LOG.md

RAG Implementation Log

Progress Tracking

Implementation of the RAG system based on PLAN.md — COMPLETE ✓

2026-03-26 — Full Implementation

Phase 1: Environment & Dependencies ✓

• Created requirements.txt with all necessary Python packages
• Created config.py with:
  • Document source paths (lectures, datasheets, app notes, source code)
  • Ollama configuration (base URL, model selection)
  • Chunking parameters (size, overlap for code and prose)
  • ChromaDB persistence settings

Phase 2: Document Ingestion ✓

• Implemented ingest/code_loader.py

  • Loads instructor solution source files (.cpp, .h, .c)
  • Skips student templates (noise reduction)
  • Adds source headers for citation tracking

• Implemented ingest/pptx_extract.py

  • Extracts text from PowerPoint slides
  • One document per slide for granular retrieval
  • Preserves slide numbers for citations

• Implemented ingest/pdf_ocr.py

  • Fast path: pdfplumber for native-text PDFs
  • Fallback: PaddleOCR for image-heavy/scanned PDFs
  • Sparsity detection to choose best extraction method
  • Page-level granularity for citations

• Implemented ingest/chunker.py

  • Overlapping text chunks (langchain RecursiveCharacterTextSplitter)
  • Different strategies for code vs prose
  • Code separators: function/class boundaries
  • Chunk metadata includes source, page, assignment info

Phase 3: Embedding & Vector Store ✓

• Implemented vectorstore/embedder.py

  • Calls Ollama /api/embeddings endpoint
  • Wraps nomic-embed-text model (768-dim vectors)
  • Includes error handling with zero-vector fallback

• Implemented vectorstore/store.py

  • ChromaDB persistent client management
  • Custom OllamaEmbeddingFunction class for integration
  • add_documents() — store chunks with embeddings
  • query() — retrieve top-k similar chunks
  • Cosine similarity metric for document retrieval

Phase 4: Query Pipeline ✓

• Implemented query/retriever.py

  • Simple wrapper around vector store queries
  • Configurable top-k retrieval (default 5)

• Implemented query/prompt_builder.py

  • System prompt guides LLM to use context only
  • Formats retrieved chunks with source citations
  • Builds structured messages for Ollama chat API

• Implemented query/generator.py

  • Calls Ollama /api/chat endpoint
  • Handles errors gracefully
  • Returns response text directly

Phase 5: CLI Scripts ✓

• Implemented scripts/ingest_all.py

  • Orchestrates full pipeline: extraction → chunking → embedding → storage
  • Walks all document directories recursively
  • Separates code vs prose for appropriate chunking
  • --dry-run flag for OCR quality testing
  • Prints summary statistics per category

• Implemented scripts/query_cli.py

  • Interactive loop for asking questions
  • Shows retrieved chunks on --verbose flag
  • Displays source citations with each answer
  • Clean formatting for terminal output

• Implemented scripts/launch_ui.py

  • Gradio web interface on localhost:7860
  • Text input for questions
  • Toggle to show/hide retrieved sources
  • User-friendly markdown output for answers

Phase 6: Testing ✓

• Implemented tests/test_ingest.py

  • Verifies code loader finds instructor files
  • Checks that student directories are skipped
  • Tests chunking respects size bounds
  • Code chunks use appropriate larger sizes

• Implemented tests/test_retrieval.py

  • Tests ChromaDB collection initialization
  • Validates add_documents and query interface
  • Checks retrieval respects top-k parameter
  • Tests retrieve function structure

• Implemented tests/test_end_to_end.py

  • Full pipeline interface tests
  • Prompt building with context validation
  • Generation interface verification
  • Graceful skipping when Ollama unavailable

Documentation & Configuration ✓

• Created .gitignore to exclude:

  • Virtual environment
  • ChromaDB persistent storage
  • Cache and build artifacts

• Created README.md with:

  • Quick start guide
  • Installation instructions
  • Configuration options
  • Example queries
  • Architecture diagram
  • Troubleshooting guide
  • Known limitations

Implementation Statistics

• Total Python files: 17
• Total lines of code: ~1400
• Phases completed: 6/6 ✓

Directory Structure (Final)

rag/
├── README.md                    # User guide
├── PLAN.md                      # Architecture plan
├── LOG.md                       # This file
├── requirements.txt             # Python dependencies
├── config.py                    # Centralized configuration
├── .gitignore                   # Git exclusions
├── ingest/                      # Document extraction
│   ├── __init__.py
│   ├── code_loader.py
│   ├── pptx_extract.py
│   ├── pdf_ocr.py
│   └── chunker.py
├── vectorstore/                 # Vector storage
│   ├── __init__.py
│   ├── embedder.py
│   └── store.py
├── query/                       # Query pipeline
│   ├── __init__.py
│   ├── retriever.py
│   ├── prompt_builder.py
│   └── generator.py
├── scripts/                     # CLI tools
│   ├── ingest_all.py
│   ├── query_cli.py
│   └── launch_ui.py
├── tests/                       # Test suite
│   ├── __init__.py
│   ├── test_ingest.py
│   ├── test_retrieval.py
│   └── test_end_to_end.py
└── chroma_db/                   # Vector storage (gitignored)
    └── [ChromaDB data]

Next Steps for Usage

1. Install dependencies: pip install -r requirements.txt
2. Ensure Ollama is running: ollama serve
3. Ingest documents: python scripts/ingest_all.py
4. Query:
   • CLI: python scripts/query_cli.py
   • Web UI: python scripts/launch_ui.py

Key Design Decisions

1. Pdfplumber + PaddleOCR fallback — Fast for native PDFs, handles scanned documents
2. ChromaDB — Embedded vector store, no server needed, persistent on disk
3. Ollama local inference — Privacy-respecting, no API costs, full control
4. Instructor-only code indexing — Reduces noise, focuses on solutions
5. Page/slide-level granularity — Precise citations, better UX
6. Separate code chunking strategy — Respects function boundaries
7. Modular architecture — Each component independently testable

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Implementation Status: READY FOR TESTING ✓

All core functionality implemented. System is ready for:

• Installing dependencies
• Running ingestion pipeline
• Testing with CLI and web UI
• Integration into course workflow