refindery 0.2.0

A local, single-machine retrieval engine over the web pages you read.

Refindery

A local, single-machine retrieval engine over the web pages you read.

Upstream capture systems (browser extensions, history readers) extract main-body text and POST it here. Refindery chunks, embeds, indexes, clusters, and extracts entities from that text, then serves hybrid retrieval over it via a local HTTP API and an MCP server.

📖 Full documentation: https://hbmartin.github.io/refindery/

Companion projects

• Refindery Chrome Extension — capture pages as you browse.
• Browser History Refindery — import and search your browser history.

This is a retrieval engine, not a Q&A system. It returns ranked, grounded passages with provenance. Synthesis is the caller's job — typically an LLM agent (e.g. Claude via MCP) that treats Refindery as a tool. No generation appears on the query path.

Jobs to be done

1. Refind — "I read something about X, take me back to it."
2. Synthesize — "What have I learned about Y?" (agent-mediated; Refindery supplies the passages)
3. Resurface — "What have I been reading a lot about?" (clusters, similarity)

Quickstart

The fastest path on macOS (Homebrew, no Docker) writes a private .env with a generated auth token and the daemon-free LanceDB profile:

VOYAGE_API_KEY=... ./scripts/setup-macos.sh --start

On later runs, start the server with the generated environment:

uv run --env-file .env refindery serve

Then ingest a page and search it:

curl -s -X POST http://127.0.0.1:8000/v1/pages \
  -H "Authorization: Bearer $REFINDERY_AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"url": "https://example.com/article", "title": "An Article",
       "body_extracted": "Plain text main body content...",
       "fetched_at": "2026-07-08T10:00:00Z", "source": "extension"}'

curl -s -X POST http://127.0.0.1:8000/v1/search \
  -H "Authorization: Bearer $REFINDERY_AUTH_TOKEN" -H "Content-Type: application/json" \
  -d '{"query": "main body content"}'

Other install paths (Docker/Qdrant, manual, fully containerized) are in the Installation guide.

Documentation

The documentation site is the canonical reference:

• Getting started — install, quickstart, and validation.
• Guides — ingest, search, MCP, evaluation, clustering, entities, models, deletion.
• Configuration — the settings model, deployment profiles, auth, tuning, observability.
• Architecture — hexagonal ports/adapters, data flow, and the data model.
• Operations — reset, purge, job leases, and accepted risks.
• Reference — HTTP API, upstream ingest API, MCP tools, CLI, and the Python API.

Development

See CONTRIBUTING.md and the Contributing docs. TL;DR: uv sync --all-groups --extra ner, then uv run ruff format . && uv run ruff check . && uv run pytest && uv run ty check && uv run pyrefly check.

Preview the docs locally with uv run zensical serve.

License

MIT — see LICENSE.txt.