🎉 News

• [x] [2024.10.18]🎯🎯📢📢We’ve added a link to a LightRAG Introduction Video. Thanks to the author!
• [x] [2024.10.17]🎯🎯📢📢We have created a Discord channel! Welcome to join for sharing and discussions! 🎉🎉
• [x] [2024.10.16]🎯🎯📢📢LightRAG now supports Ollama models!
• [x] [2024.10.15]🎯🎯📢📢LightRAG now supports Hugging Face models!

Install

• Install from source (Recommend)

cd LightRAG
pip install -e .

• Install from PyPI

  pip install lightrag-hku

Quick Start

• All the code can be found in the examples.
• Set OpenAI API key in environment if using OpenAI models: export OPENAI_API_KEY="sk-...".
• Download the demo text "A Christmas Carol by Charles Dickens":

  curl https://raw.githubusercontent.com/gusye1234/nano-graphrag/main/tests/mock_data.txt > ./book.txt

  Use the below Python snippet (in a script) to initialize LightRAG and perform queries:

from lightrag import LightRAG, QueryParam
from lightrag.llm import gpt_4o_mini_complete, gpt_4o_complete

#########
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
# import nest_asyncio 
# nest_asyncio.apply() 
#########

WORKING_DIR = "./dickens"

WORKING_DIR = "./dickens"

if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)

rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=gpt_4o_mini_complete  # Use gpt_4o_mini_complete LLM model
    # llm_model_func=gpt_4o_complete  # Optionally, use a stronger model
)

with open("./book.txt") as f:
    rag.insert(f.read())

# Perform naive search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))

# Perform local search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="local")))

# Perform global search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="global")))

# Perform hybrid search
print(rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid")))

Batch Insert

# Batch Insert: Insert multiple texts at once
rag.insert(["TEXT1", "TEXT2",...])

Incremental Insert

# Incremental Insert: Insert new documents into an existing LightRAG instance
rag = LightRAG(working_dir="./dickens")

with open("./newText.txt") as f:
    rag.insert(f.read())

Evaluation

Dataset

The dataset used in LightRAG can be downloaded from TommyChien/UltraDomain.

Generate Query

LightRAG uses the following prompt to generate high-level queries, with the corresponding code in example/generate_query.py.

Batch Eval

To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in example/batch_eval.py.

Overall Performance Table

Reproduce

All the code can be found in the ./reproduce directory.

Step-0 Extract Unique Contexts

First, we need to extract unique contexts in the datasets.

Step-1 Insert Contexts

For the extracted contexts, we insert them into the LightRAG system.

Step-2 Generate Queries

We extract tokens from the first and the second half of each context in the dataset, then combine them as dataset descriptions to generate queries.

Step-3 Query

For the queries generated in Step-2, we will extract them and query LightRAG.

Code Structure

.
├── examples
│   ├── batch_eval.py
│   ├── generate_query.py
│   ├── lightrag_azure_openai_demo.py
│   ├── lightrag_bedrock_demo.py
│   ├── lightrag_hf_demo.py
│   ├── lightrag_ollama_demo.py
│   ├── lightrag_openai_compatible_demo.py
│   └── lightrag_openai_demo.py
├── lightrag
│   ├── __init__.py
│   ├── base.py
│   ├── lightrag.py
│   ├── llm.py
│   ├── operate.py
│   ├── prompt.py
│   ├── storage.py
│   └── utils.py
├── reproduce
│   ├── Step_0.py
│   ├── Step_1.py
│   ├── Step_2.py
│   └── Step_3.py
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── README.md
├── requirements.txt
└── setup.py

Star History

Citation

@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}
}