fastrag#
A Python SDK for the whole life of a RAG system: ingest sources, keep a
vector collection in sync with them as those sources change, query it, and
benchmark the result. pip install fastrag-ai gets you typed, swappable pieces
— fetchers, parsers, chunkers, embedders, vector stores, rerankers — composed
into a pipeline you own, not a framework you configure.
Note
Want to see a RAG chatbot answering questions end to end — backend,
dashboard, and the widget it’s serving — before writing any Python? The
repository root’s README.md brings up the whole composed product
(docker compose up -d, no install beyond Docker) in a few commands. The
guide below is for building your own pipeline with the SDK directly.
Install the package, index a source, ask it a question. Start here.
fastrag.serve is a FastAPI app — create_app(engine=, chat_store=) — that
streams grounded answers over SSE, for a client with no Python at all.
Every public class and function, generated from its docstring.
Clone it, run the Compose stack, and find your way around the source.