LightRAG

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

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/learn @HKUDS/LightRAG

About this skill

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README

🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation

🎉 News

[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.
[2025.10]🎯[Scalability Enhancement]: Eliminated processing bottlenecks to support Large-Scale Datasets Efficiently.
[2025.09]🎯[New Feature] Enhances knowledge graph extraction accuracy for Open-Sourced LLMs such as Qwen3-30B-A3B.
[2025.08]🎯[New Feature] Reranker is now supported, significantly boosting performance for mixed queries (set as default query mode).
[2025.08]🎯[New Feature] Added Document Deletion with automatic KG regeneration to ensure optimal query performance.
[2025.06]🎯[New Release] Our team has released RAG-Anything — an All-in-One Multimodal RAG system for seamless processing of text, images, tables, and equations.
[2025.06]🎯[New Feature] LightRAG now supports comprehensive multimodal data handling through 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 for details.
[2025.03]🎯[New Feature] LightRAG now supports citation functionality, enabling proper source attribution and enhanced document traceability.
[2025.02]🎯[New Feature] You can now use MongoDB as an all-in-one storage solution for unified data management.
[2025.02]🎯[New Release] Our team has released VideoRAG-a RAG system for understanding extremely long-context videos
[2025.01]🎯[New Release] Our team has released MiniRAG making RAG simpler with small models.
[2025.01]🎯You can now use PostgreSQL as an all-in-one storage solution for data management.
[2024.11]🎯[New Resource] A comprehensive guide to LightRAG is now available on LearnOpenCV. — explore in-depth tutorials and best practices. Many thanks to the blog author for this excellent contribution!
[2024.11]🎯[New Feature] Introducing the LightRAG WebUI — an interface that allows you to insert, query, and visualize LightRAG knowledge through an intuitive web-based dashboard.
[2024.11]🎯[New Feature] You can now use Neo4J for Storage-enabling graph database support.
[2024.10]🎯[New Feature] We've added a link to a LightRAG Introduction Video. — a walkthrough of LightRAG's capabilities. Thanks to the author for this excellent contribution!
[2024.10]🎯[New Channel] We have created a Discord channel!💬 Welcome to join our community for sharing, discussions, and collaboration! 🎉🎉
[2024.10]🎯[New Feature] LightRAG now supports Ollama models!

<details> <summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;"> Algorithm Flowchart </summary>

Figure 1: LightRAG Indexing Flowchart - Img Caption : Source Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : Source

</details>

Installation

💡 Using uv for Package Management: This project uses 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)

Note: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.

📦 Offline Deployment: For offline or air-gapped environments, see the Offline Deployment Guide for instructions on pre-installing all dependencies and cache files.

Install LightRAG Server

The LightRAG Server is designed to provide Web UI and API support. The Web UI facilitates document indexing, knowledge graph exploration, and a simple RAG query interface. LightRAG Server also provide an Ollama compatible interfaces, aiming to emulate LightRAG as an Ollama chat model. This allows AI chat bot, such as Open WebUI, to access LightRAG easily.

Install from PyPI

### Install LightRAG Server as tool using uv (recommended)
uv tool install "lightrag-hku[api]"

### Or using pip
# python -m venv .venv
# source .venv/bin/activate  # Windows: .venv\Scripts\activate
# pip install "lightrag-hku[api]"

### Build front-end artifacts
cd lightrag_webui
bun install --frozen-lockfile
bun run build
cd ..

# Setup env file
# Obtain the env.example file by downloading it from the GitHub repository root
# or by copying it from a local source checkout.
cp env.example .env  # Update the .env with your LLM and embedding configurations
# Launch the server
lightrag-server

Installation from Source

git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG

# Using uv (recommended)
# Note: uv sync automatically creates a virtual environment in .venv/
uv sync --extra api
source .venv/bin/activate  # Activate the virtual environment (Linux/macOS)
# Or on Windows: .venv\Scripts\activate

### Or using pip with virtual environment
# python -m venv .venv
# source .venv/bin/activate  # Windows: .venv\Scripts\activate
# pip install -e ".[api]"

# Build front-end artifacts
cd lightrag_webui
bun install --frozen-lockfile
bun run build
cd ..

# setup env file
cp env.example .env  # Update the .env with your LLM and embedding configurations
# Launch API-WebUI server
lightrag-server

Launching the LightRAG Server with Docker Compose

git clone https://github.com/HKUDS/LightRAG.git
cd LightR

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