Dify

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.

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.

Open-source RAG engine based on deep document understanding with citation-backed responses. RAGFlow extracts tables, images, and structured data from complex documents, providing truthful question-answering with well-founded citations.

All-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows. Combines vector indexes (sparse/dense), graph networks and relational databases. This is an OSS framework.