From 7b44707a33bd66e2fc2f18d7d803960d77cac679 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Tue, 22 Oct 2024 12:26:43 -0700 Subject: [PATCH 01/24] inital commit for yellowhammer jupyter assistant --- examples/001-getting-started.ipynb | 2 +- examples/Demo.ipynb | 133 ++++++++ pyproject.toml | 1 + src/yellowhammer/__init__.py | 11 + src/yellowhammer/datalab-api-prompt.md | 447 +++++++++++++++++++++++++ src/yellowhammer/llm.py | 61 ++++ src/yellowhammer/magics.py | 86 +++++ src/yellowhammer/prompt.py | 13 + src/yellowhammer/system-prompt.md | 1 + src/yellowhammer/test.ipynb | 189 +++++++++++ src/yellowhammmer/__init__.py | 5 - uv.lock | 4 +- 12 files changed, 946 insertions(+), 7 deletions(-) create mode 100644 examples/Demo.ipynb create mode 100755 src/yellowhammer/datalab-api-prompt.md create mode 100644 src/yellowhammer/llm.py create mode 100644 src/yellowhammer/magics.py create mode 100644 src/yellowhammer/prompt.py create mode 100644 src/yellowhammer/system-prompt.md create mode 100644 src/yellowhammer/test.ipynb delete mode 100644 src/yellowhammmer/__init__.py diff --git a/examples/001-getting-started.ipynb b/examples/001-getting-started.ipynb index 5bf27ef..3c8eb0e 100644 --- a/examples/001-getting-started.ipynb +++ b/examples/001-getting-started.ipynb @@ -123,7 +123,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/examples/Demo.ipynb b/examples/Demo.ipynb new file mode 100644 index 0000000..20475f3 --- /dev/null +++ b/examples/Demo.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext yellowhammer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", + "metadata": {}, + "outputs": [], + "source": [ + "import os, getpass\n", + "os.environ['LLM_PROVIDER'] = \"OPENAI\"\n", + "os.environ['LLM_API_KEY'] = getpass.getpass(\"Enter your LLM API key\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Datalab is a data management platform designed for scientists and researchers to manage their experimental data efficiently. It allows users to organize, analyze, and visualize their data through a structured interface. Datalab uses the concept of \"data blocks\" to handle files associated with samples, enabling users to parse data according to scientific schemas and create plots for better data interpretation. The platform also provides an API for programmatic access, allowing users to automate tasks such as data entry, file uploads, and data analysis." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "What is datalab?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "To create a new sample in Datalab with the specified ID, name, and formula, we will use the `create_item` method of the `DatalabClient`. This method requires the item ID, item type, and the data that matches the schema for samples. In this case, we will set the `item_id` to 'llm-test3', the `name` to 'virtual sample', and the `chemform` to 'RbCl'." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "Create a sample with ID llm-test3, sample name \"virtual sample\", formula RbCl" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "33c0e59e-586d-48f3-aa5f-f6fa743dcc96", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from datalab_api import DatalabClient\n", + "import os\n", + "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", + " json_data = {\n", + " \"name\": \"virtual sample\",\n", + " \"chemform\": \"RbCl\"\n", + " }\n", + " client.create_item(item_id=\"llm-test3\", item_type=\"samples\", item_data=json_data)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "yellowhammer", + "language": "python", + "name": "yellowhammer" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index ed363dc..a8ab03f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,6 +8,7 @@ dependencies = [ "datalab-api>=0.2.4", "jupyter-ai>=2.24.1", "jupyterlab>=4.2.5", + "langchain>=0.2.16", "langchain-anthropic>=0.1.23", "langchain-openai>=0.1.25", "notebook>=7.2.2", diff --git a/src/yellowhammer/__init__.py b/src/yellowhammer/__init__.py index 77003b9..9c8eac8 100644 --- a/src/yellowhammer/__init__.py +++ b/src/yellowhammer/__init__.py @@ -6,3 +6,14 @@ __version__ = "develop" __all__ = ("__version__",) + + +from .magics import datalabMagics + +def load_ipython_extension(ipython): + """ + Any module file that define a function named `load_ipython_extension` + can be loaded via `%load_ext module.path` or be configured to be + autoloaded by IPython at startup time. + """ + ipython.register_magics(datalabMagics) diff --git a/src/yellowhammer/datalab-api-prompt.md b/src/yellowhammer/datalab-api-prompt.md new file mode 100755 index 0000000..5497102 --- /dev/null +++ b/src/yellowhammer/datalab-api-prompt.md @@ -0,0 +1,447 @@ +Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/. +Each method of the DatalabClient class will return a dictionary constructed directly +from the JSON response of the Datalab API. + +Datalab uses "data blocks" to take a file attached to a sample, parse it +according to some scientific schema, and then make a plot. + +The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. + +Assume the `DATALAB_API_KEY` has been set an environment variable. + +Python API +This package implements basic functionality for displaying and manipulating entries: + +```python +from datalab_api import DatalabClient + +with DatalabClient("https://demo.datalab-org.io") as client: + + # Get the info about this datalab instance + client.get_info() + + # Get the current user's info + client.authenticate() + + # Search for items with the string + items = client.search_items("search-values") + + # List all items of a given type + # Types can be 'samples' or 'starting_materials' + items = client.get_items(item_type="samples") + + # Get more info on a particular item called 'test' + item = client.get_item(item_id="test") + + # Create a new item with some data that matches the corresponding `item_type` schema + json_data = {"chemform": "NaCl"} + client.create_item(item_id="test_new", item_type="samples", item_data=json_data) + + # Attach a file to an item and get the uploaded ID + file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") + file_id = file_response["file_id"] + + # Create a data block for a sample, then show the plot + client.create_data_block(item_id="test", file_ids=file_id) + + # Download all files attached to a sample and return their paths + file_paths = client.get_item_files(item_id="test") + + # Get the item graph, useful for finding relationships + graph = client.get_item_graph() + +``` + +Here is an abridged JSONSchema for a sample, that also has some info about other +types. + +```json +{ + "title": "Sample", + "description": "A model for representing an experimental sample.", + "type": "object", + "properties": { + "blocks_obj": { + "title": "Blocks Obj", + "default": {}, + "type": "object" + }, + "display_order": { + "title": "Display Order", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "collections": { + "title": "Collections", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Collection" + } + }, + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "samples", + "const": "samples", + "pattern": "^samples$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "description": { + "title": "Description", + "type": "string" + }, + "date": { + "title": "Date", + "type": "date", + "format": "date-time" + }, + "name": { + "title": "Name", + "type": "string" + }, + "files": { + "title": "Files", + "type": "array", + "items": { + "$ref": "#/definitions/File" + } + }, + "file_ObjectIds": { + "title": "File Objectids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "chemform": { + "title": "Chemform", + "example": [ + "Na3P", + "LiNiO2@C" + ], + "type": "string" + }, + "synthesis_constituents": { + "title": "Synthesis Constituents", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Constituent" + } + }, + "synthesis_description": { + "title": "Synthesis Description", + "type": "string" + } + }, + "required": [ + "item_id" + ], + "definitions": { + "KnownType": { + "title": "KnownType", + "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", + "enum": [ + "samples", + "starting_materials", + "blocks", + "files", + "people", + "collections" + ], + "type": "string" + }, + "File": { + "title": "File", + "description": "A model for representing a file that has been tracked or uploaded to datalab.", + "type": "object", + "properties": { + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "files", + "const": "files", + "pattern": "^files$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "size": { + "title": "Size", + "description": "The size of the file on disk in bytes.", + "type": "integer" + }, + "last_modified_remote": { + "title": "Last Modified Remote", + "description": "The last date/time at which the remote file was modified.", + "type": "date", + "format": "date-time" + }, + "item_ids": { + "title": "Item Ids", + "description": "A list of item IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "blocks": { + "title": "Blocks", + "description": "A list of block IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "name": { + "title": "Name", + "description": "The filename on disk.", + "type": "string" + }, + "extension": { + "title": "Extension", + "description": "The file extension that the file was uploaded with.", + "type": "string" + }, + "original_name": { + "title": "Original Name", + "description": "The raw filename as uploaded.", + "type": "string" + }, + "location": { + "title": "Location", + "description": "The location of the file on disk.", + "type": "string" + }, + "url_path": { + "title": "Url Path", + "description": "The path to a remote file.", + "type": "string" + }, + "source": { + "title": "Source", + "description": "The source of the file, e.g. 'remote' or 'uploaded'.", + "type": "string" + }, + "time_added": { + "title": "Time Added", + "description": "The timestamp for the original file upload.", + "type": "string", + "format": "date-time" + }, + "metadata": { + "title": "Metadata", + "description": "Any additional metadata.", + "type": "object" + }, + "representation": { + "title": "Representation" + }, + "source_server_name": { + "title": "Source Server Name", + "description": "The server name at which the file is stored.", + "type": "string" + }, + "source_path": { + "title": "Source Path", + "description": "The path to the file on the remote resource.", + "type": "string" + }, + "is_live": { + "title": "Is Live", + "description": "Whether or not the file should be watched for future updates.", + "type": "boolean" + } + }, + "required": [ + "item_ids", + "blocks", + "name", + "extension", + "time_added", + "is_live" + ] + }, + "EntryReference": { + "title": "EntryReference", + "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", + "type": "object", + "properties": { + "type": { + "title": "Type", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "immutable_id": { + "title": "Immutable Id", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + } + }, + "required": [ + "type" + ] + }, + "InlineSubstance": { + "title": "InlineSubstance", + "type": "object", + "properties": { + "name": { + "title": "Name", + "type": "string" + }, + "chemform": { + "title": "Chemform", + "type": "string" + } + }, + "required": [ + "name" + ] + }, + "Constituent": { + "title": "Constituent", + "description": "A constituent of a sample.", + "type": "object", + "properties": { + "item": { + "title": "Item", + "anyOf": [ + { + "$ref": "#/definitions/EntryReference" + }, + { + "$ref": "#/definitions/InlineSubstance" + } + ] + }, + "quantity": { + "title": "Quantity", + "minimum": 0, + "type": "number" + }, + "unit": { + "title": "Unit", + "default": "g", + "type": "string" + } + }, + "required": [ + "item", + "quantity" + ] + } + } +} +``` diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py new file mode 100644 index 0000000..bed2644 --- /dev/null +++ b/src/yellowhammer/llm.py @@ -0,0 +1,61 @@ +from langchain_core.prompts import ChatPromptTemplate +from pydantic import BaseModel, Field +from .prompt import SYSTEM_PROMPT +from typing import Union + +# Pydantic schema to use with .with_structured_output() +class code(BaseModel): + """Schema for code solutions""" + prefix: str = Field(description="Description of the problem and approach") + imports: str = Field(description="Code block import statements") + code: str = Field(description="Code block not including import statements") + +class ConversationalResponse(BaseModel): + """Respond in a conversational manner. Be kind and helpful.""" + response: str = Field(description="A conversational response to the user's query") + +class FinalResponse(BaseModel): + """The final response can be either a code solution or a conversational response""" + final_output: Union[code, ConversationalResponse] + +def get_chain( + api_provider, + api_key, + api_model=None, + api_temperature=0, + ): + + # API provider logic + if api_provider.lower() == "openai": + from langchain_openai import ChatOpenAI + if api_model is None: + api_model = "gpt-4o-mini" + llm = ChatOpenAI( + model=api_model, + temperature=api_temperature, + openai_api_key=api_key + ) + + elif api_provider.lower() == "anthropic": + from langchain_anthropic import ChatAnthropic + if api_model is None: + api_model = "claude-3-5-sonnet-20240620" + llm = ChatAnthropic( + model=api_model, + temperature=api_temperature, + anthropic_api_key=api_key, + ) + + # Prompt + prompt = ChatPromptTemplate.from_messages( + [ + ("system", SYSTEM_PROMPT), # datalab API info is passed via {context} to the system prompt + ("placeholder", "{messages}"), + ] + ) + + # Create a chain where the final output takes the FinalResponse schema + chain = prompt | llm.with_structured_output(FinalResponse, include_raw=False) + + # Returns a runnable chain which accepts datalab API documentation "context" and user question "messages" + return chain \ No newline at end of file diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py new file mode 100644 index 0000000..8bd94cd --- /dev/null +++ b/src/yellowhammer/magics.py @@ -0,0 +1,86 @@ +""" +Magics to support LLM interactions in IPython/Jupyter. +Adapted from fperez/jupytee and jan-janssen/LangSim. +""" +import os + +from IPython import get_ipython +from IPython.core.magic import ( + Magics, + magics_class, + line_cell_magic, +) +from IPython.core.magic_arguments import ( + magic_arguments, + argument, + parse_argstring, +) +from IPython.display import Markdown +from .llm import get_chain, code, FinalResponse, ConversationalResponse +from .prompt import API_PROMPT + + +def get_output(messages, temp=0.1): + env = os.environ + agent_executor = get_chain( + api_provider=env.get("LLM_PROVIDER", "OPENAI"), + api_key=env.get("LLM_API_KEY"), + api_model=env.get("LLM_MODEL", None), + api_temperature=env.get("LLM_TEMP", temp), + ) + + return agent_executor.invoke({"context": API_PROMPT, "messages": messages}) + +# Class to manage state and expose the main magics +@magics_class +class datalabMagics(Magics): + def __init__(self, shell): + super(datalabMagics, self).__init__(shell) + self.messages = [] + + # A datalab magic that returns a code block + @magic_arguments() + @argument( + "prompt", + nargs="*", + help="""Prompt for code generation. When used as a line magic, + it runs to the end of the line. In cell mode, the entire cell + is considered the code generation prompt. + """, + ) + + @argument( + "-T", + "--temp", + type=float, + default=0.0, + help="""Temperature, float in [0,1]. Higher values push the algorithm + to generate more aggressive/"creative" output. [default=0.1].""", + ) + + @line_cell_magic + def llm(self, line, cell=None): + """ + Chat with the LLM. Return either conversation or code. + """ + args = parse_argstring(self.llm, line) #self.llm is a bound method + + if cell is None: + prompt = " ".join(args.prompt) + else: + prompt = cell + + self.messages.append(("human", prompt)) + response = get_output(self.messages).final_output + + if isinstance(response, ConversationalResponse): + output = response.response + self.messages.append(("ai", output)) + return Markdown(output) + + elif isinstance(response, code): + output = response + self.messages.append(("ai", output.prefix)) + cell_fill = output.imports + "\n" + output.code + get_ipython().set_next_input(cell_fill) + return Markdown(output.prefix) \ No newline at end of file diff --git a/src/yellowhammer/prompt.py b/src/yellowhammer/prompt.py new file mode 100644 index 0000000..efb5d12 --- /dev/null +++ b/src/yellowhammer/prompt.py @@ -0,0 +1,13 @@ +from pathlib import Path + +def load_file_content(file_path): + with open(file_path, 'r', encoding='utf-8') as file: + return file.read() + +# Load API_PROMPT +api_prompt_path = Path(__file__).parent / "datalab-api-prompt.md" +API_PROMPT = load_file_content(api_prompt_path) + +# Load SYSTEM_PROMPT +system_prompt_path = Path(__file__).parent / "system-prompt.md" +SYSTEM_PROMPT = load_file_content(system_prompt_path) \ No newline at end of file diff --git a/src/yellowhammer/system-prompt.md b/src/yellowhammer/system-prompt.md new file mode 100644 index 0000000..18ce00c --- /dev/null +++ b/src/yellowhammer/system-prompt.md @@ -0,0 +1 @@ +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question: \ No newline at end of file diff --git a/src/yellowhammer/test.ipynb b/src/yellowhammer/test.ipynb new file mode 100644 index 0000000..4961327 --- /dev/null +++ b/src/yellowhammer/test.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "from langchain_community.document_loaders import TextLoader\n", + "\n", + "path = \"datalab-api-prompt.md\"\n", + "loader = TextLoader(path)\n", + "API_PROMPT = loader.load()[0].page_content\n", + "\n", + "path = \"system-prompt.md\"\n", + "loader = TextLoader(path)\n", + "SYSTEM_PROMPT = loader.load()[0].page_content\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/.\\nEach method of the DatalabClient class will return a dictionary constructed directly\\nfrom the JSON response of the Datalab API.\\n\\nDatalab uses \"data blocks\" to take a file attached to a sample, parse it\\naccording to some scientific schema, and then make a plot.\\n\\nThe rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed.\\n\\nAssume the `DATALAB_API_KEY` has been set an environment variable.\\n\\nPython API\\nThis package implements basic functionality for displaying and manipulating entries:\\n\\n```python\\nfrom datalab_api import DatalabClient\\n\\nwith DatalabClient(\"https://demo.datalab-org.io\") as client:\\n\\n # Get the info about this datalab instance\\n client.get_info()\\n\\n # Get the current user\\'s info\\n client.authenticate()\\n\\n # Search for items with the string\\n items = client.search_items(\"search-values\")\\n\\n # List all items of a given type\\n # Types can be \\'samples\\' or \\'starting_materials\\'\\n items = client.get_items(item_type=\"samples\")\\n\\n # Get more info on a particular item called \\'test\\'\\n item = client.get_item(item_id=\"test\")\\n\\n # Create a new item with some data that matches the corresponding `item_type` schema\\n json_data = {\"chemform\": \"NaCl\"}\\n client.create_item(item_id=\"test_new\", item_type=\"samples\", item_data=json_data)\\n\\n # Attach a file to an item and get the uploaded ID\\n file_response = client.upload_file(filepath=\"my_echem_data.mpr\", item_id=\"test\")\\n file_id = file_response[\"file_id\"]\\n\\n # Create a data block for a sample, then show the plot\\n client.create_data_block(item_id=\"test\", file_ids=file_id)\\n\\n # Download all files attached to a sample and return their paths\\n file_paths = client.get_item_files(item_id=\"test\")\\n\\n # Get the item graph, useful for finding relationships\\n graph = client.get_item_graph()\\n\\n```\\n\\nHere is an abridged JSONSchema for a sample, that also has some info about other\\ntypes.\\n\\n```json \\n{\\n \"title\": \"Sample\",\\n \"description\": \"A model for representing an experimental sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"blocks_obj\": {\\n \"title\": \"Blocks Obj\",\\n \"default\": {},\\n \"type\": \"object\"\\n },\\n \"display_order\": {\\n \"title\": \"Display Order\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"collections\": {\\n \"title\": \"Collections\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Collection\"\\n }\\n },\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"samples\",\\n \"const\": \"samples\",\\n \"pattern\": \"^samples$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"description\": {\\n \"title\": \"Description\",\\n \"type\": \"string\"\\n },\\n \"date\": {\\n \"title\": \"Date\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"files\": {\\n \"title\": \"Files\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/File\"\\n }\\n },\\n \"file_ObjectIds\": {\\n \"title\": \"File Objectids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"example\": [\\n \"Na3P\",\\n \"LiNiO2@C\"\\n ],\\n \"type\": \"string\"\\n },\\n \"synthesis_constituents\": {\\n \"title\": \"Synthesis Constituents\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Constituent\"\\n }\\n },\\n \"synthesis_description\": {\\n \"title\": \"Synthesis Description\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item_id\"\\n ],\\n \"definitions\": {\\n \"KnownType\": {\\n \"title\": \"KnownType\",\\n \"description\": \"An enumeration of the types of entry known by this implementation, should be made dynamic in the future.\",\\n \"enum\": [\\n \"samples\",\\n \"starting_materials\",\\n \"blocks\",\\n \"files\",\\n \"people\",\\n \"collections\"\\n ],\\n \"type\": \"string\"\\n },\\n \"File\": {\\n \"title\": \"File\",\\n \"description\": \"A model for representing a file that has been tracked or uploaded to datalab.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"files\",\\n \"const\": \"files\",\\n \"pattern\": \"^files$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"size\": {\\n \"title\": \"Size\",\\n \"description\": \"The size of the file on disk in bytes.\",\\n \"type\": \"integer\"\\n },\\n \"last_modified_remote\": {\\n \"title\": \"Last Modified Remote\",\\n \"description\": \"The last date/time at which the remote file was modified.\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"item_ids\": {\\n \"title\": \"Item Ids\",\\n \"description\": \"A list of item IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"blocks\": {\\n \"title\": \"Blocks\",\\n \"description\": \"A list of block IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"description\": \"The filename on disk.\",\\n \"type\": \"string\"\\n },\\n \"extension\": {\\n \"title\": \"Extension\",\\n \"description\": \"The file extension that the file was uploaded with.\",\\n \"type\": \"string\"\\n },\\n \"original_name\": {\\n \"title\": \"Original Name\",\\n \"description\": \"The raw filename as uploaded.\",\\n \"type\": \"string\"\\n },\\n \"location\": {\\n \"title\": \"Location\",\\n \"description\": \"The location of the file on disk.\",\\n \"type\": \"string\"\\n },\\n \"url_path\": {\\n \"title\": \"Url Path\",\\n \"description\": \"The path to a remote file.\",\\n \"type\": \"string\"\\n },\\n \"source\": {\\n \"title\": \"Source\",\\n \"description\": \"The source of the file, e.g. \\'remote\\' or \\'uploaded\\'.\",\\n \"type\": \"string\"\\n },\\n \"time_added\": {\\n \"title\": \"Time Added\",\\n \"description\": \"The timestamp for the original file upload.\",\\n \"type\": \"string\",\\n \"format\": \"date-time\"\\n },\\n \"metadata\": {\\n \"title\": \"Metadata\",\\n \"description\": \"Any additional metadata.\",\\n \"type\": \"object\"\\n },\\n \"representation\": {\\n \"title\": \"Representation\"\\n },\\n \"source_server_name\": {\\n \"title\": \"Source Server Name\",\\n \"description\": \"The server name at which the file is stored.\",\\n \"type\": \"string\"\\n },\\n \"source_path\": {\\n \"title\": \"Source Path\",\\n \"description\": \"The path to the file on the remote resource.\",\\n \"type\": \"string\"\\n },\\n \"is_live\": {\\n \"title\": \"Is Live\",\\n \"description\": \"Whether or not the file should be watched for future updates.\",\\n \"type\": \"boolean\"\\n }\\n },\\n \"required\": [\\n \"item_ids\",\\n \"blocks\",\\n \"name\",\\n \"extension\",\\n \"time_added\",\\n \"is_live\"\\n ]\\n },\\n \"EntryReference\": {\\n \"title\": \"EntryReference\",\\n \"description\": \"A reference to a database entry by ID and type.\\\\n\\\\nCan include additional arbitarary metadata useful for\\\\ninlining the item data.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"type\": {\\n \"title\": \"Type\",\\n \"type\": \"string\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable Id\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"type\"\\n ]\\n },\\n \"InlineSubstance\": {\\n \"title\": \"InlineSubstance\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"name\"\\n ]\\n },\\n \"Constituent\": {\\n \"title\": \"Constituent\",\\n \"description\": \"A constituent of a sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"item\": {\\n \"title\": \"Item\",\\n \"anyOf\": [\\n {\\n \"$ref\": \"#/definitions/EntryReference\"\\n },\\n {\\n \"$ref\": \"#/definitions/InlineSubstance\"\\n }\\n ]\\n },\\n \"quantity\": {\\n \"title\": \"Quantity\",\\n \"minimum\": 0,\\n \"type\": \"number\"\\n },\\n \"unit\": {\\n \"title\": \"Unit\",\\n \"default\": \"g\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item\",\\n \"quantity\"\\n ]\\n }\\n }\\n}\\n```\\n'" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "API_PROMPT" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def load_file_content(file_path):\n", + " with open(file_path, 'r', encoding='utf-8') as file:\n", + " return file.read()\n", + "\n", + "# Load API_PROMPT\n", + "api_prompt_path = \"datalab-api-prompt.md\"\n", + "API_PROMPT_ = load_file_content(api_prompt_path)\n", + "\n", + "# Load SYSTEM_PROMPT\n", + "system_prompt_path = \"system-prompt.md\"\n", + "SYSTEM_PROMPT_ = load_file_content(system_prompt_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11679" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(API_PROMPT_)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11679" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(API_PROMPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "698" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(SYSTEM_PROMPT_)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "698" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(SYSTEM_PROMPT)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question:'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SYSTEM_PROMPT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/yellowhammmer/__init__.py b/src/yellowhammmer/__init__.py deleted file mode 100644 index 9485887..0000000 --- a/src/yellowhammmer/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from importlib import metadata - -__version__ = metadata.version("yellowhammer") - -__all__ = ("__version__",) diff --git a/uv.lock b/uv.lock index 5939b9a..c78ff28 100644 --- a/uv.lock +++ b/uv.lock @@ -3053,12 +3053,13 @@ wheels = [ [[package]] name = "yellowhammer" -version = "0.1.dev8+g117550e.d20241008" +version = "0.1.dev11+gf3789f6.d20241022" source = { editable = "." } dependencies = [ { name = "datalab-api" }, { name = "jupyter-ai" }, { name = "jupyterlab" }, + { name = "langchain" }, { name = "langchain-anthropic" }, { name = "langchain-openai" }, { name = "notebook" }, @@ -3076,6 +3077,7 @@ requires-dist = [ { name = "datalab-api", specifier = ">=0.2.4" }, { name = "jupyter-ai", specifier = ">=2.24.1" }, { name = "jupyterlab", specifier = ">=4.2.5" }, + { name = "langchain", specifier = ">=0.2.16" }, { name = "langchain-anthropic", specifier = ">=0.1.23" }, { name = "langchain-openai", specifier = ">=0.1.25" }, { name = "notebook", specifier = ">=7.2.2" }, From bf94201ed72f709757a52674d0f87954edda6a8a Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 23 Oct 2024 17:51:27 -0700 Subject: [PATCH 02/24] minor changes for initial commit --- src/yellowhammer/magics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 8bd94cd..4da2e57 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -33,9 +33,9 @@ def get_output(messages, temp=0.1): # Class to manage state and expose the main magics @magics_class -class datalabMagics(Magics): +class DatalabMagics(Magics): def __init__(self, shell): - super(datalabMagics, self).__init__(shell) + super(DatalabMagics, self).__init__(shell) self.messages = [] # A datalab magic that returns a code block From d93d90e4ea2d3f9a826413c4481775716c10055d Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 23 Oct 2024 17:52:18 -0700 Subject: [PATCH 03/24] minor changes for initial commit --- examples/001-getting-started.ipynb | 131 ------------ examples/Demo_anthropic.ipynb | 197 ++++++++++++++++++ examples/{Demo.ipynb => Demo_openai.ipynb} | 3 +- .../datalab-api-prompt.md | 0 .../yellowhammer => prompts}/system-prompt.md | 0 pyproject.toml | 6 +- src/yellowhammer/__init__.py | 5 +- src/yellowhammer/llm.py | 33 +-- src/yellowhammer/magics.py | 19 +- src/yellowhammer/prompt.py | 14 +- src/yellowhammer/test.ipynb | 39 +++- uv.lock | 2 +- 12 files changed, 285 insertions(+), 164 deletions(-) delete mode 100644 examples/001-getting-started.ipynb create mode 100644 examples/Demo_anthropic.ipynb rename examples/{Demo.ipynb => Demo_openai.ipynb} (98%) rename {src/yellowhammer => prompts}/datalab-api-prompt.md (100%) rename {src/yellowhammer => prompts}/system-prompt.md (100%) diff --git a/examples/001-getting-started.ipynb b/examples/001-getting-started.ipynb deleted file mode 100644 index 3c8eb0e..0000000 --- a/examples/001-getting-started.ipynb +++ /dev/null @@ -1,131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "929301f6-5982-4662-b68f-9e72269cace7", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext jupyter_ai " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f7f8325c-18b8-4943-b7e8-14a839060958", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Here's the information about Datalab in markdown format:\n", - "\n", - "# Datalab\n", - "\n", - "Datalab is a tool developed by Google Cloud Platform that provides an interactive environment for data exploration, analysis, and machine learning. Key features include:\n", - "\n", - "## Features\n", - "\n", - "- **Jupyter Notebooks**: Uses Jupyter notebooks for interactive coding and visualization\n", - "- **Cloud Integration**: Seamlessly integrates with Google Cloud services\n", - "- **Big Data Support**: Designed to work with large datasets using BigQuery and other Google Cloud data services\n", - "- **Pre-installed Libraries**: Comes with popular data science libraries like pandas, numpy, and scikit-learn\n", - "- **Collaborative**: Allows for easy sharing and collaboration on data projects\n", - "\n", - "## Use Cases\n", - "\n", - "- Data exploration and visualization\n", - "- Machine learning model development\n", - "- Big data analysis\n", - "- Prototyping data pipelines\n", - "\n", - "## Advantages\n", - "\n", - "- Easy setup and configuration\n", - "- Cost-effective (pay only for resources used)\n", - "- Scalable to handle large datasets\n", - "- Integrates well with other Google Cloud services\n", - "\n", - "Datalab is particularly useful for data scientists and analysts who work with Google Cloud Platform and need a powerful, cloud-based environment for their data projects." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": { - "text/markdown": { - "jupyter_ai": { - "model_id": "claude-3-5-sonnet-20240620", - "provider_id": "anthropic-chat" - } - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "%%ai anthropic-chat:claude-3-5-sonnet-20240620\n", - "Do you know what datalab is?" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "12aaa0b1-6ccd-4645-b578-d1fde5cd2614", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "UsageError: Line magic function `%%yellowhammer` not found.\n" - ] - } - ], - "source": [ - "\"\"\" (mock up ) \"\"\"\n", - "%%yellowhammer\n", - "Do you know what datalab is?" - ] - }, - { - "cell_type": "markdown", - "id": "b943d5eb-a83a-4ed5-b531-e69a44d6d701", - "metadata": {}, - "source": [ - "Yes, *datalab* is ACTUALLY an open source research data management tool for chemistry and materials science, and here is the functionality I know about..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8d93f30b-3dde-457a-8aa9-9d08cc19f3ad", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Demo_anthropic.ipynb b/examples/Demo_anthropic.ipynb new file mode 100644 index 0000000..ab73625 --- /dev/null +++ b/examples/Demo_anthropic.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext yellowhammer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your LLM API key ········\n" + ] + } + ], + "source": [ + "import os\n", + "import getpass\n", + "os.environ['LLM_PROVIDER'] = \"ANTHROPIC\"\n", + "os.environ['LLM_API_KEY'] = getpass.getpass(\"Enter your LLM API key\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your Datalab API key ········\n" + ] + } + ], + "source": [ + "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Datalab is a data management platform designed to help scientists manage their experimental data, plan experiments, analyze data, and plot results. It provides a structured way to store, organize, and interact with scientific data, particularly in the context of materials science and chemistry experiments. \n", + "\n", + "Some key features of Datalab include:\n", + "\n", + "1. Sample management: You can create, store, and retrieve information about experimental samples, including their chemical composition, synthesis methods, and related metadata.\n", + "\n", + "2. File attachments: Datalab allows you to attach files (such as raw data or images) to sample entries, making it easy to keep all relevant information together.\n", + "\n", + "3. Data blocks: These are used to parse attached files according to scientific schemas and generate plots, facilitating data analysis and visualization.\n", + "\n", + "4. Search functionality: You can search for items (samples, materials, etc.) across the Datalab instance.\n", + "\n", + "5. Relationship tracking: Datalab can track relationships between different items, helping to maintain the context of experiments and materials.\n", + "\n", + "6. API access: Datalab provides a Python API that allows programmatic interaction with the platform, enabling integration with other tools and scripts.\n", + "\n", + "Datalab is particularly useful for maintaining experimental workflows, ensuring data provenance, and facilitating collaboration among scientists. It helps in organizing complex scientific data in a structured manner, making it easier to retrieve, analyze, and share research findings." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "What is datalab?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "To create a sample with the specified details using the Datalab API, we'll use the `create_item` method of the `DatalabClient`. Here's what the code will do:\n", + "\n", + "1. Import the necessary module\n", + "2. Create a DatalabClient instance\n", + "3. Prepare the sample data as a dictionary\n", + "4. Use the create_item method to create the sample\n", + "5. Print the response to confirm the sample creation" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "Create a sample with ID llm-test4, sample name \"virtual sample (Claude)\", formula FrCl" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6ef8a1c2-50c8-4807-b84b-008c0bfb4efa", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample creation response:\n", + "{'collections': [], 'creator_ids': [{'$oid': '66abc00dcb992f4b299aa60a'}], 'creators': [{'contact_email': None, 'display_name': 'Yue Wu'}], 'date': '2024-10-22T20:06:55.888755', 'item_id': 'llm-test4', 'name': 'virtual sample (Claude)', 'nblocks': 0, 'refcode': 'demo:YASTWQ', 'type': 'samples'}\n" + ] + } + ], + "source": [ + "from datalab_api import DatalabClient\n", + "# Create a DatalabClient instance\n", + "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", + " # Prepare the sample data\n", + " sample_data = {\n", + " \"item_id\": \"llm-test4\",\n", + " \"name\": \"virtual sample (Claude)\",\n", + " \"chemform\": \"FrCl\",\n", + " \"type\": \"samples\"\n", + " }\n", + " \n", + " # Create the sample\n", + " response = client.create_item(item_id=\"llm-test4\", item_type=\"samples\", item_data=sample_data)\n", + " \n", + " # Print the response\n", + " print(\"Sample creation response:\")\n", + " print(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f3241bc-ba1f-4807-babb-3a928baad6ea", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "yellowhammer", + "language": "python", + "name": "yellowhammer" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Demo.ipynb b/examples/Demo_openai.ipynb similarity index 98% rename from examples/Demo.ipynb rename to examples/Demo_openai.ipynb index 20475f3..02c507e 100644 --- a/examples/Demo.ipynb +++ b/examples/Demo_openai.ipynb @@ -17,7 +17,8 @@ "metadata": {}, "outputs": [], "source": [ - "import os, getpass\n", + "import os\n", + "import getpass\n", "os.environ['LLM_PROVIDER'] = \"OPENAI\"\n", "os.environ['LLM_API_KEY'] = getpass.getpass(\"Enter your LLM API key\")" ] diff --git a/src/yellowhammer/datalab-api-prompt.md b/prompts/datalab-api-prompt.md similarity index 100% rename from src/yellowhammer/datalab-api-prompt.md rename to prompts/datalab-api-prompt.md diff --git a/src/yellowhammer/system-prompt.md b/prompts/system-prompt.md similarity index 100% rename from src/yellowhammer/system-prompt.md rename to prompts/system-prompt.md diff --git a/pyproject.toml b/pyproject.toml index a8ab03f..e4bff8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,4 +36,8 @@ ignore_missing_imports = true follow_imports = "skip" [tool.uv] -dev-dependencies = ["ipykernel>=6.29.5", "pre-commit>=4.0.0", "pytest>=8.3.3"] +dev-dependencies = [ + "ipykernel>=6.29.5", + "pre-commit>=4.0.0", + "pytest>=8.3.3", +] diff --git a/src/yellowhammer/__init__.py b/src/yellowhammer/__init__.py index 9c8eac8..c883b66 100644 --- a/src/yellowhammer/__init__.py +++ b/src/yellowhammer/__init__.py @@ -1,4 +1,5 @@ from importlib.metadata import PackageNotFoundError, version +from .magics import DatalabMagics try: __version__ = version("yellowhammer") @@ -8,12 +9,10 @@ __all__ = ("__version__",) -from .magics import datalabMagics - def load_ipython_extension(ipython): """ Any module file that define a function named `load_ipython_extension` can be loaded via `%load_ext module.path` or be configured to be autoloaded by IPython at startup time. """ - ipython.register_magics(datalabMagics) + ipython.register_magics(DatalabMagics) diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index bed2644..a2601ac 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -3,41 +3,45 @@ from .prompt import SYSTEM_PROMPT from typing import Union + # Pydantic schema to use with .with_structured_output() class code(BaseModel): """Schema for code solutions""" + prefix: str = Field(description="Description of the problem and approach") imports: str = Field(description="Code block import statements") code: str = Field(description="Code block not including import statements") + class ConversationalResponse(BaseModel): """Respond in a conversational manner. Be kind and helpful.""" + response: str = Field(description="A conversational response to the user's query") + class FinalResponse(BaseModel): """The final response can be either a code solution or a conversational response""" + final_output: Union[code, ConversationalResponse] -def get_chain( - api_provider, - api_key, - api_model=None, - api_temperature=0, - ): +def get_chain( + api_provider, + api_key, + api_model=None, + api_temperature=0, +): # API provider logic if api_provider.lower() == "openai": from langchain_openai import ChatOpenAI + if api_model is None: api_model = "gpt-4o-mini" - llm = ChatOpenAI( - model=api_model, - temperature=api_temperature, - openai_api_key=api_key - ) + llm = ChatOpenAI(model=api_model, temperature=api_temperature, openai_api_key=api_key) elif api_provider.lower() == "anthropic": from langchain_anthropic import ChatAnthropic + if api_model is None: api_model = "claude-3-5-sonnet-20240620" llm = ChatAnthropic( @@ -49,7 +53,10 @@ def get_chain( # Prompt prompt = ChatPromptTemplate.from_messages( [ - ("system", SYSTEM_PROMPT), # datalab API info is passed via {context} to the system prompt + ( + "system", + SYSTEM_PROMPT, + ), # datalab API info is passed via {context} to the system prompt ("placeholder", "{messages}"), ] ) @@ -58,4 +65,4 @@ def get_chain( chain = prompt | llm.with_structured_output(FinalResponse, include_raw=False) # Returns a runnable chain which accepts datalab API documentation "context" and user question "messages" - return chain \ No newline at end of file + return chain diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 4da2e57..fef7f6b 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -2,6 +2,7 @@ Magics to support LLM interactions in IPython/Jupyter. Adapted from fperez/jupytee and jan-janssen/LangSim. """ + import os from IPython import get_ipython @@ -16,7 +17,7 @@ parse_argstring, ) from IPython.display import Markdown -from .llm import get_chain, code, FinalResponse, ConversationalResponse +from .llm import get_chain, code, ConversationalResponse from .prompt import API_PROMPT @@ -31,11 +32,12 @@ def get_output(messages, temp=0.1): return agent_executor.invoke({"context": API_PROMPT, "messages": messages}) + # Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): - super(DatalabMagics, self).__init__(shell) + super().__init__(shell) self.messages = [] # A datalab magic that returns a code block @@ -48,7 +50,6 @@ def __init__(self, shell): is considered the code generation prompt. """, ) - @argument( "-T", "--temp", @@ -57,13 +58,12 @@ def __init__(self, shell): help="""Temperature, float in [0,1]. Higher values push the algorithm to generate more aggressive/"creative" output. [default=0.1].""", ) - @line_cell_magic def llm(self, line, cell=None): """ Chat with the LLM. Return either conversation or code. """ - args = parse_argstring(self.llm, line) #self.llm is a bound method + args = parse_argstring(self.llm, line) # self.llm is a bound method if cell is None: prompt = " ".join(args.prompt) @@ -77,10 +77,15 @@ def llm(self, line, cell=None): output = response.response self.messages.append(("ai", output)) return Markdown(output) - + elif isinstance(response, code): output = response self.messages.append(("ai", output.prefix)) cell_fill = output.imports + "\n" + output.code get_ipython().set_next_input(cell_fill) - return Markdown(output.prefix) \ No newline at end of file + return Markdown(output.prefix) + + +# If testing interactively, it's convenient to %run as a script in Jupyter +if __name__ == "__main__": + get_ipython().register_magics(DatalabMagics) diff --git a/src/yellowhammer/prompt.py b/src/yellowhammer/prompt.py index efb5d12..1658829 100644 --- a/src/yellowhammer/prompt.py +++ b/src/yellowhammer/prompt.py @@ -1,13 +1,19 @@ from pathlib import Path + def load_file_content(file_path): - with open(file_path, 'r', encoding='utf-8') as file: + with open(file_path, encoding="utf-8") as file: return file.read() + # Load API_PROMPT -api_prompt_path = Path(__file__).parent / "datalab-api-prompt.md" +api_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "datalab-api-prompt.md" API_PROMPT = load_file_content(api_prompt_path) # Load SYSTEM_PROMPT -system_prompt_path = Path(__file__).parent / "system-prompt.md" -SYSTEM_PROMPT = load_file_content(system_prompt_path) \ No newline at end of file +system_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "system-prompt.md" +SYSTEM_PROMPT = load_file_content(system_prompt_path) + +if __name__ == "__main__": + print(SYSTEM_PROMPT) + print(API_PROMPT) diff --git a/src/yellowhammer/test.ipynb b/src/yellowhammer/test.ipynb index 4961327..7ff7249 100644 --- a/src/yellowhammer/test.ipynb +++ b/src/yellowhammer/test.ipynb @@ -15,7 +15,7 @@ "\n", "path = \"system-prompt.md\"\n", "loader = TextLoader(path)\n", - "SYSTEM_PROMPT = loader.load()[0].page_content\n" + "SYSTEM_PROMPT = loader.load()[0].page_content" ] }, { @@ -45,9 +45,10 @@ "outputs": [], "source": [ "def load_file_content(file_path):\n", - " with open(file_path, 'r', encoding='utf-8') as file:\n", + " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", " return file.read()\n", "\n", + "\n", "# Load API_PROMPT\n", "api_prompt_path = \"datalab-api-prompt.md\"\n", "API_PROMPT_ = load_file_content(api_prompt_path)\n", @@ -114,7 +115,7 @@ } ], "source": [ - "len(SYSTEM_PROMPT_)\n" + "len(SYSTEM_PROMPT_)" ] }, { @@ -157,6 +158,38 @@ "SYSTEM_PROMPT" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name '__file__' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Load API_PROMPT\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m api_prompt_path \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;18;43m__file__\u001b[39;49m)\u001b[38;5;241m.\u001b[39mparent \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompts\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatalab-api-prompt.md\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 9\u001b[0m API_PROMPT \u001b[38;5;241m=\u001b[39m load_file_content(api_prompt_path)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Load SYSTEM_PROMPT\u001b[39;00m\n", + "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined" + ] + } + ], + "source": [ + "def load_file_content(file_path):\n", + " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", + " return file.read()\n", + "\n", + "\n", + "# Load API_PROMPT\n", + "api_prompt_path = Path(__file__).parent / \"prompts\" / \"datalab-api-prompt.md\"\n", + "API_PROMPT = load_file_content(api_prompt_path)\n", + "\n", + "# Load SYSTEM_PROMPT\n", + "system_prompt_path = Path(__file__).parent / \"prompts\" / \"system-prompt.md\"\n", + "SYSTEM_PROMPT = load_file_content(system_prompt_path)" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/uv.lock b/uv.lock index c78ff28..d6a0291 100644 --- a/uv.lock +++ b/uv.lock @@ -3053,7 +3053,7 @@ wheels = [ [[package]] name = "yellowhammer" -version = "0.1.dev11+gf3789f6.d20241022" +version = "0.1.dev12+g7b44707.d20241024" source = { editable = "." } dependencies = [ { name = "datalab-api" }, From 4f3128f6728ec7bc529eb1b9b38441d2fc1ce48f Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 23 Oct 2024 17:53:02 -0700 Subject: [PATCH 04/24] pre-commit update --- examples/Demo_anthropic.ipynb | 12 +++++++----- examples/Demo_openai.ipynb | 11 +++++------ prompts/datalab-api-prompt.md | 2 +- prompts/system-prompt.md | 2 +- 4 files changed, 14 insertions(+), 13 deletions(-) diff --git a/examples/Demo_anthropic.ipynb b/examples/Demo_anthropic.ipynb index ab73625..b56cf27 100644 --- a/examples/Demo_anthropic.ipynb +++ b/examples/Demo_anthropic.ipynb @@ -27,8 +27,9 @@ "source": [ "import os\n", "import getpass\n", - "os.environ['LLM_PROVIDER'] = \"ANTHROPIC\"\n", - "os.environ['LLM_API_KEY'] = getpass.getpass(\"Enter your LLM API key\")" + "\n", + "os.environ[\"LLM_PROVIDER\"] = \"ANTHROPIC\"\n", + "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" ] }, { @@ -146,6 +147,7 @@ ], "source": [ "from datalab_api import DatalabClient\n", + "\n", "# Create a DatalabClient instance\n", "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", " # Prepare the sample data\n", @@ -153,12 +155,12 @@ " \"item_id\": \"llm-test4\",\n", " \"name\": \"virtual sample (Claude)\",\n", " \"chemform\": \"FrCl\",\n", - " \"type\": \"samples\"\n", + " \"type\": \"samples\",\n", " }\n", - " \n", + "\n", " # Create the sample\n", " response = client.create_item(item_id=\"llm-test4\", item_type=\"samples\", item_data=sample_data)\n", - " \n", + "\n", " # Print the response\n", " print(\"Sample creation response:\")\n", " print(response)" diff --git a/examples/Demo_openai.ipynb b/examples/Demo_openai.ipynb index 02c507e..59cfd16 100644 --- a/examples/Demo_openai.ipynb +++ b/examples/Demo_openai.ipynb @@ -19,8 +19,9 @@ "source": [ "import os\n", "import getpass\n", - "os.environ['LLM_PROVIDER'] = \"OPENAI\"\n", - "os.environ['LLM_API_KEY'] = getpass.getpass(\"Enter your LLM API key\")" + "\n", + "os.environ[\"LLM_PROVIDER\"] = \"OPENAI\"\n", + "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" ] }, { @@ -101,11 +102,9 @@ "source": [ "from datalab_api import DatalabClient\n", "import os\n", + "\n", "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", - " json_data = {\n", - " \"name\": \"virtual sample\",\n", - " \"chemform\": \"RbCl\"\n", - " }\n", + " json_data = {\"name\": \"virtual sample\", \"chemform\": \"RbCl\"}\n", " client.create_item(item_id=\"llm-test3\", item_type=\"samples\", item_data=json_data)" ] } diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md index 5497102..9beb219 100755 --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -55,7 +55,7 @@ with DatalabClient("https://demo.datalab-org.io") as client: Here is an abridged JSONSchema for a sample, that also has some info about other types. -```json +```json { "title": "Sample", "description": "A model for representing an experimental sample.", diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index 18ce00c..18e81c6 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -1 +1 @@ -You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question: \ No newline at end of file +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question: From 82ce847e3bf0cb11eab7b197c7a13958af64160b Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Sat, 26 Oct 2024 22:26:46 -0700 Subject: [PATCH 05/24] add error parsing logic to llm chain --- .gitignore | 5 + src/yellowhammer/llm.py | 22 +++- src/yellowhammer/magics.py | 2 +- src/yellowhammer/test.ipynb | 222 ------------------------------------ 4 files changed, 26 insertions(+), 225 deletions(-) delete mode 100644 src/yellowhammer/test.ipynb diff --git a/.gitignore b/.gitignore index 82f9275..6a7416b 100644 --- a/.gitignore +++ b/.gitignore @@ -160,3 +160,8 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +# Test notebook +examples/Demo_yue.ipynb +examples/yue_examples +examples/FA-Mn-H2PO2.raw \ No newline at end of file diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index a2601ac..fff1ee3 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -25,6 +25,25 @@ class FinalResponse(BaseModel): final_output: Union[code, ConversationalResponse] +def error_parser(output): + """ + Parse the API output to handle errors gracefully. + """ + if output["parsing_error"]: + raw_output = str(output["raw"].content) + error = output["parsing_error"] + out_string = f"Error parsing LLM output. Parse error: {error}. \n Raw output: {raw_output}" + return FinalResponse(final_output=ConversationalResponse(response=out_string)) + + elif not output["parsed"]: + raw_output = str(output["raw"].content) + out_string = f"Error in LLM response. \n Raw output: {raw_output}" + return FinalResponse(final_output=ConversationalResponse(response=out_string)) + + else: + # Return the parsed output (should be FinalResponse) + return output["parsed"] + def get_chain( api_provider, api_key, @@ -62,7 +81,6 @@ def get_chain( ) # Create a chain where the final output takes the FinalResponse schema - chain = prompt | llm.with_structured_output(FinalResponse, include_raw=False) - + chain = prompt | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser # Returns a runnable chain which accepts datalab API documentation "context" and user question "messages" return chain diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index fef7f6b..5342c2a 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -54,7 +54,7 @@ def __init__(self, shell): "-T", "--temp", type=float, - default=0.0, + default=0.1, help="""Temperature, float in [0,1]. Higher values push the algorithm to generate more aggressive/"creative" output. [default=0.1].""", ) diff --git a/src/yellowhammer/test.ipynb b/src/yellowhammer/test.ipynb deleted file mode 100644 index 7ff7249..0000000 --- a/src/yellowhammer/test.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from langchain_community.document_loaders import TextLoader\n", - "\n", - "path = \"datalab-api-prompt.md\"\n", - "loader = TextLoader(path)\n", - "API_PROMPT = loader.load()[0].page_content\n", - "\n", - "path = \"system-prompt.md\"\n", - "loader = TextLoader(path)\n", - "SYSTEM_PROMPT = loader.load()[0].page_content" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/.\\nEach method of the DatalabClient class will return a dictionary constructed directly\\nfrom the JSON response of the Datalab API.\\n\\nDatalab uses \"data blocks\" to take a file attached to a sample, parse it\\naccording to some scientific schema, and then make a plot.\\n\\nThe rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed.\\n\\nAssume the `DATALAB_API_KEY` has been set an environment variable.\\n\\nPython API\\nThis package implements basic functionality for displaying and manipulating entries:\\n\\n```python\\nfrom datalab_api import DatalabClient\\n\\nwith DatalabClient(\"https://demo.datalab-org.io\") as client:\\n\\n # Get the info about this datalab instance\\n client.get_info()\\n\\n # Get the current user\\'s info\\n client.authenticate()\\n\\n # Search for items with the string\\n items = client.search_items(\"search-values\")\\n\\n # List all items of a given type\\n # Types can be \\'samples\\' or \\'starting_materials\\'\\n items = client.get_items(item_type=\"samples\")\\n\\n # Get more info on a particular item called \\'test\\'\\n item = client.get_item(item_id=\"test\")\\n\\n # Create a new item with some data that matches the corresponding `item_type` schema\\n json_data = {\"chemform\": \"NaCl\"}\\n client.create_item(item_id=\"test_new\", item_type=\"samples\", item_data=json_data)\\n\\n # Attach a file to an item and get the uploaded ID\\n file_response = client.upload_file(filepath=\"my_echem_data.mpr\", item_id=\"test\")\\n file_id = file_response[\"file_id\"]\\n\\n # Create a data block for a sample, then show the plot\\n client.create_data_block(item_id=\"test\", file_ids=file_id)\\n\\n # Download all files attached to a sample and return their paths\\n file_paths = client.get_item_files(item_id=\"test\")\\n\\n # Get the item graph, useful for finding relationships\\n graph = client.get_item_graph()\\n\\n```\\n\\nHere is an abridged JSONSchema for a sample, that also has some info about other\\ntypes.\\n\\n```json \\n{\\n \"title\": \"Sample\",\\n \"description\": \"A model for representing an experimental sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"blocks_obj\": {\\n \"title\": \"Blocks Obj\",\\n \"default\": {},\\n \"type\": \"object\"\\n },\\n \"display_order\": {\\n \"title\": \"Display Order\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"collections\": {\\n \"title\": \"Collections\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Collection\"\\n }\\n },\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"samples\",\\n \"const\": \"samples\",\\n \"pattern\": \"^samples$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"description\": {\\n \"title\": \"Description\",\\n \"type\": \"string\"\\n },\\n \"date\": {\\n \"title\": \"Date\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"files\": {\\n \"title\": \"Files\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/File\"\\n }\\n },\\n \"file_ObjectIds\": {\\n \"title\": \"File Objectids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"example\": [\\n \"Na3P\",\\n \"LiNiO2@C\"\\n ],\\n \"type\": \"string\"\\n },\\n \"synthesis_constituents\": {\\n \"title\": \"Synthesis Constituents\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Constituent\"\\n }\\n },\\n \"synthesis_description\": {\\n \"title\": \"Synthesis Description\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item_id\"\\n ],\\n \"definitions\": {\\n \"KnownType\": {\\n \"title\": \"KnownType\",\\n \"description\": \"An enumeration of the types of entry known by this implementation, should be made dynamic in the future.\",\\n \"enum\": [\\n \"samples\",\\n \"starting_materials\",\\n \"blocks\",\\n \"files\",\\n \"people\",\\n \"collections\"\\n ],\\n \"type\": \"string\"\\n },\\n \"File\": {\\n \"title\": \"File\",\\n \"description\": \"A model for representing a file that has been tracked or uploaded to datalab.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"files\",\\n \"const\": \"files\",\\n \"pattern\": \"^files$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"size\": {\\n \"title\": \"Size\",\\n \"description\": \"The size of the file on disk in bytes.\",\\n \"type\": \"integer\"\\n },\\n \"last_modified_remote\": {\\n \"title\": \"Last Modified Remote\",\\n \"description\": \"The last date/time at which the remote file was modified.\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"item_ids\": {\\n \"title\": \"Item Ids\",\\n \"description\": \"A list of item IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"blocks\": {\\n \"title\": \"Blocks\",\\n \"description\": \"A list of block IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"description\": \"The filename on disk.\",\\n \"type\": \"string\"\\n },\\n \"extension\": {\\n \"title\": \"Extension\",\\n \"description\": \"The file extension that the file was uploaded with.\",\\n \"type\": \"string\"\\n },\\n \"original_name\": {\\n \"title\": \"Original Name\",\\n \"description\": \"The raw filename as uploaded.\",\\n \"type\": \"string\"\\n },\\n \"location\": {\\n \"title\": \"Location\",\\n \"description\": \"The location of the file on disk.\",\\n \"type\": \"string\"\\n },\\n \"url_path\": {\\n \"title\": \"Url Path\",\\n \"description\": \"The path to a remote file.\",\\n \"type\": \"string\"\\n },\\n \"source\": {\\n \"title\": \"Source\",\\n \"description\": \"The source of the file, e.g. \\'remote\\' or \\'uploaded\\'.\",\\n \"type\": \"string\"\\n },\\n \"time_added\": {\\n \"title\": \"Time Added\",\\n \"description\": \"The timestamp for the original file upload.\",\\n \"type\": \"string\",\\n \"format\": \"date-time\"\\n },\\n \"metadata\": {\\n \"title\": \"Metadata\",\\n \"description\": \"Any additional metadata.\",\\n \"type\": \"object\"\\n },\\n \"representation\": {\\n \"title\": \"Representation\"\\n },\\n \"source_server_name\": {\\n \"title\": \"Source Server Name\",\\n \"description\": \"The server name at which the file is stored.\",\\n \"type\": \"string\"\\n },\\n \"source_path\": {\\n \"title\": \"Source Path\",\\n \"description\": \"The path to the file on the remote resource.\",\\n \"type\": \"string\"\\n },\\n \"is_live\": {\\n \"title\": \"Is Live\",\\n \"description\": \"Whether or not the file should be watched for future updates.\",\\n \"type\": \"boolean\"\\n }\\n },\\n \"required\": [\\n \"item_ids\",\\n \"blocks\",\\n \"name\",\\n \"extension\",\\n \"time_added\",\\n \"is_live\"\\n ]\\n },\\n \"EntryReference\": {\\n \"title\": \"EntryReference\",\\n \"description\": \"A reference to a database entry by ID and type.\\\\n\\\\nCan include additional arbitarary metadata useful for\\\\ninlining the item data.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"type\": {\\n \"title\": \"Type\",\\n \"type\": \"string\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable Id\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"type\"\\n ]\\n },\\n \"InlineSubstance\": {\\n \"title\": \"InlineSubstance\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"name\"\\n ]\\n },\\n \"Constituent\": {\\n \"title\": \"Constituent\",\\n \"description\": \"A constituent of a sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"item\": {\\n \"title\": \"Item\",\\n \"anyOf\": [\\n {\\n \"$ref\": \"#/definitions/EntryReference\"\\n },\\n {\\n \"$ref\": \"#/definitions/InlineSubstance\"\\n }\\n ]\\n },\\n \"quantity\": {\\n \"title\": \"Quantity\",\\n \"minimum\": 0,\\n \"type\": \"number\"\\n },\\n \"unit\": {\\n \"title\": \"Unit\",\\n \"default\": \"g\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item\",\\n \"quantity\"\\n ]\\n }\\n }\\n}\\n```\\n'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "API_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = \"datalab-api-prompt.md\"\n", - "API_PROMPT_ = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = \"system-prompt.md\"\n", - "SYSTEM_PROMPT_ = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question:'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SYSTEM_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name '__file__' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Load API_PROMPT\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m api_prompt_path \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;18;43m__file__\u001b[39;49m)\u001b[38;5;241m.\u001b[39mparent \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompts\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatalab-api-prompt.md\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 9\u001b[0m API_PROMPT \u001b[38;5;241m=\u001b[39m load_file_content(api_prompt_path)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Load SYSTEM_PROMPT\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined" - ] - } - ], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = Path(__file__).parent / \"prompts\" / \"datalab-api-prompt.md\"\n", - "API_PROMPT = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = Path(__file__).parent / \"prompts\" / \"system-prompt.md\"\n", - "SYSTEM_PROMPT = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From bdb22c207bfb9c0937b19a847acf5320f29f0210 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Sun, 27 Oct 2024 17:40:01 -0700 Subject: [PATCH 06/24] pre-commit autoupdate --- .gitignore | 3 ++- src/yellowhammer/llm.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 6a7416b..167efc0 100644 --- a/.gitignore +++ b/.gitignore @@ -164,4 +164,5 @@ cython_debug/ # Test notebook examples/Demo_yue.ipynb examples/yue_examples -examples/FA-Mn-H2PO2.raw \ No newline at end of file +examples/FA-Mn-H2PO2.raw +src/yellowhammer/yue_test.ipynb diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index fff1ee3..effde10 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -34,16 +34,17 @@ def error_parser(output): error = output["parsing_error"] out_string = f"Error parsing LLM output. Parse error: {error}. \n Raw output: {raw_output}" return FinalResponse(final_output=ConversationalResponse(response=out_string)) - + elif not output["parsed"]: raw_output = str(output["raw"].content) out_string = f"Error in LLM response. \n Raw output: {raw_output}" return FinalResponse(final_output=ConversationalResponse(response=out_string)) - + else: # Return the parsed output (should be FinalResponse) return output["parsed"] + def get_chain( api_provider, api_key, From 9635fe60943271ec8552b96fd9489a408e751295 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Sun, 27 Oct 2024 18:06:50 -0700 Subject: [PATCH 07/24] Remove temporary test files --- .gitignore | 4 + src/yellowhammer/test.ipynb | 222 ------------------------------------ 2 files changed, 4 insertions(+), 222 deletions(-) delete mode 100644 src/yellowhammer/test.ipynb diff --git a/.gitignore b/.gitignore index 82f9275..07f9d85 100644 --- a/.gitignore +++ b/.gitignore @@ -160,3 +160,7 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +src/yellowhammer/yue_test.ipynb +src/yellowhammer/test.ipynb +examples/Demo_yue.ipynb +examples/yue_examples \ No newline at end of file diff --git a/src/yellowhammer/test.ipynb b/src/yellowhammer/test.ipynb deleted file mode 100644 index 7ff7249..0000000 --- a/src/yellowhammer/test.ipynb +++ /dev/null @@ -1,222 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "from langchain_community.document_loaders import TextLoader\n", - "\n", - "path = \"datalab-api-prompt.md\"\n", - "loader = TextLoader(path)\n", - "API_PROMPT = loader.load()[0].page_content\n", - "\n", - "path = \"system-prompt.md\"\n", - "loader = TextLoader(path)\n", - "SYSTEM_PROMPT = loader.load()[0].page_content" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/.\\nEach method of the DatalabClient class will return a dictionary constructed directly\\nfrom the JSON response of the Datalab API.\\n\\nDatalab uses \"data blocks\" to take a file attached to a sample, parse it\\naccording to some scientific schema, and then make a plot.\\n\\nThe rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed.\\n\\nAssume the `DATALAB_API_KEY` has been set an environment variable.\\n\\nPython API\\nThis package implements basic functionality for displaying and manipulating entries:\\n\\n```python\\nfrom datalab_api import DatalabClient\\n\\nwith DatalabClient(\"https://demo.datalab-org.io\") as client:\\n\\n # Get the info about this datalab instance\\n client.get_info()\\n\\n # Get the current user\\'s info\\n client.authenticate()\\n\\n # Search for items with the string\\n items = client.search_items(\"search-values\")\\n\\n # List all items of a given type\\n # Types can be \\'samples\\' or \\'starting_materials\\'\\n items = client.get_items(item_type=\"samples\")\\n\\n # Get more info on a particular item called \\'test\\'\\n item = client.get_item(item_id=\"test\")\\n\\n # Create a new item with some data that matches the corresponding `item_type` schema\\n json_data = {\"chemform\": \"NaCl\"}\\n client.create_item(item_id=\"test_new\", item_type=\"samples\", item_data=json_data)\\n\\n # Attach a file to an item and get the uploaded ID\\n file_response = client.upload_file(filepath=\"my_echem_data.mpr\", item_id=\"test\")\\n file_id = file_response[\"file_id\"]\\n\\n # Create a data block for a sample, then show the plot\\n client.create_data_block(item_id=\"test\", file_ids=file_id)\\n\\n # Download all files attached to a sample and return their paths\\n file_paths = client.get_item_files(item_id=\"test\")\\n\\n # Get the item graph, useful for finding relationships\\n graph = client.get_item_graph()\\n\\n```\\n\\nHere is an abridged JSONSchema for a sample, that also has some info about other\\ntypes.\\n\\n```json \\n{\\n \"title\": \"Sample\",\\n \"description\": \"A model for representing an experimental sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"blocks_obj\": {\\n \"title\": \"Blocks Obj\",\\n \"default\": {},\\n \"type\": \"object\"\\n },\\n \"display_order\": {\\n \"title\": \"Display Order\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"collections\": {\\n \"title\": \"Collections\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Collection\"\\n }\\n },\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"samples\",\\n \"const\": \"samples\",\\n \"pattern\": \"^samples$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"description\": {\\n \"title\": \"Description\",\\n \"type\": \"string\"\\n },\\n \"date\": {\\n \"title\": \"Date\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"files\": {\\n \"title\": \"Files\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/File\"\\n }\\n },\\n \"file_ObjectIds\": {\\n \"title\": \"File Objectids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"example\": [\\n \"Na3P\",\\n \"LiNiO2@C\"\\n ],\\n \"type\": \"string\"\\n },\\n \"synthesis_constituents\": {\\n \"title\": \"Synthesis Constituents\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Constituent\"\\n }\\n },\\n \"synthesis_description\": {\\n \"title\": \"Synthesis Description\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item_id\"\\n ],\\n \"definitions\": {\\n \"KnownType\": {\\n \"title\": \"KnownType\",\\n \"description\": \"An enumeration of the types of entry known by this implementation, should be made dynamic in the future.\",\\n \"enum\": [\\n \"samples\",\\n \"starting_materials\",\\n \"blocks\",\\n \"files\",\\n \"people\",\\n \"collections\"\\n ],\\n \"type\": \"string\"\\n },\\n \"File\": {\\n \"title\": \"File\",\\n \"description\": \"A model for representing a file that has been tracked or uploaded to datalab.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"revision\": {\\n \"title\": \"Revision\",\\n \"default\": 1,\\n \"type\": \"integer\"\\n },\\n \"revisions\": {\\n \"title\": \"Revisions\",\\n \"type\": \"object\"\\n },\\n \"creator_ids\": {\\n \"title\": \"Creator Ids\",\\n \"default\": [],\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"creators\": {\\n \"title\": \"Creators\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/Person\"\\n }\\n },\\n \"type\": {\\n \"title\": \"Type\",\\n \"default\": \"files\",\\n \"const\": \"files\",\\n \"pattern\": \"^files$\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable ID\",\\n \"type\": \"string\"\\n },\\n \"last_modified\": {\\n \"title\": \"Last Modified\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"relationships\": {\\n \"title\": \"Relationships\",\\n \"type\": \"array\",\\n \"items\": {\\n \"$ref\": \"#/definitions/TypedRelationship\"\\n }\\n },\\n \"size\": {\\n \"title\": \"Size\",\\n \"description\": \"The size of the file on disk in bytes.\",\\n \"type\": \"integer\"\\n },\\n \"last_modified_remote\": {\\n \"title\": \"Last Modified Remote\",\\n \"description\": \"The last date/time at which the remote file was modified.\",\\n \"type\": \"date\",\\n \"format\": \"date-time\"\\n },\\n \"item_ids\": {\\n \"title\": \"Item Ids\",\\n \"description\": \"A list of item IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"blocks\": {\\n \"title\": \"Blocks\",\\n \"description\": \"A list of block IDs associated with this file.\",\\n \"type\": \"array\",\\n \"items\": {\\n \"type\": \"string\"\\n }\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"description\": \"The filename on disk.\",\\n \"type\": \"string\"\\n },\\n \"extension\": {\\n \"title\": \"Extension\",\\n \"description\": \"The file extension that the file was uploaded with.\",\\n \"type\": \"string\"\\n },\\n \"original_name\": {\\n \"title\": \"Original Name\",\\n \"description\": \"The raw filename as uploaded.\",\\n \"type\": \"string\"\\n },\\n \"location\": {\\n \"title\": \"Location\",\\n \"description\": \"The location of the file on disk.\",\\n \"type\": \"string\"\\n },\\n \"url_path\": {\\n \"title\": \"Url Path\",\\n \"description\": \"The path to a remote file.\",\\n \"type\": \"string\"\\n },\\n \"source\": {\\n \"title\": \"Source\",\\n \"description\": \"The source of the file, e.g. \\'remote\\' or \\'uploaded\\'.\",\\n \"type\": \"string\"\\n },\\n \"time_added\": {\\n \"title\": \"Time Added\",\\n \"description\": \"The timestamp for the original file upload.\",\\n \"type\": \"string\",\\n \"format\": \"date-time\"\\n },\\n \"metadata\": {\\n \"title\": \"Metadata\",\\n \"description\": \"Any additional metadata.\",\\n \"type\": \"object\"\\n },\\n \"representation\": {\\n \"title\": \"Representation\"\\n },\\n \"source_server_name\": {\\n \"title\": \"Source Server Name\",\\n \"description\": \"The server name at which the file is stored.\",\\n \"type\": \"string\"\\n },\\n \"source_path\": {\\n \"title\": \"Source Path\",\\n \"description\": \"The path to the file on the remote resource.\",\\n \"type\": \"string\"\\n },\\n \"is_live\": {\\n \"title\": \"Is Live\",\\n \"description\": \"Whether or not the file should be watched for future updates.\",\\n \"type\": \"boolean\"\\n }\\n },\\n \"required\": [\\n \"item_ids\",\\n \"blocks\",\\n \"name\",\\n \"extension\",\\n \"time_added\",\\n \"is_live\"\\n ]\\n },\\n \"EntryReference\": {\\n \"title\": \"EntryReference\",\\n \"description\": \"A reference to a database entry by ID and type.\\\\n\\\\nCan include additional arbitarary metadata useful for\\\\ninlining the item data.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"type\": {\\n \"title\": \"Type\",\\n \"type\": \"string\"\\n },\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"immutable_id\": {\\n \"title\": \"Immutable Id\",\\n \"type\": \"string\"\\n },\\n \"item_id\": {\\n \"title\": \"Item Id\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n },\\n \"refcode\": {\\n \"title\": \"Refcode\",\\n \"minLength\": 1,\\n \"maxLength\": 40,\\n \"pattern\": \"^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"type\"\\n ]\\n },\\n \"InlineSubstance\": {\\n \"title\": \"InlineSubstance\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"name\": {\\n \"title\": \"Name\",\\n \"type\": \"string\"\\n },\\n \"chemform\": {\\n \"title\": \"Chemform\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"name\"\\n ]\\n },\\n \"Constituent\": {\\n \"title\": \"Constituent\",\\n \"description\": \"A constituent of a sample.\",\\n \"type\": \"object\",\\n \"properties\": {\\n \"item\": {\\n \"title\": \"Item\",\\n \"anyOf\": [\\n {\\n \"$ref\": \"#/definitions/EntryReference\"\\n },\\n {\\n \"$ref\": \"#/definitions/InlineSubstance\"\\n }\\n ]\\n },\\n \"quantity\": {\\n \"title\": \"Quantity\",\\n \"minimum\": 0,\\n \"type\": \"number\"\\n },\\n \"unit\": {\\n \"title\": \"Unit\",\\n \"default\": \"g\",\\n \"type\": \"string\"\\n }\\n },\\n \"required\": [\\n \"item\",\\n \"quantity\"\\n ]\\n }\\n }\\n}\\n```\\n'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "API_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = \"datalab-api-prompt.md\"\n", - "API_PROMPT_ = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = \"system-prompt.md\"\n", - "SYSTEM_PROMPT_ = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "11679" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(API_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT_)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "698" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(SYSTEM_PROMPT)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question:'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "SYSTEM_PROMPT" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name '__file__' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 8\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# Load API_PROMPT\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m api_prompt_path \u001b[38;5;241m=\u001b[39m Path(\u001b[38;5;18;43m__file__\u001b[39;49m)\u001b[38;5;241m.\u001b[39mparent \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompts\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatalab-api-prompt.md\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 9\u001b[0m API_PROMPT \u001b[38;5;241m=\u001b[39m load_file_content(api_prompt_path)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Load SYSTEM_PROMPT\u001b[39;00m\n", - "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined" - ] - } - ], - "source": [ - "def load_file_content(file_path):\n", - " with open(file_path, \"r\", encoding=\"utf-8\") as file:\n", - " return file.read()\n", - "\n", - "\n", - "# Load API_PROMPT\n", - "api_prompt_path = Path(__file__).parent / \"prompts\" / \"datalab-api-prompt.md\"\n", - "API_PROMPT = load_file_content(api_prompt_path)\n", - "\n", - "# Load SYSTEM_PROMPT\n", - "system_prompt_path = Path(__file__).parent / \"prompts\" / \"system-prompt.md\"\n", - "SYSTEM_PROMPT = load_file_content(system_prompt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From d9d0d0844b0bd24f70ab1246b566a429b89f58d4 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Mon, 28 Oct 2024 10:38:11 -0700 Subject: [PATCH 08/24] added langgraph dependency --- pyproject.toml | 1 + uv.lock | 54 +++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 54 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e4bff8f..804383b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,7 @@ dependencies = [ "langchain-openai>=0.1.25", "notebook>=7.2.2", "yellowhammer", + "langgraph>=0.2.39", ] [project.urls] diff --git a/uv.lock b/uv.lock index d6a0291..e250e01 100644 --- a/uv.lock +++ b/uv.lock @@ -846,6 +846,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/56/95/9377bcb415797e44274b51d46e3249eba641711cf3348050f76ee7b15ffc/httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0", size = 76395 }, ] +[[package]] +name = "httpx-sse" +version = "0.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/4c/60/8f4281fa9bbf3c8034fd54c0e7412e66edbab6bc74c4996bd616f8d0406e/httpx-sse-0.4.0.tar.gz", hash = "sha256:1e81a3a3070ce322add1d3529ed42eb5f70817f45ed6ec915ab753f961139721", size = 12624 } +wheels = [ + { url = 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"0.1.dev13+g9635fe6.d20241028" source = { editable = "." } dependencies = [ { name = "datalab-api" }, @@ -3062,6 +3112,7 @@ dependencies = [ { name = "langchain" }, { name = "langchain-anthropic" }, { name = "langchain-openai" }, + { name = "langgraph" }, { name = "notebook" }, ] @@ -3080,6 +3131,7 @@ requires-dist = [ { name = "langchain", specifier = ">=0.2.16" }, { name = "langchain-anthropic", specifier = ">=0.1.23" }, { name = "langchain-openai", specifier = ">=0.1.25" }, + { name = "langgraph", specifier = ">=0.2.39" }, { name = "notebook", specifier = ">=7.2.2" }, { name = "yellowhammer" }, ] From 972e3b4f39c5817436c885b3e37480fedf7b62f3 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Sat, 11 Jan 2025 14:25:30 -0800 Subject: [PATCH 09/24] update architecture to accept multimodal inputs --- .DS_Store | Bin 0 -> 6148 bytes .gitignore | 3 + prompts/datalab-api-prompt.md | 10 +- prompts/datalap-api-prompt_block-search.md | 484 +++++++++++++++++++++ pyproject.toml | 3 + src/yellowhammer/llm.py | 51 ++- src/yellowhammer/magics.py | 126 +++++- uv.lock | 13 +- 8 files changed, 646 insertions(+), 44 deletions(-) create mode 100644 .DS_Store create mode 100644 prompts/datalap-api-prompt_block-search.md diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..94e8e06d0b3c0eea52a799b60b44125cd5fb1383 GIT binary patch literal 6148 zcmeHKJ8Hu~5S?*U2;8_#xmWNF7NeX%7qG!0jV*_Sgp{gst{ly8K7<&_jUkPB12b=T zcHRoTLZcB8-F)oVA}bMT;fC^UVQO}6KCwk+6bQ#1uX2!QdH?L+hDr5&!niFsU$T?q zU;g1Vyu`ipL}sY~6`%rCfC^B7n-s9#3u~8wj8uRMP=Q|s?E6sQhBa{v^iKzZj{v|1 zX*aBWmH-w@0BhnHhzv}F3Jj{|h@nA8zGPiZ90P+cn!|_YlQkz4^{3kdX>d zflCD*V!N{b{{+7>|6h{0qXJamt`yLw>$?qJDSPYW<*e5h_!e$8KX5awor2))80hU7 g8*9gpUKDl3);O<;W1!QKcRG+i1Evd&3jDVMXN%qytN;K2 literal 0 HcmV?d00001 diff --git a/.gitignore b/.gitignore index 6c93a85..1a5cd1d 100644 --- a/.gitignore +++ b/.gitignore @@ -170,3 +170,6 @@ examples/Demo_yue.ipynb examples/yue_examples examples/FA-Mn-H2PO2.raw src/yellowhammer/yue_test.ipynb +examples/Demo_flagship.ipynb +examples/HL1-5_5-90_30min.xrdml +examples/IMG_1648.jpg \ No newline at end of file diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md index 9beb219..6d5730a 100755 --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -1,13 +1,17 @@ -Use the datalab Python API package to query entries on the datalab instance at https://demo.datalab-org.io/. +Assume `DATALAB_URL` has been set by an environment variable. + +Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. +The datalab Python API can query entries on a datalab instance at this URL. Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. +Assume `DATALAB_API_KEY` have been set an environment variable. + Datalab uses "data blocks" to take a file attached to a sample, parse it according to some scientific schema, and then make a plot. The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. -Assume the `DATALAB_API_KEY` has been set an environment variable. Python API This package implements basic functionality for displaying and manipulating entries: @@ -15,7 +19,7 @@ This package implements basic functionality for displaying and manipulating entr ```python from datalab_api import DatalabClient -with DatalabClient("https://demo.datalab-org.io") as client: +with DatalabClient(os.getenv('DATALAB_URL')) as client: # Get the info about this datalab instance client.get_info() diff --git a/prompts/datalap-api-prompt_block-search.md b/prompts/datalap-api-prompt_block-search.md new file mode 100644 index 0000000..db25a11 --- /dev/null +++ b/prompts/datalap-api-prompt_block-search.md @@ -0,0 +1,484 @@ +# Preparing prmopt for when datalab block search is implemented in API + + +Datalab is data management platform. We refer to an instance of datalab as "a datalab". One datalab is typically used to capture experimental data for a single research group. The datalab consists of a database of "items" which have types such as "sample". Each item contains "data blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. + +Assume `DATALAB_URL` has been set by an environment variable. + +Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. +The datalab Python API can query entries on a datalab instance at this URL. +Each method of the DatalabClient class will return a dictionary constructed directly +from the JSON response of the Datalab API. + +Assume `DATALAB_API_KEY` have been set an environment variable. + +The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. + +Python API +This package implements basic functionality for displaying and manipulating entries: + +```python +from datalab_api import DatalabClient + +with DatalabClient() as client: + + # Get the info about this datalab instance + client.get_info() + + # Get the current user's info + client.authenticate() + + # Search for items with the string + items = client.search_items("search-values") + + # List all items of a given type + # Types can be 'samples' or 'starting_materials' + items = client.get_items(item_type="samples") + + # Get more info on a particular item called 'test' + item = client.get_item(item_id="test") + + # Create a new item with some data that matches the corresponding `item_type` schema + json_data = {"chemform": "NaCl"} + client.create_item(item_id="test_new", item_type="samples", item_data=json_data) + + # Attach a file to an item and get the uploaded ID + file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") + file_id = file_response["file_id"] + + # Create a data block for a sample, then show the plot + client.create_data_block(item_id="test", file_ids=file_id) + + # Download all files attached to a sample and return their paths + file_paths = client.get_item_files(item_id="test") + + # Get the item graph, useful for finding relationships + graph = client.get_item_graph() + +``` + +If you are asked to search through data blocks for unlisted information, first use the client.get_block_info() function, which returns a list of block definitions in JSON format. **IMPORTANT: block schemas are defined across the datalab and blocks are contained within samples. Samples are also defined by JSON schemas. Do not confuse blocks and samples.** + +Each block has the following structure: + attributes: Contains key details about the block. + accepted_file_extensions: Specifies a list of file extensions the block can process. If the block does not support files, this is empty or null. + description: A brief description of what the block does. + name: The name of the block, indicating its purpose. + version: The version number of the block. + id: A unique identifier for the block. + type: Specifies the general category or type of the block. + +Generic block structure +{ + "attributes": { + "accepted_file_extensions": [".txt", ".wdf"], + "description": "Visualize 1D Raman spectroscopy data.", + "name": "Raman spectroscopy", + "version": "0.1.0" + }, + "id": "raman", + "type": "block_type" +} + +How to Use + + Search by Block Type: Filter through blocks by checking the type attribute to identify blocks belonging to a specific category, such as "block_type". + File Compatibility: Identify which block can process a specific file format by searching within accepted_file_extensions. + Description and Purpose: To understand the functionality of each block, examine the description attribute. + Versioning: Note that each block's version attribute can help in identifying if certain features are available based on the block's version. + +For instance, if you need a block that supports .txt files for mass spectrometry, you would look for blocks with "accepted_file_extensions": [".txt"] and "name": "Mass spectrometry". + +Here is an abridged JSONSchema for a sample, that also has some info about other +types. + +```json +{ + "title": "Sample", + "description": "A model for representing an experimental sample.", + "type": "object", + "properties": { + "blocks_obj": { + "title": "Blocks Obj", + "default": {}, + "type": "object" + }, + "display_order": { + "title": "Display Order", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "collections": { + "title": "Collections", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Collection" + } + }, + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "samples", + "const": "samples", + "pattern": "^samples$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "description": { + "title": "Description", + "type": "string" + }, + "date": { + "title": "Date", + "type": "date", + "format": "date-time" + }, + "name": { + "title": "Name", + "type": "string" + }, + "files": { + "title": "Files", + "type": "array", + "items": { + "$ref": "#/definitions/File" + } + }, + "file_ObjectIds": { + "title": "File Objectids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "chemform": { + "title": "Chemform", + "example": [ + "Na3P", + "LiNiO2@C" + ], + "type": "string" + }, + "synthesis_constituents": { + "title": "Synthesis Constituents", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Constituent" + } + }, + "synthesis_description": { + "title": "Synthesis Description", + "type": "string" + } + }, + "required": [ + "item_id" + ], + "definitions": { + "KnownType": { + "title": "KnownType", + "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", + "enum": [ + "samples", + "starting_materials", + "blocks", + "files", + "people", + "collections" + ], + "type": "string" + }, + "File": { + "title": "File", + "description": "A model for representing a file that has been tracked or uploaded to datalab.", + "type": "object", + "properties": { + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "files", + "const": "files", + "pattern": "^files$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "size": { + "title": "Size", + "description": "The size of the file on disk in bytes.", + "type": "integer" + }, + "last_modified_remote": { + "title": "Last Modified Remote", + "description": "The last date/time at which the remote file was modified.", + "type": "date", + "format": "date-time" + }, + "item_ids": { + "title": "Item Ids", + "description": "A list of item IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "blocks": { + "title": "Blocks", + "description": "A list of block IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "name": { + "title": "Name", + "description": "The filename on disk.", + "type": "string" + }, + "extension": { + "title": "Extension", + "description": "The file extension that the file was uploaded with.", + "type": "string" + }, + "original_name": { + "title": "Original Name", + "description": "The raw filename as uploaded.", + "type": "string" + }, + "location": { + "title": "Location", + "description": "The location of the file on disk.", + "type": "string" + }, + "url_path": { + "title": "Url Path", + "description": "The path to a remote file.", + "type": "string" + }, + "source": { + "title": "Source", + "description": "The source of the file, e.g. 'remote' or 'uploaded'.", + "type": "string" + }, + "time_added": { + "title": "Time Added", + "description": "The timestamp for the original file upload.", + "type": "string", + "format": "date-time" + }, + "metadata": { + "title": "Metadata", + "description": "Any additional metadata.", + "type": "object" + }, + "representation": { + "title": "Representation" + }, + "source_server_name": { + "title": "Source Server Name", + "description": "The server name at which the file is stored.", + "type": "string" + }, + "source_path": { + "title": "Source Path", + "description": "The path to the file on the remote resource.", + "type": "string" + }, + "is_live": { + "title": "Is Live", + "description": "Whether or not the file should be watched for future updates.", + "type": "boolean" + } + }, + "required": [ + "item_ids", + "blocks", + "name", + "extension", + "time_added", + "is_live" + ] + }, + "EntryReference": { + "title": "EntryReference", + "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", + "type": "object", + "properties": { + "type": { + "title": "Type", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "immutable_id": { + "title": "Immutable Id", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + } + }, + "required": [ + "type" + ] + }, + "InlineSubstance": { + "title": "InlineSubstance", + "type": "object", + "properties": { + "name": { + "title": "Name", + "type": "string" + }, + "chemform": { + "title": "Chemform", + "type": "string" + } + }, + "required": [ + "name" + ] + }, + "Constituent": { + "title": "Constituent", + "description": "A constituent of a sample.", + "type": "object", + "properties": { + "item": { + "title": "Item", + "anyOf": [ + { + "$ref": "#/definitions/EntryReference" + }, + { + "$ref": "#/definitions/InlineSubstance" + } + ] + }, + "quantity": { + "title": "Quantity", + "minimum": 0, + "type": "number" + }, + "unit": { + "title": "Unit", + "default": "g", + "type": "string" + } + }, + "required": [ + "item", + "quantity" + ] + } + } +} +``` diff --git a/pyproject.toml b/pyproject.toml index 804383b..9ab76b6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,3 +42,6 @@ dev-dependencies = [ "pre-commit>=4.0.0", "pytest>=8.3.3", ] + +[tool.uv.sources] +datalab-api = { git = "https://github.com/datalab-org/datalab-api.git" } diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index effde10..656fd7f 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -1,30 +1,32 @@ -from langchain_core.prompts import ChatPromptTemplate from pydantic import BaseModel, Field -from .prompt import SYSTEM_PROMPT from typing import Union +# from langchain_core.prompts import ChatPromptTemplate +# from .prompt import SYSTEM_PROMPT -# Pydantic schema to use with .with_structured_output() -class code(BaseModel): +# Pydantic schemas to use with .with_structured_output() +# Docstring acts as a description for the LLM +class Code(BaseModel): """Schema for code solutions""" prefix: str = Field(description="Description of the problem and approach") imports: str = Field(description="Code block import statements") code: str = Field(description="Code block not including import statements") - class ConversationalResponse(BaseModel): """Respond in a conversational manner. Be kind and helpful.""" response: str = Field(description="A conversational response to the user's query") - class FinalResponse(BaseModel): - """The final response can be either a code solution or a conversational response""" - - final_output: Union[code, ConversationalResponse] + """ + Make sure that the final_output field exists. + final_output can contain either Code or ConversationalResponse schemas. + """ + final_output: Union[Code, ConversationalResponse] +# Check for errors in the structured output def error_parser(output): """ Parse the API output to handle errors gracefully. @@ -46,11 +48,23 @@ def error_parser(output): def get_chain( + messages, # ChatPromptTemplate api_provider, api_key, api_model=None, api_temperature=0, ): + """ + Return a runnable chain which accepts {context} (e.g. datalab API documentation) + + Args: + messages (ChatPromptTemplate): A ChatPromptTemplate instance + api_provider (str): API provider name (e.g. "openai", "anthropic") + api_key (str): LLM API key to use + api_model (str, optional): The specific model to use e.g. "gpt-4o-mini" + api_temperature (int, optional): LLM temperature + """ + # API provider logic if api_provider.lower() == "openai": from langchain_openai import ChatOpenAI @@ -63,25 +77,16 @@ def get_chain( from langchain_anthropic import ChatAnthropic if api_model is None: - api_model = "claude-3-5-sonnet-20240620" + # api_model = "claude-3-5-sonnet-20240620" + # https://docs.anthropic.com/en/docs/about-claude/models#model-names + api_model = "claude-3-5-sonnet-latest" llm = ChatAnthropic( model=api_model, temperature=api_temperature, anthropic_api_key=api_key, ) - # Prompt - prompt = ChatPromptTemplate.from_messages( - [ - ( - "system", - SYSTEM_PROMPT, - ), # datalab API info is passed via {context} to the system prompt - ("placeholder", "{messages}"), - ] - ) - # Create a chain where the final output takes the FinalResponse schema - chain = prompt | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser - # Returns a runnable chain which accepts datalab API documentation "context" and user question "messages" + chain = messages | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser + return chain diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 5342c2a..fd65191 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -4,6 +4,8 @@ """ import os +import re +import base64 from IPython import get_ipython from IPython.core.magic import ( @@ -17,28 +19,60 @@ parse_argstring, ) from IPython.display import Markdown -from .llm import get_chain, code, ConversationalResponse -from .prompt import API_PROMPT +from langchain_core.prompts import ChatPromptTemplate +from .llm import get_chain, Code, ConversationalResponse +from .prompt import API_PROMPT, SYSTEM_PROMPT +def get_output(messages, image_dict, temp=0.1): + """ + Send current message list to LLM and return the response + """ -def get_output(messages, temp=0.1): env = os.environ + + # Convert list of tuples to ChatPromptTemplate + messages = ChatPromptTemplate(messages) + agent_executor = get_chain( + messages=messages, api_provider=env.get("LLM_PROVIDER", "OPENAI"), api_key=env.get("LLM_API_KEY"), api_model=env.get("LLM_MODEL", None), api_temperature=env.get("LLM_TEMP", temp), - ) + ) + + # Add image data to LLM input as key-value pairs + args = {"context": API_PROMPT} + args.update(image_dict) + + return agent_executor.invoke(args) - return agent_executor.invoke({"context": API_PROMPT, "messages": messages}) +def parse_paths(input_string): + # A simple regex pattern to match file-path-like substrings: + # [A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+ + # - one or more letters, digits, underscores, dots, slashes, or dashes, + # containing at least one dot that leads to a plausible extension + file_path_pattern = r'([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)' + # Find all file paths + paths = re.findall(file_path_pattern, input_string) + + # Remove those file paths from the original string + remaining_text = re.sub(file_path_pattern, '', input_string) + + # Clean up extra whitespace + remaining_text = re.sub(r'\s+', ' ', remaining_text).strip() + + return {"text": remaining_text, "paths": paths} # Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): super().__init__(shell) - self.messages = [] + + self.messages = [("system", SYSTEM_PROMPT),] # Initialize with system prompt. + self.images = [] # Initialize with empty list of images. # A datalab magic that returns a code block @magic_arguments() @@ -61,30 +95,102 @@ def __init__(self, shell): @line_cell_magic def llm(self, line, cell=None): """ - Chat with the LLM. Return either conversation or code. + Multimodal LLM interaction with Jupyter magics """ args = parse_argstring(self.llm, line) # self.llm is a bound method + # Parse the prompt to extract any image paths if cell is None: prompt = " ".join(args.prompt) else: prompt = cell + prompt = parse_paths(prompt) + prompt_text = prompt["text"] + paths = prompt["paths"] # TODO implement check or make parser only output local paths + + # Add the text component of prompt to the message list + self.messages.append(("human", prompt_text)) + + # Add the image components of the query if found + if paths: + for path in enumerate(paths): + # first get the image data + try: + with open(path[1], "rb") as image_file: + image_data = base64.b64encode(image_file.read()).decode("utf-8") + # append image to the list of images + self.images.append(image_data) + + # generate variable name "image_n" and inject it into a message + image_variable_name = f"image_{len(self.images)}" + image_message = [ + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{{{image_variable_name}}}"}, + } + ] + self.messages.append(("human", image_message)) + except: + print(f"Error: Could not read image file {path[1]}") + pass + + # Add the image message to the message list - self.messages.append(("human", prompt)) - response = get_output(self.messages).final_output + # Create a dict from self.images to pass the image data to langchain + image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} + # Get the output from the LLM + response = get_output(messages=self.messages, image_dict=image_dict).final_output + + # Add LLM response to the message list if isinstance(response, ConversationalResponse): output = response.response self.messages.append(("ai", output)) return Markdown(output) - elif isinstance(response, code): + elif isinstance(response, Code): output = response self.messages.append(("ai", output.prefix)) cell_fill = output.imports + "\n" + output.code get_ipython().set_next_input(cell_fill) return Markdown(output.prefix) + @line_cell_magic + def chat(self, line, cell=None): + """ + Live chat with LLM + """ + while True: + try: + user_input = input("User: ") + # print(f'User: {user_input}') + + if user_input.lower() in ["quit", "exit", "q"]: + print("Goodbye!") + break + + self.messages.append(("human", user_input)) + response = get_output(self.messages).final_output + + if isinstance(response, ConversationalResponse): + output = response.response + self.messages.append(("ai", output)) + print(f'Assistant: {output}\n') + + elif isinstance(response, code): + output = response + self.messages.append(("ai", output.prefix)) + cell_fill = output.imports + "\n" + output.code + print(f'Assistant: {output.prefix}') + get_ipython().set_next_input(cell_fill) + break + + except: + # fallback if input() is not available + # user_input = "What do you know about LangGraph?" + # print("User: " + user_input) + # stream_graph_updates(user_input) + break # If testing interactively, it's convenient to %run as a script in Jupyter if __name__ == "__main__": diff --git a/uv.lock b/uv.lock index e250e01..24ae9c3 100644 --- a/uv.lock +++ b/uv.lock @@ -519,17 +519,14 @@ wheels = [ [[package]] name = "datalab-api" -version = "0.2.4" -source = { registry = "https://pypi.org/simple" } +version = "0.2.7.post5+ge8f3058" +source = { git = "https://github.com/datalab-org/datalab-api.git#e8f30588c5e5500850bd29b72439ee0d80e6d81c" } dependencies = [ { name = "bokeh" }, { name = "httpx" }, + { name = "numpy" }, { name = "rich" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/cf/83/e3cf103831ecedf6e07859a246b02eca06816f46bdefcd428b5ec0d337e1/datalab_api-0.2.4.tar.gz", hash = "sha256:81d356c508bb895e30964bd9097137e3e9b5a80604ed2db88164525e710b270b", size = 18711 } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c7/4b/ea46f265b740b9d5d06cea294d127d12a18dbd53a8487db07bf97805b8ab/datalab_api-0.2.4-py3-none-any.whl", hash = "sha256:6912473f726cd0ae1070e69daca2fbae38201b94c443ae63c8c51e4269de5b35", size = 15854 }, -] [[package]] name = "debugpy" @@ -3103,7 +3100,7 @@ wheels = [ [[package]] name = "yellowhammer" -version = "0.1.dev13+g9635fe6.d20241028" +version = "0.1.dev23+g6ccad75.d20241109" source = { editable = "." } dependencies = [ { name = "datalab-api" }, @@ -3125,7 +3122,7 @@ dev = [ [package.metadata] requires-dist = [ - { name = "datalab-api", specifier = ">=0.2.4" }, + { name = "datalab-api", git = "https://github.com/datalab-org/datalab-api.git" }, { name = "jupyter-ai", specifier = ">=2.24.1" }, { name = "jupyterlab", specifier = ">=4.2.5" }, { name = "langchain", specifier = ">=0.2.16" }, From 8fcf6d2dfaccf67598e05e296ec3d71e31187634 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Sat, 11 Jan 2025 14:40:30 -0800 Subject: [PATCH 10/24] pre-commit checks --- .gitignore | 2 +- src/yellowhammer/llm.py | 7 +++- src/yellowhammer/magics.py | 82 +++++++++++++++++++------------------- 3 files changed, 47 insertions(+), 44 deletions(-) diff --git a/.gitignore b/.gitignore index 1a5cd1d..30b4161 100644 --- a/.gitignore +++ b/.gitignore @@ -172,4 +172,4 @@ examples/FA-Mn-H2PO2.raw src/yellowhammer/yue_test.ipynb examples/Demo_flagship.ipynb examples/HL1-5_5-90_30min.xrdml -examples/IMG_1648.jpg \ No newline at end of file +examples/IMG_1648.jpg diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index 656fd7f..323a10b 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -13,11 +13,13 @@ class Code(BaseModel): imports: str = Field(description="Code block import statements") code: str = Field(description="Code block not including import statements") + class ConversationalResponse(BaseModel): """Respond in a conversational manner. Be kind and helpful.""" response: str = Field(description="A conversational response to the user's query") + class FinalResponse(BaseModel): """ Make sure that the final_output field exists. @@ -26,6 +28,7 @@ class FinalResponse(BaseModel): final_output: Union[Code, ConversationalResponse] + # Check for errors in the structured output def error_parser(output): """ @@ -48,7 +51,7 @@ def error_parser(output): def get_chain( - messages, # ChatPromptTemplate + messages, # ChatPromptTemplate api_provider, api_key, api_model=None, @@ -88,5 +91,5 @@ def get_chain( # Create a chain where the final output takes the FinalResponse schema chain = messages | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser - + return chain diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index fd65191..a5d2487 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -23,6 +23,7 @@ from .llm import get_chain, Code, ConversationalResponse from .prompt import API_PROMPT, SYSTEM_PROMPT + def get_output(messages, image_dict, temp=0.1): """ Send current message list to LLM and return the response @@ -39,7 +40,7 @@ def get_output(messages, image_dict, temp=0.1): api_key=env.get("LLM_API_KEY"), api_model=env.get("LLM_MODEL", None), api_temperature=env.get("LLM_TEMP", temp), - ) + ) # Add image data to LLM input as key-value pairs args = {"context": API_PROMPT} @@ -47,32 +48,36 @@ def get_output(messages, image_dict, temp=0.1): return agent_executor.invoke(args) + def parse_paths(input_string): # A simple regex pattern to match file-path-like substrings: # [A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+ # - one or more letters, digits, underscores, dots, slashes, or dashes, # containing at least one dot that leads to a plausible extension - file_path_pattern = r'([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)' + file_path_pattern = r"([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)" # Find all file paths paths = re.findall(file_path_pattern, input_string) # Remove those file paths from the original string - remaining_text = re.sub(file_path_pattern, '', input_string) - + remaining_text = re.sub(file_path_pattern, "", input_string) + # Clean up extra whitespace - remaining_text = re.sub(r'\s+', ' ', remaining_text).strip() + remaining_text = re.sub(r"\s+", " ", remaining_text).strip() return {"text": remaining_text, "paths": paths} + # Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): super().__init__(shell) - self.messages = [("system", SYSTEM_PROMPT),] # Initialize with system prompt. - self.images = [] # Initialize with empty list of images. + self.messages = [ + ("system", SYSTEM_PROMPT), + ] # Initialize with system prompt. + self.images = [] # Initialize with empty list of images. # A datalab magic that returns a code block @magic_arguments() @@ -106,11 +111,11 @@ def llm(self, line, cell=None): prompt = cell prompt = parse_paths(prompt) prompt_text = prompt["text"] - paths = prompt["paths"] # TODO implement check or make parser only output local paths + paths = prompt["paths"] # TODO implement check or make parser only output local paths # Add the text component of prompt to the message list self.messages.append(("human", prompt_text)) - + # Add the image components of the query if found if paths: for path in enumerate(paths): @@ -120,17 +125,19 @@ def llm(self, line, cell=None): image_data = base64.b64encode(image_file.read()).decode("utf-8") # append image to the list of images self.images.append(image_data) - + # generate variable name "image_n" and inject it into a message image_variable_name = f"image_{len(self.images)}" image_message = [ { "type": "image_url", - "image_url": {"url": f"data:image/jpeg;base64,{{{image_variable_name}}}"}, + "image_url": { + "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" + }, } ] self.messages.append(("human", image_message)) - except: + except FileNotFoundError: print(f"Error: Could not read image file {path[1]}") pass @@ -161,37 +168,30 @@ def chat(self, line, cell=None): Live chat with LLM """ while True: - try: - user_input = input("User: ") - # print(f'User: {user_input}') - - if user_input.lower() in ["quit", "exit", "q"]: - print("Goodbye!") - break - - self.messages.append(("human", user_input)) - response = get_output(self.messages).final_output - - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - print(f'Assistant: {output}\n') - - elif isinstance(response, code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code - print(f'Assistant: {output.prefix}') - get_ipython().set_next_input(cell_fill) - break - - except: - # fallback if input() is not available - # user_input = "What do you know about LangGraph?" - # print("User: " + user_input) - # stream_graph_updates(user_input) + user_input = input("User: ") + # print(f'User: {user_input}') + + if user_input.lower() in ["quit", "exit", "q"]: + print("Goodbye!") + break + + self.messages.append(("human", user_input)) + response = get_output(self.messages).final_output + + if isinstance(response, ConversationalResponse): + output = response.response + self.messages.append(("ai", output)) + print(f"Assistant: {output}\n") + + elif isinstance(response, Code): + output = response + self.messages.append(("ai", output.prefix)) + cell_fill = output.imports + "\n" + output.code + print(f"Assistant: {output.prefix}") + get_ipython().set_next_input(cell_fill) break + # If testing interactively, it's convenient to %run as a script in Jupyter if __name__ == "__main__": get_ipython().register_magics(DatalabMagics) From 2d895a987313e5827487803ee6ed8b6a5b5e82c6 Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Mon, 27 Jan 2025 14:43:58 +0000 Subject: [PATCH 11/24] Remove DS_STORE and add to gitignore --- .DS_Store | Bin 6148 -> 0 bytes .gitignore | 1 + 2 files changed, 1 insertion(+) delete mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 94e8e06d0b3c0eea52a799b60b44125cd5fb1383..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ8Hu~5S?*U2;8_#xmWNF7NeX%7qG!0jV*_Sgp{gst{ly8K7<&_jUkPB12b=T zcHRoTLZcB8-F)oVA}bMT;fC^UVQO}6KCwk+6bQ#1uX2!QdH?L+hDr5&!niFsU$T?q zU;g1Vyu`ipL}sY~6`%rCfC^B7n-s9#3u~8wj8uRMP=Q|s?E6sQhBa{v^iKzZj{v|1 zX*aBWmH-w@0BhnHhzv}F3Jj{|h@nA8zGPiZ90P+cn!|_YlQkz4^{3kdX>d zflCD*V!N{b{{+7>|6h{0qXJamt`yLw>$?qJDSPYW<*e5h_!e$8KX5awor2))80hU7 g8*9gpUKDl3);O<;W1!QKcRG+i1Evd&3jDVMXN%qytN;K2 diff --git a/.gitignore b/.gitignore index 9122838..ea15cee 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ __pycache__/ *.py[cod] *$py.class +**.DS_STORE # C extensions *.so From 8e993aca5cfec59555c2cfd57dcecd445a4ee9df Mon Sep 17 00:00:00 2001 From: Matthew Evans Date: Mon, 27 Jan 2025 14:47:15 +0000 Subject: [PATCH 12/24] Bump uv version and relock --- .github/workflows/ci.yml | 4 ++-- uv.lock | 9 +++------ 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 06fc48b..d8f4762 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -29,7 +29,7 @@ jobs: - name: Set up uv uses: astral-sh/setup-uv@v3 with: - version: "0.4.x" + version: "0.5.x" enable-cache: true - name: Install dependencies @@ -58,7 +58,7 @@ jobs: - name: Set up uv uses: astral-sh/setup-uv@v3 with: - version: "0.4.x" + version: "0.5.x" enable-cache: true - name: Install locked versions of dependencies diff --git a/uv.lock b/uv.lock index 24ae9c3..4f2b0de 100644 --- a/uv.lock +++ b/uv.lock @@ -443,7 +443,7 @@ name = "click" version = "8.1.7" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 } wheels = [ @@ -914,7 +914,7 @@ name = "ipykernel" version = "6.29.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "appnope", marker = "platform_system == 'Darwin'" }, + { name = "appnope", marker = "sys_platform == 'darwin'" }, { name = "comm" }, { name = "debugpy" }, { name = "ipython" }, @@ -2101,8 +2101,6 @@ version = "6.0.0" source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/18/c7/8c6872f7372eb6a6b2e4708b88419fb46b857f7a2e1892966b851cc79fc9/psutil-6.0.0.tar.gz", hash = "sha256:8faae4f310b6d969fa26ca0545338b21f73c6b15db7c4a8d934a5482faa818f2", size = 508067 } wheels = [ - { url = "https://files.pythonhosted.org/packages/c5/66/78c9c3020f573c58101dc43a44f6855d01bbbd747e24da2f0c4491200ea3/psutil-6.0.0-cp27-none-win32.whl", hash = "sha256:02b69001f44cc73c1c5279d02b30a817e339ceb258ad75997325e0e6169d8b35", size = 249766 }, - { url = "https://files.pythonhosted.org/packages/e1/3f/2403aa9558bea4d3854b0e5e567bc3dd8e9fbc1fc4453c0aa9aafeb75467/psutil-6.0.0-cp27-none-win_amd64.whl", hash = "sha256:21f1fb635deccd510f69f485b87433460a603919b45e2a324ad65b0cc74f8fb1", size = 253024 }, { url = "https://files.pythonhosted.org/packages/0b/37/f8da2fbd29690b3557cca414c1949f92162981920699cd62095a984983bf/psutil-6.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:c588a7e9b1173b6e866756dde596fd4cad94f9399daf99ad8c3258b3cb2b47a0", size = 250961 }, { url = "https://files.pythonhosted.org/packages/35/56/72f86175e81c656a01c4401cd3b1c923f891b31fbcebe98985894176d7c9/psutil-6.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ed2440ada7ef7d0d608f20ad89a04ec47d2d3ab7190896cd62ca5fc4fe08bf0", size = 287478 }, { url = "https://files.pythonhosted.org/packages/19/74/f59e7e0d392bc1070e9a70e2f9190d652487ac115bb16e2eff6b22ad1d24/psutil-6.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5fd9a97c8e94059b0ef54a7d4baf13b405011176c3b6ff257c247cae0d560ecd", size = 290455 }, @@ -2910,7 +2908,7 @@ name = "tqdm" version = "4.66.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "platform_system == 'Windows'" }, + { name = "colorama", marker = "sys_platform == 'win32'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/58/83/6ba9844a41128c62e810fddddd72473201f3eacde02046066142a2d96cc5/tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad", size = 169504 } wheels = [ @@ -3100,7 +3098,6 @@ wheels = [ [[package]] name = "yellowhammer" -version = "0.1.dev23+g6ccad75.d20241109" source = { editable = "." } dependencies = [ { name = "datalab-api" }, From 245daf67ee5161b03079601beebd10bffc3736d0 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 29 Jan 2025 10:40:41 -0800 Subject: [PATCH 13/24] add image upload example to yellowhammer demo notebooks --- .gitignore | 2 +- examples/Demo_anthropic.ipynb | 33 +++++++++++++++++++++++++-------- examples/Demo_openai.ipynb | 29 +++++++++++++++++++++++++++-- examples/IMG_1648.jpg | Bin 0 -> 279774 bytes 4 files changed, 53 insertions(+), 11 deletions(-) create mode 100644 examples/IMG_1648.jpg diff --git a/.gitignore b/.gitignore index ea15cee..127a1b0 100644 --- a/.gitignore +++ b/.gitignore @@ -170,4 +170,4 @@ examples/yue_examples examples/FA-Mn-H2PO2.raw examples/Demo_flagship.ipynb examples/HL1-5_5-90_30min.xrdml -examples/IMG_1648.jpg +# examples/IMG_1648.jpg diff --git a/examples/Demo_anthropic.ipynb b/examples/Demo_anthropic.ipynb index b56cf27..f720d3a 100644 --- a/examples/Demo_anthropic.ipynb +++ b/examples/Demo_anthropic.ipynb @@ -17,7 +17,7 @@ "metadata": {}, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "Enter your LLM API key ········\n" @@ -39,7 +39,7 @@ "metadata": {}, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "Enter your Datalab API key ········\n" @@ -168,18 +168,35 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "9f3241bc-ba1f-4807-babb-3a928baad6ea", + "execution_count": 2, + "id": "446c3e95", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/markdown": [ + "The image shows a clear glass vial or sample container with a black screw-on cap. Inside the vial, there appears to be a dark-colored powder or fine material collected at the bottom. The vial is sitting on what looks like a wooden surface with a warm, golden-brown color. The vial appears to be the type commonly used in laboratory settings for storing chemical samples, reagents, or synthesized materials. The powder inside could be a chemical compound or experimental sample, though without more context it's not possible to identify the specific substance." + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "What do you see in this image: IMG_1648.jpg" + ] } ], "metadata": { "kernelspec": { - "display_name": "yellowhammer", + "display_name": ".venv", "language": "python", - "name": "yellowhammer" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/examples/Demo_openai.ipynb b/examples/Demo_openai.ipynb index 59cfd16..1eaf80f 100644 --- a/examples/Demo_openai.ipynb +++ b/examples/Demo_openai.ipynb @@ -107,13 +107,38 @@ " json_data = {\"name\": \"virtual sample\", \"chemform\": \"RbCl\"}\n", " client.create_item(item_id=\"llm-test3\", item_type=\"samples\", item_data=json_data)" ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "40a0b4df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "I can't identify or describe the contents of the image. However, it appears to show a clear container with a black substance at the bottom. If you have any specific questions about it or need assistance with something related, feel free to ask!" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "what do you see in this image: IMG_1648.jpg?" + ] } ], "metadata": { "kernelspec": { - "display_name": "yellowhammer", + "display_name": ".venv", "language": "python", - "name": "yellowhammer" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/examples/IMG_1648.jpg b/examples/IMG_1648.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7cec060947ec45697d5fa6f1f6885babd8639afa GIT binary patch literal 279774 zcmb5Vc~lc;8$SHZOg55`1WZU|O8}9u$l@!OB8G&3;8ufzpk)bQ-(oEqEFdb&BnUxN z7De&3RTD%-q!KK(6cq*pp>{FgM!~*~ENxJ*KrxN1zj=S(Ip1I3KVLW};hdbz-1E%b z_kCU0eLYM4O9sH&v|-~006`Fl!2iI~Admtwkw_vE$RrYpLMBsa)-)QGO0!|IEUj&A z>};3YEa%w^gwFPkD;;^uU3^!p^zdBey~@scwZEU2ztC%y*Z&Rzktr0K1&u?aal9Os 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Date: Tue, 18 Feb 2025 14:27:24 +0000 Subject: [PATCH 14/24] Remove duplicated function --- src/yellowhammer/llm.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index dca47ae..323a10b 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -50,26 +50,6 @@ def error_parser(output): return output["parsed"] -def error_parser(output): - """ - Parse the API output to handle errors gracefully. - """ - if output["parsing_error"]: - raw_output = str(output["raw"].content) - error = output["parsing_error"] - out_string = f"Error parsing LLM output. Parse error: {error}. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) - - elif not output["parsed"]: - raw_output = str(output["raw"].content) - out_string = f"Error in LLM response. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) - - else: - # Return the parsed output (should be FinalResponse) - return output["parsed"] - - def get_chain( messages, # ChatPromptTemplate api_provider, From 794058974dd70a509ef82bbf885c8ada2abd7b34 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Fri, 14 Mar 2025 13:20:45 -0700 Subject: [PATCH 15/24] update anthropic model to claude 3.7 --- src/yellowhammer/llm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py index 323a10b..233b2f5 100644 --- a/src/yellowhammer/llm.py +++ b/src/yellowhammer/llm.py @@ -80,9 +80,9 @@ def get_chain( from langchain_anthropic import ChatAnthropic if api_model is None: - # api_model = "claude-3-5-sonnet-20240620" # https://docs.anthropic.com/en/docs/about-claude/models#model-names - api_model = "claude-3-5-sonnet-latest" + # api_model = "claude-3-5-sonnet-latest" + api_model = "claude-3-7-sonnet-latest" llm = ChatAnthropic( model=api_model, temperature=api_temperature, From f4193b8c14293c879bbea50d3cd9b94c27d4b8ac Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Tue, 18 Mar 2025 11:58:03 -0700 Subject: [PATCH 16/24] update prompt to improve sample searching --- prompts/datalab-api-prompt.md | 71 +++++++++++++++---- ...ch.md => datalab-api-prompt_deprecated.md} | 46 ++---------- src/yellowhammer/magics.py | 2 +- 3 files changed, 64 insertions(+), 55 deletions(-) mode change 100755 => 100644 prompts/datalab-api-prompt.md rename prompts/{datalap-api-prompt_block-search.md => datalab-api-prompt_deprecated.md} (82%) mode change 100644 => 100755 diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md old mode 100755 new mode 100644 index 6d5730a..46c6afa --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -1,25 +1,21 @@ +Datalab consists of a database of "items" which have types such as "sample". Each item contains "blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. + Assume `DATALAB_URL` has been set by an environment variable. +Assume `DATALAB_API_KEY` have been set an environment variable. Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. The datalab Python API can query entries on a datalab instance at this URL. -Each method of the DatalabClient class will return a dictionary constructed directly -from the JSON response of the Datalab API. - -Assume `DATALAB_API_KEY` have been set an environment variable. - -Datalab uses "data blocks" to take a file attached to a sample, parse it -according to some scientific schema, and then make a plot. +Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. - Python API This package implements basic functionality for displaying and manipulating entries: ```python from datalab_api import DatalabClient -with DatalabClient(os.getenv('DATALAB_URL')) as client: +with DatalabClient() as client: # Get the info about this datalab instance client.get_info() @@ -27,8 +23,15 @@ with DatalabClient(os.getenv('DATALAB_URL')) as client: # Get the current user's info client.authenticate() - # Search for items with the string - items = client.search_items("search-values") + # Get information about all available data block types + block_list = client.get_block_info() + + # Search for items containing a specific block + # (Currently not implemented) + items = client.() + + # Search for items using free text + items = client.search_items("search string") # List all items of a given type # Types can be 'samples' or 'starting_materials' @@ -56,8 +59,48 @@ with DatalabClient(os.getenv('DATALAB_URL')) as client: ``` -Here is an abridged JSONSchema for a sample, that also has some info about other -types. +If you are asked to provide information about the blocks associated with this datalab, you can use client.get_block_info() to return a list of block schema definitions in JSON format. + +Each block schema JSON has the following structure: + attributes: Contains key details about the block. + accepted_file_extensions: Specifies a list of file extensions the block can process. If the block does not support files, this is empty or null. + description: A brief description of what the block does. + name: The name of the block, indicating its purpose. + version: The version number of the block. + id: A unique identifier for the block. + type: Specifies the general category or type of the block. + +An example of a block instance: +{ + "attributes": { + "accepted_file_extensions": [".txt", ".wdf"], + "description": "Visualize 1D Raman spectroscopy data.", + "name": "Raman spectroscopy", + "version": "0.1.0" + }, + "id": "raman", + "type": "block_type" +} + +You may need to combine block searching with item searching. For example, `items = client.get_items(item_type="samples")` will retrieve a list of `sample` time instances in dict format. You can then search for samples containing blocks within these results by checking for 'nblocks' and 'blocks'. Use the `client.get_items()` output for search if possible. + +An example of a sample instance: +{ + 'blocks': [{'blocktype': 'media', 'title': 'Media'}, + {'blocktype': 'xrd', 'title': 'Powder XRD'}], + 'chemform': 'LiNixMnyCozO2', + 'collections': [], + 'creators': [{'contact_email': 'researcher@institution.edu', + 'display_name': 'researcher@institution.edu'}], + 'date': 'YYYY-MM-DDThh:mm:ss', + 'item_id': 'cathode_material_e1', + 'name': 'Cathode material electrode films', + 'nblocks': 2, + 'refcode': 'demo:XXXXXX' + 'type': 'samples' +} + +To help you work with items, here is an example of a JSON schema for a sample (a type of item). This information is retrieved by `client.get_item`. Use this as a reference if you need to manipulate item information in detail. ```json { @@ -448,4 +491,4 @@ types. } } } -``` +``` \ No newline at end of file diff --git a/prompts/datalap-api-prompt_block-search.md b/prompts/datalab-api-prompt_deprecated.md old mode 100644 new mode 100755 similarity index 82% rename from prompts/datalap-api-prompt_block-search.md rename to prompts/datalab-api-prompt_deprecated.md index db25a11..2dd66a8 --- a/prompts/datalap-api-prompt_block-search.md +++ b/prompts/datalab-api-prompt_deprecated.md @@ -1,26 +1,24 @@ -# Preparing prmopt for when datalab block search is implemented in API - - -Datalab is data management platform. We refer to an instance of datalab as "a datalab". One datalab is typically used to capture experimental data for a single research group. The datalab consists of a database of "items" which have types such as "sample". Each item contains "data blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. - Assume `DATALAB_URL` has been set by an environment variable. Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. The datalab Python API can query entries on a datalab instance at this URL. -Each method of the DatalabClient class will return a dictionary constructed directly -from the JSON response of the Datalab API. +Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. Assume `DATALAB_API_KEY` have been set an environment variable. +Datalab uses "data blocks" to take a file attached to a sample, parse it +according to some scientific schema, and then make a plot. + The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. + Python API This package implements basic functionality for displaying and manipulating entries: ```python from datalab_api import DatalabClient -with DatalabClient() as client: +with DatalabClient(os.getenv('DATALAB_URL')) as client: # Get the info about this datalab instance client.get_info() @@ -57,38 +55,6 @@ with DatalabClient() as client: ``` -If you are asked to search through data blocks for unlisted information, first use the client.get_block_info() function, which returns a list of block definitions in JSON format. **IMPORTANT: block schemas are defined across the datalab and blocks are contained within samples. Samples are also defined by JSON schemas. Do not confuse blocks and samples.** - -Each block has the following structure: - attributes: Contains key details about the block. - accepted_file_extensions: Specifies a list of file extensions the block can process. If the block does not support files, this is empty or null. - description: A brief description of what the block does. - name: The name of the block, indicating its purpose. - version: The version number of the block. - id: A unique identifier for the block. - type: Specifies the general category or type of the block. - -Generic block structure -{ - "attributes": { - "accepted_file_extensions": [".txt", ".wdf"], - "description": "Visualize 1D Raman spectroscopy data.", - "name": "Raman spectroscopy", - "version": "0.1.0" - }, - "id": "raman", - "type": "block_type" -} - -How to Use - - Search by Block Type: Filter through blocks by checking the type attribute to identify blocks belonging to a specific category, such as "block_type". - File Compatibility: Identify which block can process a specific file format by searching within accepted_file_extensions. - Description and Purpose: To understand the functionality of each block, examine the description attribute. - Versioning: Note that each block's version attribute can help in identifying if certain features are available based on the block's version. - -For instance, if you need a block that supports .txt files for mass spectrometry, you would look for blocks with "accepted_file_extensions": [".txt"] and "name": "Mass spectrometry". - Here is an abridged JSONSchema for a sample, that also has some info about other types. diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index a5d2487..380adce 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -1,6 +1,6 @@ """ Magics to support LLM interactions in IPython/Jupyter. -Adapted from fperez/jupytee and jan-janssen/LangSim. +Inspired by fperez/jupytee and jan-janssen/LangSim. """ import os From de199a9514ea60d6ccf22ce8da96f97e0710bb2d Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Thu, 27 Mar 2025 23:55:03 -0700 Subject: [PATCH 17/24] pydantic-ai refactor and tool-use PoC --- .gitignore | 1 + examples/Demo_pydantic.ipynb | 146 + prompts/datalab-api-prompt.md | 4 +- prompts/datalab-api-prompt_deprecated.md | 450 --- prompts/system-prompt.md | 14 +- pyproject.toml | 6 +- src/yellowhammer/llm_pydantic.py | 215 ++ src/yellowhammer/magics.py | 181 +- src/yellowhammer/magics_deprecated.py | 197 ++ uv.lock | 3152 +++++++++++++--------- 10 files changed, 2530 insertions(+), 1836 deletions(-) create mode 100644 examples/Demo_pydantic.ipynb delete mode 100755 prompts/datalab-api-prompt_deprecated.md create mode 100644 src/yellowhammer/llm_pydantic.py create mode 100644 src/yellowhammer/magics_deprecated.py diff --git a/.gitignore b/.gitignore index 127a1b0..fe0e7e8 100644 --- a/.gitignore +++ b/.gitignore @@ -171,3 +171,4 @@ examples/FA-Mn-H2PO2.raw examples/Demo_flagship.ipynb examples/HL1-5_5-90_30min.xrdml # examples/IMG_1648.jpg +prompts/deprecated \ No newline at end of file diff --git a/examples/Demo_pydantic.ipynb b/examples/Demo_pydantic.ipynb new file mode 100644 index 0000000..4bcbd48 --- /dev/null +++ b/examples/Demo_pydantic.ipynb @@ -0,0 +1,146 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pydantic-AI datalab agent demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")\n", + "os.environ[\"DATALAB_URL\"] = \"https://api.yellowhammer.datalab-org.io\"\n", + "\n", + "%reload_ext yellowhammer" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Based on the datalab records, Moinak Dutta appears to be conducting research focused on sodium-ion battery materials, specifically alpha-NaFeO2 (sodium iron oxide) cathode materials. The research involves:\n", + "\n", + "1. Systematic synthesis of NaFeO2 samples with multiple preparation attempts (at least 7 different sample sets)\n", + "2. Detailed electrode preparation for battery testing\n", + "3. Electrochemical characterization through coin cell assemblies\n", + "4. Exploring variations in synthesis conditions, including:\n", + " - Different synthesis temperatures (450-650°C)\n", + " - Various cooling methods (air quench, furnace cooling, liquid nitrogen quenching)\n", + " - Investigating phase purity and material characteristics\n", + "\n", + "The research seems concentrated on developing and optimizing sodium-ion battery cathode materials, with a particular focus on understanding the synthesis parameters and electrochemical performance of NaFeO2. The project involves multiple experimental iterations, suggesting a rigorous approach to material development and characterization.\n", + "Usage(requests=6,\n", + " request_tokens=45310,\n", + " response_tokens=427,\n", + " total_tokens=45737,\n", + " details=None)" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "Can you give an overview of Moinak Dutta's research?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Methods Section for NaFeO2 Synthesis and Electrochemical Characterization\n", + "\n", + "Materials Synthesis\n", + "Alpha-NaFeO2 cathode materials were synthesized via a solid-state reaction method. Stoichiometric amounts of sodium carbonate (Na2CO3) and iron oxide (Fe2O3) were thoroughly ground together using a mortar and pestle to ensure homogeneous mixing. The precursor mixture was subsequently calcined in a muffle furnace at temperatures ranging from 450-650°C for 12 hours, with variations in cooling methods including air quenching, furnace cooling, and rapid cooling using liquid nitrogen.\n", + "\n", + "Electrode Preparation\n", + "Electrode materials were prepared by mixing the synthesized NaFeO2 with carbon black and polyvinylidene fluoride (PVDF) binder in a 7:2:1 weight ratio. The mixture was homogenized using N-methyl-2-pyrrolidone (NMP) as a solvent. The resulting slurry was cast onto aluminum foil current collectors and dried under vacuum at 120°C for 12 hours.\n", + "\n", + "Electrochemical Characterization\n", + "Coin cells (CR2032 type) were assembled in an argon-filled glove box using sodium metal as the counter electrode and a glass fiber separator. The electrolyte consisted of 1 M sodium hexafluorophosphate (NaPF6) dissolved in propylene carbonate. Electrochemical cycling was performed using a battery testing system, with galvanostatic charge-discharge measurements conducted between 2.0-4.5 V vs. Na+/Na at various current densities to evaluate the material's electrochemical performance.\n", + "\n", + "Structural and Morphological Analysis\n", + "The crystalline structure of the synthesized NaFeO2 was characterized using X-ray diffraction (XRD), with particular attention to phase purity and potential beta-phase impurities. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to investigate the morphology and microstructure of the synthesized materials.\n", + "\n", + "Systematic Approach\n", + "Multiple synthesis iterations (designated MD1-1 through MD7-1) were conducted to optimize synthesis parameters, with careful documentation of temperature, cooling method, and resulting material characteristics. Each experimental series involved repeated electrochemical testing to ensure reproducibility and reliability of the results.\n", + "Usage(requests=8,\n", + " request_tokens=82151,\n", + " response_tokens=847,\n", + " total_tokens=82998,\n", + " details=None)" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%llm\n", + "Please write up a methods section for Moinak Dutta's experiments in the format of a JACS paper" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md index 46c6afa..9321db7 100644 --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -59,7 +59,7 @@ with DatalabClient() as client: ``` -If you are asked to provide information about the blocks associated with this datalab, you can use client.get_block_info() to return a list of block schema definitions in JSON format. +If you are asked to provide information about the blocks associated with this datalab, you can use client.get_block_info() to return a list of block schema definitions (the block manifest) in JSON format. Each block schema JSON has the following structure: attributes: Contains key details about the block. @@ -82,7 +82,7 @@ An example of a block instance: "type": "block_type" } -You may need to combine block searching with item searching. For example, `items = client.get_items(item_type="samples")` will retrieve a list of `sample` time instances in dict format. You can then search for samples containing blocks within these results by checking for 'nblocks' and 'blocks'. Use the `client.get_items()` output for search if possible. +You may need to combine block searching with item searching. For example, `items = client.get_items(item_type="samples")` will retrieve a list of `sample` time instances in dict format (the item manifest). You can then search for samples containing blocks within these results by checking for 'nblocks' and 'blocks'. Use the `client.get_items()` output for search if possible. An example of a sample instance: { diff --git a/prompts/datalab-api-prompt_deprecated.md b/prompts/datalab-api-prompt_deprecated.md deleted file mode 100755 index 2dd66a8..0000000 --- a/prompts/datalab-api-prompt_deprecated.md +++ /dev/null @@ -1,450 +0,0 @@ -Assume `DATALAB_URL` has been set by an environment variable. - -Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. -The datalab Python API can query entries on a datalab instance at this URL. -Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. - -Assume `DATALAB_API_KEY` have been set an environment variable. - -Datalab uses "data blocks" to take a file attached to a sample, parse it -according to some scientific schema, and then make a plot. - -The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. - - -Python API -This package implements basic functionality for displaying and manipulating entries: - -```python -from datalab_api import DatalabClient - -with DatalabClient(os.getenv('DATALAB_URL')) as client: - - # Get the info about this datalab instance - client.get_info() - - # Get the current user's info - client.authenticate() - - # Search for items with the string - items = client.search_items("search-values") - - # List all items of a given type - # Types can be 'samples' or 'starting_materials' - items = client.get_items(item_type="samples") - - # Get more info on a particular item called 'test' - item = client.get_item(item_id="test") - - # Create a new item with some data that matches the corresponding `item_type` schema - json_data = {"chemform": "NaCl"} - client.create_item(item_id="test_new", item_type="samples", item_data=json_data) - - # Attach a file to an item and get the uploaded ID - file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") - file_id = file_response["file_id"] - - # Create a data block for a sample, then show the plot - client.create_data_block(item_id="test", file_ids=file_id) - - # Download all files attached to a sample and return their paths - file_paths = client.get_item_files(item_id="test") - - # Get the item graph, useful for finding relationships - graph = client.get_item_graph() - -``` - -Here is an abridged JSONSchema for a sample, that also has some info about other -types. - -```json -{ - "title": "Sample", - "description": "A model for representing an experimental sample.", - "type": "object", - "properties": { - "blocks_obj": { - "title": "Blocks Obj", - "default": {}, - "type": "object" - }, - "display_order": { - "title": "Display Order", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "collections": { - "title": "Collections", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Collection" - } - }, - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "samples", - "const": "samples", - "pattern": "^samples$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "description": { - "title": "Description", - "type": "string" - }, - "date": { - "title": "Date", - "type": "date", - "format": "date-time" - }, - "name": { - "title": "Name", - "type": "string" - }, - "files": { - "title": "Files", - "type": "array", - "items": { - "$ref": "#/definitions/File" - } - }, - "file_ObjectIds": { - "title": "File Objectids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "chemform": { - "title": "Chemform", - "example": [ - "Na3P", - "LiNiO2@C" - ], - "type": "string" - }, - "synthesis_constituents": { - "title": "Synthesis Constituents", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Constituent" - } - }, - "synthesis_description": { - "title": "Synthesis Description", - "type": "string" - } - }, - "required": [ - "item_id" - ], - "definitions": { - "KnownType": { - "title": "KnownType", - "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", - "enum": [ - "samples", - "starting_materials", - "blocks", - "files", - "people", - "collections" - ], - "type": "string" - }, - "File": { - "title": "File", - "description": "A model for representing a file that has been tracked or uploaded to datalab.", - "type": "object", - "properties": { - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "files", - "const": "files", - "pattern": "^files$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "size": { - "title": "Size", - "description": "The size of the file on disk in bytes.", - "type": "integer" - }, - "last_modified_remote": { - "title": "Last Modified Remote", - "description": "The last date/time at which the remote file was modified.", - "type": "date", - "format": "date-time" - }, - "item_ids": { - "title": "Item Ids", - "description": "A list of item IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "blocks": { - "title": "Blocks", - "description": "A list of block IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "name": { - "title": "Name", - "description": "The filename on disk.", - "type": "string" - }, - "extension": { - "title": "Extension", - "description": "The file extension that the file was uploaded with.", - "type": "string" - }, - "original_name": { - "title": "Original Name", - "description": "The raw filename as uploaded.", - "type": "string" - }, - "location": { - "title": "Location", - "description": "The location of the file on disk.", - "type": "string" - }, - "url_path": { - "title": "Url Path", - "description": "The path to a remote file.", - "type": "string" - }, - "source": { - "title": "Source", - "description": "The source of the file, e.g. 'remote' or 'uploaded'.", - "type": "string" - }, - "time_added": { - "title": "Time Added", - "description": "The timestamp for the original file upload.", - "type": "string", - "format": "date-time" - }, - "metadata": { - "title": "Metadata", - "description": "Any additional metadata.", - "type": "object" - }, - "representation": { - "title": "Representation" - }, - "source_server_name": { - "title": "Source Server Name", - "description": "The server name at which the file is stored.", - "type": "string" - }, - "source_path": { - "title": "Source Path", - "description": "The path to the file on the remote resource.", - "type": "string" - }, - "is_live": { - "title": "Is Live", - "description": "Whether or not the file should be watched for future updates.", - "type": "boolean" - } - }, - "required": [ - "item_ids", - "blocks", - "name", - "extension", - "time_added", - "is_live" - ] - }, - "EntryReference": { - "title": "EntryReference", - "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", - "type": "object", - "properties": { - "type": { - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "immutable_id": { - "title": "Immutable Id", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - } - }, - "required": [ - "type" - ] - }, - "InlineSubstance": { - "title": "InlineSubstance", - "type": "object", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "chemform": { - "title": "Chemform", - "type": "string" - } - }, - "required": [ - "name" - ] - }, - "Constituent": { - "title": "Constituent", - "description": "A constituent of a sample.", - "type": "object", - "properties": { - "item": { - "title": "Item", - "anyOf": [ - { - "$ref": "#/definitions/EntryReference" - }, - { - "$ref": "#/definitions/InlineSubstance" - } - ] - }, - "quantity": { - "title": "Quantity", - "minimum": 0, - "type": "number" - }, - "unit": { - "title": "Unit", - "default": "g", - "type": "string" - } - }, - "required": [ - "item", - "quantity" - ] - } - } -} -``` diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index 18e81c6..b3dfcf8 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -1 +1,13 @@ -You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {context}. Answer user questions and use the provided documentation to interact with datalab. Your code responses will be output to a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. Ensure any code you provide can be executed with all required imports and variables defined. Structure your answer with a description of the code solution. Then list the imports. Finally list the functioning code block. Here is the user question: +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {}. Answer user questions and use the provided documentation to interact with datalab. You have access to these tools: + +get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local buffer (the item manifest) + +inspect_blocks: use this tool to inspect the list of blocks found in the datalab. + +item_query: query the values of the dicts in the item manifest generated by get_items with a search string. Simple search strings such as a chemical formula or user's name are likely to work better. Returns any item (samples) dicts containing that query. Returns a list of dicts. Also generates a list of item_ids that can be used with get_item_details. + +get_item_details: if the information provided by item_query is insufficient, use get_item_details to retrieve detailed information. item_query MUST be run first to generate the search list that get_item_details parses. Retrieved information is stored in the items buffer. The inspect_items tool can then be used to interrogate items buffer. + +inspect_items: get_item_details MUST be run first to generate the items buffer. You can then use this tool to inspect the items buffer, which contains detailed information about queried items. + +Here is the user question: \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 9ab76b6..50d456c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,12 +8,10 @@ dependencies = [ "datalab-api>=0.2.4", "jupyter-ai>=2.24.1", "jupyterlab>=4.2.5", - "langchain>=0.2.16", - "langchain-anthropic>=0.1.23", - "langchain-openai>=0.1.25", "notebook>=7.2.2", "yellowhammer", - "langgraph>=0.2.39", + "pydantic-ai>=0.0.46", + "nest-asyncio>=1.6.0", ] [project.urls] diff --git a/src/yellowhammer/llm_pydantic.py b/src/yellowhammer/llm_pydantic.py new file mode 100644 index 0000000..fd8e242 --- /dev/null +++ b/src/yellowhammer/llm_pydantic.py @@ -0,0 +1,215 @@ +import os +import json +from typing import Any, cast +from dataclasses import dataclass + +from datalab_api import DatalabClient + +from typing import Annotated, Any, Union +from typing_extensions import TypeAlias + +from pydantic import BaseModel, Field +from pydantic_ai import Agent, ModelRetry, RunContext +from pydantic_ai.models import KnownModelName +from .prompt import API_PROMPT, SYSTEM_PROMPT + + +# Dependencies +@dataclass +class Deps: + client: DatalabClient + datalab_api_key: str | None + datalab_url: str | None + item_manifest: dict[str, Any] | None = None + block_manifest: dict[str, Any] | None = None + item_search_id_buffer: dict[str, Any] | None = None + items_buffer: dict[str, Any] | None = None + +# Output schemas +class TextResponse(BaseModel): + text: str = Field(description="A short conversational response") + +class CodeResponse(BaseModel): + text: str = Field(description="A short description of the code") + code: str = Field(description="A code snippet") + +Response: TypeAlias = Union[TextResponse, CodeResponse] + +# Define agent and preferred model +default_model = 'anthropic:claude-3-5-haiku-latest' +model = cast(KnownModelName, os.getenv('AI_MODEL', default_model)) # let user set the model +datalab_agent = Agent( + model, + system_prompt=SYSTEM_PROMPT.format(API_PROMPT), # TODO: fix prompts + result_type=Response, + deps_type=Deps, + retries=2 +) + +# Define tools +# Can we add an option to return items of all types in the API call? +@datalab_agent.tool(retries=2) +async def get_items(ctx: RunContext[Deps], item_type: str) -> str: + """Use the datalab get_items function to get a JSON of all items in the database. + + Args: + ctx: The context. + item_type: Usually 'samples' + """ + # TODO check API key when the agent is initialized + # if ctx.deps.datalab_api_key is None: + # # if no API key is provided, say so + # return {'error': 'No datalab API key found.'} + + if ctx.deps.item_manifest == None: + with ctx.deps.client(ctx.deps.datalab_url) as client: + item_type = "samples" + ctx.deps.item_manifest = client.get_items(item_type=item_type) + return "Items retrieved." + + # Set 2 retries in decorator to prevent looping + raise ModelRetry('No data returned, try again.') + + +@datalab_agent.tool +async def inspect_blocks(ctx: RunContext[Deps]) -> str: + """Inspect retrieved blocks in the block manifest. + """ + + if ctx.deps.block_manifest == None: + with ctx.deps.client(ctx.deps.datalab_url) as client: + ctx.deps.block_manifest = client.get_block_info + + # Blocks buffer to plaintext + blocks_buffer = json.dumps(ctx.deps.block_manifest, indent=2) + + r = await datalab_agent.run(blocks_buffer, deps=ctx.deps) + + return r.data + +# # Tool to identify blocks present in the current datalab - may not be necessary +# @datalab_agent.tool +# async def block_query(ctx: RunContext[Deps], query: str) -> list[dict[str, Any]]: +# """Find blocks that are present in the current datalab." + +# Args: +# query: Search the block list for this query string. + +# Returns: +# List of dicts for each block matching the query +# """ +# if ctx.deps.block_manifest == None: +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# if client.get_block_info() == None: +# return {'error': 'No blocks found.'} +# ctx.deps.block_manifest = client.get_block_info() + +# # Check if blocks with the query exist and return them in a list +# if any(item.get('id') == query for item in ctx.deps.block_manifest): +# return [ +# item for item in ctx.deps.block_manifest +# if any(block.get('blocktype') == query for block in item.get('blocks', [])) +# ] +# else: +# return {'error': 'Queried block not found.'} + +# Helper function to search through nested dicts/lists (the item manifest) +def _search_nested(obj, query, case_sensitive=False): + """Recursively search through nested dictionaries and lists for a query. + + Args: + obj: The object to search (dict, list, or primitive value) + query: The string to search for + case_sensitive: Whether the search should be case-sensitive + + Returns: + bool: True if the query was found, False otherwise + """ + if isinstance(obj, dict): + # Look through all keys and values + return any(_search_nested(k, query, case_sensitive) for k in obj.keys()) or \ + any(_search_nested(v, query, case_sensitive) for v in obj.values()) + elif isinstance(obj, list): + # Look through all items in the list + return any(_search_nested(item, query, case_sensitive) for item in obj) + elif isinstance(obj, str): + # String comparison + if case_sensitive: + return query in obj + else: + return query.lower() in obj.lower() + elif obj is not None: + # For numbers or other types, convert to string and check + return str(query) == str(obj) + return False + +# Tool to query item manifest if it exists +@datalab_agent.tool +async def item_query(ctx: RunContext[Deps], query: str) -> list[dict[str, Any]]: + """Use the datalab search function to search through the item manifest at all nesting levels. + Generates a list of item_ids that can be used with get_item_details. + + Args: + query: Search string to find in any field at any nesting level. + + Returns: + List of dictionaries each representing one matching item + """ + if ctx.deps.item_manifest is None: + return {'error': 'Use get_items to generate items list first'} + + # Search all items, looking at all levels of nesting + matching_items = [ + item for item in ctx.deps.item_manifest + if _search_nested(item, query) + ] + + if matching_items: + ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + return matching_items + else: + return {'error': f'No items found containing "{query}"'} + +# Tool to make get_item API calls on item search id buffer +@datalab_agent.tool +async def get_item_details(ctx: RunContext[Deps]) -> dict[str, Any]: + """Use the datalab get_item function to get details JSONs of specific items in the manifest. + This function reads a list of item_ids and retrieves the corresponding items using the datalab API. + item_query must be run first to generate the item search id buffer. + """ + if ctx.deps.item_search_id_buffer == None: + return {'error': 'No item search buffer found, run item_query first.'} + + if ctx.deps.items_buffer == None: + ctx.deps.items_buffer = {} + + with ctx.deps.client(ctx.deps.datalab_url) as client: + for item_id in ctx.deps.item_search_id_buffer: + item = client.get_item(item_id=item_id) + ctx.deps.items_buffer[item_id] = item + return "Items retrieved." + + return {'error': 'No items found.'} + +# Ask agent to inspect items in the items buffer +@datalab_agent.tool +async def inspect_items(ctx: RunContext[Deps]) -> str: + """Inspect retrieved items in the items buffer. + Only use this if the items buffer has been populated with get_item_details! + """ + + if ctx.deps.items_buffer == None: + return {'error': 'No items buffer found, run get_item_details first.'} + + # Items buffer to plaintext + items_buffer = json.dumps(ctx.deps.items_buffer, indent=2) + + r = await datalab_agent.run(items_buffer, deps=ctx.deps) + + return r.data + + +# Validation may be helpful for some outputs +# @datalab_agent.result_validator +# async def validate_result(ctx: RunContext[Deps], result: Response) -> Response: +# # Do some validation here \ No newline at end of file diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 380adce..5e3c296 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -6,6 +6,7 @@ import os import re import base64 +import pprint from IPython import get_ipython from IPython.core.magic import ( @@ -19,35 +20,13 @@ parse_argstring, ) from IPython.display import Markdown -from langchain_core.prompts import ChatPromptTemplate -from .llm import get_chain, Code, ConversationalResponse +# from langchain_core.prompts import ChatPromptTemplate +from datalab_api import DatalabClient +from .llm_pydantic import datalab_agent, Deps from .prompt import API_PROMPT, SYSTEM_PROMPT - - -def get_output(messages, image_dict, temp=0.1): - """ - Send current message list to LLM and return the response - """ - - env = os.environ - - # Convert list of tuples to ChatPromptTemplate - messages = ChatPromptTemplate(messages) - - agent_executor = get_chain( - messages=messages, - api_provider=env.get("LLM_PROVIDER", "OPENAI"), - api_key=env.get("LLM_API_KEY"), - api_model=env.get("LLM_MODEL", None), - api_temperature=env.get("LLM_TEMP", temp), - ) - - # Add image data to LLM input as key-value pairs - args = {"context": API_PROMPT} - args.update(image_dict) - - return agent_executor.invoke(args) - +import asyncio +import nest_asyncio +nest_asyncio.apply() def parse_paths(input_string): # A simple regex pattern to match file-path-like substrings: @@ -67,36 +46,33 @@ def parse_paths(input_string): return {"text": remaining_text, "paths": paths} - # Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): super().__init__(shell) - self.messages = [ - ("system", SYSTEM_PROMPT), - ] # Initialize with system prompt. - self.images = [] # Initialize with empty list of images. + self.messages = None + # self.images = [] # Initialize with empty list of images. # A datalab magic that returns a code block @magic_arguments() @argument( "prompt", nargs="*", - help="""Prompt for code generation. When used as a line magic, + help="""LLM prompt. When used as a line magic, it runs to the end of the line. In cell mode, the entire cell is considered the code generation prompt. """, ) - @argument( - "-T", - "--temp", - type=float, - default=0.1, - help="""Temperature, float in [0,1]. Higher values push the algorithm - to generate more aggressive/"creative" output. [default=0.1].""", - ) + # @argument( + # "-T", + # "--temp", + # type=float, + # default=0.1, + # help="""Temperature, float in [0,1]. Higher values push the algorithm + # to generate more aggressive/"creative" output. [default=0.1].""", + # ) @line_cell_magic def llm(self, line, cell=None): """ @@ -113,85 +89,46 @@ def llm(self, line, cell=None): prompt_text = prompt["text"] paths = prompt["paths"] # TODO implement check or make parser only output local paths - # Add the text component of prompt to the message list - self.messages.append(("human", prompt_text)) - - # Add the image components of the query if found - if paths: - for path in enumerate(paths): - # first get the image data - try: - with open(path[1], "rb") as image_file: - image_data = base64.b64encode(image_file.read()).decode("utf-8") - # append image to the list of images - self.images.append(image_data) - - # generate variable name "image_n" and inject it into a message - image_variable_name = f"image_{len(self.images)}" - image_message = [ - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" - }, - } - ] - self.messages.append(("human", image_message)) - except FileNotFoundError: - print(f"Error: Could not read image file {path[1]}") - pass - - # Add the image message to the message list - - # Create a dict from self.images to pass the image data to langchain - image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} - - # Get the output from the LLM - response = get_output(messages=self.messages, image_dict=image_dict).final_output - - # Add LLM response to the message list - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - return Markdown(output) - - elif isinstance(response, Code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code + # Run the datalab agent + response = datalab_agent.run_sync( + prompt_text, + deps=Deps( + DatalabClient, + os.getenv("DATALAB_API_KEY"), + os.getenv("DATALAB_URL"), + ), + message_history=self.messages, + ) + + self.messages = response.all_messages() + + # Output text and code + if hasattr(response.data, 'code'): + cell_fill = response.data.code get_ipython().set_next_input(cell_fill) - return Markdown(output.prefix) - @line_cell_magic - def chat(self, line, cell=None): - """ - Live chat with LLM - """ - while True: - user_input = input("User: ") - # print(f'User: {user_input}') - - if user_input.lower() in ["quit", "exit", "q"]: - print("Goodbye!") - break - - self.messages.append(("human", user_input)) - response = get_output(self.messages).final_output - - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - print(f"Assistant: {output}\n") - - elif isinstance(response, Code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code - print(f"Assistant: {output.prefix}") - get_ipython().set_next_input(cell_fill) - break - - -# If testing interactively, it's convenient to %run as a script in Jupyter -if __name__ == "__main__": - get_ipython().register_magics(DatalabMagics) + if hasattr(response.data, 'text'): + usage = pprint.pformat(response.usage()) + return Markdown(response.data.text + "\n" + usage) + + return "Unable to generate response." + + # # generate variable name "image_n" and inject it into a message + # image_variable_name = f"image_{len(self.images)}" + # image_message = [ + # { + # "type": "image_url", + # "image_url": { + # "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" + # }, + # } + # ] + # self.messages.append(("human", image_message)) + # except FileNotFoundError: + # print(f"Error: Could not read image file {path[1]}") + # pass + + # # Add the image message to the message list + + # # Create a dict from self.images to pass the image data to langchain + # image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} diff --git a/src/yellowhammer/magics_deprecated.py b/src/yellowhammer/magics_deprecated.py new file mode 100644 index 0000000..380adce --- /dev/null +++ b/src/yellowhammer/magics_deprecated.py @@ -0,0 +1,197 @@ +""" +Magics to support LLM interactions in IPython/Jupyter. +Inspired by fperez/jupytee and jan-janssen/LangSim. +""" + +import os +import re +import base64 + +from IPython import get_ipython +from IPython.core.magic import ( + Magics, + magics_class, + line_cell_magic, +) +from IPython.core.magic_arguments import ( + magic_arguments, + argument, + parse_argstring, +) +from IPython.display import Markdown +from langchain_core.prompts import ChatPromptTemplate +from .llm import get_chain, Code, ConversationalResponse +from .prompt import API_PROMPT, SYSTEM_PROMPT + + +def get_output(messages, image_dict, temp=0.1): + """ + Send current message list to LLM and return the response + """ + + env = os.environ + + # Convert list of tuples to ChatPromptTemplate + messages = ChatPromptTemplate(messages) + + agent_executor = get_chain( + messages=messages, + api_provider=env.get("LLM_PROVIDER", "OPENAI"), + api_key=env.get("LLM_API_KEY"), + api_model=env.get("LLM_MODEL", None), + api_temperature=env.get("LLM_TEMP", temp), + ) + + # Add image data to LLM input as key-value pairs + args = {"context": API_PROMPT} + args.update(image_dict) + + return agent_executor.invoke(args) + + +def parse_paths(input_string): + # A simple regex pattern to match file-path-like substrings: + # [A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+ + # - one or more letters, digits, underscores, dots, slashes, or dashes, + # containing at least one dot that leads to a plausible extension + file_path_pattern = r"([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)" + + # Find all file paths + paths = re.findall(file_path_pattern, input_string) + + # Remove those file paths from the original string + remaining_text = re.sub(file_path_pattern, "", input_string) + + # Clean up extra whitespace + remaining_text = re.sub(r"\s+", " ", remaining_text).strip() + + return {"text": remaining_text, "paths": paths} + + +# Class to manage state and expose the main magics +@magics_class +class DatalabMagics(Magics): + def __init__(self, shell): + super().__init__(shell) + + self.messages = [ + ("system", SYSTEM_PROMPT), + ] # Initialize with system prompt. + self.images = [] # Initialize with empty list of images. + + # A datalab magic that returns a code block + @magic_arguments() + @argument( + "prompt", + nargs="*", + help="""Prompt for code generation. When used as a line magic, + it runs to the end of the line. In cell mode, the entire cell + is considered the code generation prompt. + """, + ) + @argument( + "-T", + "--temp", + type=float, + default=0.1, + help="""Temperature, float in [0,1]. Higher values push the algorithm + to generate more aggressive/"creative" output. [default=0.1].""", + ) + @line_cell_magic + def llm(self, line, cell=None): + """ + Multimodal LLM interaction with Jupyter magics + """ + args = parse_argstring(self.llm, line) # self.llm is a bound method + + # Parse the prompt to extract any image paths + if cell is None: + prompt = " ".join(args.prompt) + else: + prompt = cell + prompt = parse_paths(prompt) + prompt_text = prompt["text"] + paths = prompt["paths"] # TODO implement check or make parser only output local paths + + # Add the text component of prompt to the message list + self.messages.append(("human", prompt_text)) + + # Add the image components of the query if found + if paths: + for path in enumerate(paths): + # first get the image data + try: + with open(path[1], "rb") as image_file: + image_data = base64.b64encode(image_file.read()).decode("utf-8") + # append image to the list of images + self.images.append(image_data) + + # generate variable name "image_n" and inject it into a message + image_variable_name = f"image_{len(self.images)}" + image_message = [ + { + "type": "image_url", + "image_url": { + "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" + }, + } + ] + self.messages.append(("human", image_message)) + except FileNotFoundError: + print(f"Error: Could not read image file {path[1]}") + pass + + # Add the image message to the message list + + # Create a dict from self.images to pass the image data to langchain + image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} + + # Get the output from the LLM + response = get_output(messages=self.messages, image_dict=image_dict).final_output + + # Add LLM response to the message list + if isinstance(response, ConversationalResponse): + output = response.response + self.messages.append(("ai", output)) + return Markdown(output) + + elif isinstance(response, Code): + output = response + self.messages.append(("ai", output.prefix)) + cell_fill = output.imports + "\n" + output.code + get_ipython().set_next_input(cell_fill) + return Markdown(output.prefix) + + @line_cell_magic + def chat(self, line, cell=None): + """ + Live chat with LLM + """ + while True: + user_input = input("User: ") + # print(f'User: {user_input}') + + if user_input.lower() in ["quit", "exit", "q"]: + print("Goodbye!") + break + + self.messages.append(("human", user_input)) + response = get_output(self.messages).final_output + + if isinstance(response, ConversationalResponse): + output = response.response + self.messages.append(("ai", output)) + print(f"Assistant: {output}\n") + + elif isinstance(response, Code): + output = response + self.messages.append(("ai", output.prefix)) + cell_fill = output.imports + "\n" + output.code + print(f"Assistant: {output.prefix}") + get_ipython().set_next_input(cell_fill) + break + + +# If testing interactively, it's convenient to %run as a script in Jupyter +if __name__ == "__main__": + get_ipython().register_magics(DatalabMagics) diff --git a/uv.lock 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<14996988+yue-here@users.noreply.github.com> Date: Sat, 12 Apr 2025 23:19:01 -0700 Subject: [PATCH 18/24] add full text search, query inspector & code writer tools --- .gitignore | 3 +- prompts/code-prompt.md | 453 ++++++++++++++++ prompts/datalab-api-prompt.md | 495 +----------------- prompts/system-prompt.md | 18 +- pyproject.toml | 2 + src/yellowhammer/fulltext_search.py | 236 +++++++++ .../{llm.py => llm_deprecated.py} | 0 src/yellowhammer/llm_pydantic.py | 168 +++--- src/yellowhammer/magics.py | 65 ++- src/yellowhammer/prompt.py | 4 + src/yellowhammer/utils.py | 83 +++ uv.lock | 156 +++++- 12 files changed, 1103 insertions(+), 580 deletions(-) create mode 100644 prompts/code-prompt.md create mode 100644 src/yellowhammer/fulltext_search.py rename src/yellowhammer/{llm.py => llm_deprecated.py} (100%) create mode 100644 src/yellowhammer/utils.py diff --git a/.gitignore b/.gitignore index fe0e7e8..ed0f787 100644 --- a/.gitignore +++ b/.gitignore @@ -171,4 +171,5 @@ examples/FA-Mn-H2PO2.raw examples/Demo_flagship.ipynb examples/HL1-5_5-90_30min.xrdml # examples/IMG_1648.jpg -prompts/deprecated \ No newline at end of file +prompts/deprecated +examples/llm_trace.txt \ No newline at end of file diff --git a/prompts/code-prompt.md b/prompts/code-prompt.md new file mode 100644 index 0000000..3debbda --- /dev/null +++ b/prompts/code-prompt.md @@ -0,0 +1,453 @@ +Your task is to write datalab API code to fulfil the user's request. Your code responses will be used in a Jupyter notebook cell that has access to the datalab API and common python scientific libraries. + +Datalab consists of a database of "items" which have types such as "sample". Each item contains "blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. + +Assume `DATALAB_URL` has been set by an environment variable. +Assume `DATALAB_API_KEY` have been set an environment variable. + +Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. +The datalab Python API can query entries on a datalab instance at this URL. +Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. + +Python API +This package implements basic functionality for displaying and manipulating entries: + +```python +from datalab_api import DatalabClient + +with DatalabClient(DATALAB_URL) as client: + + # Get the info about this datalab instance + client.get_info() + + # Get the current user's info + client.authenticate() + + # Get information about all available data block types + block_list = client.get_block_info() + + # Search for items containing a specific block + # (Currently not implemented) + items = client.() + + # Search for items using free text + items = client.search_items("search string") + + # List all items of a given type + # Types can be 'samples' or 'starting_materials' + items = client.get_items(item_type="samples") + + # Get more info on a particular item called 'test' + item = client.get_item(item_id="test") + + # Create a new item with some data that matches the corresponding `item_type` schema + json_data = {"chemform": "NaCl"} + client.create_item(item_id="test_new", item_type="samples", item_data=json_data) + + # Attach a file to an item and get the uploaded ID + file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") + file_id = file_response["file_id"] + + # Create a data block for a sample, then show the plot + client.create_data_block(item_id="test", file_ids=file_id) + + # Download all files attached to a sample and return their paths + file_paths = client.get_item_files(item_id="test") + + # Get the item graph, useful for finding relationships + graph = client.get_item_graph() + +``` + +To help you work with items, here is an example of a JSON schema for a sample (a type of item). This information is retrieved by `client.get_item`. Use this as a reference if you need to manipulate item information in detail. + +```json +{ + "title": "Sample", + "description": "A model for representing an experimental sample.", + "type": "object", + "properties": { + "blocks_obj": { + "title": "Blocks Obj", + "default": {}, + "type": "object" + }, + "display_order": { + "title": "Display Order", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "collections": { + "title": "Collections", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Collection" + } + }, + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "samples", + "const": "samples", + "pattern": "^samples$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "description": { + "title": "Description", + "type": "string" + }, + "date": { + "title": "Date", + "type": "date", + "format": "date-time" + }, + "name": { + "title": "Name", + "type": "string" + }, + "files": { + "title": "Files", + "type": "array", + "items": { + "$ref": "#/definitions/File" + } + }, + "file_ObjectIds": { + "title": "File Objectids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "chemform": { + "title": "Chemform", + "example": [ + "Na3P", + "LiNiO2@C" + ], + "type": "string" + }, + "synthesis_constituents": { + "title": "Synthesis Constituents", + "default": [], + "type": "array", + "items": { + "$ref": "#/definitions/Constituent" + } + }, + "synthesis_description": { + "title": "Synthesis Description", + "type": "string" + } + }, + "required": [ + "item_id" + ], + "definitions": { + "KnownType": { + "title": "KnownType", + "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", + "enum": [ + "samples", + "starting_materials", + "blocks", + "files", + "people", + "collections" + ], + "type": "string" + }, + "File": { + "title": "File", + "description": "A model for representing a file that has been tracked or uploaded to datalab.", + "type": "object", + "properties": { + "revision": { + "title": "Revision", + "default": 1, + "type": "integer" + }, + "revisions": { + "title": "Revisions", + "type": "object" + }, + "creator_ids": { + "title": "Creator Ids", + "default": [], + "type": "array", + "items": { + "type": "string" + } + }, + "creators": { + "title": "Creators", + "type": "array", + "items": { + "$ref": "#/definitions/Person" + } + }, + "type": { + "title": "Type", + "default": "files", + "const": "files", + "pattern": "^files$", + "type": "string" + }, + "immutable_id": { + "title": "Immutable ID", + "type": "string" + }, + "last_modified": { + "title": "Last Modified", + "type": "date", + "format": "date-time" + }, + "relationships": { + "title": "Relationships", + "type": "array", + "items": { + "$ref": "#/definitions/TypedRelationship" + } + }, + "size": { + "title": "Size", + "description": "The size of the file on disk in bytes.", + "type": "integer" + }, + "last_modified_remote": { + "title": "Last Modified Remote", + "description": "The last date/time at which the remote file was modified.", + "type": "date", + "format": "date-time" + }, + "item_ids": { + "title": "Item Ids", + "description": "A list of item IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "blocks": { + "title": "Blocks", + "description": "A list of block IDs associated with this file.", + "type": "array", + "items": { + "type": "string" + } + }, + "name": { + "title": "Name", + "description": "The filename on disk.", + "type": "string" + }, + "extension": { + "title": "Extension", + "description": "The file extension that the file was uploaded with.", + "type": "string" + }, + "original_name": { + "title": "Original Name", + "description": "The raw filename as uploaded.", + "type": "string" + }, + "location": { + "title": "Location", + "description": "The location of the file on disk.", + "type": "string" + }, + "url_path": { + "title": "Url Path", + "description": "The path to a remote file.", + "type": "string" + }, + "source": { + "title": "Source", + "description": "The source of the file, e.g. 'remote' or 'uploaded'.", + "type": "string" + }, + "time_added": { + "title": "Time Added", + "description": "The timestamp for the original file upload.", + "type": "string", + "format": "date-time" + }, + "metadata": { + "title": "Metadata", + "description": "Any additional metadata.", + "type": "object" + }, + "representation": { + "title": "Representation" + }, + "source_server_name": { + "title": "Source Server Name", + "description": "The server name at which the file is stored.", + "type": "string" + }, + "source_path": { + "title": "Source Path", + "description": "The path to the file on the remote resource.", + "type": "string" + }, + "is_live": { + "title": "Is Live", + "description": "Whether or not the file should be watched for future updates.", + "type": "boolean" + } + }, + "required": [ + "item_ids", + "blocks", + "name", + "extension", + "time_added", + "is_live" + ] + }, + "EntryReference": { + "title": "EntryReference", + "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", + "type": "object", + "properties": { + "type": { + "title": "Type", + "type": "string" + }, + "name": { + "title": "Name", + "type": "string" + }, + "immutable_id": { + "title": "Immutable Id", + "type": "string" + }, + "item_id": { + "title": "Item Id", + "minLength": 1, + "maxLength": 40, + "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + }, + "refcode": { + "title": "Refcode", + "minLength": 1, + "maxLength": 40, + "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", + "type": "string" + } + }, + "required": [ + "type" + ] + }, + "InlineSubstance": { + "title": "InlineSubstance", + "type": "object", + "properties": { + "name": { + "title": "Name", + "type": "string" + }, + "chemform": { + "title": "Chemform", + "type": "string" + } + }, + "required": [ + "name" + ] + }, + "Constituent": { + "title": "Constituent", + "description": "A constituent of a sample.", + "type": "object", + "properties": { + "item": { + "title": "Item", + "anyOf": [ + { + "$ref": "#/definitions/EntryReference" + }, + { + "$ref": "#/definitions/InlineSubstance" + } + ] + }, + "quantity": { + "title": "Quantity", + "minimum": 0, + "type": "number" + }, + "unit": { + "title": "Unit", + "default": "g", + "type": "string" + } + }, + "required": [ + "item", + "quantity" + ] + } + } +} +``` \ No newline at end of file diff --git a/prompts/datalab-api-prompt.md b/prompts/datalab-api-prompt.md index 9321db7..12e2be4 100644 --- a/prompts/datalab-api-prompt.md +++ b/prompts/datalab-api-prompt.md @@ -1,494 +1 @@ -Datalab consists of a database of "items" which have types such as "sample". Each item contains "blocks" which contain information about the item. Block types are shared across the datalab and described by a JSON schema. - -Assume `DATALAB_URL` has been set by an environment variable. -Assume `DATALAB_API_KEY` have been set an environment variable. - -Retrieve the datalab instance URL from the environment variable `DATALAB_URL`. -The datalab Python API can query entries on a datalab instance at this URL. -Each method of the DatalabClient class will return a dictionary constructed directly from the JSON response of the Datalab API. - -The rest of this prompt contains the README for the datalab python API module `datalab_api`, which you already have installed. - -Python API -This package implements basic functionality for displaying and manipulating entries: - -```python -from datalab_api import DatalabClient - -with DatalabClient() as client: - - # Get the info about this datalab instance - client.get_info() - - # Get the current user's info - client.authenticate() - - # Get information about all available data block types - block_list = client.get_block_info() - - # Search for items containing a specific block - # (Currently not implemented) - items = client.() - - # Search for items using free text - items = client.search_items("search string") - - # List all items of a given type - # Types can be 'samples' or 'starting_materials' - items = client.get_items(item_type="samples") - - # Get more info on a particular item called 'test' - item = client.get_item(item_id="test") - - # Create a new item with some data that matches the corresponding `item_type` schema - json_data = {"chemform": "NaCl"} - client.create_item(item_id="test_new", item_type="samples", item_data=json_data) - - # Attach a file to an item and get the uploaded ID - file_response = client.upload_file(filepath="my_echem_data.mpr", item_id="test") - file_id = file_response["file_id"] - - # Create a data block for a sample, then show the plot - client.create_data_block(item_id="test", file_ids=file_id) - - # Download all files attached to a sample and return their paths - file_paths = client.get_item_files(item_id="test") - - # Get the item graph, useful for finding relationships - graph = client.get_item_graph() - -``` - -If you are asked to provide information about the blocks associated with this datalab, you can use client.get_block_info() to return a list of block schema definitions (the block manifest) in JSON format. - -Each block schema JSON has the following structure: - attributes: Contains key details about the block. - accepted_file_extensions: Specifies a list of file extensions the block can process. If the block does not support files, this is empty or null. - description: A brief description of what the block does. - name: The name of the block, indicating its purpose. - version: The version number of the block. - id: A unique identifier for the block. - type: Specifies the general category or type of the block. - -An example of a block instance: -{ - "attributes": { - "accepted_file_extensions": [".txt", ".wdf"], - "description": "Visualize 1D Raman spectroscopy data.", - "name": "Raman spectroscopy", - "version": "0.1.0" - }, - "id": "raman", - "type": "block_type" -} - -You may need to combine block searching with item searching. For example, `items = client.get_items(item_type="samples")` will retrieve a list of `sample` time instances in dict format (the item manifest). You can then search for samples containing blocks within these results by checking for 'nblocks' and 'blocks'. Use the `client.get_items()` output for search if possible. - -An example of a sample instance: -{ - 'blocks': [{'blocktype': 'media', 'title': 'Media'}, - {'blocktype': 'xrd', 'title': 'Powder XRD'}], - 'chemform': 'LiNixMnyCozO2', - 'collections': [], - 'creators': [{'contact_email': 'researcher@institution.edu', - 'display_name': 'researcher@institution.edu'}], - 'date': 'YYYY-MM-DDThh:mm:ss', - 'item_id': 'cathode_material_e1', - 'name': 'Cathode material electrode films', - 'nblocks': 2, - 'refcode': 'demo:XXXXXX' - 'type': 'samples' -} - -To help you work with items, here is an example of a JSON schema for a sample (a type of item). This information is retrieved by `client.get_item`. Use this as a reference if you need to manipulate item information in detail. - -```json -{ - "title": "Sample", - "description": "A model for representing an experimental sample.", - "type": "object", - "properties": { - "blocks_obj": { - "title": "Blocks Obj", - "default": {}, - "type": "object" - }, - "display_order": { - "title": "Display Order", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "collections": { - "title": "Collections", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Collection" - } - }, - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "samples", - "const": "samples", - "pattern": "^samples$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "description": { - "title": "Description", - "type": "string" - }, - "date": { - "title": "Date", - "type": "date", - "format": "date-time" - }, - "name": { - "title": "Name", - "type": "string" - }, - "files": { - "title": "Files", - "type": "array", - "items": { - "$ref": "#/definitions/File" - } - }, - "file_ObjectIds": { - "title": "File Objectids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "chemform": { - "title": "Chemform", - "example": [ - "Na3P", - "LiNiO2@C" - ], - "type": "string" - }, - "synthesis_constituents": { - "title": "Synthesis Constituents", - "default": [], - "type": "array", - "items": { - "$ref": "#/definitions/Constituent" - } - }, - "synthesis_description": { - "title": "Synthesis Description", - "type": "string" - } - }, - "required": [ - "item_id" - ], - "definitions": { - "KnownType": { - "title": "KnownType", - "description": "An enumeration of the types of entry known by this implementation, should be made dynamic in the future.", - "enum": [ - "samples", - "starting_materials", - "blocks", - "files", - "people", - "collections" - ], - "type": "string" - }, - "File": { - "title": "File", - "description": "A model for representing a file that has been tracked or uploaded to datalab.", - "type": "object", - "properties": { - "revision": { - "title": "Revision", - "default": 1, - "type": "integer" - }, - "revisions": { - "title": "Revisions", - "type": "object" - }, - "creator_ids": { - "title": "Creator Ids", - "default": [], - "type": "array", - "items": { - "type": "string" - } - }, - "creators": { - "title": "Creators", - "type": "array", - "items": { - "$ref": "#/definitions/Person" - } - }, - "type": { - "title": "Type", - "default": "files", - "const": "files", - "pattern": "^files$", - "type": "string" - }, - "immutable_id": { - "title": "Immutable ID", - "type": "string" - }, - "last_modified": { - "title": "Last Modified", - "type": "date", - "format": "date-time" - }, - "relationships": { - "title": "Relationships", - "type": "array", - "items": { - "$ref": "#/definitions/TypedRelationship" - } - }, - "size": { - "title": "Size", - "description": "The size of the file on disk in bytes.", - "type": "integer" - }, - "last_modified_remote": { - "title": "Last Modified Remote", - "description": "The last date/time at which the remote file was modified.", - "type": "date", - "format": "date-time" - }, - "item_ids": { - "title": "Item Ids", - "description": "A list of item IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "blocks": { - "title": "Blocks", - "description": "A list of block IDs associated with this file.", - "type": "array", - "items": { - "type": "string" - } - }, - "name": { - "title": "Name", - "description": "The filename on disk.", - "type": "string" - }, - "extension": { - "title": "Extension", - "description": "The file extension that the file was uploaded with.", - "type": "string" - }, - "original_name": { - "title": "Original Name", - "description": "The raw filename as uploaded.", - "type": "string" - }, - "location": { - "title": "Location", - "description": "The location of the file on disk.", - "type": "string" - }, - "url_path": { - "title": "Url Path", - "description": "The path to a remote file.", - "type": "string" - }, - "source": { - "title": "Source", - "description": "The source of the file, e.g. 'remote' or 'uploaded'.", - "type": "string" - }, - "time_added": { - "title": "Time Added", - "description": "The timestamp for the original file upload.", - "type": "string", - "format": "date-time" - }, - "metadata": { - "title": "Metadata", - "description": "Any additional metadata.", - "type": "object" - }, - "representation": { - "title": "Representation" - }, - "source_server_name": { - "title": "Source Server Name", - "description": "The server name at which the file is stored.", - "type": "string" - }, - "source_path": { - "title": "Source Path", - "description": "The path to the file on the remote resource.", - "type": "string" - }, - "is_live": { - "title": "Is Live", - "description": "Whether or not the file should be watched for future updates.", - "type": "boolean" - } - }, - "required": [ - "item_ids", - "blocks", - "name", - "extension", - "time_added", - "is_live" - ] - }, - "EntryReference": { - "title": "EntryReference", - "description": "A reference to a database entry by ID and type.\n\nCan include additional arbitarary metadata useful for\ninlining the item data.", - "type": "object", - "properties": { - "type": { - "title": "Type", - "type": "string" - }, - "name": { - "title": "Name", - "type": "string" - }, - "immutable_id": { - "title": "Immutable Id", - "type": "string" - }, - "item_id": { - "title": "Item Id", - "minLength": 1, - "maxLength": 40, - "pattern": "^(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - }, - "refcode": { - "title": "Refcode", - "minLength": 1, - "maxLength": 40, - "pattern": "^[a-z]{2,10}:(?:[a-zA-Z0-9]+|[a-zA-Z0-9][a-zA-Z0-9._-]+[a-zA-Z0-9])$", - "type": "string" - } - }, - "required": [ - "type" - ] - }, - "InlineSubstance": { - "title": "InlineSubstance", - "type": "object", - "properties": { - "name": { - "title": "Name", - "type": "string" - }, - "chemform": { - "title": "Chemform", - "type": "string" - } - }, - "required": [ - "name" - ] - }, - "Constituent": { - "title": "Constituent", - "description": "A constituent of a sample.", - "type": "object", - "properties": { - "item": { - "title": "Item", - "anyOf": [ - { - "$ref": "#/definitions/EntryReference" - }, - { - "$ref": "#/definitions/InlineSubstance" - } - ] - }, - "quantity": { - "title": "Quantity", - "minimum": 0, - "type": "number" - }, - "unit": { - "title": "Unit", - "default": "g", - "type": "string" - } - }, - "required": [ - "item", - "quantity" - ] - } - } -} -``` \ No newline at end of file +Datalab manages scientific data by storing 'items', which represent physical samples or other experimental artifacts. Items can be e.g. 'samples' or 'cells' and are described by a JSON schema. Information retrieved by the get_item_details tool is structured in this way. Items can contain one or more 'blocks' which contain additional data about the item, such as experimental measurements or images. \ No newline at end of file diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index b3dfcf8..03faf4b 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -1,13 +1,19 @@ -You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here is the documentation of the datalab API: {}. Answer user questions and use the provided documentation to interact with datalab. You have access to these tools: +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here are notes on how data is structured in datalab: {}. In each step, carefully assess whether you have sufficient information to answer the user's query, and if not, obtain more information using the provded tools. -get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local buffer (the item manifest) +You have access to these tools: -inspect_blocks: use this tool to inspect the list of blocks found in the datalab. +code_writer: you can use this tool to return code for interacting with the datalab python API. It has access to the API documentation. -item_query: query the values of the dicts in the item manifest generated by get_items with a search string. Simple search strings such as a chemical formula or user's name are likely to work better. Returns any item (samples) dicts containing that query. Returns a list of dicts. Also generates a list of item_ids that can be used with get_item_details. +get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local item manifest -get_item_details: if the information provided by item_query is insufficient, use get_item_details to retrieve detailed information. item_query MUST be run first to generate the search list that get_item_details parses. Retrieved information is stored in the items buffer. The inspect_items tool can then be used to interrogate items buffer. +item_query: get_items MUST be run first to retrieve the item manifest! You can then use this to query the item manifest using full text search. Use keywords. Returns a list of dicts representing sample (item) overviews matching the query (the search buffer). -inspect_items: get_item_details MUST be run first to generate the items buffer. You can then use this tool to inspect the items buffer, which contains detailed information about queried items. +Once you've performed a query, you can either work with the overviews for big-picture queries, or retrieve full items for deep dives. These tools are available: + +inspect_query: you can use this tool to have a LLM agent inspect the search buffer with a given prompt. If the the output of this tool is enough to answer the user's query, copy it to your final output. Otherwise, obtain more detailed information. + +get_item_details: this tool can retrieve more detailed information for every item. ONLY use this if the information provided in the overviews is insufficient! item_query MUST be run first. The inspect_items tool can then be used to inspect the retrieved information. + +inspect_items: get_item_details MUST be run first to generate the items buffer. The items buffer contains detailed information about queried items. You can use this tool to have a LLM agent inspect the items buffer with a given prompt. Please copy the output of this tool as your final response to the user. Here is the user question: \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 50d456c..c1a5e0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,8 @@ dependencies = [ "yellowhammer", "pydantic-ai>=0.0.46", "nest-asyncio>=1.6.0", + "whoosh-reloaded>=2.7.5", + "logfire>=3.12.0", ] [project.urls] diff --git a/src/yellowhammer/fulltext_search.py b/src/yellowhammer/fulltext_search.py new file mode 100644 index 0000000..450a357 --- /dev/null +++ b/src/yellowhammer/fulltext_search.py @@ -0,0 +1,236 @@ +import json +from typing import List, Dict, Any, Optional, Union +from whoosh.index import create_in +from whoosh.fields import Schema, TEXT, ID, KEYWORD +from whoosh.qparser import QueryParser, MultifieldParser, OrGroup +from whoosh.analysis import StemmingAnalyzer +from whoosh.filedb.filestore import RamStorage + +class FullTextSearchTool: + def __init__(self, samples: List[Dict]): + self.samples = samples + self.storage = RamStorage() + self._build_index() + + def _extract_nested_values(self, sample: Dict) -> Dict[str, str]: + """Extract values from nested structures for indexing""" + extracted = {} + + # Extract block titles and types + if 'blocks' in sample and isinstance(sample['blocks'], list): + block_titles = [] + block_types = [] + for block in sample['blocks']: + if 'title' in block: + block_titles.append(block['title']) + if 'blocktype' in block: + block_types.append(block['blocktype']) + + extracted['block_titles'] = ' '.join(block_titles) + extracted['block_types'] = ' '.join(block_types) + + # Extract creator names + if 'creators' in sample and isinstance(sample['creators'], list): + creator_names = [] + for creator in sample['creators']: + if 'display_name' in creator: + creator_names.append(creator['display_name']) + + extracted['creator_names'] = ' '.join(creator_names) + + return extracted + + def _build_index(self): + """Build a full-text search index from the samples with field-specific indexing in memory""" + # Create a more comprehensive schema + analyzer = StemmingAnalyzer() + schema = Schema( + id=ID(stored=True), + content=TEXT(analyzer=analyzer), + name=TEXT(stored=True), + formula=TEXT(stored=True), + item_id=ID(stored=True), + type=KEYWORD(stored=True, commas=True), + date=ID(stored=True), + creator_names=TEXT(stored=True), + block_titles=TEXT(stored=True), + block_types=KEYWORD(stored=True, commas=True) + ) + + # Create in-memory index + ix = self.storage.create_index(schema) + writer = ix.writer() + + # Add documents to index + for i, sample in enumerate(self.samples): + # Convert sample to string for indexing + content = json.dumps(sample) + + # Extract values from nested structures + nested_values = self._extract_nested_values(sample) + + writer.add_document( + id=str(i), + content=content, + name=sample.get('name', ''), + formula=sample.get('characteristic_chemical_formula', ''), + item_id=sample.get('item_id', ''), + type=sample.get('type', ''), + date=sample.get('date', ''), + creator_names=nested_values.get('creator_names', ''), + block_titles=nested_values.get('block_titles', ''), + block_types=nested_values.get('block_types', '') + ) + + writer.commit() + + def search(self, query_text: str, fields=None, limit: int = 10) -> List[Dict]: + """ + Search for samples matching the query text + + Args: + query_text: Text to search for + fields: List of specific fields to search in (None means search all fields) + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Get the in-memory index + ix = self.storage.open_index() + + with ix.searcher() as searcher: + if fields and isinstance(fields, list): + # Search in specific fields with OR between fields + parser = MultifieldParser(fields, ix.schema, group=OrGroup) + else: + # Default to searching in content field (which has everything) + parser = QueryParser("content", ix.schema) + + # Make query more flexible by wrapping terms in wildcards if simple term + if len(query_text.split()) == 1 and not any(c in query_text for c in ':"*?'): + query_text = f"*{query_text}*" + + # Parse the query + query = parser.parse(query_text) + + # Search + results = searcher.search(query, limit=limit) + + # Get the original samples + matching_samples = [] + for result in results: + sample_id = int(result["id"]) + matching_samples.append(self.samples[sample_id]) + + return matching_samples + + def search_by_field(self, field_queries: Dict[str, str], limit: int = 10) -> List[Dict]: + """ + Search using field-specific queries + + Args: + field_queries: Dictionary mapping field names to query strings + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Get the in-memory index + ix = self.storage.open_index() + + with ix.searcher() as searcher: + query_parts = [] + + for field, query_text in field_queries.items(): + parser = QueryParser(field, ix.schema) + query_parts.append(parser.parse(query_text)) + + # Combine all query parts (implicit AND) + from whoosh.query import And + if query_parts: + combined_query = And(query_parts) + results = searcher.search(combined_query, limit=limit) + + matching_samples = [] + for result in results: + sample_id = int(result["id"]) + matching_samples.append(self.samples[sample_id]) + + return matching_samples + + return [] + + def natural_language_search(self, query_text: str, limit: int = 100) -> List[Dict]: + """ + Search for samples using natural language query. + This method is optimized for use with the yellowhammer agent. + + Args: + query_text: Natural language query string + limit: Maximum number of results to return + + Returns: + List of matching samples + """ + # Parse the query to determine which fields might be relevant + field_queries = enhance_fulltext_query(query_text) + + if "content" in field_queries and len(field_queries) == 1: + # If only searching general content, use normal search + return self.search(field_queries["content"], limit=limit) + else: + # Otherwise use field-specific search + return self.search_by_field(field_queries, limit=limit) + + def update_samples(self, new_samples: List[Dict]): + """ + Update the search index with new samples + + Args: + new_samples: New list of samples to index + """ + self.samples = new_samples + self.storage = RamStorage() # Create a fresh storage + self._build_index() # Rebuild the index + +# Improve the enhance_fulltext_query function to better handle natural language +def enhance_fulltext_query(query: str, data_sample: Dict = None) -> Dict[str, str]: + """ + Parse a natural language query into field-specific queries. + This is a simple implementation that could be replaced with an LLM call. + + Args: + query: Natural language query + data_sample: Sample of the data structure (to help understand schema) + + Returns: + Dictionary of field queries + """ + query = query.lower() + field_queries = {} + + # Simple keyword mapping + if "formula" in query or "chemical" in query: + field_queries["formula"] = query.replace("formula", "").replace("chemical", "").strip() + + if "name" in query: + field_queries["name"] = query.replace("name", "").strip() + + if "creator" in query or "author" in query or "by" in query: + field_queries["creator_names"] = query.replace("creator", "").replace("author", "").replace("by", "").strip() + + if "type" in query: + field_queries["type"] = query.replace("type", "").strip() + + if "date" in query: + field_queries["date"] = query.replace("date", "").strip() + + if "id" in query or "item_id" in query: + field_queries["item_id"] = query.replace("id", "").replace("item_id", "").strip() + + # If no specific fields detected or very short query, search all content + if not field_queries or len(query.split()) <= 3: + field_queries["content"] = query + + return field_queries diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm_deprecated.py similarity index 100% rename from src/yellowhammer/llm.py rename to src/yellowhammer/llm_deprecated.py diff --git a/src/yellowhammer/llm_pydantic.py b/src/yellowhammer/llm_pydantic.py index fd8e242..3fa7a4b 100644 --- a/src/yellowhammer/llm_pydantic.py +++ b/src/yellowhammer/llm_pydantic.py @@ -11,8 +11,9 @@ from pydantic import BaseModel, Field from pydantic_ai import Agent, ModelRetry, RunContext from pydantic_ai.models import KnownModelName -from .prompt import API_PROMPT, SYSTEM_PROMPT - +from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT +from .fulltext_search import FullTextSearchTool +from .utils import process_item_for_chat # Dependencies @dataclass @@ -21,9 +22,11 @@ class Deps: datalab_api_key: str | None datalab_url: str | None item_manifest: dict[str, Any] | None = None - block_manifest: dict[str, Any] | None = None + # block_manifest: dict[str, Any] | None = None item_search_id_buffer: dict[str, Any] | None = None items_buffer: dict[str, Any] | None = None + search_buffer: list[dict] | None = None + search_tool: FullTextSearchTool | None = None # Output schemas class TextResponse(BaseModel): @@ -43,7 +46,8 @@ class CodeResponse(BaseModel): system_prompt=SYSTEM_PROMPT.format(API_PROMPT), # TODO: fix prompts result_type=Response, deps_type=Deps, - retries=2 + retries=2, + instrument=True, ) # Define tools @@ -65,27 +69,29 @@ async def get_items(ctx: RunContext[Deps], item_type: str) -> str: with ctx.deps.client(ctx.deps.datalab_url) as client: item_type = "samples" ctx.deps.item_manifest = client.get_items(item_type=item_type) + # Initialize the search tool with the fetched items + ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) return "Items retrieved." # Set 2 retries in decorator to prevent looping raise ModelRetry('No data returned, try again.') -@datalab_agent.tool -async def inspect_blocks(ctx: RunContext[Deps]) -> str: - """Inspect retrieved blocks in the block manifest. - """ +# @datalab_agent.tool +# async def inspect_blocks(ctx: RunContext[Deps]) -> str: +# """Inspect retrieved blocks in the block manifest. +# """ - if ctx.deps.block_manifest == None: - with ctx.deps.client(ctx.deps.datalab_url) as client: - ctx.deps.block_manifest = client.get_block_info +# if ctx.deps.block_manifest == None: +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# ctx.deps.block_manifest = client.get_block_info - # Blocks buffer to plaintext - blocks_buffer = json.dumps(ctx.deps.block_manifest, indent=2) +# # Blocks buffer to plaintext +# blocks_buffer = json.dumps(ctx.deps.block_manifest, indent=2) - r = await datalab_agent.run(blocks_buffer, deps=ctx.deps) +# r = await datalab_agent.run(blocks_buffer, deps=ctx.deps) - return r.data +# return r.data # # Tool to identify blocks present in the current datalab - may not be necessary # @datalab_agent.tool @@ -114,71 +120,77 @@ async def inspect_blocks(ctx: RunContext[Deps]) -> str: # return {'error': 'Queried block not found.'} # Helper function to search through nested dicts/lists (the item manifest) -def _search_nested(obj, query, case_sensitive=False): - """Recursively search through nested dictionaries and lists for a query. +# def _search_nested(obj, query, case_sensitive=False): +# """Recursively search through nested dictionaries and lists for a query. - Args: - obj: The object to search (dict, list, or primitive value) - query: The string to search for - case_sensitive: Whether the search should be case-sensitive +# Args: +# obj: The object to search (dict, list, or primitive value) +# query: The string to search for +# case_sensitive: Whether the search should be case-sensitive - Returns: - bool: True if the query was found, False otherwise - """ - if isinstance(obj, dict): - # Look through all keys and values - return any(_search_nested(k, query, case_sensitive) for k in obj.keys()) or \ - any(_search_nested(v, query, case_sensitive) for v in obj.values()) - elif isinstance(obj, list): - # Look through all items in the list - return any(_search_nested(item, query, case_sensitive) for item in obj) - elif isinstance(obj, str): - # String comparison - if case_sensitive: - return query in obj - else: - return query.lower() in obj.lower() - elif obj is not None: - # For numbers or other types, convert to string and check - return str(query) == str(obj) - return False +# Returns: +# bool: True if the query was found, False otherwise +# """ +# if isinstance(obj, dict): +# # Look through all keys and values +# return any(_search_nested(k, query, case_sensitive) for k in obj.keys()) or \ +# any(_search_nested(v, query, case_sensitive) for v in obj.values()) +# elif isinstance(obj, list): +# # Look through all items in the list +# return any(_search_nested(item, query, case_sensitive) for item in obj) +# elif isinstance(obj, str): +# # String comparison +# if case_sensitive: +# return query in obj +# else: +# return query.lower() in obj.lower() +# elif obj is not None: +# # For numbers or other types, convert to string and check +# return str(query) == str(obj) +# return False # Tool to query item manifest if it exists @datalab_agent.tool async def item_query(ctx: RunContext[Deps], query: str) -> list[dict[str, Any]]: - """Use the datalab search function to search through the item manifest at all nesting levels. - Generates a list of item_ids that can be used with get_item_details. + """Use full-text search to find items matching your query. + This searches through all fields in the item manifest including nested data. Args: - query: Search string to find in any field at any nesting level. + query: Keyword query string (e.g., "John Doe Fe3O4") Returns: List of dictionaries each representing one matching item """ if ctx.deps.item_manifest is None: return {'error': 'Use get_items to generate items list first'} - - # Search all items, looking at all levels of nesting - matching_items = [ - item for item in ctx.deps.item_manifest - if _search_nested(item, query) - ] + + # Initialize search tool if not already done + if ctx.deps.search_tool is None: + ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) + + # Perform the search using the FullTextSearchTool + matching_items = ctx.deps.search_tool.natural_language_search(query) if matching_items: + # store the query in the search buffer + ctx.deps.search_buffer = matching_items + + # Store the item IDs in the buffer for get_item_details ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] - return matching_items + + return f'Retrieved {len(matching_items)} items matching {query}' else: - return {'error': f'No items found containing "{query}"'} + return {'error': f'No items found matching query: "{query}"'} # Tool to make get_item API calls on item search id buffer @datalab_agent.tool -async def get_item_details(ctx: RunContext[Deps]) -> dict[str, Any]: +async def get_item_details(ctx: RunContext[Deps], simplify_JSON:bool = True) -> dict[str, Any]: """Use the datalab get_item function to get details JSONs of specific items in the manifest. This function reads a list of item_ids and retrieves the corresponding items using the datalab API. item_query must be run first to generate the item search id buffer. """ if ctx.deps.item_search_id_buffer == None: - return {'error': 'No item search buffer found, run item_query first.'} + return {'error': 'No search id buffer found, run item_query first.'} if ctx.deps.items_buffer == None: ctx.deps.items_buffer = {} @@ -186,6 +198,9 @@ async def get_item_details(ctx: RunContext[Deps]) -> dict[str, Any]: with ctx.deps.client(ctx.deps.datalab_url) as client: for item_id in ctx.deps.item_search_id_buffer: item = client.get_item(item_id=item_id) + + if simplify_JSON: + item = process_item_for_chat(item) ctx.deps.items_buffer[item_id] = item return "Items retrieved." @@ -193,21 +208,56 @@ async def get_item_details(ctx: RunContext[Deps]) -> dict[str, Any]: # Ask agent to inspect items in the items buffer @datalab_agent.tool -async def inspect_items(ctx: RunContext[Deps]) -> str: - """Inspect retrieved items in the items buffer. - Only use this if the items buffer has been populated with get_item_details! +async def inspect_items(ctx: RunContext[Deps], prompt: str) -> str: """ + If search item details have been successfully retrieved with a get_item_details call, + this tool uses an LLM agent to inspect these items. Prompt this agent based on the user's request. + The user does not see this tool's response so incorporate outputs into your final repsonse to the user. + Args: + prompt: A system prompt for the inspection agent + """ if ctx.deps.items_buffer == None: return {'error': 'No items buffer found, run get_item_details first.'} # Items buffer to plaintext - items_buffer = json.dumps(ctx.deps.items_buffer, indent=2) + items_buffer = json.dumps(ctx.deps.items_buffer, separators=(',', ':')) - r = await datalab_agent.run(items_buffer, deps=ctx.deps) + # Create an agent to inspect the items + inspection_agent = Agent(model, system_prompt=prompt, instrument=True) + r = await inspection_agent.run(items_buffer, deps=ctx.deps) return r.data +# Add the inspect_query function after inspect_items +@datalab_agent.tool +async def inspect_query(ctx: RunContext[Deps], prompt: str) -> str: + """ + If a search query has been successfully run with a item_query call, + this tool uses an LLM agent to inspect the search buffer. Prompt this agent based on the user's request. + item_query must be run first to generate the search buffer. + + Args: + prompt: A system prompt for the inspection agent + """ + if ctx.deps.search_buffer == None: + return {'error': 'No search buffer found, run item_query first.'} + # Search buffer to plaintext + search_buffer = json.dumps(ctx.deps.search_buffer, separators=(',', ':')) + inspection_agent = Agent(model, system_prompt=prompt, instrument=True) + result = await inspection_agent.run(search_buffer, deps=ctx.deps) + return result.data + +@datalab_agent.tool +async def code_writer(ctx: RunContext[Deps], prompt: str) -> Response: + """ + This tool is used to write code based on a prompt. It has information about the datalab API syntax. + Args: + prompt: A system prompt for the code writer agent + """ + code_agent = Agent(model, result_type = Response, system_prompt=CODE_PROMPT, instrument=True) + result = await code_agent.run(prompt, deps=ctx.deps) + return result.data # Validation may be helpful for some outputs # @datalab_agent.result_validator diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 5e3c296..bdc9928 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -8,6 +8,15 @@ import base64 import pprint +# Configure Logfire if token is available +try: + import logfire + logfire_token = os.getenv("LOGFIRE_TOKEN") + if logfire_token: + logfire.configure(token=logfire_token) +except ImportError: + pass # Logfire is optional + from IPython import get_ipython from IPython.core.magic import ( Magics, @@ -19,13 +28,22 @@ argument, parse_argstring, ) -from IPython.display import Markdown +from IPython.display import Markdown, HTML # from langchain_core.prompts import ChatPromptTemplate from datalab_api import DatalabClient from .llm_pydantic import datalab_agent, Deps from .prompt import API_PROMPT, SYSTEM_PROMPT import asyncio import nest_asyncio +import pprint +import rich +from rich.console import Console +from rich.markdown import Markdown as RichMarkdown +from rich.syntax import Syntax +from rich.panel import Panel +from rich.box import ROUNDED +from io import StringIO + nest_asyncio.apply() def parse_paths(input_string): @@ -65,14 +83,14 @@ def __init__(self, shell): is considered the code generation prompt. """, ) - # @argument( - # "-T", - # "--temp", - # type=float, - # default=0.1, - # help="""Temperature, float in [0,1]. Higher values push the algorithm - # to generate more aggressive/"creative" output. [default=0.1].""", - # ) + @argument( + "-T", + "--temp", + type=float, + default=0.1, + help="""Temperature, float in [0,1]. Higher values push the algorithm + to generate more aggressive/"creative" output. [default=0.1].""", + ) @line_cell_magic def llm(self, line, cell=None): """ @@ -93,25 +111,34 @@ def llm(self, line, cell=None): response = datalab_agent.run_sync( prompt_text, deps=Deps( - DatalabClient, - os.getenv("DATALAB_API_KEY"), - os.getenv("DATALAB_URL"), - ), + DatalabClient, + os.getenv("DATALAB_API_KEY"), + os.getenv("DATALAB_URL"), + ), message_history=self.messages, + model_settings={'temperature': args.temp}, ) self.messages = response.all_messages() - # Output text and code + if hasattr(response.data, 'text'): + # Create a string IO to capture Rich console output + console_output = StringIO() + console = Console(file=console_output, width=120, highlight=True) + + # Format main response text with Rich Markdown + console.print(RichMarkdown(response.data.text)) + + # Add usage info in a panel with syntax highlighting + usage_str = pprint.pformat(response.usage()) + usage_syntax = Syntax(usage_str, "python", theme="monokai", word_wrap=True) + console.print(Panel(usage_syntax, title="Usage Information", border_style="blue", box=ROUNDED)) + + # Output code into next cell if hasattr(response.data, 'code'): cell_fill = response.data.code get_ipython().set_next_input(cell_fill) - if hasattr(response.data, 'text'): - usage = pprint.pformat(response.usage()) - return Markdown(response.data.text + "\n" + usage) - - return "Unable to generate response." # # generate variable name "image_n" and inject it into a message # image_variable_name = f"image_{len(self.images)}" diff --git a/src/yellowhammer/prompt.py b/src/yellowhammer/prompt.py index 1658829..9a757e0 100644 --- a/src/yellowhammer/prompt.py +++ b/src/yellowhammer/prompt.py @@ -14,6 +14,10 @@ def load_file_content(file_path): system_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "system-prompt.md" SYSTEM_PROMPT = load_file_content(system_prompt_path) +# Load CODE_PROMPT +code_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "code-prompt.md" +CODE_PROMPT = load_file_content(code_prompt_path) + if __name__ == "__main__": print(SYSTEM_PROMPT) print(API_PROMPT) diff --git a/src/yellowhammer/utils.py b/src/yellowhammer/utils.py new file mode 100644 index 0000000..a49f912 --- /dev/null +++ b/src/yellowhammer/utils.py @@ -0,0 +1,83 @@ +def process_item_for_chat(item: dict) -> dict: + """Process an item to make it more suitable for LLM consumption by removing large fields + and simplifying the structure. + """ + import copy + import json + + # Work on a copy to avoid modifying the original + item_info = copy.deepcopy(item) + + # Extract filenames for easier reference + item_filenames = {} + if "files" in item_info: + for file in item_info.get("files", []): + if "immutable_id" in file and "name" in file: + item_filenames[str(file["immutable_id"])] = file["name"] + + # Keys to remove from the top level + top_level_keys_to_remove = [ + "display_order", "creator_ids", "refcode", "last_modified", + "revision", "revisions", "immutable_id", "file_ObjectIds" + ] + + for k in top_level_keys_to_remove: + item_info.pop(k, None) + + # Process blocks if they exist + big_data_keys = ["bokeh_plot_data", "b64_encoded_image"] + + if "blocks_obj" in item_info: + blocks = [] + for block_id, block in item_info.get("blocks_obj", {}).items(): + # Skip chat blocks + if block.get("blocktype") == "chat": + continue + + # Remove large or unnecessary fields + block_fields_to_remove = ["item_id", "block_id", "collection_id"] + big_data_keys + for field in block_fields_to_remove: + block.pop(field, None) + + # Replace file_id with actual filename + file_id = block.pop("file_id", None) + if file_id and file_id in item_filenames: + block["file"] = item_filenames[file_id] + + blocks.append(block) + + # Replace blocks_obj with a simpler list + item_info.pop("blocks_obj", None) + item_info["blocks"] = blocks + + # Simplify relationships + if "relationships" in item_info: + for i, relationship in enumerate(item_info["relationships"]): + item_info["relationships"][i] = { + k: v for k, v in relationship.items() + if k in ["item_id", "type", "relation"] + } + + # Simplify files to just names + if "files" in item_info: + item_info["files"] = [file["name"] for file in item_info.get("files", []) if "name" in file] + + # Simplify creators to just names + if "creators" in item_info: + item_info["creators"] = [ + creator.get("display_name") for creator in item_info.get("creators", []) + if "display_name" in creator + ] + + # Process quantity fields in various constituent types + for key in ["synthesis_constituents", "positive_electrode", "negative_electrode", "electrolyte"]: + if key in item_info: + for constituent in item_info[key]: + if "quantity" in constituent and "unit" in constituent: + constituent["quantity"] = f"{constituent.get('quantity', 'unknown')} {constituent.get('unit', '')}" + constituent.pop("unit", None) + + # Remove empty values + item_info = {k: v for k, v in item_info.items() if v} + + return item_info \ No newline at end of file diff --git a/uv.lock b/uv.lock index 9bbaebd..a459506 100644 --- a/uv.lock +++ b/uv.lock @@ -338,6 +338,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/64/b9/f412880f4837fb98d0c06bdbcbe07430f50c49db375d26fa0a2ddce26562/botocore-1.37.22-py3-none-any.whl", hash = "sha256:184db7c9314d13002bc827f511a5140574b5da1acda342d51e093dad6317de98", size = 13441302 }, ] +[[package]] +name = "cached-property" +version = "2.0.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/76/4b/3d870836119dbe9a5e3c9a61af8cc1a8b69d75aea564572e385882d5aefb/cached_property-2.0.1.tar.gz", hash = "sha256:484d617105e3ee0e4f1f58725e72a8ef9e93deee462222dbd51cd91230897641", size = 10574 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/11/0e/7d8225aab3bc1a0f5811f8e1b557aa034ac04bdf641925b30d3caf586b28/cached_property-2.0.1-py3-none-any.whl", hash = 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-3687,9 +3839,11 @@ requires-dist = [ { name = "datalab-api", git = "https://github.com/datalab-org/datalab-api.git" }, { name = "jupyter-ai", specifier = ">=2.24.1" }, { name = "jupyterlab", specifier = ">=4.2.5" }, + { name = "logfire", specifier = ">=3.12.0" }, { name = "nest-asyncio", specifier = ">=1.6.0" }, { name = "notebook", specifier = ">=7.2.2" }, { name = "pydantic-ai", specifier = ">=0.0.46" }, + { name = "whoosh-reloaded", specifier = ">=2.7.5" }, { name = "yellowhammer" }, ] From 4062b43e0c065c0fb962f0068a351d04db9e32b3 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Tue, 22 Apr 2025 23:19:49 -0700 Subject: [PATCH 19/24] add vector search and sample creation tools --- prompts/ingest-prompt.md | 16 + prompts/system-prompt.md | 2 + pyproject.toml | 2 + src/yellowhammer/llm_pydantic.py | 158 ++++- src/yellowhammer/magics.py | 8 +- src/yellowhammer/prompt.py | 8 +- uv.lock | 1060 +++++++++++++++++++++++++++++- 7 files changed, 1236 insertions(+), 18 deletions(-) create mode 100644 prompts/ingest-prompt.md diff --git a/prompts/ingest-prompt.md b/prompts/ingest-prompt.md new file mode 100644 index 0000000..c160104 --- /dev/null +++ b/prompts/ingest-prompt.md @@ -0,0 +1,16 @@ +You are an agent that helps a user create entries in the scientific data management platform "datalab". + +You have access to the tool `create_item()`. It accepts input based on the following schema: + +class CreateItemInput(BaseModel): + item_id: str = Field(description="A short identifier for the item") + name: str = Field(description="The name of the item") + chemform: str = Field(description="The chemical form of the item") + date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") + file: str = Field(description="File path to the associated file, if specified") + +Try to create an item_id relevant to the entry. +If the user specifies a date, use that. Otherwise, use today's date {}. +The tool will notify you on event of success or failure. +If there is a naming clash, try again with a different name. +For all other issues, don't retry, just report the error back to the user. diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index 03faf4b..6adca52 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -6,6 +6,8 @@ code_writer: you can use this tool to return code for interacting with the datal get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local item manifest +vector_search: only use this if the user requests it specifically! get_items MUST be run first to retrieve the item manifest! Returns the top n results matching the query. If you use just, just output the top results by rank as your final response. + item_query: get_items MUST be run first to retrieve the item manifest! You can then use this to query the item manifest using full text search. Use keywords. Returns a list of dicts representing sample (item) overviews matching the query (the search buffer). Once you've performed a query, you can either work with the overviews for big-picture queries, or retrieve full items for deep dives. These tools are available: diff --git a/pyproject.toml b/pyproject.toml index c1a5e0c..9c2ee56 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,6 +14,8 @@ dependencies = [ "nest-asyncio>=1.6.0", "whoosh-reloaded>=2.7.5", "logfire>=3.12.0", + "chromadb>=1.0.4", + "matplotlib>=3.10.1", ] [project.urls] diff --git a/src/yellowhammer/llm_pydantic.py b/src/yellowhammer/llm_pydantic.py index 3fa7a4b..246ba52 100644 --- a/src/yellowhammer/llm_pydantic.py +++ b/src/yellowhammer/llm_pydantic.py @@ -1,7 +1,9 @@ import os import json +import chromadb from typing import Any, cast from dataclasses import dataclass +from datetime import date from datalab_api import DatalabClient @@ -11,9 +13,10 @@ from pydantic import BaseModel, Field from pydantic_ai import Agent, ModelRetry, RunContext from pydantic_ai.models import KnownModelName -from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT +from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT, INGEST_PROMPT from .fulltext_search import FullTextSearchTool from .utils import process_item_for_chat +from pydantic import BaseModel, Field, ConfigDict # Dependencies @dataclass @@ -225,9 +228,9 @@ async def inspect_items(ctx: RunContext[Deps], prompt: str) -> str: # Create an agent to inspect the items inspection_agent = Agent(model, system_prompt=prompt, instrument=True) - r = await inspection_agent.run(items_buffer, deps=ctx.deps) + result = await inspection_agent.run(items_buffer, deps=ctx.deps) - return r.data + return result.data # Add the inspect_query function after inspect_items @datalab_agent.tool @@ -259,7 +262,148 @@ async def code_writer(ctx: RunContext[Deps], prompt: str) -> Response: result = await code_agent.run(prompt, deps=ctx.deps) return result.data -# Validation may be helpful for some outputs -# @datalab_agent.result_validator -# async def validate_result(ctx: RunContext[Deps], result: Response) -> Response: -# # Do some validation here \ No newline at end of file +@datalab_agent.tool +async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: + """Use chromadb to perform a vector search on the item manifest. + + Args: + query: A string to search for in the item manifest + n_results: Number of top results to return (default: 5) + + Returns: + String confirmation with number of results found + """ + if ctx.deps.item_manifest is None: + return {'error': 'Use get_items to generate items list first'} + + # Initialize the Chroma client (in-memory) + client = chromadb.Client() + + # Create or get the collection + try: + collection = client.get_collection("items") + except: + collection = client.create_collection("items") + + # Extract item IDs and text content from the manifest + item_ids = [] + documents = [] + + for item in ctx.deps.item_manifest: + item_id = str(item.get('item_id', '')) + if not item_id: + continue + + # Convert the item to a string representation for the document + item_text = json.dumps(item, ensure_ascii=False) + + item_ids.append(item_id) + documents.append(item_text) + + # Add items to the collection if there are any valid items + if item_ids and documents: + collection.add( + ids=item_ids, + documents=documents + ) + + # Perform a vector search + results = collection.query( + query_texts=[query], + n_results=min(n_results, len(item_ids)) + ) + + if not results or not results['ids'][0]: + return {'error': f'No items found matching query: "{query}"'} + + # Get the matching item IDs + matching_ids = results['ids'][0] + + # Store results in buffers for further processing + matching_items = [item for item in ctx.deps.item_manifest + if str(item.get('item_id', '')) in matching_ids] + + # Store the results in the search buffer + ctx.deps.search_buffer = matching_items + + # Store the item IDs in the buffer for get_item_details + ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + + return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' + +# Separate agent to ingest items +ingestion_agent = Agent( + model, + system_prompt=INGEST_PROMPT.format(date.today()), + result_type=Response, + deps_type=Deps, + retries=2, + instrument=True, +) + +class CreateItemInput(BaseModel): + item_id: str = Field(description="A short identifier for the item") + # item_type: str = Field(description="The type of the item") # default to 'samples' + name: str = Field(description="The name of the item") + chemform: str = Field(description="The chemical form of the item") + date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") + file: str = Field(description="File path to the associated file, if specified") + + +@ingestion_agent.tool +async def create_item(ctx: RunContext[Deps], inputs: CreateItemInput) -> str: + """ + Use the datalab client to create a new datalab item. + """ + if ctx.deps.datalab_api_key is None: + return {'error': 'No datalab API key found.'} + + if ctx.deps.datalab_url is None: + return {'error': 'No datalab URL found.'} + + with ctx.deps.client(ctx.deps.datalab_url) as client: + sample_dict = { + "name": inputs.name, + "chemform": inputs.chemform, + "date": inputs.date, + } + try: + created = client.create_item( + item_id=inputs.item_id, + item_type="samples", + item_data=sample_dict + ) + + created_item_id = created["item_id"] + + if inputs.file: + # Check if the file exists + if not os.path.exists(inputs.file): + return {'error': f'Item {inputs.item_id} created but cannot find file {inputs.file}'} + + try: + client.upload_file( + item_id=created_item_id, file_path=inputs.file + ) + except Exception as e: + return {'error': f'Item {inputs.item_id} created but file upload failed: {str(e)}'} + + return f"Item {inputs.item_id} created successfully with ID {created_item_id}." + + except Exception as e: + return {'error': f'Error creating item: {str(e)}'} + + +@datalab_agent.tool +async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: + """ + This tool is used to create a new item in the datalab. + It uses an agent to create an item specified by natural language. + + Args: + str: A system prompt for the item creation agent + """ + # Create an agent to create the item + result = await ingestion_agent.run(prompt, deps=ctx.deps) + + return result.data diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index bdc9928..7a83917 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -103,13 +103,13 @@ def llm(self, line, cell=None): prompt = " ".join(args.prompt) else: prompt = cell - prompt = parse_paths(prompt) - prompt_text = prompt["text"] - paths = prompt["paths"] # TODO implement check or make parser only output local paths + # prompt = parse_paths(prompt) + # prompt_text = prompt["text"] + # paths = prompt["paths"] # TODO implement check or make parser only output local paths # Run the datalab agent response = datalab_agent.run_sync( - prompt_text, + prompt, deps=Deps( DatalabClient, os.getenv("DATALAB_API_KEY"), diff --git a/src/yellowhammer/prompt.py b/src/yellowhammer/prompt.py index 9a757e0..871b001 100644 --- a/src/yellowhammer/prompt.py +++ b/src/yellowhammer/prompt.py @@ -1,11 +1,9 @@ from pathlib import Path - def load_file_content(file_path): with open(file_path, encoding="utf-8") as file: return file.read() - # Load API_PROMPT api_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "datalab-api-prompt.md" API_PROMPT = load_file_content(api_prompt_path) @@ -18,6 +16,12 @@ def load_file_content(file_path): code_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "code-prompt.md" CODE_PROMPT = load_file_content(code_prompt_path) +# Load INGEST_PROMPT +ingest_prompt_path = Path(__file__).parent.parent.parent / "prompts" / "ingest-prompt.md" +INGEST_PROMPT = load_file_content(ingest_prompt_path) + if __name__ == "__main__": print(SYSTEM_PROMPT) print(API_PROMPT) + print(CODE_PROMPT) + print(INGEST_PROMPT) diff --git a/uv.lock b/uv.lock 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specifier = ">=4.2.5" }, { name = "logfire", specifier = ">=3.12.0" }, + { name = "matplotlib", specifier = ">=3.10.1" }, { name = "nest-asyncio", specifier = ">=1.6.0" }, { name = "notebook", specifier = ">=7.2.2" }, { name = "pydantic-ai", specifier = ">=0.0.46" }, From b0c68a54f5fe9118bce7d67e26be152c33099625 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Tue, 29 Apr 2025 09:50:50 -0700 Subject: [PATCH 20/24] update readme and examples --- .gitignore | 4 +++- README.md | 21 ++++++++++++++++----- examples/Demo_pydantic.ipynb | 32 +++++++++++++++++++++++++++++++- 3 files changed, 50 insertions(+), 7 deletions(-) diff --git a/.gitignore b/.gitignore index ed0f787..e336ccb 100644 --- a/.gitignore +++ b/.gitignore @@ -172,4 +172,6 @@ examples/Demo_flagship.ipynb examples/HL1-5_5-90_30min.xrdml # examples/IMG_1648.jpg prompts/deprecated -examples/llm_trace.txt \ No newline at end of file +examples/llm_trace.txt +examples/Demo_anthropic.ipynb +examples/Demo_openai.ipynb \ No newline at end of file diff --git a/README.md b/README.md index d206ced..ef9dc1d 100644 --- a/README.md +++ b/README.md @@ -6,17 +6,28 @@ -A continuation of the [LLM hackathon](https://www.eventbrite.com/e/llm-hackathon-for-applications-in-materials-and-chemistry-tickets-868303598437) project, [*yeLLowhaMMer: A Multi-modal Tool-calling Agent for Accelerated Research Data Management*](https://github.com/bocarsly-group/llm-hackathon-2024). +This project is a continuation of [LLM hackathon](https://www.eventbrite.com/e/llm-hackathon-for-applications-in-materials-and-chemistry-tickets-868303598437) project, [*yeLLowhaMMer: A Multi-modal Tool-calling Agent for Accelerated Research Data Management*](https://github.com/bocarsly-group/llm-hackathon-2024). -This repository will explore using the Jupyter AI plugin to provide an agentic interface to [*datalab*](https://github.com/datalab-org/datalab), with the idea of having this as an additional UI that *datalab* users can use to interact with their data, either deployed for their instance, or run locally. +This repo explores the use of a agentic assistant for [*datalab*](https://github.com/datalab-org/datalab) using Jupyter cell magics as an UI, allowing *datalab* users to interact with the agent during their standard data processing and analysis workflow. -## Initial development tasks +See the [example notebook](examples/Demo_pydantic.ipynb) for a demonstration. -- [ ] Reproduce the hackathon project with the Jupyter AI plugin, probably by extending the `%%ai` cell magic to something like `%%yellowhammer` that includes the yellowhammer system prompt, and guides through the registration of a *datalab* API key and any provided LLM API keys. -- [ ] Use yellowhammer to generate API examples for the underlying [*datalab-python-api*](https://github.com/datalab-org/datalab-python-api) package. +## Current status +- [x] Port project from langchain to pydantic-AI +- [x] Create `%%llm` line/cell magic that passes messages to the datalab agent +- [ ] Agent guides user through the registration of a *datalab* API key and any provided LLM API keys. +- [x] Write agentic tools for interactions with *datalab* API. +- [x] Include code-writing tool that can query the underlying API directly with [*datalab-python-api*](https://github.com/datalab-org/datalab-python-api) package. +- [ ] Update datalab API for direct data retrieval from backend - [ ] Consider deploying this as a JupyterHub that *datalab* instances can link to directly. - [ ] Integrate the results much more closely into *datalab* itself, i.e., attaching notebooks to the relevant samples, and recording the provenance of AI generated data recording. +## Project structure +- [`src/yellowhammer/llm_pydantic.py`](src/yellowhammer/llm_pydantic.py): Defines the agent and tools. +- [`src/yellowhammer/magics.py`](src/yellowhammer/magics.py): Defines Jupyter magic commands. +- [`prompts`](prompts/): System prompts for each agent +- [`examples/Demo_pydantic.ipynb`](examples/Demo_pydantic.ipynb): Example notebook demonstrating usage + ## Installation This repository uses [`uv`](https://docs.astral.sh/uv/) for the entire packaging workflow. diff --git a/examples/Demo_pydantic.ipynb b/examples/Demo_pydantic.ipynb index 4bcbd48..19fcc20 100644 --- a/examples/Demo_pydantic.ipynb +++ b/examples/Demo_pydantic.ipynb @@ -4,7 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Pydantic-AI datalab agent demo" + "# Pydantic-AI datalab agent demo\n", + "\n", + "This demo queries a *datalab* instance populated by data from Josh Bocarsly's group at the University of Houston." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enter LLM API key after running this cell (model choice to be added soon)" ] }, { @@ -19,6 +28,13 @@ "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Enter *datalab* API key and define the *datalab* instance URL" + ] + }, { "cell_type": "code", "execution_count": null, @@ -31,6 +47,13 @@ "%reload_ext yellowhammer" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use natural language to query the *datalab* instance, which contains data from multiple student projects:" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -70,6 +93,13 @@ "Can you give an overview of Moinak Dutta's research?" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The agent can synthesize information from multiple samples:" + ] + }, { "cell_type": "code", "execution_count": 3, From e1ebf78d1c1f0865f63c4b48823f3fbf9042258f Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 7 May 2025 23:11:01 -0700 Subject: [PATCH 21/24] deprecated vector search --- .gitignore | 3 +- prompts/system-prompt.md | 2 - src/yellowhammer/llm_pydantic.py | 117 ++++++++++++++++--------------- 3 files changed, 61 insertions(+), 61 deletions(-) diff --git a/.gitignore b/.gitignore index e336ccb..a9481f0 100644 --- a/.gitignore +++ b/.gitignore @@ -174,4 +174,5 @@ examples/HL1-5_5-90_30min.xrdml prompts/deprecated examples/llm_trace.txt examples/Demo_anthropic.ipynb -examples/Demo_openai.ipynb \ No newline at end of file +examples/Demo_openai.ipynb +examples/Demo_pydantic copy.ipynb \ No newline at end of file diff --git a/prompts/system-prompt.md b/prompts/system-prompt.md index 6adca52..03faf4b 100644 --- a/prompts/system-prompt.md +++ b/prompts/system-prompt.md @@ -6,8 +6,6 @@ code_writer: you can use this tool to return code for interacting with the datal get_items: uses the datalab API to retrieve a list of dicts representing short overviews all the items (samples) in the current datalab and stores it in a local item manifest -vector_search: only use this if the user requests it specifically! get_items MUST be run first to retrieve the item manifest! Returns the top n results matching the query. If you use just, just output the top results by rank as your final response. - item_query: get_items MUST be run first to retrieve the item manifest! You can then use this to query the item manifest using full text search. Use keywords. Returns a list of dicts representing sample (item) overviews matching the query (the search buffer). Once you've performed a query, you can either work with the overviews for big-picture queries, or retrieve full items for deep dives. These tools are available: diff --git a/src/yellowhammer/llm_pydantic.py b/src/yellowhammer/llm_pydantic.py index 246ba52..ff4c1c0 100644 --- a/src/yellowhammer/llm_pydantic.py +++ b/src/yellowhammer/llm_pydantic.py @@ -262,74 +262,75 @@ async def code_writer(ctx: RunContext[Deps], prompt: str) -> Response: result = await code_agent.run(prompt, deps=ctx.deps) return result.data -@datalab_agent.tool -async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: - """Use chromadb to perform a vector search on the item manifest. +# Requires further development +# @datalab_agent.tool +# async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: +# """Use chromadb to perform a vector search on the item manifest. - Args: - query: A string to search for in the item manifest - n_results: Number of top results to return (default: 5) +# Args: +# query: A string to search for in the item manifest +# n_results: Number of top results to return (default: 5) - Returns: - String confirmation with number of results found - """ - if ctx.deps.item_manifest is None: - return {'error': 'Use get_items to generate items list first'} - - # Initialize the Chroma client (in-memory) - client = chromadb.Client() +# Returns: +# String confirmation with number of results found +# """ +# if ctx.deps.item_manifest is None: +# return {'error': 'Use get_items to generate items list first'} + +# # Initialize the Chroma client (in-memory) +# client = chromadb.Client() + +# # Create or get the collection +# try: +# collection = client.get_collection("items") +# except: +# collection = client.create_collection("items") + +# # Extract item IDs and text content from the manifest +# item_ids = [] +# documents = [] + +# for item in ctx.deps.item_manifest: +# item_id = str(item.get('item_id', '')) +# if not item_id: +# continue + +# # Convert the item to a string representation for the document +# item_text = json.dumps(item, ensure_ascii=False) + +# item_ids.append(item_id) +# documents.append(item_text) - # Create or get the collection - try: - collection = client.get_collection("items") - except: - collection = client.create_collection("items") +# # Add items to the collection if there are any valid items +# if item_ids and documents: +# collection.add( +# ids=item_ids, +# documents=documents +# ) - # Extract item IDs and text content from the manifest - item_ids = [] - documents = [] +# # Perform a vector search +# results = collection.query( +# query_texts=[query], +# n_results=min(n_results, len(item_ids)) +# ) - for item in ctx.deps.item_manifest: - item_id = str(item.get('item_id', '')) - if not item_id: - continue - - # Convert the item to a string representation for the document - item_text = json.dumps(item, ensure_ascii=False) - - item_ids.append(item_id) - documents.append(item_text) - - # Add items to the collection if there are any valid items - if item_ids and documents: - collection.add( - ids=item_ids, - documents=documents - ) - - # Perform a vector search - results = collection.query( - query_texts=[query], - n_results=min(n_results, len(item_ids)) - ) - - if not results or not results['ids'][0]: - return {'error': f'No items found matching query: "{query}"'} +# if not results or not results['ids'][0]: +# return {'error': f'No items found matching query: "{query}"'} - # Get the matching item IDs - matching_ids = results['ids'][0] +# # Get the matching item IDs +# matching_ids = results['ids'][0] - # Store results in buffers for further processing - matching_items = [item for item in ctx.deps.item_manifest - if str(item.get('item_id', '')) in matching_ids] +# # Store results in buffers for further processing +# matching_items = [item for item in ctx.deps.item_manifest +# if str(item.get('item_id', '')) in matching_ids] - # Store the results in the search buffer - ctx.deps.search_buffer = matching_items +# # Store the results in the search buffer +# ctx.deps.search_buffer = matching_items - # Store the item IDs in the buffer for get_item_details - ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] +# # Store the item IDs in the buffer for get_item_details +# ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] - return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' +# return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' # Separate agent to ingest items ingestion_agent = Agent( From b14e5977e941918dafc28cfc054ad1f00275e676 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Wed, 7 May 2025 23:41:38 -0700 Subject: [PATCH 22/24] Update demo notebook and agent tools --- examples/Demo_pydantic.ipynb | 259 +++++++++++++++++++++++++------ src/yellowhammer/llm_pydantic.py | 9 ++ 2 files changed, 224 insertions(+), 44 deletions(-) diff --git a/examples/Demo_pydantic.ipynb b/examples/Demo_pydantic.ipynb index 19fcc20..8b6c527 100644 --- a/examples/Demo_pydantic.ipynb +++ b/examples/Demo_pydantic.ipynb @@ -56,36 +56,70 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "text/markdown": [ - "Based on the datalab records, Moinak Dutta appears to be conducting research focused on sodium-ion battery materials, specifically alpha-NaFeO2 (sodium iron oxide) cathode materials. The research involves:\n", - "\n", - "1. Systematic synthesis of NaFeO2 samples with multiple preparation attempts (at least 7 different sample sets)\n", - "2. Detailed electrode preparation for battery testing\n", - "3. Electrochemical characterization through coin cell assemblies\n", - "4. Exploring variations in synthesis conditions, including:\n", - " - Different synthesis temperatures (450-650°C)\n", - " - Various cooling methods (air quench, furnace cooling, liquid nitrogen quenching)\n", - " - Investigating phase purity and material characteristics\n", - "\n", - "The research seems concentrated on developing and optimizing sodium-ion battery cathode materials, with a particular focus on understanding the synthesis parameters and electrochemical performance of NaFeO2. The project involves multiple experimental iterations, suggesting a rigorous approach to material development and characterization.\n", - "Usage(requests=6,\n", - " request_tokens=45310,\n", - " response_tokens=427,\n", - " total_tokens=45737,\n", - " details=None)" + "text/html": [ + "

Based on the research items in the datalab, Moinak Dutta's research focuses on sodium-ion battery materials,            \n",
+       "specifically the synthesis and electrochemical characterization of sodium iron oxide (NaFeO2).                          \n",
+       "\n",
+       "Key research highlights include:                                                                                        \n",
+       "\n",
+       " 1 Developing advanced synthesis techniques for NaFeO2 materials                                                        \n",
+       " 2 Exploring different synthesis parameters like temperature and starting materials                                     \n",
+       " 3 Preparing electrodes for coin cell battery testing                                                                   \n",
+       " 4 Systematically refining material synthesis to produce pure alpha-NaFeO2 phase                                        \n",
+       "\n",
+       "The research is primarily centered on improving sodium-ion battery electrode materials through meticulous material      \n",
+       "synthesis and electrochemical characterization. Dutta has conducted extensive experiments on solid-state synthesis      \n",
+       "methods, varying conditions to optimize the material's structure and performance for potential battery applications.    \n",
+       "
\n" ], "text/plain": [ - "" + "Based on the research items in the datalab, Moinak Dutta's research focuses on sodium-ion battery materials, \n", + "specifically the synthesis and electrochemical characterization of sodium iron oxide (NaFeO2). \n", + "\n", + "Key research highlights include: \n", + "\n", + "\u001b[1;33m 1 \u001b[0mDeveloping advanced synthesis techniques for NaFeO2 materials \n", + "\u001b[1;33m 2 \u001b[0mExploring different synthesis parameters like temperature and starting materials \n", + "\u001b[1;33m 3 \u001b[0mPreparing electrodes for coin cell battery testing \n", + "\u001b[1;33m 4 \u001b[0mSystematically refining material synthesis to produce pure alpha-NaFeO2 phase \n", + "\n", + "The research is primarily centered on improving sodium-ion battery electrode materials through meticulous material \n", + "synthesis and electrochemical characterization. Dutta has conducted extensive experiments on solid-state synthesis \n", + "methods, varying conditions to optimize the material's structure and performance for potential battery applications. \n" ] }, - "execution_count": 2, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "│       total_tokens=10910,                                                                                            │\n",
+       "│       details=None)                                                                                                  │\n",
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Methods Section for NaFeO2 Synthesis and Characterization                                                               \n",
        "\n",
-       "Materials Synthesis\n",
-       "Alpha-NaFeO2 cathode materials were synthesized via a solid-state reaction method. Stoichiometric amounts of sodium carbonate (Na2CO3) and iron oxide (Fe2O3) were thoroughly ground together using a mortar and pestle to ensure homogeneous mixing. The precursor mixture was subsequently calcined in a muffle furnace at temperatures ranging from 450-650°C for 12 hours, with variations in cooling methods including air quenching, furnace cooling, and rapid cooling using liquid nitrogen.\n",
+       "Synthesis of NaFeO2                                                                                                     \n",
        "\n",
-       "Electrode Preparation\n",
-       "Electrode materials were prepared by mixing the synthesized NaFeO2 with carbon black and polyvinylidene fluoride (PVDF) binder in a 7:2:1 weight ratio. The mixture was homogenized using N-methyl-2-pyrrolidone (NMP) as a solvent. The resulting slurry was cast onto aluminum foil current collectors and dried under vacuum at 120°C for 12 hours.\n",
+       "NaFeO2 was synthesized via a solid-state reaction method performed entirely within an argon-filled glovebox to minimize \n",
+       "moisture and oxygen exposure. Stoichiometric amounts of sodium peroxide (Na2O2) and iron oxide (Fe2O3 or Fe3O4) were    \n",
+       "precisely weighed and thoroughly ground using a ceramic mortar and pestle. The mixed precursors were heated in an       \n",
+       "alumina crucible within a box furnace, with temperatures ranging from 450-650°C and a heating ramp of 5°C min-1.        \n",
+       "Synthesis protocols included 12-24 hour dwell times at target temperatures, with variations in quenching methods        \n",
+       "including air quench and liquid nitrogen quench.                                                                        \n",
        "\n",
-       "Electrochemical Characterization\n",
-       "Coin cells (CR2032 type) were assembled in an argon-filled glove box using sodium metal as the counter electrode and a glass fiber separator. The electrolyte consisted of 1 M sodium hexafluorophosphate (NaPF6) dissolved in propylene carbonate. Electrochemical cycling was performed using a battery testing system, with galvanostatic charge-discharge measurements conducted between 2.0-4.5 V vs. Na+/Na at various current densities to evaluate the material's electrochemical performance.\n",
+       "Electrode Preparation                                                                                                   \n",
        "\n",
-       "Structural and Morphological Analysis\n",
-       "The crystalline structure of the synthesized NaFeO2 was characterized using X-ray diffraction (XRD), with particular attention to phase purity and potential beta-phase impurities. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to investigate the morphology and microstructure of the synthesized materials.\n",
+       "Coin cells were assembled in an argon-purged glovebox to ensure minimal moisture and oxygen contamination. Electrodes   \n",
+       "were prepared by mixing NaFeO2 powder with Super P carbon and polyvinylidene fluoride (PVDF) binder in                  \n",
+       "N-methyl-2-pyrrolidone (NMP) solvent. The electrode composition typically contained 5.7-12.6 mg of active material. Coin\n",
+       "cells were constructed using sodium metal as the counter electrode, with a 1M NaPF6 in propylene carbonate electrolyte  \n",
+       "(150 µL). Cell assembly followed a standardized procedure, with components layered in the following order: bottom cap,  \n",
+       "cathode, separator (pre-soaked in electrolyte), sodium metal anode, spacers, compression spring, and top cap. Cells were\n",
+       "sealed under a pressure of 0.4 tonnes.                                                                                  \n",
        "\n",
-       "Systematic Approach\n",
-       "Multiple synthesis iterations (designated MD1-1 through MD7-1) were conducted to optimize synthesis parameters, with careful documentation of temperature, cooling method, and resulting material characteristics. Each experimental series involved repeated electrochemical testing to ensure reproducibility and reliability of the results.\n",
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+       "Electrochemical Characterization                                                                                        \n",
+       "\n",
+       "Electrochemical performance was evaluated through galvanostatic charge-discharge cycling using a battery testing system.\n",
+       "Cycling protocols were designed to probe the material's electrochemical behavior, with specific current rates and       \n",
+       "voltage windows determined through systematic investigation.                                                            \n",
+       "\n",
+       "This methods section provides a comprehensive overview of the experimental approach used in synthesizing and            \n",
+       "characterizing NaFeO2 for sodium-ion battery applications.                                                              \n",
+       "
\n" + ], + "text/plain": [ + "Methods Section for NaFeO2 Synthesis and Characterization \n", + "\n", + "Synthesis of NaFeO2 \n", + "\n", + "NaFeO2 was synthesized via a solid-state reaction method performed entirely within an argon-filled glovebox to minimize \n", + "moisture and oxygen exposure. Stoichiometric amounts of sodium peroxide (Na2O2) and iron oxide (Fe2O3 or Fe3O4) were \n", + "precisely weighed and thoroughly ground using a ceramic mortar and pestle. The mixed precursors were heated in an \n", + "alumina crucible within a box furnace, with temperatures ranging from 450-650°C and a heating ramp of 5°C min-1. \n", + "Synthesis protocols included 12-24 hour dwell times at target temperatures, with variations in quenching methods \n", + "including air quench and liquid nitrogen quench. \n", + "\n", + "Electrode Preparation \n", + "\n", + "Coin cells were assembled in an argon-purged glovebox to ensure minimal moisture and oxygen contamination. Electrodes \n", + "were prepared by mixing NaFeO2 powder with Super P carbon and polyvinylidene fluoride (PVDF) binder in \n", + "N-methyl-2-pyrrolidone (NMP) solvent. The electrode composition typically contained 5.7-12.6 mg of active material. Coin\n", + "cells were constructed using sodium metal as the counter electrode, with a 1M NaPF6 in propylene carbonate electrolyte \n", + "(150 µL). Cell assembly followed a standardized procedure, with components layered in the following order: bottom cap, \n", + "cathode, separator (pre-soaked in electrolyte), sodium metal anode, spacers, compression spring, and top cap. Cells were\n", + "sealed under a pressure of 0.4 tonnes. \n", + "\n", + "Electrochemical Characterization \n", + "\n", + "Electrochemical performance was evaluated through galvanostatic charge-discharge cycling using a battery testing system.\n", + "Cycling protocols were designed to probe the material's electrochemical behavior, with specific current rates and \n", + "voltage windows determined through systematic investigation. \n", + "\n", + "This methods section provides a comprehensive overview of the experimental approach used in synthesizing and \n", + "characterizing NaFeO2 for sodium-ion battery applications. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "│ Usage(requests=8,                                                                                                    │\n",
+       "│       request_tokens=27022,                                                                                          │\n",
+       "│       response_tokens=890,                                                                                           │\n",
+       "│       total_tokens=27912,                                                                                            │\n",
+       "│       details=None)                                                                                                  │\n",
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Based on the datalab contents, the main users and their projects include:                                               \n",
+       "\n",
+       " 1 Hasindhi: Researching sodium nickel manganese oxide (Na-Ni-Mn-O) compounds for battery applications                  \n",
+       " 2 Kusalvin Dabare: Specializing in sodium cobalt oxide (Na-Co-O) materials and their electrochemical properties        \n",
+       " 3 Moinak Dutta: Focusing on sodium iron oxide (NaFeO2) synthesis and characterization                                  \n",
+       " 4 Jason Zhou: Investigating manganese antimonide (MnSb) compounds                                                      \n",
+       " 5 Matthew Evans: Contributing to X-ray diffraction analysis                                                            \n",
+       "\n",
+       "The datalab primarily supports research in advanced materials science, with a strong focus on:                          \n",
+       "\n",
+       " • Sodium-ion battery material development                                                                              \n",
+       " • Solid-state material synthesis                                                                                       \n",
+       " • Electrode preparation                                                                                                \n",
+       " • Electrochemical performance testing                                                                                  \n",
+       " • Crystal structure analysis                                                                                           \n",
+       "\n",
+       "The researchers appear to collaborate closely, sharing methodologies and techniques in material science, particularly in\n",
+       "developing next-generation battery and electronic material technologies.                                                \n",
+       "
\n" + ], + "text/plain": [ + "Based on the datalab contents, the main users and their projects include: \n", + "\n", + "\u001b[1;33m 1 \u001b[0mHasindhi: Researching sodium nickel manganese oxide (Na-Ni-Mn-O) compounds for battery applications \n", + "\u001b[1;33m 2 \u001b[0mKusalvin Dabare: Specializing in sodium cobalt oxide (Na-Co-O) materials and their electrochemical properties \n", + "\u001b[1;33m 3 \u001b[0mMoinak Dutta: Focusing on sodium iron oxide (NaFeO2) synthesis and characterization \n", + "\u001b[1;33m 4 \u001b[0mJason Zhou: Investigating manganese antimonide (MnSb) compounds \n", + "\u001b[1;33m 5 \u001b[0mMatthew Evans: Contributing to X-ray diffraction analysis \n", + "\n", + "The datalab primarily supports research in advanced materials science, with a strong focus on: \n", + "\n", + "\u001b[1;33m • \u001b[0mSodium-ion battery material development \n", + "\u001b[1;33m • \u001b[0mSolid-state material synthesis \n", + "\u001b[1;33m • \u001b[0mElectrode preparation \n", + "\u001b[1;33m • \u001b[0mElectrochemical performance testing \n", + "\u001b[1;33m • \u001b[0mCrystal structure analysis \n", + "\n", + "The researchers appear to collaborate closely, sharing methodologies and techniques in material science, particularly in\n", + "developing next-generation battery and electronic material technologies. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
+       "│ Usage(requests=8,                                                                                                    │\n",
+       "│       request_tokens=43938,                                                                                          │\n",
+       "│       response_tokens=616,                                                                                           │\n",
+       "│       total_tokens=44554,                                                                                            │\n",
+       "│       details=None)                                                                                                  │\n",
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Please relay it to the user" return result.data @@ -249,6 +253,11 @@ async def inspect_query(ctx: RunContext[Deps], prompt: str) -> str: search_buffer = json.dumps(ctx.deps.search_buffer, separators=(',', ':')) inspection_agent = Agent(model, system_prompt=prompt, instrument=True) result = await inspection_agent.run(search_buffer, deps=ctx.deps) + + # Append a message to the result so that it's passed back to the user + if result.data: + result.data += "\n\n###\n\n The tool reponse is given above. Please relay it to the user" + return result.data @datalab_agent.tool From ad4613a666963fc0bb779562fcbc9b4629844d18 Mon Sep 17 00:00:00 2001 From: Yue Wu <14996988+yue-here@users.noreply.github.com> Date: Tue, 14 Oct 2025 16:23:23 +0100 Subject: [PATCH 23/24] implement multimodal inputs --- examples/Demo_anthropic.ipynb | 216 --------------------- examples/Demo_openai.ipynb | 158 ---------------- examples/Demo_pydantic.ipynb | 347 ---------------------------------- 3 files changed, 721 deletions(-) delete mode 100644 examples/Demo_anthropic.ipynb delete mode 100644 examples/Demo_openai.ipynb delete mode 100644 examples/Demo_pydantic.ipynb diff --git a/examples/Demo_anthropic.ipynb b/examples/Demo_anthropic.ipynb deleted file mode 100644 index f720d3a..0000000 --- a/examples/Demo_anthropic.ipynb +++ /dev/null @@ -1,216 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext yellowhammer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter your LLM API key ········\n" - ] - } - ], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"LLM_PROVIDER\"] = \"ANTHROPIC\"\n", - "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Enter your Datalab API key ········\n" - ] - } - ], - "source": [ - "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Datalab is a data management platform designed to help scientists manage their experimental data, plan experiments, analyze data, and plot results. It provides a structured way to store, organize, and interact with scientific data, particularly in the context of materials science and chemistry experiments. \n", - "\n", - "Some key features of Datalab include:\n", - "\n", - "1. Sample management: You can create, store, and retrieve information about experimental samples, including their chemical composition, synthesis methods, and related metadata.\n", - "\n", - "2. File attachments: Datalab allows you to attach files (such as raw data or images) to sample entries, making it easy to keep all relevant information together.\n", - "\n", - "3. Data blocks: These are used to parse attached files according to scientific schemas and generate plots, facilitating data analysis and visualization.\n", - "\n", - "4. Search functionality: You can search for items (samples, materials, etc.) across the Datalab instance.\n", - "\n", - "5. Relationship tracking: Datalab can track relationships between different items, helping to maintain the context of experiments and materials.\n", - "\n", - "6. API access: Datalab provides a Python API that allows programmatic interaction with the platform, enabling integration with other tools and scripts.\n", - "\n", - "Datalab is particularly useful for maintaining experimental workflows, ensuring data provenance, and facilitating collaboration among scientists. It helps in organizing complex scientific data in a structured manner, making it easier to retrieve, analyze, and share research findings." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "What is datalab?" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "To create a sample with the specified details using the Datalab API, we'll use the `create_item` method of the `DatalabClient`. Here's what the code will do:\n", - "\n", - "1. Import the necessary module\n", - "2. Create a DatalabClient instance\n", - "3. Prepare the sample data as a dictionary\n", - "4. Use the create_item method to create the sample\n", - "5. Print the response to confirm the sample creation" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "Create a sample with ID llm-test4, sample name \"virtual sample (Claude)\", formula FrCl" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6ef8a1c2-50c8-4807-b84b-008c0bfb4efa", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample creation response:\n", - "{'collections': [], 'creator_ids': [{'$oid': '66abc00dcb992f4b299aa60a'}], 'creators': [{'contact_email': None, 'display_name': 'Yue Wu'}], 'date': '2024-10-22T20:06:55.888755', 'item_id': 'llm-test4', 'name': 'virtual sample (Claude)', 'nblocks': 0, 'refcode': 'demo:YASTWQ', 'type': 'samples'}\n" - ] - } - ], - "source": [ - "from datalab_api import DatalabClient\n", - "\n", - "# Create a DatalabClient instance\n", - "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", - " # Prepare the sample data\n", - " sample_data = {\n", - " \"item_id\": \"llm-test4\",\n", - " \"name\": \"virtual sample (Claude)\",\n", - " \"chemform\": \"FrCl\",\n", - " \"type\": \"samples\",\n", - " }\n", - "\n", - " # Create the sample\n", - " response = client.create_item(item_id=\"llm-test4\", item_type=\"samples\", item_data=sample_data)\n", - "\n", - " # Print the response\n", - " print(\"Sample creation response:\")\n", - " print(response)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "446c3e95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "The image shows a clear glass vial or sample container with a black screw-on cap. Inside the vial, there appears to be a dark-colored powder or fine material collected at the bottom. The vial is sitting on what looks like a wooden surface with a warm, golden-brown color. The vial appears to be the type commonly used in laboratory settings for storing chemical samples, reagents, or synthesized materials. The powder inside could be a chemical compound or experimental sample, though without more context it's not possible to identify the specific substance." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "What do you see in this image: IMG_1648.jpg" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Demo_openai.ipynb b/examples/Demo_openai.ipynb deleted file mode 100644 index 1eaf80f..0000000 --- a/examples/Demo_openai.ipynb +++ /dev/null @@ -1,158 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "2671fc38-c9ca-49ef-948a-abb7815ca2b9", - "metadata": {}, - "outputs": [], - "source": [ - "%reload_ext yellowhammer" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "32c5ffc5-ea83-458c-8e5a-75bb15da8a2d", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"LLM_PROVIDER\"] = \"OPENAI\"\n", - "os.environ[\"LLM_API_KEY\"] = getpass.getpass(\"Enter your LLM API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2f61e78f-b8a3-4f0c-b5e4-503af30019f3", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f44cabeb-58ef-4ccd-86d7-5cb04a3f8643", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "Datalab is a data management platform designed for scientists and researchers to manage their experimental data efficiently. It allows users to organize, analyze, and visualize their data through a structured interface. Datalab uses the concept of \"data blocks\" to handle files associated with samples, enabling users to parse data according to scientific schemas and create plots for better data interpretation. The platform also provides an API for programmatic access, allowing users to automate tasks such as data entry, file uploads, and data analysis." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "What is datalab?" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "d10442e5-7c5c-40cd-becf-9c2e9a79fd45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "To create a new sample in Datalab with the specified ID, name, and formula, we will use the `create_item` method of the `DatalabClient`. This method requires the item ID, item type, and the data that matches the schema for samples. In this case, we will set the `item_id` to 'llm-test3', the `name` to 'virtual sample', and the `chemform` to 'RbCl'." - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "Create a sample with ID llm-test3, sample name \"virtual sample\", formula RbCl" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "33c0e59e-586d-48f3-aa5f-f6fa743dcc96", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/yue/Library/CloudStorage/OneDrive-Personal/code/yellowhammer/.venv/lib/python3.10/site-packages/datalab_api/_base.py:165: UserWarning: Found API URL https://demo-api.datalab-org.io in HTML meta tag. Creating client with this URL instead.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from datalab_api import DatalabClient\n", - "import os\n", - "\n", - "with DatalabClient(\"https://demo.datalab-org.io\") as client:\n", - " json_data = {\"name\": \"virtual sample\", \"chemform\": \"RbCl\"}\n", - " client.create_item(item_id=\"llm-test3\", item_type=\"samples\", item_data=json_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "40a0b4df", - "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "I can't identify or describe the contents of the image. However, it appears to show a clear container with a black substance at the bottom. If you have any specific questions about it or need assistance with something related, feel free to ask!" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%llm\n", - "what do you see in this image: IMG_1648.jpg?" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.9" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Demo_pydantic.ipynb b/examples/Demo_pydantic.ipynb deleted file mode 100644 index 8b6c527..0000000 --- a/examples/Demo_pydantic.ipynb +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pydantic-AI datalab agent demo\n", - "\n", - "This demo queries a *datalab* instance populated by data from Josh Bocarsly's group at the University of Houston." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter LLM API key after running this cell (model choice to be added soon)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass(\"Enter your Anthropic API key\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Enter *datalab* API key and define the *datalab* instance URL" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")\n", - "os.environ[\"DATALAB_URL\"] = \"https://api.yellowhammer.datalab-org.io\"\n", - "\n", - "%reload_ext yellowhammer" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use natural language to query the *datalab* instance, which contains data from multiple student projects:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Based on the research items in the datalab, Moinak Dutta's research focuses on sodium-ion battery materials,            \n",
-       "specifically the synthesis and electrochemical characterization of sodium iron oxide (NaFeO2).                          \n",
-       "\n",
-       "Key research highlights include:                                                                                        \n",
-       "\n",
-       " 1 Developing advanced synthesis techniques for NaFeO2 materials                                                        \n",
-       " 2 Exploring different synthesis parameters like temperature and starting materials                                     \n",
-       " 3 Preparing electrodes for coin cell battery testing                                                                   \n",
-       " 4 Systematically refining material synthesis to produce pure alpha-NaFeO2 phase                                        \n",
-       "\n",
-       "The research is primarily centered on improving sodium-ion battery electrode materials through meticulous material      \n",
-       "synthesis and electrochemical characterization. Dutta has conducted extensive experiments on solid-state synthesis      \n",
-       "methods, varying conditions to optimize the material's structure and performance for potential battery applications.    \n",
-       "
\n" - ], - "text/plain": [ - "Based on the research items in the datalab, Moinak Dutta's research focuses on sodium-ion battery materials, \n", - "specifically the synthesis and electrochemical characterization of sodium iron oxide (NaFeO2). \n", - "\n", - "Key research highlights include: \n", - "\n", - "\u001b[1;33m 1 \u001b[0mDeveloping advanced synthesis techniques for NaFeO2 materials \n", - "\u001b[1;33m 2 \u001b[0mExploring different synthesis parameters like temperature and starting materials \n", - "\u001b[1;33m 3 \u001b[0mPreparing electrodes for coin cell battery testing \n", - "\u001b[1;33m 4 \u001b[0mSystematically refining material synthesis to produce pure alpha-NaFeO2 phase \n", - "\n", - "The research is primarily centered on improving sodium-ion battery electrode materials through meticulous material \n", - "synthesis and electrochemical characterization. Dutta has conducted extensive experiments on solid-state synthesis \n", - "methods, varying conditions to optimize the material's structure and performance for potential battery applications. \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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-       "│ Usage(requests=5,                                                                                                    │\n",
-       "│       request_tokens=10497,                                                                                          │\n",
-       "│       response_tokens=413,                                                                                           │\n",
-       "│       total_tokens=10910,                                                                                            │\n",
-       "│       details=None)                                                                                                  │\n",
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Methods Section for NaFeO2 Synthesis and Characterization                                                               \n",
-       "\n",
-       "Synthesis of NaFeO2                                                                                                     \n",
-       "\n",
-       "NaFeO2 was synthesized via a solid-state reaction method performed entirely within an argon-filled glovebox to minimize \n",
-       "moisture and oxygen exposure. Stoichiometric amounts of sodium peroxide (Na2O2) and iron oxide (Fe2O3 or Fe3O4) were    \n",
-       "precisely weighed and thoroughly ground using a ceramic mortar and pestle. The mixed precursors were heated in an       \n",
-       "alumina crucible within a box furnace, with temperatures ranging from 450-650°C and a heating ramp of 5°C min-1.        \n",
-       "Synthesis protocols included 12-24 hour dwell times at target temperatures, with variations in quenching methods        \n",
-       "including air quench and liquid nitrogen quench.                                                                        \n",
-       "\n",
-       "Electrode Preparation                                                                                                   \n",
-       "\n",
-       "Coin cells were assembled in an argon-purged glovebox to ensure minimal moisture and oxygen contamination. Electrodes   \n",
-       "were prepared by mixing NaFeO2 powder with Super P carbon and polyvinylidene fluoride (PVDF) binder in                  \n",
-       "N-methyl-2-pyrrolidone (NMP) solvent. The electrode composition typically contained 5.7-12.6 mg of active material. Coin\n",
-       "cells were constructed using sodium metal as the counter electrode, with a 1M NaPF6 in propylene carbonate electrolyte  \n",
-       "(150 µL). Cell assembly followed a standardized procedure, with components layered in the following order: bottom cap,  \n",
-       "cathode, separator (pre-soaked in electrolyte), sodium metal anode, spacers, compression spring, and top cap. Cells were\n",
-       "sealed under a pressure of 0.4 tonnes.                                                                                  \n",
-       "\n",
-       "Electrochemical Characterization                                                                                        \n",
-       "\n",
-       "Electrochemical performance was evaluated through galvanostatic charge-discharge cycling using a battery testing system.\n",
-       "Cycling protocols were designed to probe the material's electrochemical behavior, with specific current rates and       \n",
-       "voltage windows determined through systematic investigation.                                                            \n",
-       "\n",
-       "This methods section provides a comprehensive overview of the experimental approach used in synthesizing and            \n",
-       "characterizing NaFeO2 for sodium-ion battery applications.                                                              \n",
-       "
\n" - ], - "text/plain": [ - "Methods Section for NaFeO2 Synthesis and Characterization \n", - "\n", - "Synthesis of NaFeO2 \n", - "\n", - "NaFeO2 was synthesized via a solid-state reaction method performed entirely within an argon-filled glovebox to minimize \n", - "moisture and oxygen exposure. Stoichiometric amounts of sodium peroxide (Na2O2) and iron oxide (Fe2O3 or Fe3O4) were \n", - "precisely weighed and thoroughly ground using a ceramic mortar and pestle. The mixed precursors were heated in an \n", - "alumina crucible within a box furnace, with temperatures ranging from 450-650°C and a heating ramp of 5°C min-1. \n", - "Synthesis protocols included 12-24 hour dwell times at target temperatures, with variations in quenching methods \n", - "including air quench and liquid nitrogen quench. \n", - "\n", - "Electrode Preparation \n", - "\n", - "Coin cells were assembled in an argon-purged glovebox to ensure minimal moisture and oxygen contamination. Electrodes \n", - "were prepared by mixing NaFeO2 powder with Super P carbon and polyvinylidene fluoride (PVDF) binder in \n", - "N-methyl-2-pyrrolidone (NMP) solvent. The electrode composition typically contained 5.7-12.6 mg of active material. Coin\n", - "cells were constructed using sodium metal as the counter electrode, with a 1M NaPF6 in propylene carbonate electrolyte \n", - "(150 µL). Cell assembly followed a standardized procedure, with components layered in the following order: bottom cap, \n", - "cathode, separator (pre-soaked in electrolyte), sodium metal anode, spacers, compression spring, and top cap. Cells were\n", - "sealed under a pressure of 0.4 tonnes. \n", - "\n", - "Electrochemical Characterization \n", - "\n", - "Electrochemical performance was evaluated through galvanostatic charge-discharge cycling using a battery testing system.\n", - "Cycling protocols were designed to probe the material's electrochemical behavior, with specific current rates and \n", - "voltage windows determined through systematic investigation. \n", - "\n", - "This methods section provides a comprehensive overview of the experimental approach used in synthesizing and \n", - "characterizing NaFeO2 for sodium-ion battery applications. \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
-       "│ Usage(requests=8,                                                                                                    │\n",
-       "│       request_tokens=27022,                                                                                          │\n",
-       "│       response_tokens=890,                                                                                           │\n",
-       "│       total_tokens=27912,                                                                                            │\n",
-       "│       details=None)                                                                                                  │\n",
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Based on the datalab contents, the main users and their projects include:                                               \n",
-       "\n",
-       " 1 Hasindhi: Researching sodium nickel manganese oxide (Na-Ni-Mn-O) compounds for battery applications                  \n",
-       " 2 Kusalvin Dabare: Specializing in sodium cobalt oxide (Na-Co-O) materials and their electrochemical properties        \n",
-       " 3 Moinak Dutta: Focusing on sodium iron oxide (NaFeO2) synthesis and characterization                                  \n",
-       " 4 Jason Zhou: Investigating manganese antimonide (MnSb) compounds                                                      \n",
-       " 5 Matthew Evans: Contributing to X-ray diffraction analysis                                                            \n",
-       "\n",
-       "The datalab primarily supports research in advanced materials science, with a strong focus on:                          \n",
-       "\n",
-       " • Sodium-ion battery material development                                                                              \n",
-       " • Solid-state material synthesis                                                                                       \n",
-       " • Electrode preparation                                                                                                \n",
-       " • Electrochemical performance testing                                                                                  \n",
-       " • Crystal structure analysis                                                                                           \n",
-       "\n",
-       "The researchers appear to collaborate closely, sharing methodologies and techniques in material science, particularly in\n",
-       "developing next-generation battery and electronic material technologies.                                                \n",
-       "
\n" - ], - "text/plain": [ - "Based on the datalab contents, the main users and their projects include: \n", - "\n", - "\u001b[1;33m 1 \u001b[0mHasindhi: Researching sodium nickel manganese oxide (Na-Ni-Mn-O) compounds for battery applications \n", - "\u001b[1;33m 2 \u001b[0mKusalvin Dabare: Specializing in sodium cobalt oxide (Na-Co-O) materials and their electrochemical properties \n", - "\u001b[1;33m 3 \u001b[0mMoinak Dutta: Focusing on sodium iron oxide (NaFeO2) synthesis and characterization \n", - "\u001b[1;33m 4 \u001b[0mJason Zhou: Investigating manganese antimonide (MnSb) compounds \n", - "\u001b[1;33m 5 \u001b[0mMatthew Evans: Contributing to X-ray diffraction analysis \n", - "\n", - "The datalab primarily supports research in advanced materials science, with a strong focus on: \n", - "\n", - "\u001b[1;33m • \u001b[0mSodium-ion battery material development \n", - "\u001b[1;33m • \u001b[0mSolid-state material synthesis \n", - "\u001b[1;33m • \u001b[0mElectrode preparation \n", - "\u001b[1;33m • \u001b[0mElectrochemical performance testing \n", - "\u001b[1;33m • \u001b[0mCrystal structure analysis \n", - "\n", - "The researchers appear to collaborate closely, sharing methodologies and techniques in material science, particularly in\n", - "developing next-generation battery and electronic material technologies. \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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-       "│ Usage(requests=8,                                                                                                    │\n",
-       "│       request_tokens=43938,                                                                                          │\n",
-       "│       response_tokens=616,                                                                                           │\n",
-       "│       total_tokens=44554,                                                                                            │\n",
-       "│       details=None)                                                                                                  │\n",
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We can first specify the model we want to use, and the API key. This can be done by setting environment variables or through a `.env` file, but here we do it explicitly in the notebook.\n", + "\n", + "In this demo, we use the Google Gemini model which has a generous free tier. You can get a free API key here: https://aistudio.google.com/app/apikey\n", + "\n", + "Any model supported by Pydantic AI can be used. The list of model names is here: https://ai.pydantic.dev/api/models/base/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3dcd2d79", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "# Specify the AI model\n", + "os.environ[\"AI_MODEL\"] = \"google-gla:gemini-2.5-flash\"\n", + "\n", + "# Use the correct environment variable name for the API key\n", + "# os.environ[\"GEMINI_API_KEY\"] = getpass.getpass(\"Enter your API key:\")" + ] + }, + { + "cell_type": "markdown", + "id": "0f4cc192", + "metadata": {}, + "source": [ + "We now need to connect to a _datalab_ instance using its API key. Here we connect to a locally hosted instance, but normally you will connect to a remote instance such as `https://demo-api.datalab-org.io`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d8dabcbe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Logfire project URL: https://logfire-us.pydantic.dev/yue-here/datalab-1\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mLogfire\u001b[0m project URL: \u001b]8;id=750970;https://logfire-us.pydantic.dev/yue-here/datalab-1\u001b\\\u001b[4;36mhttps://logfire-us.pydantic.dev/yue-here/datalab-1\u001b[0m\u001b]8;;\u001b\\\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set the Datalab API key and URL\n", + "# os.environ[\"DATALAB_API_KEY\"] = getpass.getpass(\"Enter your Datalab API key\")\n", + "# os.environ[\"DATALAB_URL\"] = \"http://api.localhost:5001/\"\n", + "os.environ[\"DATALAB_URL\"] = \"http://localhost:5001/\"\n", + "\n", + "# Load the yellowhammer extension\n", + "%reload_ext yellowhammer" + ] + }, + { + "cell_type": "markdown", + "id": "f7e3c6eb", + "metadata": {}, + "source": [ + "Now we can start using the `%%llm` cell magic to talk to _datalab_!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3a663b5b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15:02:56.043 datalab_agent run\n", + "15:02:56.044 chat gemini-2.5-flash\n", + "15:02:57.397 running 1 tool\n", + "15:02:57.397 running tool: item_query\n", + "15:02:57.406 chat gemini-2.5-flash\n", + "15:02:59.331 inspection_agent run\n", + "15:02:59.332 chat gemini-2.5-flash\n" + ] + }, + { + "data": { + "text/html": [ + "
Moinak Dutta's research primarily focuses on materials related to energy storage, specifically utilizing alpha-NaFeO2.  \n",
+       "The main types of experiments conducted include Powder X-ray Diffraction (XRD) for material characterization and        \n",
+       "electrochemical cycling to evaluate battery cell performance. The materials investigated are alpha-NaFeO2 as well as    \n",
+       "electrodes and battery cells constructed with alpha-NaFeO2. The provided overviews do not contain specific key findings \n",
+       "from these experiments.                                                                                                 \n",
+       "
\n" + ], + "text/plain": [ + "Moinak Dutta's research primarily focuses on materials related to energy storage, specifically utilizing alpha-NaFeO2. \n", + "The main types of experiments conducted include Powder X-ray Diffraction (XRD) for material characterization and \n", + "electrochemical cycling to evaluate battery cell performance. The materials investigated are alpha-NaFeO2 as well as \n", + "electrodes and battery cells constructed with alpha-NaFeO2. The provided overviews do not contain specific key findings \n", + "from these experiments. \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
+       "│ Usage(requests=2, request_tokens=2181, response_tokens=73, total_tokens=2477, details={'thoughts_tokens': 223,       │\n",
+       "│ 'text_prompt_tokens': 2181, 'cached_content_tokens': 867, 'text_cache_tokens': 867})                                 │\n",
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I cannot identify the specific item from the image alone. The item_query tool returned 100 items matching 'powder'.     \n",
+       "Could you please provide more details about the powder, such as its name, properties, or any associated experiment IDs, \n",
+       "so I can narrow down the search?                                                                                        \n",
+       "
\n" + ], + "text/plain": [ + "I cannot identify the specific item from the image alone. The \u001b[1;36;40mitem_query\u001b[0m tool returned 100 items matching 'powder'. \n", + "Could you please provide more details about the powder, such as its name, properties, or any associated experiment IDs, \n", + "so I can narrow down the search? \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭───────────────────────────────────────────────── Usage Information ──────────────────────────────────────────────────╮\n",
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+       "│ 'text_cache_tokens': 995})                                                                                           │\n",
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"14:17:46.565 chat gemini-2.5-flash\n", + "14:17:47.792 running 1 tool\n", + "14:17:47.792 running tool: get_item_details\n", + "14:17:47.793 chat gemini-2.5-flash\n", + "14:17:49.091 running 1 tool\n", + "14:17:49.091 running tool: get_item_details\n", + "14:17:49.092 chat gemini-2.5-flash\n", + "14:17:51.629 running 1 tool\n", + "14:17:51.630 running tool: item_query\n", + "14:17:51.632 chat gemini-2.5-flash\n", + "14:17:53.719 running 1 tool\n", + "14:17:53.720 running tool: get_items\n", + "14:17:54.070 chat gemini-2.5-flash\n", + "14:17:56.374 running 1 tool\n", + "14:17:56.374 running tool: item_query\n", + "14:17:56.379 chat gemini-2.5-flash\n", + "14:17:57.771 running 1 tool\n", + "14:17:57.771 running tool: get_item_details\n", + "14:17:58.821 chat gemini-2.5-flash\n", + "14:18:00.775 inspection_agent run\n", + "14:18:00.777 chat gemini-2.5-flash\n" + ] + }, + { + "data": { + "text/html": [ + "
Moinak Dutta's research centers on the synthesis and electrochemical characterization of alpha-NaFeO2, likely for       \n",
+       "sodium-ion battery applications. The core of the research involves optimizing the solid-state synthesis of alpha-NaFeO2 \n",
+       "from starting materials like Sodium Peroxide (Na2O2) and Iron (III) Oxide (Fe2O3) or Iron (II,III) oxide (Fe3O4). A key \n",
+       "challenge is minimizing the formation of an undesirable beta phase impurity, with various synthesis parameters,         \n",
+       "including heating temperatures (450°C to 700°C), dwell times (2 to 72 hours), and cooling methods (air quench, liquid N2\n",
+       "quench, furnace cool), being systematically explored. The purity of the synthesized alpha-NaFeO2 is routinely assessed  \n",
+       "using Powder X-ray Diffraction (XRD).                                                                                   \n",
+       "\n",
+       "Pure samples of alpha-NaFeO2 (such as MD7-1, noted as                                                                   \n",
+       "
\n" + ], + "text/plain": [ + "Moinak Dutta's research centers on the synthesis and electrochemical characterization of alpha-NaFeO2, likely for \n", + "sodium-ion battery applications. The core of the research involves optimizing the solid-state synthesis of alpha-NaFeO2 \n", + "from starting materials like Sodium Peroxide (Na2O2) and Iron (III) Oxide (Fe2O3) or Iron (II,III) oxide (Fe3O4). A key \n", + "challenge is minimizing the formation of an undesirable beta phase impurity, with various synthesis parameters, \n", + "including heating temperatures (450°C to 700°C), dwell times (2 to 72 hours), and cooling methods (air quench, liquid N2\n", + "quench, furnace cool), being systematically explored. The purity of the synthesized alpha-NaFeO2 is routinely assessed \n", + "using Powder X-ray Diffraction (XRD). \n", + "\n", + "Pure samples of alpha-NaFeO2 (such as MD7-1, noted as \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Here are notes on how data is structured in datalab: {}. In each step, carefully assess whether you have sufficient information to answer the user's query, and if not, obtain more information using the provded tools. +You are a virtual assistant that helps scientists use the data management platform datalab to manage their experimental data, plan experiments, analyse data and plot results. Here are notes on how data is structured in datalab: {}. In each step, carefully assess whether you have sufficient information to answer the user's query, and if not, obtain more information using the provded tools. You can also assess images if multimodal input is provided. You have access to these tools: @@ -10,10 +10,10 @@ item_query: get_items MUST be run first to retrieve the item manifest! You can t Once you've performed a query, you can either work with the overviews for big-picture queries, or retrieve full items for deep dives. These tools are available: -inspect_query: you can use this tool to have a LLM agent inspect the search buffer with a given prompt. If the the output of this tool is enough to answer the user's query, copy it to your final output. Otherwise, obtain more detailed information. +inspect_query: use this tool to hand off to another agent that will inspect the search buffer and deliver the final answer to the user. Provide a clear prompt explaining what the user wants to guide this agent. get_item_details: this tool can retrieve more detailed information for every item. ONLY use this if the information provided in the overviews is insufficient! item_query MUST be run first. The inspect_items tool can then be used to inspect the retrieved information. -inspect_items: get_item_details MUST be run first to generate the items buffer. The items buffer contains detailed information about queried items. You can use this tool to have a LLM agent inspect the items buffer with a given prompt. Please copy the output of this tool as your final response to the user. +inspect_items: get_item_details MUST be run first to generate the items buffer. The items buffer contains detailed information about queried items. Use this tool to hand off to another agent that will inspect the items buffer and deliver the final answer to the user. Provide a clear prompt explaining what the user wants to guide this agent. Here is the user question: \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 9c2ee56..4c07f5e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,12 +10,13 @@ dependencies = [ "jupyterlab>=4.2.5", "notebook>=7.2.2", "yellowhammer", - "pydantic-ai>=0.0.46", "nest-asyncio>=1.6.0", "whoosh-reloaded>=2.7.5", "logfire>=3.12.0", "chromadb>=1.0.4", "matplotlib>=3.10.1", + "pydantic-ai>=0.3.3", + "python-dotenv>=1.1.0", ] [project.urls] diff --git a/src/yellowhammer/llm.py b/src/yellowhammer/llm.py new file mode 100644 index 0000000..03afc3a --- /dev/null +++ b/src/yellowhammer/llm.py @@ -0,0 +1,377 @@ +import os +import json +import chromadb +from typing import Any, cast +from dataclasses import dataclass +from datetime import date +from datalab_api import DatalabClient +from typing import Annotated, Any, Union +from typing_extensions import TypeAlias +from pydantic import BaseModel, Field +from pydantic_ai import Agent, ModelRetry, RunContext +from pydantic_ai.models import KnownModelName +from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT, INGEST_PROMPT +from .fulltext_search import FullTextSearchTool +from .utils import process_item_for_chat +from pydantic import BaseModel, Field, ConfigDict +from dotenv import load_dotenv + +# Load environment variables from .env file for local development +load_dotenv() + +# Context dependencies for agent +@dataclass +class Deps: + client: DatalabClient + datalab_api_key: str | None + datalab_url: str | None + item_manifest: dict[str, Any] | None = None + item_search_id_buffer: dict[str, Any] | None = None + items_buffer: dict[str, Any] | None = None + search_buffer: list[dict] | None = None + search_tool: FullTextSearchTool | None = None + +# Output schemas +class TextResponse(BaseModel): + text: str = Field(description="A conversational response") + +class CodeResponse(BaseModel): + text: str = Field(description="A description of the code") + code: str = Field(description="A code snippet") + +Response: TypeAlias = Union[TextResponse, CodeResponse] + +# Define preferred model +default_model = 'google-gla:gemini-2.5-flash-lite' +model = cast(KnownModelName, os.getenv('AI_MODEL', default_model)) # let user set the model via env var + +# API key will be detected from environment variables. For the main providers: +# OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY +# TODO: add getpass to example notebook for user to input API key +# PydanticAI known models: https://ai.pydantic.dev/api/models/base/ + +# Use Output Functions to hand off to a new agent that outputs directly to the user +# Use this to avoid rephrasing by the main agent. +# Recently implemented in https://github.com/pydantic/pydantic-ai/pull/1785 + +# Direct output function for item inspection +async def inspect_items_output(ctx: RunContext[Deps], prompt: str) -> str: + """ + If search item details have been successfully retrieved with a get_item_details call, + this function uses an LLM agent to inspect these items and returns the result directly. + + Args: + prompt: A system prompt for the inspection agent + """ + if ctx.deps.items_buffer == None: + return "Error: No items buffer found, run get_item_details first." + + # Items buffer to plaintext + items_buffer = json.dumps(ctx.deps.items_buffer, separators=(',', ':')) + + # Create an agent to inspect the items + inspection_agent = Agent(model, system_prompt=prompt, result_type=Response, instrument=True) + result = await inspection_agent.run(items_buffer, deps=ctx.deps) + + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No inspection result available." + +# Direct output function for query inspection +async def inspect_query_output(ctx: RunContext[Deps], prompt: str) -> str: + """ + If a search query has been successfully run with a item_query call, + this function uses an LLM agent to inspect the search buffer and returns the result directly. + + Args: + prompt: A system prompt for the inspection agent + """ + if ctx.deps.search_buffer == None: + return "Error: No search buffer found, run item_query first." + + # Search buffer to plaintext + search_buffer = json.dumps(ctx.deps.search_buffer, separators=(',', ':')) + inspection_agent = Agent(model, system_prompt=prompt, result_type=Response, instrument=True) + result = await inspection_agent.run(search_buffer, deps=ctx.deps) + + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No inspection result available." + +# Direct output function for code writing +async def code_writer_output(ctx: RunContext[Deps], prompt: str) -> str: + """ + This function uses an LLM agent to write code based on a prompt. + It has information about the datalab API syntax and returns the result directly. + + Args: + prompt: A system prompt for the code writer agent + """ + code_agent = Agent(model, result_type=Response, system_prompt=CODE_PROMPT, instrument=True) + result = await code_agent.run(prompt, deps=ctx.deps) + + # Return the result directly - no need to ask main agent to rephrase + return result.output if result.output else "No code generation result available." + + +# Define main agent +datalab_agent = Agent[Deps, Union[Response, str]]( + model, + system_prompt=SYSTEM_PROMPT.format(API_PROMPT), + output_type=[Response, inspect_items_output, inspect_query_output, code_writer_output], # Output can be Response or output function + deps_type=Deps, + retries=2, + instrument=True, # Enable logging +) + +# Define tools +# TODO add an option to return items of all types in the API call +# @datalab_agent.tool(retries=2) +# async def get_items(ctx: RunContext[Deps], item_type: str) -> str: +# """Use the datalab get_items function to get a JSON of all items in the database. + +# Args: +# ctx: The context. +# item_type: Usually 'samples' + +# Returns: + +# """ +# # TODO check API key when the agent is initialized +# # if ctx.deps.datalab_api_key is None: +# # # if no API key is provided, say so +# # return {'error': 'No datalab API key found.'} + +# if ctx.deps.item_manifest is None: +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# item_type = "samples" +# ctx.deps.item_manifest = client.get_items(item_type=item_type) +# # Initialize the search tool with the fetched items +# ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) +# return "Items retrieved." + +# if ctx.deps.item_manifest is not None and ctx.deps.search_tool is None: +# ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) + +# if ctx.deps.item_manifest is not None: +# return "Items already available." + +# # Set 2 retries in decorator to prevent looping +# raise ModelRetry('No data returned, try again.') + +# Tool to query item manifest if it exists +@datalab_agent.tool +async def item_query(ctx: RunContext[Deps], query: str) -> str | dict[str, Any] | list[dict[str, Any]]: + """Use full-text search to find items matching your query. + This searches through all fields in the item manifest including nested data. + + Args: + query: Keyword query string (e.g., "John Doe Fe3O4") + + Returns: + List of dictionaries each representing one matching item + """ + if ctx.deps.item_manifest is None: + return {'error': 'Use get_items to generate items list first'} + + # Initialize search tool if not already done + if ctx.deps.search_tool is None: + ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) + + # Perform the search using the FullTextSearchTool + matching_items = ctx.deps.search_tool.natural_language_search(query) + + if matching_items: + # store the query in the search buffer + ctx.deps.search_buffer = matching_items + + # Store the item IDs in the buffer for get_item_details + ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + + return f'Retrieved {len(matching_items)} items matching {query}' + else: + return {'error': f'No items found matching query: "{query}"'} + +# Tool to make get_item API calls on item search id buffer +@datalab_agent.tool +async def get_item_details(ctx: RunContext[Deps], simplify_JSON: bool = True) -> str | dict[str, Any]: + """Use the datalab get_item function to get details JSONs of specific items in the manifest. + This function reads a list of item_ids and retrieves the corresponding items using the datalab API. + item_query must be run first to generate the item search id buffer. + """ + if ctx.deps.item_search_id_buffer == None: + return {'error': 'No search id buffer found, run item_query first.'} + + if ctx.deps.items_buffer == None: + ctx.deps.items_buffer = {} + + with ctx.deps.client(ctx.deps.datalab_url) as client: + for item_id in ctx.deps.item_search_id_buffer: + item = client.get_item(item_id=item_id) + + if simplify_JSON: + item = process_item_for_chat(item) + ctx.deps.items_buffer[item_id] = item + return "Items retrieved." + + return {'error': 'No items found.'} + +# For further development +# @datalab_agent.tool +# async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: +# """Use chromadb to perform a vector search on the item manifest. + +# Args: +# query: A string to search for in the item manifest +# n_results: Number of top results to return (default: 5) + +# Returns: +# String confirmation with number of results found +# """ +# if ctx.deps.item_manifest is None: +# return {'error': 'Use get_items to generate items list first'} + +# # Initialize the Chroma client (in-memory) +# client = chromadb.Client() + +# # Create or get the collection +# try: +# collection = client.get_collection("items") +# except: +# collection = client.create_collection("items") + +# # Extract item IDs and text content from the manifest +# item_ids = [] +# documents = [] + +# for item in ctx.deps.item_manifest: +# item_id = str(item.get('item_id', '')) +# if not item_id: +# continue + +# # Convert the item to a string representation for the document +# item_text = json.dumps(item, ensure_ascii=False) + +# item_ids.append(item_id) +# documents.append(item_text) + +# # Add items to the collection if there are any valid items +# if item_ids and documents: +# collection.add( +# ids=item_ids, +# documents=documents +# ) + +# # Perform a vector search +# results = collection.query( +# query_texts=[query], +# n_results=min(n_results, len(item_ids)) +# ) + +# if not results or not results['ids'][0]: +# return {'error': f'No items found matching query: "{query}"'} + +# # Get the matching item IDs +# matching_ids = results['ids'][0] + +# # Store results in buffers for further processing +# matching_items = [item for item in ctx.deps.item_manifest +# if str(item.get('item_id', '')) in matching_ids] + +# # Store the results in the search buffer +# ctx.deps.search_buffer = matching_items + +# # Store the item IDs in the buffer for get_item_details +# ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] + +# return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' + +# # Agent to ingest items +# ingestion_agent = Agent( +# model, +# system_prompt=INGEST_PROMPT.format(date.today()), +# result_type=Response, +# deps_type=Deps, +# retries=2, +# instrument=True, +# ) + +# class CreateItemInput(BaseModel): +# item_id: str = Field(description="A short identifier for the item") +# # item_type: str = Field(description="The type of the item") # default to 'samples' +# name: str = Field(description="The name of the item") +# chemform: str = Field(description="The chemical form of the item") +# date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") +# file: str = Field(description="File path to the associated file, if specified") + + +# @ingestion_agent.tool +# async def create_item(ctx: RunContext[Deps], inputs: CreateItemInput) -> str: +# """ +# Use the datalab client to create a new datalab item. +# """ +# if ctx.deps.datalab_api_key is None: +# return {'error': 'No datalab API key found.'} + +# if ctx.deps.datalab_url is None: +# return {'error': 'No datalab URL found.'} + +# with ctx.deps.client(ctx.deps.datalab_url) as client: +# sample_dict = { +# "name": inputs.name, +# "chemform": inputs.chemform, +# "date": inputs.date, +# } +# try: +# created = client.create_item( +# item_id=inputs.item_id, +# item_type="samples", +# item_data=sample_dict +# ) + +# created_item_id = created["item_id"] + +# if inputs.file: +# # Check if the file exists +# if not os.path.exists(inputs.file): +# return {'error': f'Item {inputs.item_id} created but cannot find file {inputs.file}'} + +# try: +# client.upload_file( +# item_id=created_item_id, file_path=inputs.file +# ) +# except Exception as e: +# return {'error': f'Item {inputs.item_id} created but file upload failed: {str(e)}'} + +# return f"Item {inputs.item_id} created successfully with ID {created_item_id}." + +# except Exception as e: +# return {'error': f'Error creating item: {str(e)}'} + + +# @datalab_agent.tool +# async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: +# """ +# This tool is used to create a new item in the datalab. +# It uses an agent to create an item specified by natural language. + +# Args: +# str: A system prompt for the item creation agent +# """ +# # Create an agent to create the item +# result = await ingestion_agent.run(prompt, deps=ctx.deps) + +# return result.output +# return result.output +# @datalab_agent.tool +# async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: +# """ +# This tool is used to create a new item in the datalab. +# It uses an agent to create an item specified by natural language. + +# Args: +# str: A system prompt for the item creation agent +# """ +# # Create an agent to create the item +# result = await ingestion_agent.run(prompt, deps=ctx.deps) + +# return result.output +# return result.output diff --git a/src/yellowhammer/llm_deprecated.py b/src/yellowhammer/llm_deprecated.py deleted file mode 100644 index 233b2f5..0000000 --- a/src/yellowhammer/llm_deprecated.py +++ /dev/null @@ -1,95 +0,0 @@ -from pydantic import BaseModel, Field -from typing import Union -# from langchain_core.prompts import ChatPromptTemplate -# from .prompt import SYSTEM_PROMPT - - -# Pydantic schemas to use with .with_structured_output() -# Docstring acts as a description for the LLM -class Code(BaseModel): - """Schema for code solutions""" - - prefix: str = Field(description="Description of the problem and approach") - imports: str = Field(description="Code block import statements") - code: str = Field(description="Code block not including import statements") - - -class ConversationalResponse(BaseModel): - """Respond in a conversational manner. Be kind and helpful.""" - - response: str = Field(description="A conversational response to the user's query") - - -class FinalResponse(BaseModel): - """ - Make sure that the final_output field exists. - final_output can contain either Code or ConversationalResponse schemas. - """ - - final_output: Union[Code, ConversationalResponse] - - -# Check for errors in the structured output -def error_parser(output): - """ - Parse the API output to handle errors gracefully. - """ - if output["parsing_error"]: - raw_output = str(output["raw"].content) - error = output["parsing_error"] - out_string = f"Error parsing LLM output. Parse error: {error}. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) - - elif not output["parsed"]: - raw_output = str(output["raw"].content) - out_string = f"Error in LLM response. \n Raw output: {raw_output}" - return FinalResponse(final_output=ConversationalResponse(response=out_string)) - - else: - # Return the parsed output (should be FinalResponse) - return output["parsed"] - - -def get_chain( - messages, # ChatPromptTemplate - api_provider, - api_key, - api_model=None, - api_temperature=0, -): - """ - Return a runnable chain which accepts {context} (e.g. datalab API documentation) - - Args: - messages (ChatPromptTemplate): A ChatPromptTemplate instance - api_provider (str): API provider name (e.g. "openai", "anthropic") - api_key (str): LLM API key to use - api_model (str, optional): The specific model to use e.g. "gpt-4o-mini" - api_temperature (int, optional): LLM temperature - """ - - # API provider logic - if api_provider.lower() == "openai": - from langchain_openai import ChatOpenAI - - if api_model is None: - api_model = "gpt-4o-mini" - llm = ChatOpenAI(model=api_model, temperature=api_temperature, openai_api_key=api_key) - - elif api_provider.lower() == "anthropic": - from langchain_anthropic import ChatAnthropic - - if api_model is None: - # https://docs.anthropic.com/en/docs/about-claude/models#model-names - # api_model = "claude-3-5-sonnet-latest" - api_model = "claude-3-7-sonnet-latest" - llm = ChatAnthropic( - model=api_model, - temperature=api_temperature, - anthropic_api_key=api_key, - ) - - # Create a chain where the final output takes the FinalResponse schema - chain = messages | llm.with_structured_output(FinalResponse, include_raw=True) | error_parser - - return chain diff --git a/src/yellowhammer/llm_pydantic.py b/src/yellowhammer/llm_pydantic.py deleted file mode 100644 index 3dd2b99..0000000 --- a/src/yellowhammer/llm_pydantic.py +++ /dev/null @@ -1,419 +0,0 @@ -import os -import json -import chromadb -from typing import Any, cast -from dataclasses import dataclass -from datetime import date - -from datalab_api import DatalabClient - -from typing import Annotated, Any, Union -from typing_extensions import TypeAlias - -from pydantic import BaseModel, Field -from pydantic_ai import Agent, ModelRetry, RunContext -from pydantic_ai.models import KnownModelName -from .prompt import API_PROMPT, SYSTEM_PROMPT, CODE_PROMPT, INGEST_PROMPT -from .fulltext_search import FullTextSearchTool -from .utils import process_item_for_chat -from pydantic import BaseModel, Field, ConfigDict - -# Dependencies -@dataclass -class Deps: - client: DatalabClient - datalab_api_key: str | None - datalab_url: str | None - item_manifest: dict[str, Any] | None = None - # block_manifest: dict[str, Any] | None = None - item_search_id_buffer: dict[str, Any] | None = None - items_buffer: dict[str, Any] | None = None - search_buffer: list[dict] | None = None - search_tool: FullTextSearchTool | None = None - -# Output schemas -class TextResponse(BaseModel): - text: str = Field(description="A short conversational response") - -class CodeResponse(BaseModel): - text: str = Field(description="A short description of the code") - code: str = Field(description="A code snippet") - -Response: TypeAlias = Union[TextResponse, CodeResponse] - -# Define agent and preferred model -default_model = 'anthropic:claude-3-5-haiku-latest' -model = cast(KnownModelName, os.getenv('AI_MODEL', default_model)) # let user set the model -datalab_agent = Agent( - model, - system_prompt=SYSTEM_PROMPT.format(API_PROMPT), # TODO: fix prompts - result_type=Response, - deps_type=Deps, - retries=2, - instrument=True, -) - -# Define tools -# Can we add an option to return items of all types in the API call? -@datalab_agent.tool(retries=2) -async def get_items(ctx: RunContext[Deps], item_type: str) -> str: - """Use the datalab get_items function to get a JSON of all items in the database. - - Args: - ctx: The context. - item_type: Usually 'samples' - """ - # TODO check API key when the agent is initialized - # if ctx.deps.datalab_api_key is None: - # # if no API key is provided, say so - # return {'error': 'No datalab API key found.'} - - if ctx.deps.item_manifest == None: - with ctx.deps.client(ctx.deps.datalab_url) as client: - item_type = "samples" - ctx.deps.item_manifest = client.get_items(item_type=item_type) - # Initialize the search tool with the fetched items - ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) - return "Items retrieved." - - # Set 2 retries in decorator to prevent looping - raise ModelRetry('No data returned, try again.') - - -# @datalab_agent.tool -# async def inspect_blocks(ctx: RunContext[Deps]) -> str: -# """Inspect retrieved blocks in the block manifest. -# """ - -# if ctx.deps.block_manifest == None: -# with ctx.deps.client(ctx.deps.datalab_url) as client: -# ctx.deps.block_manifest = client.get_block_info - -# # Blocks buffer to plaintext -# blocks_buffer = json.dumps(ctx.deps.block_manifest, indent=2) - -# r = await datalab_agent.run(blocks_buffer, deps=ctx.deps) - -# return r.data - -# # Tool to identify blocks present in the current datalab - may not be necessary -# @datalab_agent.tool -# async def block_query(ctx: RunContext[Deps], query: str) -> list[dict[str, Any]]: -# """Find blocks that are present in the current datalab." - -# Args: -# query: Search the block list for this query string. - -# Returns: -# List of dicts for each block matching the query -# """ -# if ctx.deps.block_manifest == None: -# with ctx.deps.client(ctx.deps.datalab_url) as client: -# if client.get_block_info() == None: -# return {'error': 'No blocks found.'} -# ctx.deps.block_manifest = client.get_block_info() - -# # Check if blocks with the query exist and return them in a list -# if any(item.get('id') == query for item in ctx.deps.block_manifest): -# return [ -# item for item in ctx.deps.block_manifest -# if any(block.get('blocktype') == query for block in item.get('blocks', [])) -# ] -# else: -# return {'error': 'Queried block not found.'} - -# Helper function to search through nested dicts/lists (the item manifest) -# def _search_nested(obj, query, case_sensitive=False): -# """Recursively search through nested dictionaries and lists for a query. - -# Args: -# obj: The object to search (dict, list, or primitive value) -# query: The string to search for -# case_sensitive: Whether the search should be case-sensitive - -# Returns: -# bool: True if the query was found, False otherwise -# """ -# if isinstance(obj, dict): -# # Look through all keys and values -# return any(_search_nested(k, query, case_sensitive) for k in obj.keys()) or \ -# any(_search_nested(v, query, case_sensitive) for v in obj.values()) -# elif isinstance(obj, list): -# # Look through all items in the list -# return any(_search_nested(item, query, case_sensitive) for item in obj) -# elif isinstance(obj, str): -# # String comparison -# if case_sensitive: -# return query in obj -# else: -# return query.lower() in obj.lower() -# elif obj is not None: -# # For numbers or other types, convert to string and check -# return str(query) == str(obj) -# return False - -# Tool to query item manifest if it exists -@datalab_agent.tool -async def item_query(ctx: RunContext[Deps], query: str) -> list[dict[str, Any]]: - """Use full-text search to find items matching your query. - This searches through all fields in the item manifest including nested data. - - Args: - query: Keyword query string (e.g., "John Doe Fe3O4") - - Returns: - List of dictionaries each representing one matching item - """ - if ctx.deps.item_manifest is None: - return {'error': 'Use get_items to generate items list first'} - - # Initialize search tool if not already done - if ctx.deps.search_tool is None: - ctx.deps.search_tool = FullTextSearchTool(ctx.deps.item_manifest) - - # Perform the search using the FullTextSearchTool - matching_items = ctx.deps.search_tool.natural_language_search(query) - - if matching_items: - # store the query in the search buffer - ctx.deps.search_buffer = matching_items - - # Store the item IDs in the buffer for get_item_details - ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] - - return f'Retrieved {len(matching_items)} items matching {query}' - else: - return {'error': f'No items found matching query: "{query}"'} - -# Tool to make get_item API calls on item search id buffer -@datalab_agent.tool -async def get_item_details(ctx: RunContext[Deps], simplify_JSON:bool = True) -> dict[str, Any]: - """Use the datalab get_item function to get details JSONs of specific items in the manifest. - This function reads a list of item_ids and retrieves the corresponding items using the datalab API. - item_query must be run first to generate the item search id buffer. - """ - if ctx.deps.item_search_id_buffer == None: - return {'error': 'No search id buffer found, run item_query first.'} - - if ctx.deps.items_buffer == None: - ctx.deps.items_buffer = {} - - with ctx.deps.client(ctx.deps.datalab_url) as client: - for item_id in ctx.deps.item_search_id_buffer: - item = client.get_item(item_id=item_id) - - if simplify_JSON: - item = process_item_for_chat(item) - ctx.deps.items_buffer[item_id] = item - return "Items retrieved." - - return {'error': 'No items found.'} - -# Ask agent to inspect items in the items buffer -@datalab_agent.tool -async def inspect_items(ctx: RunContext[Deps], prompt: str) -> str: - """ - If search item details have been successfully retrieved with a get_item_details call, - this tool uses an LLM agent to inspect these items. Prompt this agent based on the user's request. - The user does not see this tool's response so incorporate outputs into your final repsonse to the user. - - Args: - prompt: A system prompt for the inspection agent - """ - if ctx.deps.items_buffer == None: - return {'error': 'No items buffer found, run get_item_details first.'} - - # Items buffer to plaintext - items_buffer = json.dumps(ctx.deps.items_buffer, separators=(',', ':')) - - # Create an agent to inspect the items - inspection_agent = Agent(model, system_prompt=prompt, instrument=True) - result = await inspection_agent.run(items_buffer, deps=ctx.deps) - - # Append a message to the result so that it's passed back to the user - if result.data: - result.data += "\n\n###\n\n The tool reponse is given above. Please relay it to the user" - - return result.data - -# Add the inspect_query function after inspect_items -@datalab_agent.tool -async def inspect_query(ctx: RunContext[Deps], prompt: str) -> str: - """ - If a search query has been successfully run with a item_query call, - this tool uses an LLM agent to inspect the search buffer. Prompt this agent based on the user's request. - item_query must be run first to generate the search buffer. - - Args: - prompt: A system prompt for the inspection agent - """ - if ctx.deps.search_buffer == None: - return {'error': 'No search buffer found, run item_query first.'} - # Search buffer to plaintext - search_buffer = json.dumps(ctx.deps.search_buffer, separators=(',', ':')) - inspection_agent = Agent(model, system_prompt=prompt, instrument=True) - result = await inspection_agent.run(search_buffer, deps=ctx.deps) - - # Append a message to the result so that it's passed back to the user - if result.data: - result.data += "\n\n###\n\n The tool reponse is given above. Please relay it to the user" - - return result.data - -@datalab_agent.tool -async def code_writer(ctx: RunContext[Deps], prompt: str) -> Response: - """ - This tool is used to write code based on a prompt. It has information about the datalab API syntax. - Args: - prompt: A system prompt for the code writer agent - """ - code_agent = Agent(model, result_type = Response, system_prompt=CODE_PROMPT, instrument=True) - result = await code_agent.run(prompt, deps=ctx.deps) - return result.data - -# Requires further development -# @datalab_agent.tool -# async def vector_search(ctx: RunContext[Deps], query: str, n_results: int = 5) -> str: -# """Use chromadb to perform a vector search on the item manifest. - -# Args: -# query: A string to search for in the item manifest -# n_results: Number of top results to return (default: 5) - -# Returns: -# String confirmation with number of results found -# """ -# if ctx.deps.item_manifest is None: -# return {'error': 'Use get_items to generate items list first'} - -# # Initialize the Chroma client (in-memory) -# client = chromadb.Client() - -# # Create or get the collection -# try: -# collection = client.get_collection("items") -# except: -# collection = client.create_collection("items") - -# # Extract item IDs and text content from the manifest -# item_ids = [] -# documents = [] - -# for item in ctx.deps.item_manifest: -# item_id = str(item.get('item_id', '')) -# if not item_id: -# continue - -# # Convert the item to a string representation for the document -# item_text = json.dumps(item, ensure_ascii=False) - -# item_ids.append(item_id) -# documents.append(item_text) - -# # Add items to the collection if there are any valid items -# if item_ids and documents: -# collection.add( -# ids=item_ids, -# documents=documents -# ) - -# # Perform a vector search -# results = collection.query( -# query_texts=[query], -# n_results=min(n_results, len(item_ids)) -# ) - -# if not results or not results['ids'][0]: -# return {'error': f'No items found matching query: "{query}"'} - -# # Get the matching item IDs -# matching_ids = results['ids'][0] - -# # Store results in buffers for further processing -# matching_items = [item for item in ctx.deps.item_manifest -# if str(item.get('item_id', '')) in matching_ids] - -# # Store the results in the search buffer -# ctx.deps.search_buffer = matching_items - -# # Store the item IDs in the buffer for get_item_details -# ctx.deps.item_search_id_buffer = [item.get('item_id') for item in matching_items if 'item_id' in item] - -# return f'Retrieved {len(matching_items)} items matching vector search for "{query}"' - -# Separate agent to ingest items -ingestion_agent = Agent( - model, - system_prompt=INGEST_PROMPT.format(date.today()), - result_type=Response, - deps_type=Deps, - retries=2, - instrument=True, -) - -class CreateItemInput(BaseModel): - item_id: str = Field(description="A short identifier for the item") - # item_type: str = Field(description="The type of the item") # default to 'samples' - name: str = Field(description="The name of the item") - chemform: str = Field(description="The chemical form of the item") - date: str = Field(description="Date of item creation, ISO formatted (YYYY-MM-DD)") - file: str = Field(description="File path to the associated file, if specified") - - -@ingestion_agent.tool -async def create_item(ctx: RunContext[Deps], inputs: CreateItemInput) -> str: - """ - Use the datalab client to create a new datalab item. - """ - if ctx.deps.datalab_api_key is None: - return {'error': 'No datalab API key found.'} - - if ctx.deps.datalab_url is None: - return {'error': 'No datalab URL found.'} - - with ctx.deps.client(ctx.deps.datalab_url) as client: - sample_dict = { - "name": inputs.name, - "chemform": inputs.chemform, - "date": inputs.date, - } - try: - created = client.create_item( - item_id=inputs.item_id, - item_type="samples", - item_data=sample_dict - ) - - created_item_id = created["item_id"] - - if inputs.file: - # Check if the file exists - if not os.path.exists(inputs.file): - return {'error': f'Item {inputs.item_id} created but cannot find file {inputs.file}'} - - try: - client.upload_file( - item_id=created_item_id, file_path=inputs.file - ) - except Exception as e: - return {'error': f'Item {inputs.item_id} created but file upload failed: {str(e)}'} - - return f"Item {inputs.item_id} created successfully with ID {created_item_id}." - - except Exception as e: - return {'error': f'Error creating item: {str(e)}'} - - -@datalab_agent.tool -async def item_creator(ctx: RunContext[Deps], prompt: str) -> str: - """ - This tool is used to create a new item in the datalab. - It uses an agent to create an item specified by natural language. - - Args: - str: A system prompt for the item creation agent - """ - # Create an agent to create the item - result = await ingestion_agent.run(prompt, deps=ctx.deps) - - return result.data diff --git a/src/yellowhammer/magics.py b/src/yellowhammer/magics.py index 7a83917..4934b78 100644 --- a/src/yellowhammer/magics.py +++ b/src/yellowhammer/magics.py @@ -29,10 +29,11 @@ parse_argstring, ) from IPython.display import Markdown, HTML -# from langchain_core.prompts import ChatPromptTemplate from datalab_api import DatalabClient -from .llm_pydantic import datalab_agent, Deps +from .fulltext_search import FullTextSearchTool +from .llm import datalab_agent, Deps from .prompt import API_PROMPT, SYSTEM_PROMPT +from .utils import process_prompt_with_images import asyncio import nest_asyncio import pprint @@ -46,32 +47,14 @@ nest_asyncio.apply() -def parse_paths(input_string): - # A simple regex pattern to match file-path-like substrings: - # [A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+ - # - one or more letters, digits, underscores, dots, slashes, or dashes, - # containing at least one dot that leads to a plausible extension - file_path_pattern = r"([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)" - - # Find all file paths - paths = re.findall(file_path_pattern, input_string) - - # Remove those file paths from the original string - remaining_text = re.sub(file_path_pattern, "", input_string) - - # Clean up extra whitespace - remaining_text = re.sub(r"\s+", " ", remaining_text).strip() - - return {"text": remaining_text, "paths": paths} - -# Class to manage state and expose the main magics @magics_class class DatalabMagics(Magics): def __init__(self, shell): super().__init__(shell) self.messages = None - # self.images = [] # Initialize with empty list of images. + self.item_manifest = None + self.search_tool = None # A datalab magic that returns a code block @magic_arguments() @@ -94,26 +77,42 @@ def __init__(self, shell): @line_cell_magic def llm(self, line, cell=None): """ - Multimodal LLM interaction with Jupyter magics + Multimodal LLM interaction with Jupyter magics. + Supports text prompts and local image files. """ - args = parse_argstring(self.llm, line) # self.llm is a bound method + args = parse_argstring(self.llm, line) - # Parse the prompt to extract any image paths + # Get the prompt if cell is None: prompt = " ".join(args.prompt) else: prompt = cell - # prompt = parse_paths(prompt) - # prompt_text = prompt["text"] - # paths = prompt["paths"] # TODO implement check or make parser only output local paths - # Run the datalab agent + # Process prompt for images and file paths + messages, warnings = process_prompt_with_images(prompt) + + # Display any warnings to the user + for warning in warnings: + print(f"⚠️ {warning}") + + # Retrieve item manifest using get_items() and initialize search tool if needed + if self.item_manifest is None or self.search_tool is None: + with DatalabClient(os.getenv("DATALAB_URL")) as client: + self.item_manifest = client.get_items(item_type="samples") + self.search_tool = FullTextSearchTool(self.item_manifest) + + item_manifest = self.item_manifest + search_tool = self.search_tool + + # Run the datalab agent with processed messages (including images) response = datalab_agent.run_sync( - prompt, + messages, # May include both text and BinaryContent for images deps=Deps( - DatalabClient, - os.getenv("DATALAB_API_KEY"), - os.getenv("DATALAB_URL"), + client=DatalabClient, + datalab_api_key=os.getenv("DATALAB_API_KEY"), + datalab_url=os.getenv("DATALAB_URL"), + item_manifest=item_manifest, + search_tool=search_tool, ), message_history=self.messages, model_settings={'temperature': args.temp}, @@ -138,24 +137,3 @@ def llm(self, line, cell=None): if hasattr(response.data, 'code'): cell_fill = response.data.code get_ipython().set_next_input(cell_fill) - - - # # generate variable name "image_n" and inject it into a message - # image_variable_name = f"image_{len(self.images)}" - # image_message = [ - # { - # "type": "image_url", - # "image_url": { - # "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" - # }, - # } - # ] - # self.messages.append(("human", image_message)) - # except FileNotFoundError: - # print(f"Error: Could not read image file {path[1]}") - # pass - - # # Add the image message to the message list - - # # Create a dict from self.images to pass the image data to langchain - # image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} diff --git a/src/yellowhammer/magics_deprecated.py b/src/yellowhammer/magics_deprecated.py deleted file mode 100644 index 380adce..0000000 --- a/src/yellowhammer/magics_deprecated.py +++ /dev/null @@ -1,197 +0,0 @@ -""" -Magics to support LLM interactions in IPython/Jupyter. -Inspired by fperez/jupytee and jan-janssen/LangSim. -""" - -import os -import re -import base64 - -from IPython import get_ipython -from IPython.core.magic import ( - Magics, - magics_class, - line_cell_magic, -) -from IPython.core.magic_arguments import ( - magic_arguments, - argument, - parse_argstring, -) -from IPython.display import Markdown -from langchain_core.prompts import ChatPromptTemplate -from .llm import get_chain, Code, ConversationalResponse -from .prompt import API_PROMPT, SYSTEM_PROMPT - - -def get_output(messages, image_dict, temp=0.1): - """ - Send current message list to LLM and return the response - """ - - env = os.environ - - # Convert list of tuples to ChatPromptTemplate - messages = ChatPromptTemplate(messages) - - agent_executor = get_chain( - messages=messages, - api_provider=env.get("LLM_PROVIDER", "OPENAI"), - api_key=env.get("LLM_API_KEY"), - api_model=env.get("LLM_MODEL", None), - api_temperature=env.get("LLM_TEMP", temp), - ) - - # Add image data to LLM input as key-value pairs - args = {"context": API_PROMPT} - args.update(image_dict) - - return agent_executor.invoke(args) - - -def parse_paths(input_string): - # A simple regex pattern to match file-path-like substrings: - # [A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+ - # - one or more letters, digits, underscores, dots, slashes, or dashes, - # containing at least one dot that leads to a plausible extension - file_path_pattern = r"([A-Za-z0-9_.\\/-]+\.[A-Za-z0-9_]+)" - - # Find all file paths - paths = re.findall(file_path_pattern, input_string) - - # Remove those file paths from the original string - remaining_text = re.sub(file_path_pattern, "", input_string) - - # Clean up extra whitespace - remaining_text = re.sub(r"\s+", " ", remaining_text).strip() - - return {"text": remaining_text, "paths": paths} - - -# Class to manage state and expose the main magics -@magics_class -class DatalabMagics(Magics): - def __init__(self, shell): - super().__init__(shell) - - self.messages = [ - ("system", SYSTEM_PROMPT), - ] # Initialize with system prompt. - self.images = [] # Initialize with empty list of images. - - # A datalab magic that returns a code block - @magic_arguments() - @argument( - "prompt", - nargs="*", - help="""Prompt for code generation. When used as a line magic, - it runs to the end of the line. In cell mode, the entire cell - is considered the code generation prompt. - """, - ) - @argument( - "-T", - "--temp", - type=float, - default=0.1, - help="""Temperature, float in [0,1]. Higher values push the algorithm - to generate more aggressive/"creative" output. [default=0.1].""", - ) - @line_cell_magic - def llm(self, line, cell=None): - """ - Multimodal LLM interaction with Jupyter magics - """ - args = parse_argstring(self.llm, line) # self.llm is a bound method - - # Parse the prompt to extract any image paths - if cell is None: - prompt = " ".join(args.prompt) - else: - prompt = cell - prompt = parse_paths(prompt) - prompt_text = prompt["text"] - paths = prompt["paths"] # TODO implement check or make parser only output local paths - - # Add the text component of prompt to the message list - self.messages.append(("human", prompt_text)) - - # Add the image components of the query if found - if paths: - for path in enumerate(paths): - # first get the image data - try: - with open(path[1], "rb") as image_file: - image_data = base64.b64encode(image_file.read()).decode("utf-8") - # append image to the list of images - self.images.append(image_data) - - # generate variable name "image_n" and inject it into a message - image_variable_name = f"image_{len(self.images)}" - image_message = [ - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{{{image_variable_name}}}" - }, - } - ] - self.messages.append(("human", image_message)) - except FileNotFoundError: - print(f"Error: Could not read image file {path[1]}") - pass - - # Add the image message to the message list - - # Create a dict from self.images to pass the image data to langchain - image_dict = {f"image_{i+1}": image for i, image in enumerate(self.images)} - - # Get the output from the LLM - response = get_output(messages=self.messages, image_dict=image_dict).final_output - - # Add LLM response to the message list - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - return Markdown(output) - - elif isinstance(response, Code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code - get_ipython().set_next_input(cell_fill) - return Markdown(output.prefix) - - @line_cell_magic - def chat(self, line, cell=None): - """ - Live chat with LLM - """ - while True: - user_input = input("User: ") - # print(f'User: {user_input}') - - if user_input.lower() in ["quit", "exit", "q"]: - print("Goodbye!") - break - - self.messages.append(("human", user_input)) - response = get_output(self.messages).final_output - - if isinstance(response, ConversationalResponse): - output = response.response - self.messages.append(("ai", output)) - print(f"Assistant: {output}\n") - - elif isinstance(response, Code): - output = response - self.messages.append(("ai", output.prefix)) - cell_fill = output.imports + "\n" + output.code - print(f"Assistant: {output.prefix}") - get_ipython().set_next_input(cell_fill) - break - - -# If testing interactively, it's convenient to %run as a script in Jupyter -if __name__ == "__main__": - get_ipython().register_magics(DatalabMagics) diff --git a/src/yellowhammer/utils.py b/src/yellowhammer/utils.py index a49f912..33fbb22 100644 --- a/src/yellowhammer/utils.py +++ b/src/yellowhammer/utils.py @@ -1,3 +1,148 @@ +import os +import re +from pathlib import Path +from typing import Tuple, List + +def parse_file_paths(prompt: str) -> Tuple[str, List[str]]: + """ + Parse a prompt string to extract file paths and return cleaned prompt with paths list. + + Matches paths that: + - Have common file extensions (jpg, jpeg, png, gif, bmp, pdf, txt, etc.) + - Can be absolute or relative paths + - Can contain common path characters (letters, numbers, underscores, dots, slashes, dashes) + + Args: + prompt: The input prompt string that may contain file paths + + Returns: + Tuple of (cleaned_prompt, list_of_paths) + """ + # Pattern matches file paths with extensions + # Supports: alphanumeric, underscores, dots, forward/back slashes, dashes, colons (for absolute paths) + # This pattern is more permissive to capture complex paths like /var/folders/... + file_path_pattern = r'(?:^|(?<=\s))([A-Za-z0-9_.:~/\\\-]+\.(?:jpg|jpeg|png|gif|bmp|tiff|pdf|txt|doc|docx|xls|xlsx|csv|json|xml|raw|xrdml|xy))(?:(?=\s)|(?=[?!,;.])|$)' + + # Find all file paths + matches = re.finditer(file_path_pattern, prompt, re.IGNORECASE) + paths = [match.group(1) for match in matches] + + # Remove file paths from the prompt + cleaned_prompt = re.sub(file_path_pattern, '', prompt, flags=re.IGNORECASE) + + # Clean up extra whitespace + cleaned_prompt = re.sub(r'\s+', ' ', cleaned_prompt).strip() + + return cleaned_prompt, paths + + +def verify_and_load_image(file_path: str) -> dict | None: + """ + Verify a file path exists and if it's an image, load it as BinaryContent. + + Args: + file_path: Path to the file (relative or absolute) + + Returns: + Dictionary with 'type' and 'content' keys if successful image, None otherwise + - For images: {'type': 'image', 'content': BinaryContent object, 'path': str} + - For non-images: {'type': 'file', 'path': str} (for future extension) + - For non-existent files: None + """ + from pydantic_ai import BinaryContent + + # Supported image extensions + IMAGE_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.webp'} + + # Convert to Path object for easier manipulation + path = Path(file_path) + + # Check if file exists + if not path.exists(): + return None + + # Get file extension + extension = path.suffix.lower() + + # Check if it's an image + if extension in IMAGE_EXTENSIONS: + try: + # Read file content + data = path.read_bytes() + + # Map extension to media type + media_type_map = { + '.jpg': 'image/jpeg', + '.jpeg': 'image/jpeg', + '.png': 'image/png', + '.gif': 'image/gif', + '.bmp': 'image/bmp', + '.tiff': 'image/tiff', + '.webp': 'image/webp' + } + + media_type = media_type_map.get(extension, 'image/jpeg') + + # Create BinaryContent for the image + binary_content = BinaryContent(data=data, media_type=media_type) + + return { + 'type': 'image', + 'content': binary_content, + 'path': str(path.absolute()) + } + except Exception as e: + print(f"Error reading image file {file_path}: {e}") + return None + else: + # For non-image files, return file info for future extension + return { + 'type': 'file', + 'path': str(path.absolute()) + } + + +def process_prompt_with_images(prompt: str) -> Tuple[List, List[str]]: + """ + Process a prompt to extract images and construct a message list for the LLM. + + This function: + 1. Parses the prompt for file paths + 2. Verifies file existence + 3. Loads images as BinaryContent + 4. Constructs a message list suitable for pydantic-ai agents + + Args: + prompt: The user's prompt string, potentially containing file paths + + Returns: + Tuple of (message_list, warnings) + - message_list: List suitable for passing to agent.run() or agent.run_sync() + - warnings: List of warning messages about missing or non-image files + """ + # Parse file paths from prompt + cleaned_prompt, file_paths = parse_file_paths(prompt) + + # Initialize message list with the cleaned text prompt + messages = [cleaned_prompt] if cleaned_prompt else [] + warnings = [] + + # Process each detected file path + for file_path in file_paths: + result = verify_and_load_image(file_path) + + if result is None: + warnings.append(f"File not found: {file_path}") + elif result['type'] == 'image': + # Add image to messages + messages.append(result['content']) + elif result['type'] == 'file': + # Non-image file - skip for now but could be extended later + warnings.append(f"File '{file_path}' is not an image format (skipped for now)") + + return messages, warnings + + def process_item_for_chat(item: dict) -> dict: """Process an item to make it more suitable for LLM consumption by removing large fields and simplifying the structure. diff --git a/tests/test_multimodal.py b/tests/test_multimodal.py new file mode 100644 index 0000000..be7575d --- /dev/null +++ b/tests/test_multimodal.py @@ -0,0 +1,120 @@ +""" +Tests for multimodal (image) support utility functions. +""" +import os +import tempfile +from pathlib import Path +from yellowhammer.utils import parse_file_paths, verify_and_load_image, process_prompt_with_images + + +def test_parse_file_paths(): + """Test parsing file paths from prompts.""" + # Test with single image + prompt1 = "What do you see in this image: IMG_1648.jpg" + cleaned, paths = parse_file_paths(prompt1) + assert cleaned == "What do you see in this image:" + assert "IMG_1648.jpg" in paths + + # Test with multiple files + prompt2 = "Compare test.png and data.csv files" + cleaned, paths = parse_file_paths(prompt2) + assert "test.png" in paths + assert "data.csv" in paths + + # Test with paths + prompt3 = "Analyze examples/IMG_1648.jpg and other_dir/file.pdf" + cleaned, paths = parse_file_paths(prompt3) + assert "examples/IMG_1648.jpg" in paths + assert "other_dir/file.pdf" in paths + + # Test with no files + prompt4 = "Just a simple question" + cleaned, paths = parse_file_paths(prompt4) + assert cleaned == "Just a simple question" + assert len(paths) == 0 + + +def test_verify_and_load_image(): + """Test image verification and loading.""" + # Test with non-existent file + result = verify_and_load_image("non_existent_file.jpg") + assert result is None + + # Create a temporary image file for testing + with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_file: + # Write minimal PNG header + tmp_file.write(b'\x89PNG\r\n\x1a\n') + tmp_path = tmp_file.name + + try: + # Test with existing image + result = verify_and_load_image(tmp_path) + assert result is not None + assert result['type'] == 'image' + assert 'content' in result + assert 'path' in result + + # Verify BinaryContent properties + from pydantic_ai import BinaryContent + assert isinstance(result['content'], BinaryContent) + assert result['content'].media_type == 'image/png' + finally: + # Clean up + os.unlink(tmp_path) + + # Create a temporary non-image file + with tempfile.NamedTemporaryFile(suffix='.txt', delete=False) as tmp_file: + tmp_file.write(b'test content') + tmp_path = tmp_file.name + + try: + # Test with non-image file + result = verify_and_load_image(tmp_path) + assert result is not None + assert result['type'] == 'file' + assert 'path' in result + finally: + os.unlink(tmp_path) + + +def test_process_prompt_with_images(): + """Test complete prompt processing with images.""" + # Test with no images + messages, warnings = process_prompt_with_images("Simple question without files") + assert len(messages) == 1 + assert messages[0] == "Simple question without files" + assert len(warnings) == 0 + + # Test with non-existent file + messages, warnings = process_prompt_with_images("Check this file: missing.jpg") + assert len(messages) == 1 + assert "Check this file:" in messages[0] + assert len(warnings) == 1 + assert "File not found" in warnings[0] + + # Create a temporary image for testing + with tempfile.NamedTemporaryFile(suffix='.jpg', delete=False) as tmp_file: + tmp_file.write(b'\xff\xd8\xff') # JPEG header + tmp_path = tmp_file.name + + try: + # Test with existing image + messages, warnings = process_prompt_with_images(f"What is in {tmp_path}?") + assert len(messages) == 2 # Text + image + assert "What is in" in messages[0] + + # Verify second message is BinaryContent + from pydantic_ai import BinaryContent + assert isinstance(messages[1], BinaryContent) + assert messages[1].media_type == 'image/jpeg' + + assert len(warnings) == 0 + finally: + os.unlink(tmp_path) + + +if __name__ == "__main__": + test_parse_file_paths() + test_verify_and_load_image() + test_process_prompt_with_images() + print("✅ All tests passed!") diff --git a/uv.lock b/uv.lock index 81eca85..7b79d7d 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 2 requires-python = ">=3.10" resolution-markers = [ "python_full_version < '3.12.4'", @@ -10,9 +11,9 @@ resolution-markers = [ name = "aiohappyeyeballs" version = "2.6.1" source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 } +sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" } 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