How to: Industry Strength Information Retrieval
* [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG) ![](https://img.shields.io/github/stars/Marker-Inc-Korea/AutoRAG.svg?cacheSeconds=86400) - AutoRAG is a RAG AutoML tool for automatically finds an optimal RAG pipeline for your data.
* [BGE](https://github.com/FlagOpen/FlagEmbedding) ![](https://img.shields.io/github/stars/FlagOpen/FlagEmbedding.svg?cacheSeconds=86400) - BGE builds one-stop retrieval toolkit for search and RAG.
* [Cognita](https://github.com/truefoundry/cognita) ![](https://img.shields.io/github/stars/truefoundry/cognita.svg?cacheSeconds=86400) - Cognita is a RAG framework for building modular and production-ready applications.
* [DocArray](https://github.com/docarray/docarray) ![](https://img.shields.io/github/stars/docarray/docarray.svg?cacheSeconds=86400) - DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API.
* [Faiss](https://github.com/facebookresearch/faiss) ![](https://img.shields.io/github/stars/facebookresearch/faiss.svg?cacheSeconds=86400) - Faiss is a library for efficient similarity search and clustering of dense vectors.
* [fastRAG](https://github.com/IntelLabs/fastRAG) ![](https://img.shields.io/github/stars/IntelLabs/fastRAG.svg?cacheSeconds=86400) - fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval.
* [GraphRAG](https://github.com/microsoft/graphrag) ![](https://img.shields.io/github/stars/microsoft/graphrag.svg?cacheSeconds=86400) - GraphRAG is a data pipeline and transformation suite that is designed to extract meaningful, structured data from unstructured text using the power of LLMs.
* [HippoRAG](https://github.com/OSU-NLP-Group/HippoRAG) ![](https://img.shields.io/github/stars/OSU-NLP-Group/HippoRAG.svg?cacheSeconds=86400) - HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables LLMs to continuously integrate knowledge across external documents.
* [JamAI Base](https://github.com/EmbeddedLLM/JamAIBase) ![](https://img.shields.io/github/stars/EmbeddedLLM/JamAIBase.svg?cacheSeconds=86400) - JamAI Base is an open-source RAG (Retrieval-Augmented Generation) backend platform that integrates an embedded database (SQLite) and an embedded vector database (LanceDB) with managed memory and RAG capabilities. It features built-in LLM, vector embeddings, and reranker orchestration and management, all accessible through a convenient, intuitive, spreadsheet-like UI and a simple REST API.
* [LangExtract](https://github.com/google/langextract) ![](https://img.shields.io/github/stars/google/langextract.svg?cacheSeconds=86400) - LangExtract is a Python library that uses LLMs to extract structured information from unstructured text documents based on user-defined instructions. It processes materials such as clinical notes or reports, identifying and organizing key details while ensuring the extracted data corresponds to the source text.
* [LightRAG](https://github.com/HKUDS/LightRAG) ![](https://img.shields.io/github/stars/HKUDS/LightRAG.svg?cacheSeconds=86400) - A simple and fast retrieval-augmented generation framework.
* [llmware](https://github.com/llmware-ai/llmware) ![](https://img.shields.io/github/stars/llmware-ai/llmware.svg?cacheSeconds=86400) - llmware provides a unified framework for building LLM-based applications (e.g, RAG, Agents), using small, specialized models that can be deployed privately, integrated with enterprise knowledge sources safely and securely, and cost-effectively tuned and adapted for any business process.
* [Mem0](https://github.com/mem0ai/mem0) ![](https://img.shields.io/github/stars/mem0ai/mem0.svg?cacheSeconds=86400) - Mem0 enhances AI assistants and agents with an intelligent memory layer, enabling personalized AI interactions.
* [NGT](https://github.com/yahoojapan/NGT) ![](https://img.shields.io/github/stars/yahoojapan/NGT.svg?cacheSeconds=86400) - NGT provides commands and a library for performing high-speed approximate nearest neighbor searches against a large volume of data in high dimensional vector data space.
* [NMSLIB](https://github.com/nmslib/nmslib) ![](https://img.shields.io/github/stars/nmslib/nmslib.svg?cacheSeconds=86400) - Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
* [Qdrant](https://github.com/qdrant/qdrant) ![](https://img.shields.io/github/stars/qdrant/qdrant.svg?cacheSeconds=86400) - An open source vector similarity search engine with extended filtering support.
* [R2R](https://github.com/SciPhi-AI/R2R) ![](https://img.shields.io/github/stars/SciPhi-AI/R2R.svg?cacheSeconds=86400) - R2R (RAG to Riches) is a comprehensive pl