# 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

---

**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