LightRAG AI技能包

LLM and Technology Stack Requirements for LightRAG

LightRAG'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-m3 and text-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-m3 or models provided by services like Jina.

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

```bash

Install LightRAG Server as tool using uv (recommended)

uv tool install "lightrag-hku[api]"

pip install "lightrag-hku[api]"

Build front-end artifacts

cd lightrag_webui bun install --frozen-lockfile bun run build cd ..

Setup env file

make dev installs the test toolchain plus the full offline stack

(API, storage backends, and provider integrations), then builds the frontend.

Or using pip with virtual environment

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

Create .env File With Setup Tool

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 risks

For 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 LightRAG Core

• Install from source (Recommended)

```bash cd LightRAG

Or: pip install -e .

* Install from PyPI

Or: pip install lightrag-hku

```

Obtain the env.example file by downloading it from the GitHub repository root

Run make env-base or copy env.example to .env before starting the server.

Quick Start

Quick Start for LightRAG Server

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.

Quick Start for LightRAG core

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:

```bash

you should run the demo code with project folder

cd LightRAG

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.py ```

For 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.

python -m venv .venv

source .venv/bin/activate # Windows: .venv\Scripts\activate

Bootstrap the development environment (recommended)

make dev source .venv/bin/activate # Activate the virtual environment (Linux/macOS)

Or on Windows: .venv\Scripts\activate

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

python -m venv .venv

source .venv/bin/activate # Windows: .venv\Scripts\activate

modify LLM and Embedding settings in .env

docker compose up ```

Historical versions of LightRAG docker images can be found here: [LightRAG Docker Images]( https://github.com/HKUDS/LightRAG/pkgs/container/lightrag) Official GHCR images published by GitHub Actions are signed with Sigstore Cosign using GitHub OIDC. See docs/DockerDeployment.md for verification commands.

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

Launch API-WebUI server

lightrag-server

* Launching the LightRAG Server with Docker Compose

provide your API-KEY for OpenAI

export OPENAI_API_KEY="sk-...your_opeai_key..."