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- [2026.03]🎯[New Feature]: Integrated OpenSearch as a unified storage backend, providing comprehensive support for all four LightRAG storage.
- [2026.03]🎯[New Feature]: Introduced a setup wizard. Support for local deployment of embedding, reranking, and storage backends via Docker.
- [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! 🎉🎉
Algorithm Flowchart
💡 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.
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
# Bootstrap the development environment (recommended)
make dev
source .venv/bin/activate # Activate the virtual environment (Linux/macOS)
# Or on Windows: .venv\Scripts\activate
# make dev installs the test toolchain plus the full offline stack
# (API, storage backends, and provider integrations), then builds the frontend.
# Run make env-base or copy env.example to .env before starting the server.
# Equivalent manual steps with uv
# Note: uv sync automatically creates a virtual environment in .venv/
uv sync --extra test --extra offline
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 ".[test,offline]"
# Build front-end artifacts
cd lightrag_webui
bun install --frozen-lockfile
bun run build
cd ..
# setup env file
make env-base # Or: cp env.example .env and update it manually
# Launch API-WebUI server
lightrag-server- Launching the LightRAG Server with Docker Compose
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG
cp env.example .env # Update the .env with your LLM and embedding configurations
# modify LLM and Embedding settings in .env
docker compose upHistorical versions of LightRAG docker images can be found here: LightRAG Docker Images
Instead of editing env.example by hand, use the interactive setup wizard to generate a configured .env and, when needed, docker-compose.final.yml:
make env-base # Required first step: LLM, embedding, reranker
make env-storage # Optional: storage backends and database services
make env-server # Optional: server port, auth, and SSL
make env-base-rewrite # Optional: force-regenerate wizard-managed compose services
make env-storage-rewrite # Optional: force-regenerate wizard-managed compose services
make env-security-check # Optional: audit the current .env for security risksFor full description of every target see docs/InteractiveSetup.md.
The setup wizards update configuration only; run make env-security-check separately to audit the
current .env for security risks before deployment.
By default, rerunning the setup preserves unchanged wizard-managed compose service blocks; use a
*-rewrite target only when you need to rebuild those managed blocks from the bundled templates.
- Install from source (Recommended)
cd LightRAG
# Note: uv sync automatically creates a virtual environment in .venv/
uv sync
source .venv/bin/activate # Activate the virtual environment (Linux/macOS)
# Or on Windows: .venv\Scripts\activate
# Or: pip install -e .- Install from PyPI
uv pip install lightrag-hku
# Or: pip install lightrag-hkuLightRAG's demands on the capabilities of Large Language Models (LLMs) are significantly higher than those of traditional RAG, as it requires the LLM to perform entity-relationship extraction tasks from documents. Configuring appropriate Embedding and Reranker models is also crucial for improving query performance.
- LLM Selection:
- It is recommended to use an LLM with at least 32 billion parameters.
- The context length should be at least 32KB, with 64KB being recommended.
- It is not recommended to choose reasoning models during the document indexing stage.
- During the query stage, it is recommended to choose models with stronger capabilities than those used in the indexing stage to achieve better query results.
- Embedding Model:
- A high-performance Embedding model is essential for RAG.
- We recommend using mainstream multilingual Embedding models, such as:
BAAI/bge-m3andtext-embedding-3-large. - Important Note: The Embedding model must be determined before document indexing, and the same model must be used during the document query phase. For certain storage solutions (e.g., PostgreSQL), the vector dimension must be defined upon initial table creation. Therefore, when changing embedding models, it is necessary to delete the existing vector-related tables and allow LightRAG to recreate them with the new dimensions.
- Reranker Model Configuration:
- Configuring a Reranker model can significantly enhance LightRAG's retrieval performance.
- When a Reranker model is enabled, it is recommended to set the "mix mode" as the default query mode.
- We recommend using mainstream Reranker models, such as:
BAAI/bge-reranker-v2-m3or models provided by services like Jina.
The LightRAG Server is designed to provide Web UI and API support. The LightRAG Server offers a comprehensive knowledge graph visualization feature. It supports various gravity layouts, node queries, subgraph filtering, and more. For more information about LightRAG Server, please refer to LightRAG Server.
To get started with LightRAG core, refer to the sample codes available in the examples folder. Additionally, a video demo demonstration is provided to guide you through the local setup process. If you already possess an OpenAI API key, you can run the demo right away:
### you should run the demo code with project folder
cd LightRAG
### provide your API-KEY for OpenAI
export OPENAI_API_KEY="sk-...your_opeai_key..."
### download the demo document of "A Christmas Carol" by Charles Dickens
curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt
### run the demo code
python examples/lightrag_openai_demo.pyFor a streaming response implementation example, please see examples/lightrag_openai_compatible_demo.py. Prior to execution, ensure you modify the sample code's LLM and embedding configurations accordingly.
Note 1: When running the demo program, please be aware that different test scripts may use different embedding models. If you switch to a different embedding model, you must clear the data directory (./dickens); otherwise, the program may encounter errors. If you wish to retain the LLM cache, you can preserve the kv_store_llm_response_cache.json file while clearing the data directory.
Note 2: Only lightrag_openai_demo.py and lightrag_openai_compatible_demo.py are officially supported sample codes. Other sample files are community contributions that haven't undergone full testing and optimization.
For the complete Core API reference — including init parameters, QueryParam, LLM/embedding provider examples (OpenAI, Ollama, Azure, Gemini, HuggingFace, LlamaIndex), reranker injection, insert operations, entity/relation management, and delete/merge — see docs/ProgramingWithCore.md.
⚠️ If you would like to integrate LightRAG into your project, we recommend utilizing the REST API provided by the LightRAG Server. LightRAG Core is typically intended for embedded applications or for researchers who wish to conduct studies and evaluations.
LightRAG integrates with RAG-Anything for end-to-end multimodal RAG across PDFs, Office documents, images, tables, and formulas. For setup and usage examples, see docs/AdvancedFeatures.md.
LightRAG provides additional capabilities including token usage tracking, knowledge graph data export, LLM cache management, Langfuse observability integration, and RAGAS-based evaluation. See docs/AdvancedFeatures.md.
LightRAG consistently outperforms NaiveRAG, RQ-RAG, HyDE, and GraphRAG across agriculture, computer science, legal, and mixed domains. For the full evaluation methodology, prompts, and reproduce steps, see docs/Reproduce.md.
Overall Performance Table
| Agriculture | CS | Legal | Mix | |||||
|---|---|---|---|---|---|---|---|---|
| NaiveRAG | LightRAG | NaiveRAG | LightRAG | NaiveRAG | LightRAG | NaiveRAG | LightRAG | |
| Comprehensiveness | 32.4% | 67.6% | 38.4% | 61.6% | 16.4% | 83.6% | 38.8% | 61.2% |
| Diversity | 23.6% | 76.4% | 38.0% | 62.0% | 13.6% | 86.4% | 32.4% | 67.6% |
| Empowerment | 32.4% | 67.6% | 38.8% | 61.2% | 16.4% | 83.6% | 42.8% | 57.2% |
| Overall | 32.4% | 67.6% | 38.8% | 61.2% | 15.2% | 84.8% | 40.0% | 60.0% |
| RQ-RAG | LightRAG | RQ-RAG | LightRAG | RQ-RAG | LightRAG | RQ-RAG | LightRAG | |
| Comprehensiveness | 31.6% | 68.4% | 38.8% | 61.2% | 15.2% | 84.8% | 39.2% | 60.8% |
| Diversity | 29.2% | 70.8% | 39.2% | 60.8% | 11.6% | 88.4% | 30.8% | 69.2% |
| Empowerment | 31.6% | 68.4% | 36.4% | 63.6% | 15.2% | 84.8% | 42.4% | 57.6% |
| Overall | 32.4% | 67.6% | 38.0% | 62.0% | 14.4% | 85.6% | 40.0% | 60.0% |
| HyDE | LightRAG | HyDE | LightRAG | HyDE | LightRAG | HyDE | LightRAG | |
| Comprehensiveness | 26.0% | 74.0% | 41.6% | 58.4% | 26.8% | 73.2% | 40.4% | 59.6% |
| Diversity | 24.0% | 76.0% | 38.8% | 61.2% | 20.0% | 80.0% | 32.4% | 67.6% |
| Empowerment | 25.2% | 74.8% | 40.8% | 59.2% | 26.0% | 74.0% | 46.0% | 54.0% |
| Overall | 24.8% | 75.2% | 41.6% | 58.4% | 26.4% | 73.6% | 42.4% | 57.6% |
| GraphRAG | LightRAG | GraphRAG | LightRAG | GraphRAG | LightRAG | GraphRAG | LightRAG | |
| Comprehensiveness | 45.6% | 54.4% | 48.4% | 51.6% | 48.4% | 51.6% | 50.4% | 49.6% |
| Diversity | 22.8% | 77.2% | 40.8% | 59.2% | 26.4% | 73.6% | 36.0% | 64.0% |
| Empowerment | 41.2% | 58.8% | 45.2% | 54.8% | 43.6% | 56.4% | 50.8% | 49.2% |
| Overall | 45.2% | 54.8% | 48.0% | 52.0% | 47.2% | 52.8% | 50.4% | 49.6% |
Ecosystem & Extensions
|
📸
RAG-AnythingMultimodal RAG |
🎥
VideoRAGExtreme Long-Context Video RAG |
✨
MiniRAGExtremely Simple RAG |
Please read our Contributing Guide before submitting a pull request.
@article{guo2024lightrag,
title={LightRAG: Simple and Fast Retrieval-Augmented Generation},
author={Zirui Guo and Lianghao Xia and Yanhua Yu and Tu Ao and Chao Huang},
year={2024},
eprint={2410.05779},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
