Embedchain

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 Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone, providing automatic chunking, embedding, chat history management, and query optimization.

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.

Programming framework for RAG and AI applications with cutting-edge optimization capabilities, featuring the lowest framework overhead and automatic improvement based on example data.

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.