ragsmith 0.0.2

Async RAG toolkit on top of Postgres/pgvector and Voyage AI.

ragsmith

Async RAG toolkit on top of Postgres + pgvector and Voyage AI, with zero direct or indirect numpy / pandas dependency.

Highlights

• 100% async (asyncpg, httpx).
• Voyage embeddings via the raw HTTP API (no SDK, no numpy).
• pgvector storage with cosine similarity search.
• Strict tooling: ruff (all rules), pytest, 100% coverage in CI.
• Python 3.14+.

Install

uv sync

Local pgvector database

A docker-compose.yml is provided for local development and integration testing:

docker compose up -d           # start Postgres + pgvector on localhost:5432
docker compose down -v         # stop and wipe the volume

Connection string (also written in .env.example):

DATABASE_URL=postgresql://postgres:postgres@localhost:5432/ragsmith

Usage

import asyncio
import os

from ragsmith import PgVectorStore, VoyageClient, Retriever, Document, chunk_text


async def main() -> None:
    async with VoyageClient() as voyage:
        store = await PgVectorStore.from_dsn(os.environ["DATABASE_URL"], dim=1024)
        try:
            chunks = chunk_text("Long document text...", max_chars=500)
            vectors = await voyage.embed([c.text for c in chunks], input_type="document")
            await store.upsert(
                Document(content=c.text, embedding=v)
                for c, v in zip(chunks, vectors, strict=True)
            )

            retriever = Retriever(voyage, store)
            for hit in await retriever.retrieve("what is rag?", k=3):
                print(hit.score, hit.document.content)
        finally:
            await store.aclose()


asyncio.run(main())

Tests

uv run pytest                              # unit + doctests, 100% coverage gate
uv run pytest -m integration --no-cov      # live tests against pgvector + Voyage
uv run python examples/quickstart.py       # end-to-end demo

Integration tests skip cleanly when DATABASE_URL / VOYAGE_API_KEY are absent. Start the local DB with docker compose up -d and source .env before running them.

Documentation

Full API reference: https://gghez.github.io/ragsmith/

Releasing

Releases are fully automated through GitHub Actions and PyPI Trusted Publishing (OIDC, no API token stored).

1. Bump version in pyproject.toml and commit (e.g. chore: bump to 0.2.0).

2. Tag and push:

   git tag v0.2.0
   git push origin v0.2.0
   

3. The Release workflow runs uv build, publishes the sdist + wheel to PyPI and creates a matching GitHub Release with the artifacts attached.

One-time PyPI setup: register the project on PyPI as a Trusted Publisher pointing to gghez/ragsmith, workflow release.yml, environment pypi.

License

MIT