retrieval-augmented-generation
16 repositories with 10k+ stars are tagged retrieval-augmented-generation.
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RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
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A no-code RAG server that keeps answers in sync with your documents. Works with 8 vector databases. Built on Pathway.
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[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
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📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG
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Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
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An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
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This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
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Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
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Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.
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Unified framework for building enterprise RAG pipelines with small, specialized models
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[MLsys2026 Best Paper]: https://arxiv.org/abs/2506.08276. RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.
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💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
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Agent S: an open agentic framework that uses computers like a human
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Retrieval and Retrieval-augmented LLMs
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Pocket Flow: 100-line LLM framework. Let Agents build Agents!