rag-anchor

A Python library for adding RAG (Retrieval-Augmented Generation) to an existing project: ingest documents, store them as vectors, and answer questions from them.

Status: under development. The version published on PyPI (0.0.0) only reserves the name. The first working release will be 0.1.0.

Install

pip install rag-anchor

Use

from rag_anchor import GoogleProvider, MemoryStore, RagAnchor

provider = GoogleProvider()  # reads GEMINI_API_KEY
rag = RagAnchor(store=MemoryStore(), embedder=provider, generator=provider)

rag.ingest_directory("notes/")
answer = rag.ask("Where did you work in 2022?")

print(answer.text)
for hit in answer.hits:
    print(f"  {hit.score:.2f}  {hit.chunk.content[:60]}")

Or from the command line:

rag-anchor build notes/          # ingest a directory -> index.json
rag-anchor ask "..." -v          # answer, with the score of each excerpt
rag-anchor chunks                # see what was actually indexed

Principles

• No web dependency in the core — usable from Django, FastAPI, a cron script or a notebook.
• No infrastructure required — the index can be a plain file; Postgres with pgvector is an option, not a prerequisite.
• Similarity scores are always exposed, so "the information is absent from the corpus" can be told apart from "retrieval missed it".

Development

pip install -e ".[dev]"
pytest

examples/ holds a small demo corpus about a fictional person, which the test suite runs the whole pipeline against without touching the network.