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gyrfalcon-rag

Hybrid retrieval for RAG (BM25 and dense vectors, fused) in one Rust engine, built to be fast on any CPU. One pip install gyrfalcon-rag, then import gyrfalcon. No server, no vector database. The PyPI name gyrfalcon is already taken, so the distribution name is gyrfalcon-rag.

from gyrfalcon import Collection

with Collection("./my-index") as col:              # creates the collection on first use
    col.add("intro", "Gyrfalcons are the largest falcons.")
    col.add_many({"a": "first text", "b": "second text"})
    col.add_path("docs/")                           # PDF, HTML, Markdown, and text files
    for hit in col.search("largest falcon", k=5):
        print(f"{hit.score:.3f} {hit.doc_id} {hit.text[:80]}")
  • update(doc_id, text) replaces a document atomically; delete(doc_id) removes it.
  • Writes are durable when the call returns; searches see them at once.
  • Two tiers. lite (default) embeds with static potion-retrieval-32M and needs no runtime. full embeds with int8 bge-small-en-v1.5 and needs ONNX Runtime: pip install "gyrfalcon-rag[full]".
  • PDFs need PDFium: pip install "gyrfalcon-rag[pdf]".
  • Models download on first use into $GYR_MODELS (default ~/.cache/gyrfalcon/models).

Building from the repository: pip install maturin && maturin develop --release in crates/gyrfalcon-py, then pytest crates/gyrfalcon-py/tests.

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