quivr
An opinionated RAG framework for integrating generative AI into applications, supporting any LLM, vector store, and file type.
What is quivr?
Quivr is an opinionated Retrieval-Augmented Generation (RAG) framework that simplifies adding generative AI capabilities to applications. It handles document ingestion, retrieval, and generation workflows, allowing developers to integrate AI assistants or knowledge bases without building RAG systems from scratch. The framework supports multiple LLMs, vector stores, and file formats for flexibility.
Developers and product teams building AI-powered applications like chatbots, knowledge assistants, or document analysis tools who want a pre-built, customizable RAG solution.
Quivr reduces development time by providing a production-ready RAG core with extensive customization options, avoiding vendor lock-in through support for any LLM or vector store. Its opinionated design ensures best practices while allowing integration into existing products.
Overview
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: PGVector, Faiss. Any Files. Anyway you want.
Use Cases
Best For
- Adding AI-powered Q&A to existing applications
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