RAG

[tbrandenburg]

@task Turn this jupyter notebook https://colab.research.google.com/github/docling-project/docling/blob/main/docs/examples/rag_langchain.ipynb into one main python file
and test it with a dummy PDF.
Keep it very simple and just turn the notebook steps into one python file.
Run it without an HuggingFace token.
Embed it in an uv project.
I want to run it with "uv run pyrag"

[github-actions]

🌿 Branch created: task-machine-41

[github-actions]

Context

Stakeholders need the LangChain RAG Colab notebook converted into a simple, single Python entrypoint that lives inside a uv-managed project. The resulting CLI must run without any Hugging Face token, accept a dummy PDF for validation, and be invocable through uv run pyrag while keeping dependencies lightweight and configuration clear.

Role Descriptions

Role: business-analyst

• Agent Path: .github/agents/08-business-product/business-analyst.md

Role: ai-engineer

• Agent Path: .github/agents/05-data-ai/ai-engineer.md

Role: backend-developer

• Agent Path: .github/agents/01-core-development/backend-developer.md

Role: python-expert

• Agent Path: .github/agents/02-language-specialists/python-expert.md

Role: cli-developer

• Agent Path: .github/agents/06-developer-experience/cli-developer.md

Role: test-automator

• Agent Path: .github/agents/04-quality-security/test-automator.md

Role: qa-expert

• Agent Path: .github/agents/04-quality-security/qa-expert.md

Role: error-detective

• Agent Path: .github/agents/04-quality-security/error-detective.md

Role: project-manager

• Agent Path: .github/agents/08-business-product/project-manager.md

Chronologic Task List

• [business-analyst] Feature 1 – Requirements capture — Review output/41/issue_conversation.md plus any repo notes to list user stories, constraints (no Hugging Face token, uv-based CLI, dummy PDF validation), acceptance criteria, and external dependencies in output/41/feature1_requirements.md so downstream roles can trace every need.
  • Summary: Documented user stories, constraints, acceptance criteria, and dependencies in output/41/feature1_requirements.md to guide downstream roles.
• [ai-engineer] Feature 1 – Architecture blueprint — Read output/41/feature1_requirements.md and describe the end-to-end RAG flow (PDF ingestion, chunking, embedding choice, retrieval, answer synthesis) plus runtime resources, data artifacts, and fallback behaviors in output/41/feature1_architecture.md, ensuring it stays executable within a uv project.
  • Summary: Captured uv-friendly RAG architecture covering ingestion, embeddings, vector store, runtime needs, artifacts, and fallbacks in output/41/feature1_architecture.md.
• [backend-developer] Feature 1 – Technical design — Using output/41/feature1_architecture.md, specify the module/function layout (e.g., loaders, vector store helpers, CLI interface), config surface, logging, and error-handling scheme in output/41/feature1_design.md, referencing concrete repo paths such as pyrag/__main__.py, pyrag/pipeline.py, and dependency sections in pyproject.toml.
  • Summary: Produced output/41/feature1_design.md outlining modules, config surfaces, logging, and error handling mapped to concrete repo paths.
• [python-expert] Feature 1 – Coding implementation — Follow output/41/feature1_design.md to convert the Colab notebook into production code by creating the described modules (e.g., pyrag/pipeline.py, pyrag/doc_loader.py, pyrag/embed.py, pyrag/__main__.py), ensuring the script runs offline without Hugging Face tokens, handling CLI arguments, and documenting decisions plus any deviations in output/41/feature1_impl_notes.md.
  • Summary: Built the full pyrag package with config, ingestion, embedding, vector store, pipeline, and CLI modules plus offline-safe fallbacks, then logged the decisions inside output/41/feature1_impl_notes.md.
• [cli-developer] Feature 1 – Coding: uv + CLI wiring — After the Python modules exist, configure pyproject.toml, uv.lock, and any README snippets so uv run pyrag executes the entrypoint defined in pyrag/__main__.py; add helpful CLI usage details, default paths for the dummy PDF, and troubleshooting steps to output/41/feature1_cli_notes.md.
  • Summary: Added a uv-managed pyproject.toml/uv.lock, README quickstart, and CLI notes so uv run pyrag calls pyrag.__main__.cli (currently using the argparse path for stability) with only pypdf as a runtime dependency.
• [test-automator] Feature 1 – Testing — Create a repeatable dummy PDF fixture under tests/fixtures/dummy.pdf, add scripted tests (e.g., tests/test_pyrag_dummy_pdf.py or an integration shell) that call uv run pyrag --pdf tests/fixtures/dummy.pdf --query "test", and capture execution logs, assertions, and dataset cleanup notes in output/41/feature1_test_report.md.
  • Summary: Added the dummy PDF fixture, uv-driven CLI pytest coverage, and logged execution evidence plus data-handling notes in output/41/feature1_test_report.md.
• [qa-expert] Feature 1 – Stabilization: QA review — Evaluate output/41/feature1_test_report.md, inspect the implemented files, confirm acceptance criteria traced from output/41/feature1_requirements.md, and record coverage metrics, open defects, and release readiness in output/41/feature1_qa_signoff.md.
  • Summary: Logged acceptance-pass evidence plus open coverage risks in output/41/feature1_qa_signoff.md, recommending a conditional go pending expanded negative-path automation.
• [error-detective] Feature 1 – Stabilization: defect analysis — Parse runtime logs from the integration runs (attach snippets or hashes inside output/41/feature1_test_report.md), reproduce any anomalies with uv run pyrag, and document root causes plus mitigation steps in output/41/feature1_error_analysis.md to prevent regressions.
  • Summary: Reproduced the verbose CLI logs, explained the constant embedding + LLM fallback signals, and recorded mitigations in output/41/feature1_error_analysis.md so future runs can distinguish expected vs. real defects.
• [project-manager] Feature 1 – Replanning — Review output/41/feature1_qa_signoff.md and output/41/feature1_error_analysis.md, then compile an updated backlog with priority ranking, ownership, and timeline cues in output/41/feature1_replan.md, calling out any scope changes or follow-up work required for future cycles.
  • Summary: Rebuilt output/41/feature1_replan.md with prioritized backlog, owners, and timeline cues, highlighting scope adjustments and follow-up risks for future cycles.