{"repo":"HKUDS/LightRAG","free":true,"listed":false,"github":"https://github.com/HKUDS/LightRAG","clone":"git clone https://github.com/HKUDS/LightRAG.git","description":"[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation","language":"Python","stars":38901,"topics":["docling","genai","gpt","graphrag","knowledge-graph","large-language-models","llm","mineru","rag","ragas","retrieval-augmented-generation"],"license":"MIT","category":"ai_agent_framework","readme_excerpt":"
\n\n
\n \"LightRAG\n
\n\n# πŸš€ LightRAG: Simple and Fast Retrieval-Augmented Generation\n\n
\n \"HKUDS%2FLightRAG\n
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\n \"LightRAG\n
\n\n---\n\n
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\n \"LiteWrite\"\n\n \n \n \n \n
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\n\n---\n\n## πŸŽ‰ News\n- [2026.07]🎯[New Feature]: Add **Smart Heading** recognition feature for word documents.\n- [2026.05]🎯[New Feature]: **Merge RagAnything into LightRAG**πŸŽ‰. Multimodal content parsing and extraction via **MinerU / Docling** services.\n- [2026.05]🎯[New Feature]: Introducing four selectable text chunking strategies: `Fix`, `Recursive`, `Vector`, and `Paragraph`.\n- [2026.05]🎯[New Feature]: **Role-specific LLM configuration** support, 4 distinct roles: EXTRACT, QUERY, KEYWORDS, and VLM, with independent LLM settings.\n- [2026.03]🎯[New Feature]: Integrated **OpenSearch** as a unified storage backend, providing comprehensive support for all four LightRAG storage.\n- [2026.03]🎯[New Feature]: Introduced a setup wizard. Support for local deployment of embedding, reranking, and storage backends via Docker.\n- [2025.11]🎯[New Feature]: Integrated **RAGAS for Evaluation** and **Langfuse for Tracing**. Updated the API to return retrieved contexts alongside query results to support context precision metrics.\n- [2025.10]🎯[Scalability Enhancement]: Eliminated processing bottlenecks to support **Large-Scale Datasets Efficiently**.\n- [2025.09]🎯[New Feature] Enhances knowledge graph extraction accuracy for **Open-Sourced LLMs** such as Qwen3-30B-A3B.\n- [2025.08]🎯[New Feature] **Reranker** is now supported, significantly boosting performance for mixed queries (set as default query mode).\n- [2025.08]🎯[New Feature] Added **Document Deletion** with automatic KG regeneration to ensure optimal query performance.\n- [2025.06]🎯[New Release] Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) β€” an **All-in-One Multimodal RAG** system for seamless processing of text, images, tables, and equations.\n- [2025.06]🎯[New Feature] LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.\n- [2025.03]🎯[New Feature] LightRAG now supports citation functionality, enabling proper source attribution and enhanced document traceability.\n- [2025.02]🎯[New Feature] You can now use MongoDB as an all-in-one storage solution for unified data management.\n- [2025.02]🎯[New Release] Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG)-a RAG system for understanding extremely long-context videos\n- [2025.01]🎯[New Release] Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.\n- [2025.01]🎯You can now use PostgreSQL as an all-in-one storage solution for data management.\n- [2024.11]🎯[New Resource] A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). β€” explore in-depth tutorials and best practices. Many thanks to the blog author for this excellent contribution!\n- [2024.11]🎯[New Feature] Introducing the LightRAG WebUI β€” an interface that allows you to insert, query, and visualize LightRAG knowledge through an intuitive web-based dashboard.\n- [2024.11]🎯[New Feature] You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)-enabling graph database support.\n- [2024.10]🎯[New Feature] We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). β€” a walkthrough of LightRAG's capabilities. Thanks to the author for this excellent contribution!\n- [2024.10]🎯[New Channel] We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)!πŸ’¬ Welcome to join our community for sharing, discussions, and collaboration! πŸŽ‰πŸŽ‰\n\n
\n \n Algorithm Flowchart\n \n\n![LightRAG Indexing Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-VectorDB-Json-KV-Store-Indexing-Flowchart-scaled.jpg)\n*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*\n![LightRAG Retrieval and Querying Flowchart](https://learnopencv.com/wp-content/uploads/2024/11/LightRAG-Querying-Flowchart-Dual-Level-Retrieval-Generation-Knowledge-Graphs-scaled.jpg)\n*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*\n\n
\n\n## Installation\n\n**πŸ’‘ Using uv for Package Management**: This project uses [uv](https://docs.astral.sh/uv/) for fast and reliable Python package management. Install uv first: `curl -LsSf https://astral.sh/uv/install.sh | sh` (Unix/macOS) or `powershell -c \"irm https://astral.sh/uv/install.ps1 | iex\"` (Windows)\n\n> **Note**: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.\n>\n> **πŸ“¦ Offline Deployment**: For offline or air-gapped environments, see the [Offline Deployment Guide](./docs/OfflineDeployment.md) for instructions on pre-installing all dependencies and cache files.\n\n### Install LightRAG Server\n\n* Install from PyPI\n\n```bash\n### Install LightRAG Server as tool using uv (recommended)\nuv tool install \"lightrag-hku[api]\"\n\n### Or using pip\n# python -m venv .venv\n# source .venv/bin/activate # Windows: .venv\\Scripts\\activate\n# pip install \"lightrag-hku[api]\"\n\n# Setup env file\n# Obtain the env.example file by downloading it from the GitHub repository root\n# or by copying it from a local source checkout.\ncp env.example .env # Update the .env with your LLM and embedding configurations\n# Launch the server. It binds to all interfaces (0.0.0.0) by default.\n# SECURITY: before exposing it on a network, configure authentication in .env\n# (LIGHTRAG_API_KEY, or AUTH_ACCOUNTS together with TOKEN_SECRET), or bind to\n# 127.0.0.1 for local-only access; without auth every endpoint is public.\n# Note: the Ollama-compatible /api/* routes stay open by default for client\n# compatibility; set WHITELIST_PATHS=/health to require auth on them too.\nlightrag-server\n```\n\n* 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