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RAGFreeOpen Source
QUIVR
Production-ready RAG for any LLM, vector store, or file type
Apache-2.0
ABOUT
Building a production-ready Retrieval-Augmented Generation pipeline from scratch means wiring together document ingestion, chunking, embedding, vector storage, retrieval, and LLM orchestration. Quivr provides an opinionated, ready-made RAG stack that handles all of this out of the box, letting developers focus on their product instead of plumbing. It works with any LLM, any vector store, and any file format.
INSTALL
pip install quivr-coreINTEGRATION GUIDE
1. Build a personal "second brain" AI assistant that answers questions across private documents
2. Automate customer support by grounding chatbots in product docs and knowledge bases
3. Power internal documentation Q&A for engineering teams with multi-format file ingestion
4. Create custom RAG workflows and chatbots over PDFs, text, Markdown, and more
TAGS
pythonragknowledge-basechatbotdocument-qagenai