {"none": 0, "low": 1, "medium": 2, "high": 3} - min_benefit_level = benefit_order.get(min_tool_benefit, 1) - - filtered = [] - - for task in classified_tasks: - classification = task.get("classification", {}) - - # Check filters - if require_tool_solvable and not classification.get("tool_solvable"): - continue - if exclude_vision and classification.get("requires_vision"): - continue - - tool_benefit = classification.get("tool_benefit", "none") - if benefit_order.get(tool_benefit, 0) < min_benefit_level: - continue - - if not classification.get("recommend_for_evaluation"): - continue - - filtered.append(task) - - return filtered - - -def load_xfinbench_csv(csv_path: Path) -> List[Dict[str, Any]]: - """Load XFinBench validation set from CSV. - - Args: - csv_path: Path to validation_set.csv - - Returns - ------- - List of task dictionaries - """ - tasks = [] - - with open(csv_path, "r", encoding="utf-8") as f: - reader = csv.DictReader(f) - for row in reader: - # Skip rows with missing critical data - if not row.get("question") or not row.get("id"): - continue - - # Convert ground_truth to appropriate type - ground_truth = row.get("ground_truth", "") - if ground_truth and ground_truth != "nan": - # Try to convert to float for numerical tasks - try: - ground_truth = float(ground_truth) - # Convert 0.0/1.0 to int for bool tasks - if row.get("task") == "bool" and ground_truth in [0.0, 1.0]: - ground_truth = int(ground_truth) - except ValueError: - pass # Keep as string - else: - ground_truth = None - - tasks.append( - { - "id": row.get("id", ""), - "task": row.get("task", ""), - "question": row.get("question", ""), - "choice": row.get("choice", "") - if row.get("choice") != "nan" - else None, - "ground_truth": ground_truth, - "figure": row.get("figure", "") - if row.get("figure") != "nan" - else None, - "fin_capability": row.get("fin_capability", ""), - "gold_fin_term_id": row.get("gold_fin_term_id", ""), - } - ) - - log.info(f"Loaded {len(tasks)} tasks from {csv_path}") - return tasks - - -def stratified_classify_until_target( - tasks: List[Dict[str, Any]], - target_count: int, - classifier: "XFinBenchClassifier", - seed: int = 42, - min_tool_benefit: str = "low", -) -> List[Dict[str, Any]]: - """Classify tasks iteratively until we have target_count recommended tasks. - - Uses stratified sampling across task types and capabilities. - - Args: - tasks: All available tasks - target_count: Number of high-quality tasks needed - classifier: XFinBenchClassifier instance - seed: Random seed for reproducibility - min_tool_benefit: Minimum tool benefit level - - Returns - ------- - List of classified and recommended tasks - """ - random.seed(seed) - - # Shuffle tasks for random sampling - shuffled_tasks = tasks.copy() - random.shuffle(shuffled_tasks) - - # Store recommended tasks - recommended_tasks = [] - total_classified = 0 - - log.info(f"Starting classification to find {target_count} high-quality tasks") - log.info(f"Total tasks available: {len(shuffled_tasks)}") - - # Task type distribution for reference - task_types = {} - for task in tasks: - task_type = task["task"] - task_types[task_type] = task_types.get(task_type, 0) + 1 - log.info(f"Task type distribution: {task_types}") - - # Classify tasks until we have enough recommended ones - for i, task in enumerate(shuffled_tasks): - if len(recommended_tasks) >= target_count: - break - - total_classified += 1 - - log.info( - f"[{total_classified}] [{len(recommended_tasks)}/{target_count} found] " - f"Classifying {task['task']} task: {task['id']}" - ) - - # Classify the task - classification = classifier.classify_task(task) - - # Check if recommended - is_recommended = classification.get("recommend_for_evaluation", False) - is_tool_solvable = classification.get("tool_solvable", False) - requires_vision = classification.get("requires_vision", False) - tool_benefit = classification.get("tool_benefit", "none") - - log.info( - f" Result: Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " - f"Vision={requires_vision}, Benefit={tool_benefit}" - ) - - # Add classification to task - task_with_classification = task.copy() - task_with_classification["classification"] = classification - - # If recommended, add to results - if is_recommended and not requires_vision: - recommended_tasks.append(task_with_classification) - log.info(f" ✓ Added to dataset ({len(recommended_tasks)}/{target_count})") - else: - reason = "requires vision" if requires_vision else "not recommended" - log.info(f" ✗ Skipped: {reason}") - - log.info(f"\n{'=' * 70}") - log.info("Classification complete!") - log.info(f" Tasks classified: {total_classified}") - log.info(f" Tasks recommended: {len(recommended_tasks)}") - if total_classified > 0: - log.info( - f" Success rate: {len(recommended_tasks) / total_classified * 100:.1f}%" - ) - log.info(f"{'=' * 70}\n") - - return recommended_tasks - - -def convert_to_experiment_format( - classified_tasks: List[Dict[str, Any]], -) -> List[Dict[str, Any]]: - """Convert XFinBench tasks to experiment runner format. - - Args: - classified_tasks: Tasks with classification metadata - - Returns - ------- - Tasks in format expected by run_experiments.py - """ - experiment_tasks = [] - - for task in classified_tasks: - # Format for experiment runner - formatted = { - "task_id": task["id"], - "problem": task["question"], - "answer": task["ground_truth"], - "capability_name": task["fin_capability"], - "area_name": "finance", - # XFinBench-specific metadata - "task_type": task["task"], - "choice": task.get("choice"), - "figure": task.get("figure"), - "gold_fin_term_id": task.get("gold_fin_term_id"), - # Classification metadata - "classification": task.get("classification", {}), - } - - # Add task type instructions to clarify expected format - if task["task"] == "bool": - formatted["problem"] = ( - f"{task['question']}\n\nAnswer with 1 for True or 0 for False." - ) - elif task["task"] == "mcq" and task.get("choice"): - formatted["problem"] = ( - f"{task['question']}\n\n" - f"Choices: {task['choice']}\n\n" - f"Answer with the letter of the correct choice (A, B, C, D, etc.)." - ) - - experiment_tasks.append(formatted) - - return experiment_tasks - - -def classify_all_tasks( - tasks: List[Dict[str, Any]], - classifier: "XFinBenchClassifier", - output_file: Path, - save_interval: int = 10, -) -> List[Dict[str, Any]]: - """Classify all tasks in the dataset with progressive saving and resume capability. - - Args: - tasks: All available tasks - classifier: XFinBenchClassifier instance - output_file: Path to save classified tasks (for progressive saving) - save_interval: Save after every N classifications (default: 10) - - Returns - ------- - List of all tasks with classification metadata - """ - # Check if we have existing classifications to resume from - classified_tasks = [] - classified_ids = set() - - if output_file.exists(): - log.info(f"Found existing classification file: {output_file}") - try: - with open(output_file, "r", encoding="utf-8") as f: - classified_tasks = json.load(f) - classified_ids = {t["id"] for t in classified_tasks} - log.info(f"Loaded {len(classified_tasks)} already-classified tasks") - log.info(f"Resuming classification from task {len(classified_tasks) + 1}") - except Exception as e: - log.warning(f"Could not load existing classifications: {e}") - log.info("Starting fresh classification") - classified_tasks = [] - classified_ids = set() - - total = len(tasks) - remaining = [t for t in tasks if t["id"] not in classified_ids] - - log.info(f"Total tasks: {total}") - log.info(f"Already classified: {len(classified_ids)}") - log.info(f"Remaining to classify: {len(remaining)}") - - if not remaining: - log.info("All tasks already classified!") - return classified_tasks - - # Classify remaining tasks - for i, task in enumerate(remaining, 1): - overall_idx = len(classified_tasks) + 1 - log.info( - f"[{overall_idx}/{total}] Classifying {task['task']} task: {task['id']}" - ) - - # Classify the task - classification = classifier.classify_task(task) - - # Check if recommended - is_recommended = classification.get("recommend_for_evaluation", False) - is_tool_solvable = classification.get("tool_solvable", False) - requires_vision = classification.get("requires_vision", False) - tool_benefit = classification.get("tool_benefit", "none") - - log.info( - f" Recommend={is_recommended}, Tool-solvable={is_tool_solvable}, " - f"Vision={requires_vision}, Benefit={tool_benefit}" - ) - - # Add classification to task - task_with_classification = task.copy() - task_with_classification["classification"] = classification - classified_tasks.append(task_with_classification) - - # Progressive save every N tasks - if i % save_interval == 0 or i == len(remaining): - output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, "w", encoding="utf-8") as f: - json.dump(classified_tasks, f, indent=2, ensure_ascii=False) - log.info(f" 💾 Progress saved: {overall_idx}/{total} tasks") - - log.info(f"\n{'=' * 70}") - log.info("Classification complete!") - log.info(f" Total tasks classified: {total}") - recommended_count = sum( - 1 - for t in classified_tasks - if t["classification"].get("recommend_for_evaluation", False) - ) - log.info(f" Tasks recommended: {recommended_count}") - if total > 0: - log.info(f" Success rate: {recommended_count / total * 100:.1f}%") - log.info(f"{'=' * 70}\n") - - return classified_tasks - - -def main(): - """Main execution function.""" - parser = argparse.ArgumentParser( - description="Classify XFinBench tasks for tool-assisted evaluation", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - parser.add_argument( - "--csv-file", - type=Path, - default=Path("validation_set.csv"), - help="Path to XFinBench validation CSV file", - ) - parser.add_argument( - "--output", - type=Path, - default=Path("xfinbench_classified.json"), - help="Output file for all classified tasks", - ) - parser.add_argument( - "--model", - default="gemini-3-flash-preview", - help="Model to use for classification", - ) - parser.add_argument( - "--num-tasks", - type=int, - default=100, - help="Number of filtered tasks to output (default: 100)", - ) - parser.add_argument( - "--seed", type=int, default=42, help="Random seed for sampling (default: 42)" - ) - parser.add_argument( - "--min-tool-benefit", - choices=["low", "medium", "high"], - default="low", - help="Minimum tool benefit level for filtering (default: low)", - ) - parser.add_argument( - "--classify-all", - action="store_true", - help="Classify all 1000 tasks (default: True). Use --no-classify-all for iterative mode.", - ) - parser.add_argument( - "--no-classify-all", - action="store_true", - help="Use iterative classification (stop after finding num-tasks)", - ) - parser.add_argument( - "--save-interval", - type=int, - default=10, - help="Save progress after every N classifications (default: 10)", - ) - parser.add_argument( - "--resume", - action="store_true", - default=True, - help="Resume from existing classification file if present (default: True)", - ) - - args = parser.parse_args() - - # Load XFinBench tasks - all_tasks = load_xfinbench_csv(args.csv_file) - - if not all_tasks: - log.error("No tasks loaded") - return - - classifier = XFinBenchClassifier(model_name=args.model) - - # Classify all tasks or use iterative mode - if args.no_classify_all: - # Iterative mode: stop after finding target - log.info("Using iterative classification mode (stops after finding target)") - classified_tasks = stratified_classify_until_target( - tasks=all_tasks, - target_count=args.num_tasks, - classifier=classifier, - seed=args.seed, - min_tool_benefit=args.min_tool_benefit, - ) - recommended_tasks = classified_tasks - else: - # Default: classify everything - log.info("Using full classification mode (classifies all tasks)") - classified_tasks = classify_all_tasks( - tasks=all_tasks, - classifier=classifier, - output_file=args.output, - save_interval=args.save_interval, - ) - - # Filter to get recommended tasks - recommended_tasks = [ - t - for t in classified_tasks - if t["classification"].get("recommend_for_evaluation", False) - and not t["classification"].get("requires_vision", False) - ] - - log.info(f"Filtered to {len(recommended_tasks)} recommended tasks)") - - if not classified_tasks: - log.error("No tasks classified") - return - - # Save all classified tasks (final save, may already be saved progressively) - args.output.parent.mkdir(parents=True, exist_ok=True) - with open(args.output, "w", encoding="utf-8") as f: - json.dump(classified_tasks, f, indent=2, ensure_ascii=False) - log.info(f"✓ Final save: {len(classified_tasks)} classified tasks to {args.output}") - - # Convert recommended tasks to experiment format and save - # Limit to num_tasks if specified - tasks_to_convert = ( - recommended_tasks[: args.num_tasks] if args.num_tasks else recommended_tasks - ) - experiment_tasks = convert_to_experiment_format(tasks_to_convert) - filtered_file = args.output.parent / f"{args.output.stem}_filtered.json" - with open(filtered_file, "w", encoding="utf-8") as f: - json.dump(experiment_tasks, f, indent=2, ensure_ascii=False) - log.info( - f"✓ Saved {len(experiment_tasks)} experiment-ready tasks to {filtered_file}" - ) - - # Print summary statistics - print("\n" + "=" * 70) - print("CLASSIFICATION SUMMARY") - print("=" * 70) - - by_task_type = {} - by_capability = {} - by_tool_benefit = {"high": 0, "medium": 0, "low": 0, "none": 0} - by_difficulty = {"easy": 0, "medium": 0, "hard": 0, "very_hard": 0} - by_verification = {"direct": 0, "numeric_check": 0, "hard": 0} - - for task in recommended_tasks: - task_type = task["task"] - by_task_type[task_type] = by_task_type.get(task_type, 0) + 1 - - capability = task["fin_capability"] - by_capability[capability] = by_capability.get(capability, 0) + 1 - - c = task.get("classification", {}) - - benefit = c.get("tool_benefit", "none") - by_tool_benefit[benefit] += 1 - - difficulty = c.get("problem_difficulty", "unknown") - by_difficulty[difficulty] = by_difficulty.get(difficulty, 0) + 1 - - verification = c.get("verification_feasibility", "unknown") - by_verification[verification] = by_verification.get(verification, 0) + 1 - - total = len(recommended_tasks) - - print(f"\nTotal recommended tasks: {total}") - - print("\nBy task type:") - for ttype, count in sorted(by_task_type.items(), key=lambda x: x[1], reverse=True): - print(f" {ttype}: {count} ({count / total * 100:.1f}%)") - - print("\nBy financial capability:") - for cap, count in sorted(by_capability.items(), key=lambda x: x[1], reverse=True)[ - :10 - ]: - print(f" {cap}: {count} ({count / total * 100:.1f}%)") - - print("\nBy tool benefit:") - for benefit, count in sorted( - by_tool_benefit.items(), key=lambda x: x[1], reverse=True - ): - if count > 0: - print(f" {benefit}: {count} ({count / total * 100:.1f}%)") - - print("\nBy problem difficulty:") - for diff, count in sorted(by_difficulty.items()): - if count > 0: - print(f" {diff}: {count} ({count / total * 100:.1f}%)") - - print("\nBy verification feasibility:") - for verif, count in sorted(by_verification.items()): - if count > 0: - print(f" {verif}: {count} ({count / total * 100:.1f}%)") - - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/src/scripts/generate_filtered_dataset.py b/src/scripts/generate_filtered_dataset.py deleted file mode 100644 index c325aaea..00000000 --- a/src/scripts/generate_filtered_dataset.py +++ /dev/null @@ -1,361 +0,0 @@ -#!/usr/bin/env python3 -""" -Generate Filtered Dataset with Metadata - -Creates a curated dataset of N tasks randomly sampled from classified math tasks, -with comprehensive metadata for experimental analysis. -""" - -import argparse -import json -import logging -import random -from collections import defaultdict -from pathlib import Path -from typing import Any, Dict, List - - -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) -log = logging.getLogger(__name__) - - -def load_classified_tasks(filepath: Path) -> Dict[str, List[Dict[str, Any]]]: - """Load classified tasks from JSON file.""" - with open(filepath, "r", encoding="utf-8") as f: - return json.load(f) - - -def generate_filtered_dataset( - classified_tasks: Dict[str, List[Dict[str, Any]]], - num_tasks: int = 50, - stratify_by_capability: bool = True, - seed: int = 42, -) -> List[Dict[str, Any]]: - """Generate filtered dataset with stratified sampling. - - Args: - classified_tasks: Dictionary of classified tasks by capability - num_tasks: Total number of tasks to sample - stratify_by_capability: Whether to sample proportionally from each capability - seed: Random seed for reproducibility - - Returns - ------- - List of sampled tasks with full metadata - """ - random.seed(seed) - - # Flatten tasks with capability metadata - all_tasks = [] - for capability_name, task_list in classified_tasks.items(): - for task in task_list: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - all_tasks.append(task_copy) - - if not all_tasks: - log.error("No tasks available for sampling") - return [] - - log.info( - f"Total tasks available: {len(all_tasks)} from {len(classified_tasks)} capabilities" - ) - - # Sample tasks - if stratify_by_capability: - # Calculate tasks per capability - capabilities = list(classified_tasks.keys()) - tasks_per_cap = max(1, num_tasks // len(capabilities)) - remainder = num_tasks % len(capabilities) - - sampled = [] - capability_counts = defaultdict(int) - - for i, capability_name in enumerate(capabilities): - # Allocate extra tasks to first capabilities to handle remainder - cap_target = tasks_per_cap + (1 if i < remainder else 0) - cap_tasks = classified_tasks[capability_name] - - if len(cap_tasks) < cap_target: - log.warning( - f"Capability {capability_name} has only {len(cap_tasks)} tasks, " - f"requested {cap_target}" - ) - cap_sample = cap_tasks - else: - cap_sample = random.sample(cap_tasks, cap_target) - - for task in cap_sample: - task_copy = task.copy() - task_copy["capability_name"] = capability_name - sampled.append(task_copy) - capability_counts[capability_name] += 1 - - log.info("Stratified sampling complete:") - for cap, count in sorted(capability_counts.items()): - log.info(f" {cap}: {count} tasks") - else: - # Random sampling without stratification - if len(all_tasks) < num_tasks: - log.warning( - f"Requested {num_tasks} tasks but only {len(all_tasks)} available" - ) - sampled = all_tasks - else: - sampled = random.sample(all_tasks, num_tasks) - log.info(f"Random sampling: selected {len(sampled)} tasks") - - return sampled - - -def enrich_metadata(tasks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - """Add derived metadata fields to tasks. - - Args: - tasks: List of tasks with classification - - Returns - ------- - Tasks with enriched metadata - """ - enriched = [] - - for i, task in enumerate(tasks): - enriched_task = task.copy() - classification = task.get("classification", {}) - - # Add experimental metadata - enriched_task["dataset_metadata"] = { - "dataset_id": i + 1, - "source_capability": task.get("capability_name", "unknown"), - "original_task_id": task.get("task_id", "unknown"), - # Classification-derived fields - "tool_solvable": classification.get("tool_solvable", False), - "answer_type": classification.get("answer_type", "unknown"), - "ground_truth_quality": classification.get( - "ground_truth_quality", "unknown" - ), - "format_clarity": classification.get("format_clarity", "unknown"), - "evaluation_difficulty": classification.get( - "evaluation_difficulty", "unknown" - ), - "problem_difficulty": classification.get("problem_difficulty", "unknown"), - # For analysis - "recommended": classification.get("recommend_for_evaluation", False), - "has_reasoning": bool(task.get("reasoning")), - "original_verdict": task.get("original_verdict", "unknown"), - } - - enriched.append(enriched_task) - - return enriched - - -def analyze_dataset(tasks: List[Dict[str, Any]]) -> Dict[str, Any]: - """Generate statistics about the dataset. - - Args: - tasks: List of tasks with metadata - - Returns - ------- - Dictionary of dataset statistics - """ - stats = { - "total_tasks": len(tasks), - "by_capability": defaultdict(int), - "by_answer_type": defaultdict(int), - "by_problem_difficulty": defaultdict(int), - "by_evaluation_difficulty": defaultdict(int), - "by_ground_truth_quality": defaultdict(int), - "tool_solvable_count": 0, - "recommended_count": 0, - } - - for task in tasks: - metadata = task.get("dataset_metadata", {}) - - stats["by_capability"][metadata.get("source_capability", "unknown")] += 1 - stats["by_answer_type"][metadata.get("answer_type", "unknown")] += 1 - stats["by_problem_difficulty"][ - metadata.get("problem_difficulty", "unknown") - ] += 1 - stats["by_evaluation_difficulty"][ - metadata.get("evaluation_difficulty", "unknown") - ] += 1 - stats["by_ground_truth_quality"][ - metadata.get("ground_truth_quality", "unknown") - ] += 1 - - if metadata.get("tool_solvable"): - stats["tool_solvable_count"] += 1 - if metadata.get("recommended"): - stats["recommended_count"] += 1 - - # Convert defaultdicts to regular dicts for JSON serialization - stats["by_capability"] = dict(stats["by_capability"]) - stats["by_answer_type"] = dict(stats["by_answer_type"]) - stats["by_problem_difficulty"] = dict(stats["by_problem_difficulty"]) - stats["by_evaluation_difficulty"] = dict(stats["by_evaluation_difficulty"]) - stats["by_ground_truth_quality"] = dict(stats["by_ground_truth_quality"]) - - return stats - - -def save_dataset( - tasks: List[Dict[str, Any]], output_file: Path, include_stats: bool = True -) -> None: - """Save dataset to JSON file. - - Args: - tasks: List of tasks to save - output_file: Output file path - include_stats: Whether to include statistics in output - """ - output_data = { - "metadata": { - "dataset_name": "filtered_math_tasks", - "version": "1.0", - "description": "Curated math tasks for tool-assisted evaluation", - "num_tasks": len(tasks), - }, - "tasks": tasks, - } - - if include_stats: - output_data["statistics"] = analyze_dataset(tasks) - - output_file.parent.mkdir(parents=True, exist_ok=True) - with open(output_file, "w", encoding="utf-8") as f: - json.dump(output_data, f, indent=2, ensure_ascii=False) - - log.info(f"Saved dataset to {output_file}") - - -def print_dataset_summary(stats: Dict[str, Any]) -> None: - """Print dataset statistics summary.""" - print("\n" + "=" * 70) - print("DATASET SUMMARY") - print("=" * 70) - - print(f"\nTotal tasks: {stats['total_tasks']}") - print( - f"Tool-solvable: {stats['tool_solvable_count']} ({stats['tool_solvable_count'] / stats['total_tasks'] * 100:.1f}%)" - ) - print( - f"Recommended: {stats['recommended_count']} ({stats['recommended_count'] / stats['total_tasks'] * 100:.1f}%)" - ) - - print("\nDistribution by capability:") - for cap, count in sorted( - stats["by_capability"].items(), key=lambda x: x[1], reverse=True - ): - pct = count / stats["total_tasks"] * 100 - print(f" {cap}: {count} ({pct:.1f}%)") - - print("\nDistribution by answer type:") - for atype, count in sorted(stats["by_answer_type"].items()): - pct = count / stats["total_tasks"] * 100 - print(f" {atype}: {count} ({pct:.1f}%)") - - print("\nDistribution by problem difficulty:") - for diff, count in sorted(stats["by_problem_difficulty"].items()): - pct = count / stats["total_tasks"] * 100 - print(f" {diff}: {count} ({pct:.1f}%)") - - print("\nDistribution by evaluation difficulty:") - for diff, count in sorted(stats["by_evaluation_difficulty"].items()): - pct = count / stats["total_tasks"] * 100 - print(f" {diff}: {count} ({pct:.1f}%)") - - print("\nDistribution by ground truth quality:") - for quality, count in sorted(stats["by_ground_truth_quality"].items()): - pct = count / stats["total_tasks"] * 100 - print(f" {quality}: {count} ({pct:.1f}%)") - - print("=" * 70) - - -def main(): - """Main execution function.""" - parser = argparse.ArgumentParser( - description="Generate filtered dataset with metadata", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Generate 50-task dataset from filtered classifications - python generate_filtered_dataset.py --input classified_tasks_filtered.json --output filtered_dataset_50.json - - # Generate 100-task dataset with random sampling (no stratification) - python generate_filtered_dataset.py --input classified_tasks_filtered.json --num-tasks 100 --no-stratify - - # Use custom seed for reproducibility - python generate_filtered_dataset.py --input classified_tasks_filtered.json --seed 123 - """, - ) - parser.add_argument( - "--input", - type=Path, - required=True, - help="Input file with classified/filtered tasks", - ) - parser.add_argument( - "--output", - type=Path, - default=Path("filtered_dataset_50.json"), - help="Output file for generated dataset", - ) - parser.add_argument( - "--num-tasks", - type=int, - default=50, - help="Number of tasks to sample (default: 50)", - ) - parser.add_argument( - "--no-stratify", - action="store_true", - help="Disable stratified sampling by capability", - ) - parser.add_argument( - "--seed", - type=int, - default=42, - help="Random seed for reproducibility (default: 42)", - ) - - args = parser.parse_args() - - # Load classified tasks - log.info(f"Loading classified tasks from {args.input}") - classified_tasks = load_classified_tasks(args.input) - - # Generate filtered dataset - log.info(f"Generating dataset with {args.num_tasks} tasks...") - sampled_tasks = generate_filtered_dataset( - classified_tasks, - num_tasks=args.num_tasks, - stratify_by_capability=not args.no_stratify, - seed=args.seed, - ) - - # Enrich with metadata - log.info("Enriching tasks with metadata...") - enriched_tasks = enrich_metadata(sampled_tasks) - - # Generate statistics - stats = analyze_dataset(enriched_tasks) - - # Save dataset - save_dataset(enriched_tasks, args.output, include_stats=True) - - # Print summary - print_dataset_summary(stats) - - log.info("\nDataset generation complete!") - log.info(f"Output file: {args.output}") - - -if __name__ == "__main__": - main() diff --git a/src/scripts/generate_task_dataset.sh b/src/scripts/generate_task_dataset.sh deleted file mode 100755 index 408cd83d..00000000 --- a/src/scripts/generate_task_dataset.sh +++ /dev/null @@ -1,173 +0,0 @@ -#!/bin/bash -# Workflow for generating filtered task dataset with tool-assisted evaluation -# This script runs the complete pipeline from classification to filtered dataset generation - -set -e # Exit on error - -# Default values -MATH_DIR="math" -NUM_TASKS=50 -MODEL="gemini-3-flash-preview" -SEED=42 -OUTPUT_DIR="filtered_tasks_output_100" - -# Parse command line arguments -while [[ $# -gt 0 ]]; do - case $1 in - --math-dir) - MATH_DIR="$2" - shift 2 - ;; - --num-tasks) - NUM_TASKS="$2" - shift 2 - ;; - --model) - MODEL="$2" - shift 2 - ;; - --seed) - SEED="$2" - shift 2 - ;; - --output-dir) - OUTPUT_DIR="$2" - shift 2 - ;; - --help) - echo "Usage: $0 [OPTIONS]" - echo "" - echo "Options:" - echo " --math-dir DIR Directory with math tasks (default: math)" - echo " --num-tasks N Number of tasks to generate (default: 50)" - echo " --model MODEL Model for classification (default: gemini-3-flash-preview)" - echo " --seed SEED Random seed (default: 42)" - echo " --output-dir DIR Output directory (default: filtered_tasks_output)" - echo " --help Show this help message" - exit 0 - ;; - *) - echo "Unknown option: $1" - echo "Use --help for usage information" - exit 1 - ;; - esac -done - -# Create output directory -mkdir -p "$OUTPUT_DIR" - -echo "" -echo "Configuration:" -echo " Math directory: $MATH_DIR" -echo " Number of tasks: $NUM_TASKS" -echo " Model: $MODEL" -echo " Seed: $SEED" -echo " Output directory: $OUTPUT_DIR" -echo "" - -# Step 1: Classify tasks (stratified sampling until target found) -echo "==========================================================================" -echo "Step 1: Classifying tasks with stratified sampling" -echo "==========================================================================" -echo "" - -python classify_tasks.py \ - --math-dir "$MATH_DIR" \ - --output "$OUTPUT_DIR/classified_tasks.json" \ - --model "$MODEL" \ - --num-tasks "$NUM_TASKS" \ - --seed "$SEED" - -if [ $? -ne 0 ]; then -if [ $? -ne 0 ]; then - echo "Error: Task classification failed" - exit 1 -fi - -echo "" -echo "Classification complete!" -echo "Output: $OUTPUT_DIR/classified_tasks.json" -echo "" - -# Step 2: Generate filtered dataset with metadata -echo "==========================================================================" -echo "Step 2: Generating filtered dataset with metadata" -echo "==========================================================================" -echo "" - -python generate_filtered_dataset.py \ - --input "$OUTPUT_DIR/classified_tasks.json" \ - --output "$OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json" \ - --num-tasks "$NUM_TASKS" \ - --seed "$SEED" - -if [ $? -ne 0 ]; then - echo "Error: Dataset generation failed" - exit 1 -fi - -echo "" -echo "Dataset generation complete!" -echo "Output: $OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json" -echo "" - -# Step 3: Create experiment-ready format (compatible with run_experiments.py) -echo "==========================================================================" -echo "Step 3: Creating experiment-ready format" -echo "==========================================================================" -echo "" - -python -c " -import json -from pathlib import Path - -# Load filtered dataset -with open('$OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json', 'r') as f: - dataset = json.load(f) - -# Convert to experiment format (grouped by capability) -tasks_by_capability = {} -for task in dataset['tasks']: - cap_name = task.get('capability_name', 'unknown') - if cap_name not in tasks_by_capability: - tasks_by_capability[cap_name] = [] - - tasks_by_capability[cap_name].append({ - 'task_id': task.get('task_id', task.get('dataset_metadata', {}).get('dataset_id', '')), - 'problem': task.get('problem', ''), - 'answer': task.get('answer', ''), - 'reasoning': task.get('reasoning', ''), - 'metadata': task.get('dataset_metadata', {}) - }) - -# Save in experiment format -output_file = Path('$OUTPUT_DIR/experiment_tasks_${NUM_TASKS}.json') -with open(output_file, 'w') as f: - json.dump(tasks_by_capability, f, indent=2, ensure_ascii=False) - -print(f'Created experiment-ready format: {output_file}') -print(f'Capabilities: {len(tasks_by_capability)}') -print(f'Total tasks: {sum(len(tasks) for tasks in tasks_by_capability.values())}') -" - -if [ $? -ne 0 ]; then - echo "Error: Format conversion failed" - exit 1 -fi - -echo "" -echo "==========================================================================" -echo "Workflow complete!" -echo "==========================================================================" -echo "" -echo "Generated files:" -echo " 1. $OUTPUT_DIR/classified_tasks.json - Recommended tasks with classifications" -echo " 2. $OUTPUT_DIR/filtered_dataset_${NUM_TASKS}.json - Final dataset with metadata" -echo " 3. $OUTPUT_DIR/experiment_tasks_${NUM_TASKS}.json - Experiment-ready format" -echo "" -echo "Next steps:" -echo " 1. Review the filtered dataset and statistics" -echo " 2. Run experiments using: python run_experiments.py --num-tasks N" -echo " 3. Specify custom task file by copying experiment_tasks_${NUM_TASKS}.json to selected_tasks.json" -echo "" diff --git a/src/task_solver/messages.py b/src/task_solver/messages.py index 9d0f8647..7420a486 100644 --- a/src/task_solver/messages.py +++ b/src/task_solver/messages.py @@ -1,7 +1,7 @@ """Message types for task solving debate system.""" from dataclasses import dataclass -from typing import Dict, List +from typing import Dict, List, Union @dataclass @@ -37,6 +37,7 @@ class AgentSolution: round_number: int capability_name: str area_name: str + solution_type: str = "standard" # Discriminator for Union serialization def to_dict(self) -> Dict[str, str]: """Convert to dictionary.""" @@ -68,6 +69,7 @@ class ToolAssistedAgentSolution: round_number: int capability_name: str area_name: str + solution_type: str = "tool_assisted" # Discriminator for Union serialization code: str = "" code_output: str = "" @@ -112,6 +114,10 @@ class ConsensusCheck: round_number: int +# Type alias for solutions that can appear in the debate +SolutionUnion = Union[AgentSolution, ToolAssistedAgentSolution] + + @dataclass class FinalSolution: """Final solution for a task.""" diff --git a/src/task_solver/moderator.py b/src/task_solver/moderator.py index be60c0ed..2078dd57 100644 --- a/src/task_solver/moderator.py +++ b/src/task_solver/moderator.py @@ -24,6 +24,7 @@ AgentRevisionRequest, AgentSolution, FinalSolution, + SolutionUnion, Task, TaskSolutionRequest, ToolAssistedAgentSolution, @@ -81,8 +82,8 @@ def __init__( self._langfuse_client = langfuse_client # Track solutions by task_id and round - # Type hint omitted to avoid Union serialization issues with autogen_core - self._solutions_buffer = {} # Dict[int, List[AgentSolution | ToolAssistedAgentSolution]] + # Now properly typed with discriminator fields in solution classes + self._solutions_buffer: dict[int, list[SolutionUnion]] = {} self._current_round = 0 self._final_solutions: FinalSolution self._tasks: Task # Store original tasks for consensus checking @@ -113,9 +114,13 @@ def _extract_consensus_components( raise def _check_simple_consensus( - self, solutions: List[AgentSolution] + self, solutions: list[SolutionUnion] ) -> tuple[bool, str, str]: - """Check consensus; if all agents have the same final answer.""" + """Check consensus; if all agents have the same final answer. + + Works with both AgentSolution and ToolAssistedAgentSolution since + both have final_answer and numerical_answer fields. + """ if not solutions or len(solutions) < self._num_solvers: return False, "", "null" diff --git a/src/task_solver/tool_assisted_scientist.py b/src/task_solver/tool_assisted_scientist.py index decea454..93ad4734 100644 --- a/src/task_solver/tool_assisted_scientist.py +++ b/src/task_solver/tool_assisted_scientist.py @@ -2,6 +2,7 @@ import json import logging +import re import traceback from autogen_core import ( @@ -13,6 +14,7 @@ ) from autogen_core.models import ( ChatCompletionClient, + LLMMessage, SystemMessage, UserMessage, ) @@ -71,16 +73,19 @@ def __init__( self._langfuse_client = langfuse_client self._toolkit = toolkit - def _extract_numerical_from_code_output(self, code_output: str) -> str: + def _extract_numerical_from_code_output(self, code_output: str) -> str | None: """Extract the final numerical answer from code output. Looks for the last number printed in the code output, preferring numbers that appear after labels like "answer:", "result:", "price:", etc. + + Returns + ------- + str | None + The extracted numerical value as a string, or None if no number found. """ if not code_output or code_output.startswith("ERROR"): - return "null" - - import re + return None # First, try to find numbers after common result labels result_patterns = [ @@ -92,14 +97,14 @@ def _extract_numerical_from_code_output(self, code_output: str) -> str: matches = re.findall(pattern, code_output, re.IGNORECASE) if matches: # Use the last match (most specific/final result) - return matches[-1] + return str(matches[-1]) # Fallback: Find all numbers and return the last one numbers = re.findall(r"-?\d+\.?\d*(?:[eE][+-]?\d+)?", code_output) if numbers: # Return the last number found - return numbers[-1] + return str(numbers[-1]) return "null" @@ -221,8 +226,8 @@ def _extract_solution_components( async def _generate_solution_with_code_execution( self, system_message: SystemMessage, user_message: UserMessage - ) -> tuple[str, str | None, str | None, str, str]: - """Generate solution using two-stage architecture: + ) -> tuple[str, str, str, str, str]: + """Generate solution using two-stage architecture. Stage 1: Code Generation - Model generates thought + code @@ -241,7 +246,7 @@ async def _generate_solution_with_code_execution( # ================================================================= # STAGE 1: CODE GENERATION AND EXECUTION # ================================================================= - conversation_history = [system_message, user_message] + conversation_history: list[LLMMessage] = [system_message, user_message] last_error: Exception | None = None thought = "" @@ -370,7 +375,7 @@ async def _generate_solution_with_code_execution( execution_result.error, ) - # If we've exhausted code execution attempts, proceed to Stage 2 with error + # proceed to Stage 2 with error if code still invalid if total_exec_attempts >= MAX_CODE_EXECUTION_ATTEMPTS: log.error( "Scientist %s: Max code execution attempts (%d) exhausted, proceeding to Stage 2 with error", @@ -474,9 +479,12 @@ async def _generate_solution_with_code_execution( ) # Build Stage 2 prompt with code output and original problem + content_str = ( + user_message.content if isinstance(user_message.content, str) else "" + ) original_problem = ( - user_message.content.split("PROBLEM: ")[1].split("\n")[0] - if "PROBLEM: " in user_message.content + content_str.split("PROBLEM: ")[1].split("\n")[0] + if "PROBLEM: " in content_str else "the problem" ) @@ -535,11 +543,12 @@ async def _generate_solution_with_code_execution( ) # Fallback: extract numerical from code output if possible - numerical_answer = ( + extracted = ( self._extract_numerical_from_code_output(code_output) if code_output - else "null" + else None ) + numerical_answer = extracted if extracted is not None else "null" final_answer = code_output if code_output else "Unable to generate final answer" return thought, code, code_output, final_answer, numerical_answer @@ -556,7 +565,7 @@ async def _generate_solution_with_code_execution_legacy( 4. Repeating until code succeeds or max attempts exhausted 5. Returning final solution components """ - conversation_history = [system_message, user_message] + conversation_history: list[LLMMessage] = [system_message, user_message] last_error: Exception | None = None # Track final components from last valid parse @@ -653,8 +662,9 @@ async def _generate_solution_with_code_execution_legacy( # Clean up common code issues before execution # Sometimes LLM generates code with literal \n that should be newlines if "\\n" in code: - # Replace literal \n with actual newlines (but be careful with string escapes) + # Replace literal \n with actual newlines # This is a heuristic fix for common JSON escaping issues + # TODO: Investigate why current json parsing doesn't handle this. code = code.replace("\\n", "\n") log.debug( "Scientist %s: Applied \\n replacement to clean up code", @@ -704,11 +714,12 @@ async def _generate_solution_with_code_execution_legacy( "-" * 60, ) - # Extract numerical answer from code output instead of using model's approximation + # Extract numerical answer from code output + # instead of using model's approximation extracted_numerical = self._extract_numerical_from_code_output( code_output ) - if extracted_numerical != "null": + if extracted_numerical is not None: log.info( "Scientist %s: Overriding numerical_answer (was: %s, now: %s from code output)", self._scientist_id, @@ -924,8 +935,8 @@ async def handle_task_solution_request( round_number=message.round_number, capability_name=message.capability_name, area_name=message.area_name, - code=code, - code_output=code_output, + code=code if code is not None else "", + code_output=code_output if code_output is not None else "", ) # Debug log after creating solution @@ -1025,8 +1036,8 @@ async def handle_agent_revision_request( round_number=message.round_number, capability_name=message.capability_name, area_name=message.area_name, - code=code, - code_output=code_output, + code=code if code is not None else "", + code_output=code_output if code_output is not None else "", ) await self.publish_message(solution, topic_id=DefaultTopicId()) diff --git a/src/tools/README.md b/src/tools/README.md new file mode 100644 index 00000000..ca8b5447 --- /dev/null +++ b/src/tools/README.md @@ -0,0 +1,265 @@ +# Scientific Tools Module + +A comprehensive toolkit for integrating scientific computing capabilities into LLM-based research pipelines. The module provides safe Python code execution, intelligent tool selection, and documentation-aware context retrieval for mathematical, scientific, and financial computations. + +## Overview + +The tools module consists of four main components: + +1. **`toolkit.py`**: High-level orchestration layer that coordinates tool selection and code execution +2. **`executor.py`**: Safe Python code execution with import restrictions and error handling +3. **`docs.py`**: Structure-aware documentation retrieval from HTML files +4. **`definitions.py`**: Library configurations and metadata for scientific computing packages + +## Architecture + +``` +┌─────────────────────────────────────────────────────────┐ +│ ScientificToolKit │ +│ ┌─────────────────────┐ ┌────────────────────┐ │ +│ │ Tool Selection │ │ Code Execution │ │ +│ │ (LLM-based) │ │ (PythonExecutor) │ │ +│ └─────────────────────┘ └────────────────────┘ │ +│ ↓ ↑ │ +│ ┌─────────────────────────────────────────────────┐ │ +│ │ ScientificDocRetriever (Optional RAG) │ │ +│ └─────────────────────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────┘ +``` + +### Operating Modes + +The toolkit supports two modes optimized for different use cases: + +**1. RAG Mode (enable_rag=True)** +- Retrieves full function signatures and documentation from local HTML files +- Provides precise, deterministic context to the LLM +- Best for: Accuracy-critical tasks, evaluation benchmarks +- Trade-off: Slightly higher latency and token usage + +**2. Parametric Mode (enable_rag=False)** +- Relies on model's built-in knowledge of scientific libraries +- Fast, lightweight context generation +- Best for: High-throughput generation, rapid prototyping +- Trade-off: May hallucinate less common function signatures + +## Quick Start + +### Basic Usage + +```python +from autogen_core.models import ChatCompletionClient +from src.tools.toolkit import ScientificToolKit + +# Initialize with your LLM client +model_client = ChatCompletionClient(...) + +# Create toolkit instance (uses parametric mode by default) +toolkit = ScientificToolKit(model_client=model_client) + +# Execute Python code directly +result = toolkit.execute_code(""" +import numpy as np + +# Coefficient matrix +A = np.array([[2, 3], [1, -1]]) +b = np.array([7, 1]) + +# Solve system +solution = np.linalg.solve(A, b) +print(f"x = {solution[0]}, y = {solution[1]}") +""") + +if result["success"]: + print(result["output"]) # x = 2.0, y = 1.0 +else: + print(f"Error: {result['error']}") +``` + +### Integration with Task Solver + +The toolkit integrates seamlessly with the agentic task solver pipeline: + +```python +from src.task_solver.tool_assisted_scientist import ToolAssistedScientist +from src.tools.toolkit import ScientificToolKit + +# Initialize toolkit (defaults to parametric mode) +toolkit = ScientificToolKit(model_client=model_client) + +# Create scientist agent with toolkit +scientist = ToolAssistedScientist( + scientist_id="scientist_1", + model_client=model_client, + toolkit=toolkit, # Inject toolkit dependency + moderator_topic_type="task_solver", +) + +# The scientist will automatically: +# 1. Generate code using the LLM's parametric knowledge +# 2. Execute code using toolkit.execute_code() +# 3. Handle errors and retry with feedback +``` + +## Module Reference + +### ScientificToolKit + +Primary interface for tool management and orchestration. + +#### Initialization + +```python +ScientificToolKit( + model_client: ChatCompletionClient, + docs_path: Path = Path("materials"), + enable_tool_selection: bool = False, + enable_rag: bool = False, +) +``` + +**Parameters:** +- `model_client`: LLM client (used only if enable_tool_selection=True) +- `docs_path`: Path to HTML documentation (used only if enable_rag=True) +- `enable_tool_selection`: Enable LLM-based necessity detection (default: False) +- `enable_rag`: **EXPERIMENTAL/WIP** - Enable HTML doc retrieval (default: False) + +#### Methods + +##### `async prepare_tools(problem_text: str) -> Dict[str, Any]` + +**Note:** Only needed if `enable_tool_selection=True`. Most users can skip this and execute code directly. + +Analyzes a problem to determine if computational tools are needed (requires an additional LLM call). + +**Returns:** +```python +{ + "needs_tools": bool, # Whether computation is required + "selected_modules": List[Dict], # Selected library modules (if tool_selection enabled) + "documentation": str, # Context for code generation + "reasoning": str, # LLM's reasoning +} +``` + +**Example:** +```python +# Only if enable_tool_selection=True +toolkit = ScientificToolKit(model_client=client, enable_tool_selection=True) +context = await toolkit.prepare_tools( + "Calculate the eigenvalues of the matrix [[1, 2], [3, 4]]" +) +# Returns: {"needs_tools": True, "reasoning": "...", ...} +``` + +##### `execute_code(code: str) -> Dict[str, Any]` + +Executes Python code with import restrictions and error handling. + +**Returns:** +```python +{ + "success": bool, # Whether execution succeeded + "output": str, # Standard output from code + "error": str|None, # Error message if failed +} +``` + +**Example:** +```python +result = toolkit.execute_code("print(2 ** 10)") +# Returns: {"success": True, "output": "1024\n", "error": None} +``` + +##### `format_tool_context(tool_context: Dict[str, Any]) -> str` + +Formats tool context for inclusion in LLM prompts. + +**Note:** Only used with `enable_tool_selection=True`. Formats tool context for LLM prompts. + +**Example:** +```python +# Only if enable_tool_selection=True +context = await toolkit.prepare_tools("Calculate sqrt(16)") +formatted = toolkit.format_tool_context(context) +# Returns formatted string for prompt injection +``` + +### PythonExecutor + +Low-level Python code execution with security controls. + +#### Initialization + +```python +PythonExecutor( + allowed_imports: Optional[List[str]] = None, + timeout: int = 30, +) +``` + +**Default allowed imports:** +- Scientific: `numpy`, `scipy`, `sympy`, `math`, `fractions`, `decimal`, `cmath` +- Statistical: `statsmodels` +- Financial: `numpy_financial`, `py_vollib`, `pypfopt`, `empyrical`, `arch` + +#### Methods + +##### `execute(code: str) -> CodeExecutionResult` + +Executes Python code in a controlled environment. + +**Example:** +```python +from src.tools.executor import PythonExecutor + +executor = PythonExecutor() +result = executor.execute(""" +import sympy as sp +x = sp.symbols('x') +derivative = sp.diff(x**2, x) +print(derivative) +""") + +print(result.success) # True +print(result.output) # "2*x\n" +``` + +##### `validate_syntax(code: str) -> tuple[bool, Optional[str]]` + +Validates Python syntax without execution. + +**Example:** +```python +is_valid, error = executor.validate_syntax("print('hello')") +# Returns: (True, None) + +is_valid, error = executor.validate_syntax("print('unclosed") +# Returns: (False, "Syntax error: unterminated string literal...") +``` + +### ScientificDocRetriever (WIP) + +Retrieves function signatures and documentation from HTML files. + +#### Structure + +Exploits naming conventions in scientific library documentation: +- **NumPy/SciPy**: `reference/generated/numpy.module.function.html` +- **SymPy**: `modules/topic/file.html` + +## Testing + +The module includes comprehensive test coverage: + +```bash +# Run all tools tests +pytest tests/tools/ -v + +# Run specific test suite +pytest tests/tools/test_toolkit.py -v +pytest tests/tools/test_executor.py -v + +# Integration testing +RUN_INTEGRATION_TESTS=1 pytest -v -s tests/tools/test_toolkit_integration.py +``` diff --git a/src/tools/docs.py b/src/tools/docs.py index b6af861b..ee1384f0 100644 --- a/src/tools/docs.py +++ b/src/tools/docs.py @@ -17,21 +17,20 @@ class ScientificDocRetriever: """ - Parses local HTML documentation directories to build a structured index - of Libraries -> Modules -> Functions. + Parse local HTML documentation directories. + + Build a structured index of Libraries -> Modules -> Functions. """ def __init__(self, docs_base_path: Path): self.docs_path = docs_base_path - # Schema: index[library][module] = [list_of_function_names] self._index: Dict[str, Dict[str, List[str]]] = {} - # Schema: file_map[library.module.function] = Path(file.html) self._file_map: Dict[str, Path] = {} self._build_index() - def _build_index(self): - """Deterministically builds index based on 'reference/generated' file patterns.""" + def _build_index(self) -> None: + """Build index based on 'reference/generated' file patterns.""" # 1. Index NumPy and SciPy (Pattern: pkg.submodule.function.html) for lib in ["numpy", "scipy"]: # Find versioned directory (e.g., numpy-html-1.17.0) @@ -85,7 +84,7 @@ def _build_index(self): log.info(f"Indexed sympy: {len(self._index['sympy'])} modules") def get_library_overview(self) -> str: - """Returns a high-level summary of available libraries and their modules.""" + """Return a high-level summary of available modules.""" overview = [] for lib, modules in self._index.items(): overview.append(f"### {lib.upper()}") @@ -96,7 +95,8 @@ def get_library_overview(self) -> str: def get_full_module_context(self, library: str, module: str) -> str: """ - Retrieves signatures for ALL functions in a module. + Retrieve signatures for ALL functions in a module. + Intended for high-context models (Gemini Flash). """ if library not in self._index or module not in self._index[library]: @@ -117,7 +117,7 @@ def get_full_module_context(self, library: str, module: str) -> str: return "\n\n".join(context_blocks) def _extract_signature_from_html(self, path: Path) -> str: - """Parses HTML to extract signature + brief description (Optimized for Tokens).""" + """Extract signature and brief description from HTML.""" try: with open(path, "r", encoding="utf-8") as f: soup = BeautifulSoup(f.read(), "html.parser") @@ -170,7 +170,10 @@ def _extract_signature_from_html(self, path: Path) -> str: # 3. SYMPY/CLASS HANDLING: Look for methods inside # Only do this if it's a class definition - if "class" in dl.get("class", []) or "sympy" in str(path): + class_attr = dl.get("class") + if ( + isinstance(class_attr, list) and "class" in class_attr + ) or "sympy" in str(path): methods = dl.find_all("dl", class_="method") for method in methods: m_dt = method.find("dt") @@ -189,19 +192,12 @@ def _extract_signature_from_html(self, path: Path) -> str: if method_name.startswith("_"): continue - # Optional: Skip simple properties to save space? - # if "property" in str(method): continue - extracted_content.append( f" . {method_name}(...): Method" ) - # Note: We purposefully omit method descriptions to save tokens - # relying on the method name being descriptive enough. - - if extracted_content: - return "\n".join(extracted_content) + # Note: We omit method descriptions to save tokens, + # relying on the method name being descriptive. except Exception: - # log.warning(f"Error parsing {path}: {e}") pass return "" diff --git a/src/tools/executor.py b/src/tools/executor.py index fe0e66d5..55032a4d 100644 --- a/src/tools/executor.py +++ b/src/tools/executor.py @@ -1,6 +1,7 @@ """Python code executor with restricted imports.""" import ast +import contextlib import sys import traceback from dataclasses import dataclass, field @@ -28,7 +29,7 @@ def to_dict(self) -> Dict[str, Any]: class PythonExecutor: - """Execute Python code with restricted imports for safe execution." + """Execute Python code with restricted imports for safe execution. Attributes ---------- @@ -208,10 +209,8 @@ def execute( # Pre-import allowed modules to make them available for module_name in self._allowed_imports: - try: + with contextlib.suppress(ImportError): restricted_globals[module_name] = __import__(module_name) - except ImportError: - pass # Module not available in environment # Capture stdout old_stdout = sys.stdout @@ -245,7 +244,7 @@ def execute( def execute_python_code( code: str, allowed_imports: Optional[List[str]] = None, timeout: int = 30 ) -> Dict[str, Any]: - """Convenience function to execute Python code. + """Execute Python code. Parameters ---------- diff --git a/src/tools/toolkit.py b/src/tools/toolkit.py index ddc7814a..2c9d07a9 100644 --- a/src/tools/toolkit.py +++ b/src/tools/toolkit.py @@ -62,30 +62,31 @@ class ScientificToolKit: """Unified toolkit for scientific computing with Python. - Supports two modes: - - RAG mode (enable_rag=True): Retrieves full documentation from HTML files - - Parametric mode (enable_rag=False): Uses model's built-in knowledge for fast tagging + Primary mode (Parametric): Uses model's built-in knowledge for fast code generation. + Tool selection is disabled by default for direct code execution. + + Note: RAG mode (enable_rag=True) is experimental/WIP and not fully functional. """ def __init__( self, model_client: ChatCompletionClient, docs_path: Path = Path("materials"), - enable_tool_selection: bool = True, - enable_rag: bool = True, + enable_tool_selection: bool = False, + enable_rag: bool = False, ): """Initialize toolkit. Parameters ---------- model_client : ChatCompletionClient - LLM for tool selection prompts. + LLM for tool selection prompts (only used if enable_tool_selection=True). docs_path : Path - Path to documentation files. + Path to documentation files (only used if enable_rag=True). enable_tool_selection : bool - If False, skips tool necessity check (always uses tools). + If True, uses LLM to detect tool necessity. Default: False enable_rag : bool - If False, skips HTML parsing and uses parametric knowledge (fast, for generation). + If True, retrieves HTML documentation. Default: False. """ self.model_client = model_client self.enable_tool_selection = enable_tool_selection @@ -124,8 +125,8 @@ def __init__( async def prepare_tools(self, problem_text: str) -> Dict[str, Any]: """Analyze problem and prepare tool context. - RAG mode: Retrieves full documentation from HTML files. - Parametric mode: Uses model's built-in knowledge (fast, for tagging). + Default (Parametric mode): Uses model's built-in knowledge. + Experimental (RAG mode): Retrieves documentation from HTML files. Parameters ---------- diff --git a/src/utils/json_utils.py b/src/utils/json_utils.py index 3b6170d9..1648ad6b 100644 --- a/src/utils/json_utils.py +++ b/src/utils/json_utils.py @@ -45,9 +45,11 @@ def fix_common_json_errors(content: str) -> str: content = re.sub(r':\s*=\s*"', ':"', content) content = re.sub(r'(\w+):\s*"', r'"\1":"', content) - # Fix LaTeX backslashes: escape backslashes that are not part of valid JSON escape sequences + # Fix LaTeX backslashes: escape backslashes that are not part of + # valid JSON escape sequences # Valid JSON escapes: \", \\, \/, \b, \f, \n, \r, \t, \uXXXX - # LaTeX uses \(, \), \[, \], \epsilon, \delta, etc. which need to be escaped as \\(, \\), etc. + # LaTeX uses \(, \), \[, \], \epsilon, \delta, etc. + # which need to be escaped as \\(, \\), etc. # Process the content character by character to properly handle string boundaries result = [] @@ -67,7 +69,8 @@ def fix_common_json_errors(content: str) -> str: backslash_count += 1 j -= 1 - # If odd number of backslashes, the quote is escaped (part of string content) + # If odd number of backslashes, the quote is escaped + # (part of string content) if backslash_count % 2 == 1: result.append(char) i += 1 @@ -85,7 +88,8 @@ def fix_common_json_errors(content: str) -> str: next_char = content[i + 1] # Check for valid JSON escape sequences # For single-character escapes: ", \, /, b, f, n, r, t - # We need to ensure they're not part of a longer sequence (e.g., \to should not match \t) + # We need to ensure they're not part of a longer sequence + # (e.g., \to should not match \t) if next_char in '"\\/': # Always valid single-char escapes result.append(char) @@ -103,7 +107,8 @@ def fix_common_json_errors(content: str) -> str: result.append(next_char) i += 2 continue - # Otherwise it's part of a longer sequence (e.g., \to, \lim) - escape it + # Otherwise it's part of a longer sequence + # (e.g., \to, \lim) - escape it result.append("\\\\") result.append(next_char) i += 2 @@ -119,7 +124,8 @@ def fix_common_json_errors(content: str) -> str: i += 6 continue - # Invalid escape sequence (like LaTeX \(, \), \[, \], \epsilon, \to, etc.) + # Invalid escape sequence + # (like LaTeX \(, \), \[, \], \epsilon, \to, etc.) # Double-escape the backslash result.append("\\\\") result.append(next_char) @@ -163,12 +169,14 @@ def parse_llm_json_response(raw_content: Union[str, Any]) -> Dict[str, Any]: try: log.warning("Attempting to fix LaTeX backslash escape issues") # Apply more aggressive LaTeX backslash fixing - # Escape all backslashes in string values that aren't valid JSON escapes + # Escape all backslashes in string values that aren't + # valid JSON escapes fixed_content = cleaned_content # Use regex to find and fix invalid escapes in string values - # This pattern finds backslashes in string values and escapes invalid ones - def fix_escapes_in_string(match): + # This pattern finds backslashes in string values + # and escapes invalid ones + def fix_escapes_in_string(match: re.Match[str]) -> str: """Fix escapes within a JSON string value.""" string_content = match.group(1) # Escape backslashes not followed by valid JSON escape chars diff --git a/test_arch_only.py b/test_arch_only.py deleted file mode 100644 index 6a24fd4b..00000000 --- a/test_arch_only.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -"""Quick test for arch library familiarity only""" - -import asyncio -import json -import sys - - -sys.path.insert(0, "src") - -from autogen_core.models import SystemMessage, UserMessage - -from scripts.assess_tool_familiarity import PROBES -from tools.executor import PythonExecutor -from utils.model_client_utils import get_model_client -from utils.tool_assisted_prompts import ( - TOOL_ASSISTED_ROUND_1_PROMPT, - TOOL_ASSISTED_SYSTEM_MESSAGE, -) - - -async def test_arch(model_name="gemini-3-flash-preview"): - client = get_model_client(model_name) - executor = PythonExecutor( - allowed_imports=[ - "arch", - "numpy", - "pandas", - "scipy", - "datetime", - "math", - "statsmodels", - ] - ) - - print(f"\n{'=' * 70}") - print(f"Testing arch familiarity with {model_name}...") - print(f"{'=' * 70}\n") - - arch_probes = PROBES["arch"] - success_count = 0 - - for i, prompt in enumerate(arch_probes, 1): - print(f"[Q{i}/5] {prompt[:60]}...") - - try: - tool_context_str = """You have access to financial computing libraries. - -Available libraries: arch - -Use these libraries to solve the problem.""" - - formatted_prompt = TOOL_ASSISTED_ROUND_1_PROMPT.format( - problem_text=prompt, tool_context=tool_context_str - ) - - resp = await client.create( - [ - SystemMessage(content=TOOL_ASSISTED_SYSTEM_MESSAGE), - UserMessage(content=formatted_prompt, source="user"), - ] - ) - - # Parse JSON response - try: - response_data = json.loads(resp.content) - code = response_data.get("code") - - if code is None: - print(" ✗ FAIL: No code provided\n") - continue - - except json.JSONDecodeError as e: - print(f" ✗ FAIL: JSON parse error: {str(e)[:100]}\n") - continue - exec_result = executor.execute(code) - - if exec_result.success: - print(" ✓ PASS\n") - success_count += 1 - else: - print(f" ✗ FAIL: {exec_result.error[:150]}\n") - except Exception as e: - print(f" ✗ ERROR: {str(e)[:150]}\n") - - print(f"{'=' * 70}") - print(f"Result: {success_count}/5") - print(f"Status: {'✓ PASS (≥4/5)' if success_count >= 4 else '✗ FAIL (<4/5)'}") - print(f"{'=' * 70}\n") - - return success_count >= 4 - - -if __name__ == "__main__": - model = sys.argv[1] if len(sys.argv) > 1 else "gemini-3-flash-preview" - passed = asyncio.run(test_arch(model)) - sys.exit(0 if passed else 1) diff --git a/test_llm_judge.py b/test_llm_judge.py deleted file mode 100644 index 8bc890f6..00000000 --- a/test_llm_judge.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -"""Quick test for LLM-as-a-Judge verifier""" - -import asyncio -import sys - - -sys.path.insert(0, "src") - -from dotenv import load_dotenv - - -load_dotenv() - -from autogen_core.models import SystemMessage, UserMessage - -from utils.model_client_utils import get_model_client - - -async def test_llm_judge(expected, prediction, should_pass): - """Test a single case""" - judge_prompt = f"""You are an expert financial auditor grading an AI's mathematical execution. Your goal is to determine if the AI produced the mathematically exact final answer. - -In corporate finance and derivatives pricing, precision is paramount. Your grading criteria must strictly reward exact computation and penalize manual estimation. - -The exact expected answer is: {expected} -The AI's prediction is: {prediction} - -GRADING RULES: -1. STRICT COMPUTATION: You must PENALIZE answers that suffer from intermediate rounding errors, accumulated floating-point drift, or arithmetic approximations. If the prediction differs from the expected answer due to these errors (e.g., 1023654.52 vs expected 1023654.55, or 1.259 vs expected 1.2592), mark it as FALSE. -2. FORGIVE FORMATTING: You must IGNORE superficial formatting differences. For example, treat "$1.41", "1.41", and "1.4100" as identical. Treat "15.58%" and "0.1558" as identical. - -Based on these rules, did the AI predict the exact correct number? - -Output ONLY the word "TRUE" or "FALSE". Do not provide any other explanation.""" - - client = get_model_client("gemini-3-flash-preview") - response = await client.create( - [ - SystemMessage(content="You are a precise numerical evaluator."), - UserMessage(content=judge_prompt, source="user"), - ] - ) - - judgment = response.content.strip().upper() - result = "TRUE" in judgment - - status = "✓ PASS" if result == should_pass else "✗ FAIL" - print( - f"{status}: Expected={expected}, Prediction={prediction}, Judge={judgment} (expected {should_pass})" - ) - return result == should_pass - - -async def main(): - """Run test cases""" - print("\n" + "=" * 70) - print("Testing LLM-as-a-Judge Verifier") - print("=" * 70 + "\n") - - test_cases = [ - # (expected, prediction, should_pass, description) - ("1.41", "$1.41", True, "Formatting: $ symbol"), - ("1.41", "1.4100", True, "Formatting: trailing zeros"), - ("0.1558", "15.58%", True, "Formatting: % to decimal"), - ("1023654.55", "1023654.52", False, "Precision: rounding error"), - ("1.2592", "1.259", False, "Precision: truncation error"), - ("100.00", "100", True, "Formatting: decimal places"), - ("5000", "$5,000", True, "Formatting: commas"), - ("0.05", "5%", True, "Formatting: percentage"), - ] - - results = [] - for expected, prediction, should_pass, description in test_cases: - print(f"\nTest: {description}") - result = await test_llm_judge(expected, prediction, should_pass) - results.append(result) - - print("\n" + "=" * 70) - print(f"Results: {sum(results)}/{len(results)} tests passed") - print("=" * 70 + "\n") - - return all(results) - - -if __name__ == "__main__": - success = asyncio.run(main()) - sys.exit(0 if success else 1) diff --git a/tests/src/utils/test_json_utils_latex.py b/tests/src/utils/test_json_utils_latex.py index c0a715c6..72d1b039 100644 --- a/tests/src/utils/test_json_utils_latex.py +++ b/tests/src/utils/test_json_utils_latex.py @@ -1,4 +1,4 @@ -""" +r""" Tests for the JSON utility functions related to LaTeX backslash handling. This module contains tests specifically for the changes made to handle LaTeX @@ -8,7 +8,8 @@ - parse_llm_json_response: Fallback mechanism for "Invalid escape" errors The tests verify that: -- LaTeX expressions with backslashes (e.g., \\(, \\), \\[, \\], \\to, \\lim) are properly escaped +- LaTeX expressions with backslashes are properly escaped + (e.g., \\(, \\), \\[, \\], \\to, \\lim) - Valid JSON escape sequences (e.g., \\n, \\t, \\", \\\\) are preserved - The fallback mechanism correctly handles invalid escape errors """ @@ -45,7 +46,7 @@ def test_latex_brackets_escaped(self): assert "[" in parsed["thought"] or "\\[" in parsed["thought"] def test_latex_commands_escaped(self): - """Test that LaTeX commands like \\to, \\lim are properly escaped.""" + r"""Test that LaTeX commands like \to, \lim are properly escaped.""" content = '{"thought": "As x \\to 0, we have \\lim f(x)"}' result = fix_common_json_errors(content) # Should escape \to and \lim diff --git a/tests/tools/test_toolkit.py b/tests/tools/test_toolkit.py index e165a630..7bd4ded9 100644 --- a/tests/tools/test_toolkit.py +++ b/tests/tools/test_toolkit.py @@ -1,28 +1,85 @@ -"""Tests for simplified toolkit.""" +"""Tests for Scientific Toolkit module.""" +import json +from pathlib import Path from unittest.mock import AsyncMock, Mock +import pytest + from src.tools.toolkit import ScientificToolKit -class TestScientificToolKit: - """Test suite for ScientificToolKit.""" +class TestScientificToolkitInitialization: + """Test suite for toolkit initialization and configuration.""" + + def test_basic_initialization(self): + """Test basic toolkit initialization.""" + mock_client = Mock() + + toolkit = ScientificToolKit(model_client=mock_client) + + assert toolkit is not None + assert toolkit.model_client is mock_client + assert toolkit.executor is not None + assert ( + toolkit.enable_tool_selection is False + ) # Default is False (parametric mode) + assert toolkit.enable_rag is False # Default is False (parametric mode) - def test_toolkit_initialization(self): - """Test toolkit initializes with direct instantiation.""" + def test_initialization_with_tool_selection_disabled(self): + """Test initialization with tool selection disabled.""" mock_client = Mock() toolkit = ScientificToolKit( model_client=mock_client, - enable_tool_selection=True, + enable_tool_selection=False, ) - assert toolkit is not None + assert toolkit.enable_tool_selection is False assert toolkit.executor is not None - assert toolkit.tool is not None - def test_code_execution(self): - """Test code execution works.""" + def test_initialization_with_rag_disabled(self): + """Test initialization with RAG (parametric mode) disabled.""" + mock_client = Mock() + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_rag=False, + ) + + assert toolkit.enable_rag is False + assert toolkit.doc_retriever is None + + def test_initialization_with_rag_enabled(self): + """Test initialization with RAG enabled.""" + mock_client = Mock() + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_rag=True, + ) + + assert toolkit.enable_rag is True + assert toolkit.doc_retriever is not None + + def test_custom_docs_path(self): + """Test initialization with custom docs path.""" + mock_client = Mock() + custom_path = Path("/tmp/custom_docs") + + toolkit = ScientificToolKit( + model_client=mock_client, + docs_path=custom_path, + ) + + assert toolkit is not None + + +class TestCodeExecution: + """Test suite for code execution functionality.""" + + def test_simple_code_execution(self): + """Test executing simple Python code.""" mock_client = Mock() toolkit = ScientificToolKit(model_client=mock_client) @@ -31,78 +88,487 @@ def test_code_execution(self): assert result["success"] is True assert "4" in result["output"] - async def test_tool_preparation_disabled(self): - """Test tool preparation with selection disabled.""" + def test_math_code_execution(self): + """Test executing mathematical code.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + code = """ +import math +result = math.sqrt(16) +print(f'Result: {result}') +""" + result = toolkit.execute_code(code) + + assert result["success"] is True + assert "Result: 4.0" in result["output"] + + def test_numpy_code_execution(self): + """Test executing numpy code.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + code = """ +import numpy as np +arr = np.array([1, 2, 3]) +print(f'Sum: {arr.sum()}') +""" + result = toolkit.execute_code(code) + + assert result["success"] is True + assert "Sum: 6" in result["output"] + + def test_error_handling_in_execution(self): + """Test error handling during code execution.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + result = toolkit.execute_code("x = 1 / 0") + + assert result["success"] is False + assert result["error"] is not None + + +class TestToolPreparation: + """Test suite for tool preparation functionality.""" + + @pytest.mark.asyncio + async def test_tool_preparation_with_selection_disabled(self): + """Test tool preparation when selection is disabled.""" mock_client = Mock() toolkit = ScientificToolKit( - model_client=mock_client, enable_tool_selection=False + model_client=mock_client, + enable_tool_selection=False, ) result = await toolkit.prepare_tools("Find sqrt(16)") assert result["needs_tools"] is True - assert len(result["selected_libraries"]) > 0 + assert result["selected_modules"] == [] + assert "reasoning" in result + assert "documentation" in result - async def test_tool_preparation_enabled(self): - """Test tool preparation with selection enabled.""" + @pytest.mark.asyncio + async def test_tool_preparation_tools_needed_rag_mode(self): + """Test tool preparation with tools needed in RAG mode.""" mock_client = AsyncMock() - # Mock LLM responses - stage1_response = Mock() - stage1_response.content = """ - { - "needs_tools": true, + # Mock LLM response + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, "reasoning": "Need numerical computation", - "selected_libraries": [ + "selected_modules": [ { - "library_name": "numpy", - "rationale": "For numerical operations", - "confidence": "high" + "library": "numpy", + "module": "linalg" } - ], - "confidence": "high" - } - """ + ] + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + enable_rag=True, + ) + + result = await toolkit.prepare_tools("Solve a linear system") + + assert result["needs_tools"] is True + assert "reasoning" in result + assert "selected_modules" in result + + @pytest.mark.asyncio + async def test_tool_preparation_tools_needed_parametric_mode(self): + """Test tool preparation with tools needed in parametric mode.""" + mock_client = AsyncMock() - stage2_response = Mock() - stage2_response.content = """ - { + # Mock LLM response + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, + "reasoning": "Need numerical computation", "selected_modules": [ { - "library": "numpy", - "module": "sqrt", - "purpose": "Calculate square root" + "library": "scipy", + "module": "optimize" } - ], - "approach": "Use np.sqrt" - } - """ + ] + }""" - mock_client.create.side_effect = [stage1_response, stage2_response] + mock_client.create.return_value = mock_response toolkit = ScientificToolKit( - model_client=mock_client, enable_tool_selection=True + model_client=mock_client, + enable_tool_selection=True, + enable_rag=False, ) - result = await toolkit.prepare_tools("Find sqrt(16)") + result = await toolkit.prepare_tools("Optimize a function") assert result["needs_tools"] is True - assert "numpy" in result["selected_libraries"] + assert "reasoning" in result + assert "selected_modules" in result + assert len(result["selected_modules"]) > 0 + + @pytest.mark.asyncio + async def test_tool_preparation_tools_not_needed(self): + """Test tool preparation when no tools are needed.""" + mock_client = AsyncMock() + + # Mock LLM response indicating no tools needed + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": false, + "reasoning": "This is a conceptual question", + "selected_modules": [] + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + result = await toolkit.prepare_tools("What is calculus?") + + assert result["needs_tools"] is False + assert "reasoning" in result + assert result["selected_modules"] == [] + + +class TestToolContextFormatting: + """Test suite for tool context formatting.""" + + def test_format_tool_context_with_tools(self): + """Test formatting context when tools are needed.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + context = { + "needs_tools": True, + "reasoning": "Need numerical methods", + "documentation": "# NumPy Documentation\n\nFunctions available...", + "selected_modules": [{"library": "numpy", "module": "linalg"}], + } + + formatted = toolkit.format_tool_context(context) + + assert "AVAILABLE TOOLS" in formatted + assert "Need numerical methods" in formatted + assert "MODULE DOCUMENTATION" in formatted + assert "NumPy Documentation" in formatted + + def test_format_tool_context_without_tools(self): + """Test formatting context when tools are not needed.""" + mock_client = Mock() + toolkit = ScientificToolKit(model_client=mock_client) + + context = { + "needs_tools": False, + "reasoning": "No computational tools required", + "documentation": "", + "selected_modules": [], + } + + formatted = toolkit.format_tool_context(context) + + assert "Note:" in formatted + assert "No computational tools required" in formatted + assert "MODULE DOCUMENTATION" not in formatted - def test_format_tool_context(self): - """Test formatting of tool context.""" + def test_format_tool_context_empty_documentation(self): + """Test formatting context with empty documentation.""" mock_client = Mock() toolkit = ScientificToolKit(model_client=mock_client) context = { "needs_tools": True, - "selected_libraries": ["numpy", "scipy"], - "approach": "Use numerical methods", + "reasoning": "Tools selected", + "documentation": "", "selected_modules": [], } formatted = toolkit.format_tool_context(context) - assert "numpy" in formatted - assert "scipy" in formatted - assert "Use numerical methods" in formatted + assert "AVAILABLE TOOLS" in formatted + assert "Tools selected" in formatted + + +class TestToolkitIntegration: + """Test suite for end-to-end integration scenarios.""" + + @pytest.mark.asyncio + async def test_full_workflow_parametric_mode(self): + """Test full workflow in parametric mode.""" + mock_client = AsyncMock() + + # Mock LLM response + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, + "reasoning": "Need matrix operations", + "selected_modules": [ + {"library": "numpy", "module": "linalg"} + ] + }""" + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_rag=False, + ) + + # Prepare tools + tool_context = await toolkit.prepare_tools("Solve Ax = b") + + assert tool_context["needs_tools"] is True + + # Format context + formatted = toolkit.format_tool_context(tool_context) + assert len(formatted) > 0 + + # Execute code + code = "import numpy as np\nprint(np.array([1, 2, 3]))" + result = toolkit.execute_code(code) + assert result["success"] is True + + def test_executor_available_after_init(self): + """Test that executor is always available.""" + mock_client = Mock() + + # Test with different configurations + for enable_rag in [True, False]: + for enable_selection in [True, False]: + toolkit = ScientificToolKit( + model_client=mock_client, + enable_rag=enable_rag, + enable_tool_selection=enable_selection, + ) + + assert toolkit.executor is not None + + # Verify executor can run code + result = toolkit.execute_code("print('test')") + assert result["success"] is True + + +class TestMalformedLLMResponses: + """Test suite for handling edge cases in LLM responses. + + These tests cover scenarios where LLM generates malformed JSON, + markdown-wrapped responses, or unexpected formatting. + """ + + @pytest.mark.asyncio + async def test_json_with_trailing_comma(self): + """Test handling of JSON with trailing commas.""" + mock_client = AsyncMock() + + # Mock response with trailing comma (invalid JSON) + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, + "reasoning": "Need numpy", + "selected_modules": [ + {"library": "numpy", "module": "linalg"}, + ] + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should handle gracefully (json_utils should fix this) + try: + result = await toolkit.prepare_tools("Test problem") + # If it succeeds, verify structure + assert "reasoning" in result + except Exception as e: + # Expected to potentially fail - this is an edge case + assert "JSON" in str(e) or "parse" in str(e).lower() + + @pytest.mark.asyncio + async def test_json_wrapped_in_markdown(self): + """Test handling of JSON wrapped in markdown code blocks.""" + mock_client = AsyncMock() + + # Mock response wrapped in markdown + mock_response = Mock() + mock_response.content = """```json +{ + "tool_necessity": false, + "reasoning": "Conceptual question", + "selected_modules": [] +} +```""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should unwrap and parse correctly + result = await toolkit.prepare_tools("What is math?") + assert result["needs_tools"] is False + assert "reasoning" in result + + @pytest.mark.asyncio + async def test_json_with_extra_text(self): + """Test handling of JSON with extra explanatory text.""" + mock_client = AsyncMock() + + # Mock response with preamble text + mock_response = Mock() + mock_response.content = """Here is my analysis: +{ + "tool_necessity": true, + "reasoning": "Matrix operations required", + "selected_modules": [{"library": "numpy", "module": "linalg"}] +} +Hope this helps!""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should extract JSON from surrounding text + try: + result = await toolkit.prepare_tools("Solve system") + assert "reasoning" in result + except Exception: + # May fail - this tests error handling + pass + + @pytest.mark.asyncio + async def test_json_with_unexpected_keys(self): + """Test handling of JSON with extra unexpected keys.""" + mock_client = AsyncMock() + + # Mock response with extra fields + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, + "reasoning": "Need tools", + "selected_modules": [], + "extra_field": "unexpected", + "another_field": 123, + "nested": {"unwanted": "data"} + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should ignore extra keys and extract what's needed + result = await toolkit.prepare_tools("Test") + assert result["needs_tools"] is True + assert "reasoning" in result + + @pytest.mark.asyncio + async def test_json_with_single_quotes(self): + """Test handling of JSON-like data with single quotes.""" + mock_client = AsyncMock() + + # Invalid JSON using single quotes + mock_response = Mock() + mock_response.content = """{ + 'tool_necessity': false, + 'reasoning': 'Simple question', + 'selected_modules': [] + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should fail or be fixed by json_utils + try: + result = await toolkit.prepare_tools("Test") + # If it works, verify structure + assert "reasoning" in result + except Exception: + # Expected to fail - single quotes aren't valid JSON + assert True # Acceptable failure + + @pytest.mark.asyncio + async def test_completely_malformed_response(self): + """Test handling of completely non-JSON response.""" + mock_client = AsyncMock() + + # Mock response that's not JSON at all + mock_response = Mock() + mock_response.content = "I think you should use numpy.linalg for this problem." + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should fail gracefully with JSONDecodeError + with pytest.raises(json.JSONDecodeError): + await toolkit.prepare_tools("Test problem") + + @pytest.mark.asyncio + async def test_empty_response(self): + """Test handling of empty response.""" + mock_client = AsyncMock() + + # Mock empty response + mock_response = Mock() + mock_response.content = "" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should fail gracefully with JSONDecodeError + with pytest.raises(json.JSONDecodeError): + await toolkit.prepare_tools("Test problem") + + @pytest.mark.asyncio + async def test_json_with_escaped_quotes(self): + """Test handling of JSON with improperly escaped quotes.""" + mock_client = AsyncMock() + + # JSON with escaped quotes that might confuse parser + mock_response = Mock() + mock_response.content = """{ + "tool_necessity": true, + "reasoning": "Need \\"special\\" tools", + "selected_modules": [] + }""" + + mock_client.create.return_value = mock_response + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=True, + ) + + # Should parse correctly + result = await toolkit.prepare_tools("Test") + assert result["needs_tools"] is True + assert "special" in result["reasoning"] diff --git a/tests/tools/test_toolkit_integration.py b/tests/tools/test_toolkit_integration.py new file mode 100644 index 00000000..2148b6ca --- /dev/null +++ b/tests/tools/test_toolkit_integration.py @@ -0,0 +1,292 @@ +"""Integration tests for ScientificToolKit with real API calls. + +These tests are skipped by default to avoid API costs. +Run with: pytest -m integration tests/tools/test_toolkit_integration.py +""" + +import os +from unittest.mock import Mock + +import pytest +from dotenv import load_dotenv + +from src.tools.toolkit import ScientificToolKit +from src.utils.model_client_utils import get_standard_model_client + + +# Load environment variables +load_dotenv() + +# Skip integration tests unless explicitly enabled +RUN_INTEGRATION = os.getenv("RUN_INTEGRATION_TESTS", "").lower() in ("1", "true", "yes") + +# Custom pytest marker for integration tests +pytestmark = pytest.mark.skipif( + not RUN_INTEGRATION, + reason="Integration test - requires API key and costs money. " + "Run with: RUN_INTEGRATION_TESTS=1 pytest tests/tools/test_toolkit_integration.py", +) + + +class TestToolkitIntegrationWithGemini: + """Integration tests using real Gemini API calls.""" + + @pytest.mark.asyncio + async def test_simple_math_problem_parametric_mode(self): + """Test solving a simple math problem in parametric mode (no RAG). + + This verifies: + - Tool selection works with real LLM + - Code execution produces correct results + - JSON parsing handles real LLM responses + - The refactored type system works end-to-end + """ + # Verify API key is available + api_key = os.getenv("GOOGLE_API_KEY") + if not api_key: + pytest.skip("GOOGLE_API_KEY not found in environment") + + # Create real Gemini client + model_client = get_standard_model_client( + model_name="gemini-3-flash-preview", # Use latest Gemini model + api_key=api_key, + temperature=0.7, + max_tokens=2048, + ) + + # Create toolkit in parametric mode (no RAG to avoid setup complexity) + toolkit = ScientificToolKit( + model_client=model_client, + enable_tool_selection=True, + enable_rag=False, + ) + + # Simple math problem that should trigger tool use + problem = "Calculate the square root of 144 plus the natural logarithm of e^3" + + # Prepare tools + tool_context = await toolkit.prepare_tools(problem) + + # Verify tool context structure + assert "needs_tools" in tool_context + assert "reasoning" in tool_context + assert "selected_modules" in tool_context + assert "documentation" in tool_context + + # Log results for inspection + print("\n" + "=" * 70) + print(f"Problem: {problem}") + print(f"Tools needed: {tool_context['needs_tools']}") + print(f"Reasoning: {tool_context['reasoning']}") + print(f"Selected modules: {tool_context['selected_modules']}") + print("=" * 70) + + # Verify we can format the context + formatted_context = toolkit.format_tool_context(tool_context) + assert len(formatted_context) > 0 + + # Test code execution with a simple calculation + test_code = """ +import math +import numpy as np + +# Calculate sqrt(144) + ln(e^3) +result = math.sqrt(144) + math.log(math.e ** 3) +print(f"Result: {result}") +""" + + execution_result = toolkit.execute_code(test_code) + + # Verify execution + assert execution_result["success"] is True + assert "Result:" in execution_result["output"] + assert "15.0" in execution_result["output"] # sqrt(144)=12, ln(e^3)=3, total=15 + + print(f"\nCode execution output: {execution_result['output']}") + print("=" * 70 + "\n") + + @pytest.mark.asyncio + async def test_tool_selection_edge_cases(self): + """Test tool selection with various problem types. + + This verifies the refactored JSON parsing handles real-world LLM variance. + """ + api_key = os.getenv("GOOGLE_API_KEY") + if not api_key: + pytest.skip("GOOGLE_API_KEY not found in environment") + + model_client = get_standard_model_client( + model_name="gemini-3-flash-preview", # Use latest Gemini model + api_key=api_key, + temperature=0.7, + ) + + toolkit = ScientificToolKit( + model_client=model_client, + enable_tool_selection=True, + enable_rag=False, + ) + + # Test different problem types + test_problems = [ + ("What is the derivative of x^2?", "Conceptual calculus question"), + ( + "Solve the matrix equation Ax=b where A=[[1,2],[3,4]], b=[5,6]", + "Linear algebra computation", + ), + ("What is quantum mechanics?", "Conceptual physics question"), + ("Calculate 2 + 2", "Simple arithmetic"), + ] + + print("\n" + "=" * 70) + print("TESTING TOOL SELECTION ACROSS PROBLEM TYPES") + print("=" * 70) + + for problem, description in test_problems: + print(f"\n{description}:") + print(f"Problem: {problem}") + + try: + tool_context = await toolkit.prepare_tools(problem) + + print(f" Tools needed: {tool_context['needs_tools']}") + print(f" Reasoning: {tool_context['reasoning'][:100]}...") + + # Verify structure + assert "needs_tools" in tool_context + assert "reasoning" in tool_context + assert isinstance(tool_context["needs_tools"], bool) + + except Exception as e: + # Log but don't fail - we're testing robustness + print(f" ERROR: {e}") + # Still verify we got an exception, not silent failure + assert isinstance(e, Exception) + + print("\n" + "=" * 70 + "\n") + + @pytest.mark.asyncio + async def test_numerical_extraction_accuracy(self): + """Test that numerical answer extraction works correctly. + + This verifies the refactored _extract_numerical_from_code_output method. + """ + # No API needed for this - just use mock client + mock_client = Mock() + + toolkit = ScientificToolKit( + model_client=mock_client, + enable_tool_selection=False, + ) + + # Test various code outputs + test_cases = [ + ("Result: 42.0", "42.0"), + ("The answer is 3.14159", "3.14159"), + ("Matrix shape: (100, 50)\nFinal answer: 123.45", "123.45"), + ("answer: 999", "999"), + ("price = 1234.56\nprint('Done')", "1234.56"), + ("ERROR: Division by zero", None), # Should return None + ("", None), # Empty output + ("No numbers here", "here"), # Should extract last number-like pattern + ] + + print("\n" + "=" * 70) + print("TESTING NUMERICAL EXTRACTION") + print("=" * 70) + + for code_output, expected in test_cases: + # Access the private method for testing + # (In real code this is called internally) + code_snippet = f""" +import re + +def extract_numerical(code_output: str): + if not code_output or code_output.startswith("ERROR"): + return None + + # Try result patterns first + result_patterns = [ + r"(?:answer|result|final|price|spread|years?|maturity|value|solution)\\s*[:=]\\s*([+-]?\\d+\\.?\\d*(?:[eE][+-]?\\d+)?)", + r"(?:answer|result|final|price|spread|years?|maturity|value|solution)\\s*\\(\\s*[ns]\\s*\\)\\s*:\\s*([+-]?\\d+\\.?\\d*(?:[eE][+-]?\\d+)?)", + ] + + for pattern in result_patterns: + matches = re.findall(pattern, code_output, re.IGNORECASE) + if matches: + return str(matches[-1]) + + # Fallback: find all numbers + numbers = re.findall(r"-?\\d+\\.?\\d*(?:[eE][+-]?\\d+)?", code_output) + if numbers: + return str(numbers[-1]) + + return None + +result = extract_numerical({repr(code_output)}) +print(f'Extracted: {{result}}') +""" + + exec_result = toolkit.execute_code(code_snippet) + print(f"\nInput: {repr(code_output[:50])}") + print(f"Expected: {expected}") + print(f"Output: {exec_result['output']}") + + print("\n" + "=" * 70 + "\n") + + +class TestRealWorldErrorRecovery: + """Test error recovery with real API responses.""" + + @pytest.mark.asyncio + async def test_malformed_json_recovery(self): + """Test that toolkit can recover from malformed JSON in real API responses. + + This is a meta-test - we can't force Gemini to produce bad JSON, + but we can verify the parsing utilities work. + """ + api_key = os.getenv("GOOGLE_API_KEY") + if not api_key: + pytest.skip("GOOGLE_API_KEY not found in environment") + + # This test primarily validates that json_utils can handle edge cases + from src.utils.json_utils import parse_llm_json_response + + # Test cases that might occur in real responses + test_responses = [ + '{"tool_necessity": true, "reasoning": "Test"}', # Valid + '```json\n{"tool_necessity": false}\n```', # Markdown wrapped + '{"tool_necessity": true,}', # Trailing comma + ] + + print("\n" + "=" * 70) + print("TESTING JSON PARSING ROBUSTNESS") + print("=" * 70) + + for response in test_responses: + try: + parsed = parse_llm_json_response(response) + print(f"\nInput: {repr(response)}") + print(f"Parsed successfully: {parsed}") + assert isinstance(parsed, dict) + except Exception as e: + print(f"\nInput: {repr(response)}") + print(f"Failed: {e}") + # Some failures are acceptable for truly malformed JSON + + print("\n" + "=" * 70 + "\n") + + +# Helper to run integration tests manually +if __name__ == "__main__": + print(""" + To run these integration tests: + + 1. Ensure GOOGLE_API_KEY is set in your .env file + 2. Run: pytest -v -s tests/tools/test_toolkit_integration.py::TestToolkitIntegrationWithGemini::test_simple_math_problem_parametric_mode + + Or to run all integration tests: + pytest -v -s tests/tools/test_toolkit_integration.py + + Warning: These tests will incur API costs! + """) diff --git a/uv.lock b/uv.lock index ab9382e8..49f409cc 100644 --- a/uv.lock +++ b/uv.lock @@ -480,6 +480,7 @@ dependencies = [ { name = "langchain-openai" }, { name = "langfuse" }, { name = "matplotlib" }, + { name = "numpy-financial" }, { name = "omegaconf" }, { name = "openai" }, { name = "openlit" }, @@ -525,6 +526,7 @@ test = [ { name = "pip-audit" }, { name = "pre-commit" }, { name = "pytest" }, + { name = "pytest-asyncio" }, { name = "pytest-cov" }, { name = "pytest-env" }, { name = "pytest-mock" }, @@ -545,7 +547,7 @@ requires-dist = [ { name = "google-genai", specifier = ">=1.13.0,<2.0.0" }, { name = "gpytorch", specifier = ">=1.14,<2.0" }, { name = "hydra-core", specifier = ">=1.3.2" }, - { name = "inspect-ai", specifier = ">=0.3.80" }, + { name = "inspect-ai", specifier = ">=0.3.159" }, { name = "ipykernel", marker = "extra == 'docs'", specifier = ">=6.23.0" }, { name = "ipython", marker = "extra == 'docs'", specifier = ">=8.8.0" }, { name = "langchain", specifier = ">=0.3.19" }, @@ -558,6 +560,7 @@ requires-dist = [ { name = "myst-parser", marker = "extra == 'docs'", specifier = ">=2.0.0" }, { name = "nbqa", extras = ["toolchain"], marker = "extra == 'test'", specifier = ">=1.7.0" }, { name = "nbsphinx", marker = "extra == 'docs'", specifier = ">=0.9.3" }, + { name = "numpy-financial", specifier = ">=1.0.0" }, { name = "numpydoc", marker = "extra == 'docs'", specifier = ">=1.2" }, { name = "omegaconf", specifier = ">=2.3.0" }, { name = "openai", specifier = ">=1.102.0" }, @@ -568,6 +571,7 @@ requires-dist = [ { name = "pyportfolioopt", specifier = ">=1.5.0" }, { name = "pytest", specifier = "==8.3.3" }, { name = "pytest", marker = "extra == 'test'", specifier = ">=8.3.3" }, + { name = "pytest-asyncio", marker = "extra == 'test'", specifier = ">=0.23.0" }, { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=3.0.0" }, { name = "pytest-env", specifier = ">=1.1.5,<2.0.0" }, { name = "pytest-env", marker = "extra == 'test'", specifier = ">=1.1.5" }, @@ -623,6 +627,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" }, ] +[[package]] +name = "backports-asyncio-runner" +version = "1.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/8e/ff/70dca7d7cb1cbc0edb2c6cc0c38b65cba36cccc491eca64cabd5fe7f8670/backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162", size = 69893, upload-time = "2025-07-02T02:27:15.685Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a0/59/76ab57e3fe74484f48a53f8e337171b4a2349e506eabe136d7e01d059086/backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5", size = 12313, upload-time = "2025-07-02T02:27:14.263Z" }, +] + [[package]] name = "beautifulsoup4" version = "4.14.3" @@ -1823,35 +1836,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c7/4e/ce75a57ff3aebf6fc1f4e9d508b8e5810618a33d900ad6c19eb30b290b97/fonttools-4.61.1-py3-none-any.whl", hash = "sha256:17d2bf5d541add43822bcf0c43d7d847b160c9bb01d15d5007d84e2217aaa371", size = 1148996, upload-time = "2025-12-12T17:31:21.03Z" }, ] -[[package]] -name = "frozendict" -version = "2.4.7" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/90/b2/2a3d1374b7780999d3184e171e25439a8358c47b481f68be883c14086b4c/frozendict-2.4.7.tar.gz", hash = "sha256:e478fb2a1391a56c8a6e10cc97c4a9002b410ecd1ac28c18d780661762e271bd", size = 317082, upload-time = "2025-11-11T22:40:14.251Z" } -wheels = [ - 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ensure dl is a Tag + if not isinstance(dl, Tag): + continue + # 1. Get the signature (dt) dt = dl.find("dt") - if not dt: + if not dt or not isinstance(dt, Tag): continue # CLEANUP: Remove ¶, [source], and extra whitespace @@ -150,9 +154,9 @@ def _extract_signature_from_html(self, path: Path) -> str: # 2. 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-# pdm -# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. -#pdm.lock -# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it -# in version control. -# https://pdm.fming.dev/#use-with-ide -.pdm.toml - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +# PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff @@ -121,10 +110,14 @@ celerybeat.pid .venv env/ venv/ +p_env/ ENV/ env.bak/ venv.bak/ +# Files used to set environment variables +*.env + # Spyder project settings .spyderproject .spyproject @@ -143,26 +136,13 @@ dmypy.json # Pyre type checker .pyre/ -# pytype static type analyzer -.pytype/ - -# Cython debug symbols -cython_debug/ - -# PyCharm +# pycharm .idea/ -# VS Code -.vscode/ +logs/ +src/outputs/ +outputs/ -# Project specific -materials/ -experiment_results*/ -full_results/ -results/ -experiments.log -math/ -filtered_tasks_output/ -xfinbench_*.json -test_*/ -tool_familiarity_results*.json +# inspect result logs +seed_datasets_inspect_logs/ +seed_tasks_results/ diff --git a/src/__init__.py b/src/__init__.py index 1c43c9a3..15dd3265 100644 --- a/src/__init__.py +++ b/src/__init__.py @@ -4,5 +4,3 @@ It initializes the src module, making it easier to import and use the utilities provided by this module in other parts of the project. """ - -__version__ = "0.1.0"