RAG

(26/02/18) Although I still primarily use the Lunr XML search feature, I occasionally use the AI helper and I want to get rid of the remote API calls. I'm adding this page for documenting the process.

Overview

Build a fully local RAG system that ingests ~313 markdown files from the Docusaurus site, stores them in ChromaDB with Ollama embeddings, and provides a web UI for semantic search and Q&A.

Stack

Python 3 with FastAPI for the web server
Ollama with nomic-embed-text for embeddings
ChromaDB for vector storage (persisted to disk)
Simple HTML/JS frontend served by FastAPI

Directory Structure

daw_til/
└── rag/
    ├── ingest.py          # Markdown parser + ChromaDB ingestion
    ├── server.py          # FastAPI web server + query endpoint
    ├── requirements.txt   # Python dependencies
    ├── templates/
    │   └── index.html     # Web UI (search box + results)
    └── chroma_db/         # ChromaDB persistence (gitignored)

Implementation Steps

Create rag/requirements.txt
```
chromadb
fastapi
uvicorn[standard]
ollama
python-frontmatter
```
1. Create rag/ingest.py — Markdown Ingestion Pipeline
2. Walk docs/, notes/, lists/, posts/ directories
3. Parse each .md file:
4. Extract YAML frontmatter (posts have title, slug, description, tags)
5. For docs/notes/lists: derive title from first # heading
6. Extract date from post filenames (YYYY-MM-DD pattern)
7. Categorize by directory: docs, notes, lists, posts
8. Chunking strategy:
9. Split on markdown headers (##, ###) to keep semantic sections intact
10. For sections exceeding ~1000 chars, further split on paragraphs
11. Each chunk gets metadata: source (file path), category, section (header path), title, tags (if post)
12. Generate embeddings via Ollama (nomic-embed-text)
13. Upsert into ChromaDB collection with metadata
14. Print progress (file count, chunk count, timing)
15. Create rag/server.py — FastAPI Query Server
16. GET / — serve the HTML UI
17. POST /query — accept a search query, embed it with Ollama, query ChromaDB for top-k results (default 8), return JSON with chunks + metadata + distances
18. GET /stats — return collection stats (doc count, chunk count)
19. CORS enabled for local dev
20. Create rag/templates/index.html — Web UI
21. Clean, minimal search interface
22. Search box with submit button
23. Results displayed as cards showing:
24. Matched text snippet
25. Source file path (clickable link to Docusaurus page)
26. Category badge (docs/notes/lists/posts)
27. Relevance score
28. Loading spinner during search
29. Add rag/ to .gitignore Add rag/chroma_db/ to gitignore to keep vector data out of git
Metadata Schema (ChromaDB)

Each chunk stored with:
```
{
"source": "docs/lang/JavaScript.md",  # relative file path
"category": "docs",                    # docs|notes|lists|posts
"title": "JavaScript",                 # doc/post title
"section": "Arrays > Map and Filter",  # header breadcrumb
"tags": "tech",                        # comma-separated (posts only)
"date": "2025-03-03",                  # posts only
"url": "/til/docs/lang/JavaScript"     # reconstructed Docusaurus URL
}
```

Prerequisites

Ollama installed and running with nomic-embed-text model pulled
Python 3.10+

Usage

cd rag
pip install -r requirements.txt
ollama pull nomic-embed-text

# Ingest all markdown files (run once, re-run to rebuild)
python ingest.py

# Start the web UI
python server.py
# Open http://localhost:8808

Verification

Run python ingest.py — should report ~300+ files processed and ~1000+ chunks created
Run python server.py — should start on port 8808
Open browser to http://localhost:8808
Search for "PostgreSQL" — should return relevant chunks from docs/db/PostgreSQL.md
Search for "Docker" — should return chunks from docs/server/Docker.md
Search for a conceptual query like "how to set up SSL certificates" — should return relevant Let's Encrypt / Apache / Nginx docs

ai-rag.md 4.0 KB Permalink History Raw

RAG