quivr
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.
💡 Why It Matters
Quivr addresses the challenge of integrating generative AI into applications by providing a flexible and customisable retrieval-augmented generation (RAG) framework. This is particularly beneficial for ML and AI teams who need a production-ready solution that simplifies the integration process with various large language models (LLMs) and vector stores. With over 38,000 stars, Quivr demonstrates a strong maturity level, indicating it is a reliable choice for engineering teams. However, it may not be suitable for projects requiring extensive customisation or those that need highly specific RAG implementations, where other specialised tools might be more appropriate.
🎯 When to Use
Quivr is a strong choice when teams need a straightforward, open source tool for engineering teams that can be easily integrated into existing applications. Teams should consider alternatives if they require a more tailored approach or if their use case involves unique data handling needs that Quivr does not support.
👥 Team Fit & Use Cases
Quivr is primarily used by machine learning engineers, AI developers, and software architects who are looking to implement generative AI capabilities in their products. Typical systems that include Quivr are chatbots, AI-driven applications, and any product requiring seamless integration of AI functionalities.
🎭 Best For
🏷️ Topics & Ecosystem
📊 Activity
Latest commit: 2025-07-09. Over the past 96 days, this repository gained 329 stars (+0.9% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.