h2oGPT

Open Source RAG Framework designed to be 'Conventional but Configurable', streamlining the creation of RAG applications with efficient data management, embeddings generation, and vector storage.

Python toolkit for efficient RAG research providing 36 pre-processed benchmark datasets and 23 state-of-the-art RAG algorithms in a unified, modular framework for reproduction and development.

Open-source LLM app development platform with an intuitive interface that combines AI workflow, RAG pipeline, agent capabilities, model management, and observability features for rapid prototyping and production deployment.

Simple and efficient retrieval-augmented generation framework that combines document retrieval with generation, focusing on speed and ease of use. Designed to run on standard CPUs and laptops with minimal resource requirements.

Retrieval-augmented generation framework that utilizes small, specialized models instead of large language models, significantly reducing computational and financial costs while offering cost-effective RAG solutions that can run on standard hardware.

Production-ready AI project for private, local document Q&A using RAG. 100% private with no data leaving your environment, supporting offline operation with local LLMs and vector databases.