rag-python

Production-grade Retrieval-Augmented Generation (RAG) for Python — ingest documents, ask questions, get grounded answers with multi-LLM support, hybrid search, streaming, and guardrails.

pip install rag-python
export OPENAI_API_KEY=sk-...
rag-python ingest ./docs --reindex
rag-python query "What is our leave policy?"

Author: Raghav Singla · Repo: github.com/RaghavOG/rag-python

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Why rag-python?

Capability	What you get
Ingest	TXT, MD, PDF, DOCX, CSV, JSON, HTML → chunk → embed → ChromaDB
Retrieve	Multi-query rewriting, hybrid BM25+vector, reranking, metadata filters
Generate	Multi-LLM answers with guardrails, evaluation, and retry loop
Stream	rag.query_stream() and --stream CLI for responsive UX
Offline	Local embeddings via sentence-transformers
CLI	rag-python ingest, query, docs — no code required

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Install

pip install rag-python

Extra	Install	Enables
local	pip install rag-python[local]	Offline embeddings (sentence-transformers)
hybrid	pip install rag-python[hybrid]	BM25 + vector hybrid retrieval
rerank	pip install rag-python[rerank]	Cross-encoder reranking
anthropic	pip install rag-python[anthropic]	Claude LLM
gemini	pip install rag-python[gemini]	Gemini LLM
all	pip install rag-python[all]	All optional features

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Quickstart (Python)

from rag_python import RAG

rag = RAG(llm_model="gpt-4o-mini")
rag.ingest(["./data"], reindex=True)

answer = rag.query("How many days of annual leave?")
print(answer.text)
print(answer.sources)

Streaming

stream = rag.query_stream("How many days of annual leave?")
for token in stream:
    print(token, end="", flush=True)
print(stream.result.evaluation)

Hybrid search + metadata filter

rag = RAG(
    retriever="hybrid",  # pip install rag-python[hybrid]
    metadata_filter={"filename": "leave-policy.pdf"},
)
rag.ingest(["./policies/"])
print(rag.query("annual leave policy").text)

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Quickstart (CLI)

export OPENAI_API_KEY=sk-...

rag-python ingest ./data --reindex
rag-python query "How many days of annual leave?"
rag-python query "PTO policy" --stream -v
rag-python query "benefits" --retriever hybrid

# Built-in terminal docs
rag-python docs quickstart
rag-python docs --list
rag-python --help

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Documentation

Guide	Description
Docs index	Start here
Usage	Python API, streaming, retrieval
CLI reference	All rag-python commands and flags
Configuration	Env vars and RAGConfig
Providers	OpenAI, Azure, Anthropic, Gemini, Ollama, local
Changelog	Release notes

In the terminal: rag-python docs [topic] — topics: quickstart, install, cli, config, providers, features

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Environment variables

Variable	Description
OPENAI_API_KEY	Default LLM + embeddings
ANTHROPIC_API_KEY	Claude
GEMINI_API_KEY	Gemini
AZURE_OPENAI_ENDPOINT / AZURE_OPENAI_API_KEY	Azure OpenAI
OLLAMA_BASE_URL	Local Ollama (default http://localhost:11434)
RAG_PYTHON_DATA_DIR	Document dir (default ./data)
RAG_PYTHON_CHROMA_DIR	Vector store (default ./chroma_db)

See Configuration and .env.example.

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Project layout

src/rag_python/     # pip install rag-python → import rag_python
  client.py         # RAG, RAGAnswer, query_stream
  rag_pipeline.py   # ingest / query pipeline
  providers/        # OpenAI, Azure, Anthropic, Gemini, Ollama, local
docs/               # User documentation (linked from PyPI README)
tests/
examples/

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License

MIT © Raghav Singla