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LightRAG

LightRAG 介绍

Simple and Fast Retrieval-Augmented Generation

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性能评测

	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%

安装

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

Docker 启动

cd LightRAG
cp env.example .env  # Update the .env with your LLM and embedding configurations
# modify LLM and Embedding settings in .env
docker compose up

初始化配置与启动脚本

大模型配置
嵌入模型配置
向量存储配置
图数据配置，默认 networkx
系统参数配置

# 大模型配置
export LLM_MODEL=gpt-5-mini
export LLM_BINDING_HOST=$OPENAI_BASE_URL
export LLM_BINDING_API_KEY=$OPENAI_API_KEY

#export OPENAI_LLM_REASONING_EFFORT=minimal

# 嵌入模型配置
export EMBEDDING_BINDING_HOST=$OPENAI_BASE_URL
export EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY

# 知识图谱配置
export LIGHTRAG_GRAPH_STORAGE=Neo4JStorage

export NEO4J_URI=neo4j://127.0.0.1:7687
export NEO4J_USERNAME=neo4j
export NEO4J_PASSWORD=ceshiren.com
export NEO4J_DATABASE=neo4j

# 日志
export LOG_LEVEL=DEBUG
export VERBOSE=True


# 清理数据
# rm -rf inputs rag_storage

lightrag-server --verbose

启动

(LightRAG) seveniruby-mac:LightRAG seveniruby$ bash env_openai.sh

LightRAG log file: /Users/seveniruby/projects/LightRAG/lightrag.log

WARNING:root:>> Forcing workers=1 in uvicorn mode(Ignoring workers=2)

    ╔══════════════════════════════════════════════════════════════╗
    ║                LightRAG Server v1.4.9.9/0262                 ║
    ║         Fast, Lightweight RAG Server Implementation          ║
    ╚══════════════════════════════════════════════════════════════╝


📡 Server Configuration:
    ├─ Host: 0.0.0.0
    ├─ Port: 9621
    ├─ Workers: 1
    ├─ Timeout: 300
    ├─ CORS Origins: *
    ├─ SSL Enabled: False
    ├─ Ollama Emulating Model: lightrag:latest
    ├─ Log Level: DEBUG
    ├─ Verbose Debug: True
    ├─ API Key: Not Set
    └─ JWT Auth: Disabled

📂 Directory Configuration:
    ├─ Working Directory: /Users/seveniruby/projects/LightRAG/rag_storage
    └─ Input Directory: /Users/seveniruby/projects/LightRAG/inputs

🤖 LLM Configuration:
    ├─ Binding: openai
    ├─ Host: http://127.0.0.1:8001/v1
    ├─ Model: gpt-4o
    ├─ Max Async for LLM: 4
    ├─ Summary Context Size: 12000
    ├─ LLM Cache Enabled: True
    └─ LLM Cache for Extraction Enabled: True

📊 Embedding Configuration:
    ├─ Binding: openai
    ├─ Host: http://127.0.0.1:8001/v1
    ├─ Model: text-embedding-3-large
    └─ Dimensions: 3072

⚙️ RAG Configuration:
    ├─ Summary Language: English
    ├─ Entity Types: ['Person', 'Creature', 'Organization', 'Location', 'Event', 'Concept', 'Method', 'Content', 'Data', 'Artifact', 'NaturalObject']
    ├─ Max Parallel Insert: 2
    ├─ Chunk Size: 1200
    ├─ Chunk Overlap Size: 100
    ├─ Cosine Threshold: 0.2
    ├─ Top-K: 40
    └─ Force LLM Summary on Merge: 8

💾 Storage Configuration:
    ├─ KV Storage: JsonKVStorage
    ├─ Vector Storage: NanoVectorDBStorage
    ├─ Graph Storage: Neo4JStorage
    ├─ Document Status Storage: JsonDocStatusStorage
    └─ Workspace: -

✨ Server starting up...


🌐 Server Access Information:
    ├─ WebUI (local): http://localhost:9621
    ├─ Remote Access: http://<your-ip-address>:9621
    ├─ API Documentation (local): http://localhost:9621/docs
    └─ Alternative Documentation (local): http://localhost:9621/redoc

📝 Note:
    Since the server is running on 0.0.0.0:
    - Use 'localhost' or '127.0.0.1' for local access
    - Use your machine's IP address for remote access
    - To find your IP address:
      • Windows: Run 'ipconfig' in terminal
      • Linux/Mac: Run 'ifconfig' or 'ip addr' in terminal

快速开始

知识库上传

知识图谱

知识图谱数据

LightRAG 检索

工具调用

API

智能体对接

HTTP 协议的自定义工具对接
面向智能体封装 MCP Skills
编程 SDK

LightRAG