LightRAG: Simple and Fast Retrieval-Augmented Generation

This repository hosts the code of LightRAG. The structure of this code is based on nano-graphrag.

Install

• Install from source

cd LightRAG
pip install -e .

• Install from PyPI

pip install lightrag-hku

Quick Start

• Set OpenAI API key in environment: 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:

from lightrag import LightRAG, QueryParam

rag = LightRAG(working_dir="./dickens")

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

Batch Insert

rag.insert(["TEXT1", "TEXT2",...])

Incremental Insert

rag = LightRAG(working_dir="./dickens")

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

Evaluation

Dataset

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

Generate Query

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

Given the following description of a dataset:

{description}

Please identify 5 potential users who would engage with this dataset. For each user, list 5 tasks they would perform with this dataset. Then, for each (user, task) combination, generate 5 questions that require a high-level understanding of the entire dataset.

Output the results in the following structure:
- User 1: [user description]
    - Task 1: [task description]
        - Question 1:
        - Question 2:
        - Question 3:
        - Question 4:
        - Question 5:
    - Task 2: [task description]
        ...
    - Task 5: [task description]
- User 2: [user description]
    ...
- User 5: [user description]
    ...

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.

---Role---
You are an expert tasked with evaluating two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**.
---Goal---
You will evaluate two answers to the same question based on three criteria: **Comprehensiveness**, **Diversity**, and **Empowerment**. 

- **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question?
- **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question?
- **Empowerment**: How well does the answer help the reader understand and make informed judgments about the topic?

For each criterion, choose the better answer (either Answer 1 or Answer 2) and explain why. Then, select an overall winner based on these three categories.

Here is the question:
{query}

Here are the two answers:

**Answer 1:**
{answer1}

**Answer 2:**
{answer2}

Evaluate both answers using the three criteria listed above and provide detailed explanations for each criterion.

Output your evaluation in the following JSON format:

{{
    "Comprehensiveness": {{
        "Winner": "[Answer 1 or Answer 2]",
        "Explanation": "[Provide explanation here]"
    }},
    "Empowerment": {{
        "Winner": "[Answer 1 or Answer 2]",
        "Explanation": "[Provide explanation here]"
    }},
    "Overall Winner": {{
        "Winner": "[Answer 1 or Answer 2]",
        "Explanation": "[Summarize why this answer is the overall winner based on the three criteria]"
    }}
}}

Code Structure

.
├── examples
│   ├── batch_eval.py
│   ├── generate_query.py
│   ├── insert.py
│   └── query.py
├── lightrag
│   ├── __init__.py
│   ├── base.py
│   ├── lightrag.py
│   ├── llm.py
│   ├── operate.py
│   ├── prompt.py
│   ├── storage.py
│   └── utils.jpeg
├── LICENSE
├── README.md
├── requirements.txt
└── setup.py

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={},
archivePrefix={arXiv},
primaryClass={cs.IR}
}