MTEB (Massive Text Embedding Benchmark) is a comprehensive benchmark framework for evaluating text embeddings across classification, retrieval, clustering, reranking, and more, helping select optimal embedding models for RAG systems.
TrustRAG is a RAG framework focused on reliable input and trusted output, providing complete RAG pipeline components including document parsing, chunking, retrieval, and reranking with multiple retrieval strategies and evaluation methods.
Easily use and train state of the art late-interaction retrieval methods (ColBERT) in any RAG pipeline. Designed for modularity and ease-of-use, backed by research.
Agentic Retrieval-Augmented Generation framework achieving state-of-the-art on multi-hop QA benchmarks through hierarchical retrieval with keyword, semantic, and chunk read tools.
LightRAG is a simple and fast Retrieval-Augmented Generation framework using graph-enhanced retrieval, published at EMNLP 2025.