gurbani-mcp

A local, self-hosted tool for looking up and verifying that a quote is authentically from Sri Guru Granth Sahib (SGGS). This project covers SGGS only (not other Banis).

It runs entirely on your own machine. No data leaves your computer unless you choose to expose it to an AI client (Claude, ChatGPT, etc.), and even then, only the query text you send is transmitted — never the underlying database.

• Search — keyword search with Gurbani concept expansion (e.g. "sewa" also matches "service", "selfless service")

• Verify — check a Gurmukhi quote against SGGS; get back the verbatim text + citation (Ang, Shabad, author), or a clear "not found"

• Guard — scan a block of text for every Gurmukhi quote in it and verify each one individually

• Two ways to connect an AI client: an MCP server (Claude Desktop, Claude Code, Cursor) and a plain HTTP API (ChatGPT via Custom GPT Actions, or any REST client)

Gurbani text is only ever returned from the source database — never paraphrased, summarized, or generated. See Quote Verification below.

Quick start

Prerequisites:

git clone <this-repo-url> gurbani-mcp
cd gurbani-mcp
bash scripts/setup.sh

setup.sh builds database/dist/banidb.sqlite from the official Khalis Foundation BaniDB Docker image (the dataset behind SikhiToTheMax), then installs Python dependencies. It takes several minutes the first time (downloading + seeding a ~640MB dataset); nothing about your database is uploaded anywhere.

Verify it worked:

bash scripts/test_search.sh "benefits of sewa"
uv run --extra dev pytest

Related MCP server: anna-book-search

Connect to an AI client

Claude Desktop / Claude Code (MCP)

Add this to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "gurbani": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/gurbani-mcp", "python", "-m", "mcp_server.server"]
    }
  }
}

Restart Claude Desktop. You should see a 🔨 tools icon indicating the gurbani server is connected, exposing search_gurbani, verify_quote, guard_text, get_shabad_by_ang, get_line, and get_shabad_by_line.

For Claude Code, add the same server with:

claude mcp add gurbani -- uv run --directory /absolute/path/to/gurbani-mcp python -m mcp_server.server

ChatGPT (Custom GPT Actions, via the HTTP API)

1. Start the HTTP API:

   uv run uvicorn api_server.app:app --port 8421

2. ChatGPT Actions need a public HTTPS URL — they can't reach localhost. The fastest way to get one without an account is a Cloudflare quick tunnel:

   cloudflared tunnel --url http://localhost:8421

   This prints a temporary https://<random>.trycloudflare.com URL. Treat it as sensitive while it's live — anyone with the URL can query your local API. It's meant for short sessions; for anything longer-lived, put an API key or auth layer in front of it first (not included here — see Follow-ups).

3. In ChatGPT: Explore GPTs → Create → Configure → Actions → Import from URL, and paste https://<random>.trycloudflare.com/openapi.json. ChatGPT will pick up all the endpoints (/api/search, /api/verify, /api/guard, etc.) automatically.

4. Give the GPT instructions like: "When asked to verify a Gurbani quote, always call the verify or guard action and quote its source_text back verbatim — never answer from your own memory."

Example: verifying every quote in a document

curl -s "http://localhost:8421/api/guard" --get \
  --data-urlencode "q=$(cat my_document.txt)" | jq

Returns every Gurmukhi span found in the text, each marked verified, verified_fuzzy (found, but with minor punctuation/spelling differences), or not_found — with the exact source citation (Ang, author, full line) for anything that verified.

Quote verification — how authenticity is guaranteed

Four layers, all sharing one matching core in gurbani_rag/verify.py:

1. Build gate (scripts/validate_db.py) — structural checks (row counts, Ang coverage, no gaps/duplicates) run automatically during setup.sh, so an incomplete or corrupted database can never reach runtime.

2. Retrieval — every search result comes straight from the source database with its citation attached. Authentic by construction.

3. Verify (verify_quote) — exact match first (punctuation-agnostic), then fuzzy match via FTS5 + rapidfuzz with a 0.90 confidence floor. Below that: not_found. The text returned is always the source's own — never your input echoed back.

4. Guard (guard_text) — scans arbitrary text for every Gurmukhi span and verifies each one independently. This is the tool for auditing any drafted or existing content.

See CLAUDE.md for the full architecture and schema.

Optional: semantic search

The default search is keyword + concept expansion (works well, no extra setup). An optional ChromaDB-based semantic index can also be built:

uv run python scripts/build_index.py

Running tests

uv run --extra dev pytest

tests/test_golden.py checks known-authentic quotes verify at their correct Ang, and known fakes are correctly rejected.

Data attribution

Scripture text, translations, and transliterations: Khalis Foundation BaniDB (SikhiToTheMax dataset).

BaniDB's compiled/proprietary form is not redistributed by this repo — database/dist/banidb.sqlite is always built locally from the official BaniDB Docker image (see scripts/setup.sh).

Follow-ups (not built yet)

• The HTTP API has no authentication — fine for a short-lived tunnel session, not for leaving it exposed long-term.

• No CI workflow yet.

License

MIT — see LICENSE.