lightrag-hku 0.0.5

LightRAG: Simple and Fast Retrieval-Augmented Generation

LightRAG: Simple and Fast Retrieval-Augmented Generation

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

🎉 News

• [2024.10.15]🎯🎯📢📢LightRAG now supports Hugging Face models!

Install

• Install from source

cd LightRAG
pip install -e .

• Install from PyPI

pip install lightrag-hku

Quick Start

• 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 to initialize LightRAG and perform queries:

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

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")))

Using Hugging Face Models

If you want to use Hugging Face models, you only need to set LightRAG as follows:

from lightrag.llm import hf_model_complete, hf_embedding
from transformers import AutoModel, AutoTokenizer

# Initialize LightRAG with Hugging Face model
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=hf_model_complete,  # Use Hugging Face complete model for text generation
    llm_model_name='meta-llama/Llama-3.1-8B-Instruct',  # Model name from Hugging Face
    # Use Hugging Face embedding function
    embedding_func=EmbeddingFunc(
        embedding_dim=384,
        max_token_size=5000,
        func=lambda texts: hf_embedding(
            texts, 
            tokenizer=AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2"),
            embed_model=AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
        )
    ),
)

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 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]"
    }}
}}

Overall Performance Table

	Agriculture		CS		Legal		Mix
	NaiveRAG	LightRAG	NaiveRAG	LightRAG	NaiveRAG	LightRAG	NaiveRAG	LightRAG
Comprehensiveness	32.69%	67.31%	35.44%	64.56%	19.05%	80.95%	36.36%	63.64%
Diversity	24.09%	75.91%	35.24%	64.76%	10.98%	89.02%	30.76%	69.24%
Empowerment	31.35%	68.65%	35.48%	64.52%	17.59%	82.41%	40.95%	59.05%
Overall	33.30%	66.70%	34.76%	65.24%	17.46%	82.54%	37.59%	62.40%
	RQ-RAG	LightRAG	RQ-RAG	LightRAG	RQ-RAG	LightRAG	RQ-RAG	LightRAG
Comprehensiveness	32.05%	67.95%	39.30%	60.70%	18.57%	81.43%	38.89%	61.11%
Diversity	29.44%	70.56%	38.71%	61.29%	15.14%	84.86%	28.50%	71.50%
Empowerment	32.51%	67.49%	37.52%	62.48%	17.80%	82.20%	43.96%	56.04%
Overall	33.29%	66.71%	39.03%	60.97%	17.80%	82.20%	39.61%	60.39%
	HyDE	LightRAG	HyDE	LightRAG	HyDE	LightRAG	HyDE	LightRAG
Comprehensiveness	24.39%	75.61%	36.49%	63.51%	27.68%	72.32%	42.17%	57.83%
Diversity	24.96%	75.34%	37.41%	62.59%	18.79%	81.21%	30.88%	69.12%
Empowerment	24.89%	75.11%	34.99%	65.01%	26.99%	73.01%	45.61%	54.39%
Overall	23.17%	76.83%	35.67%	64.33%	27.68%	72.32%	42.72%	57.28%
	GraphRAG	LightRAG	GraphRAG	LightRAG	GraphRAG	LightRAG	GraphRAG	LightRAG
Comprehensiveness	45.56%	54.44%	45.98%	54.02%	47.13%	52.87%	51.86%	48.14%
Diversity	19.65%	80.35%	39.64%	60.36%	25.55%	74.45%	35.87%	64.13%
Empowerment	36.69%	63.31%	45.09%	54.91%	42.81%	57.19%	52.94%	47.06%
Overall	43.62%	56.38%	45.98%	54.02%	45.70%	54.30%	51.86%	48.14%

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.

def extract_unique_contexts(input_directory, output_directory):

    os.makedirs(output_directory, exist_ok=True)

    jsonl_files = glob.glob(os.path.join(input_directory, '*.jsonl'))
    print(f"Found {len(jsonl_files)} JSONL files.")

    for file_path in jsonl_files:
        filename = os.path.basename(file_path)
        name, ext = os.path.splitext(filename)
        output_filename = f"{name}_unique_contexts.json"
        output_path = os.path.join(output_directory, output_filename)

        unique_contexts_dict = {}

        print(f"Processing file: {filename}")

        try:
            with open(file_path, 'r', encoding='utf-8') as infile:
                for line_number, line in enumerate(infile, start=1):
                    line = line.strip()
                    if not line:
                        continue
                    try:
                        json_obj = json.loads(line)
                        context = json_obj.get('context')
                        if context and context not in unique_contexts_dict:
                            unique_contexts_dict[context] = None
                    except json.JSONDecodeError as e:
                        print(f"JSON decoding error in file {filename} at line {line_number}: {e}")
        except FileNotFoundError:
            print(f"File not found: {filename}")
            continue
        except Exception as e:
            print(f"An error occurred while processing file {filename}: {e}")
            continue

        unique_contexts_list = list(unique_contexts_dict.keys())
        print(f"There are {len(unique_contexts_list)} unique `context` entries in the file {filename}.")

        try:
            with open(output_path, 'w', encoding='utf-8') as outfile:
                json.dump(unique_contexts_list, outfile, ensure_ascii=False, indent=4)
            print(f"Unique `context` entries have been saved to: {output_filename}")
        except Exception as e:
            print(f"An error occurred while saving to the file {output_filename}: {e}")

    print("All files have been processed.")

Step-1 Insert Contexts

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

def insert_text(rag, file_path):
    with open(file_path, mode='r') as f:
        unique_contexts = json.load(f)
    
    retries = 0
    max_retries = 3
    while retries < max_retries:
        try:
            rag.insert(unique_contexts)
            break
        except Exception as e:
            retries += 1
            print(f"Insertion failed, retrying ({retries}/{max_retries}), error: {e}")
            time.sleep(10)
    if retries == max_retries:
        print("Insertion failed after exceeding the maximum number of retries")

Step-2 Generate Queries

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

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')

def get_summary(context, tot_tokens=2000):
    tokens = tokenizer.tokenize(context)
    half_tokens = tot_tokens // 2

    start_tokens = tokens[1000:1000 + half_tokens]
    end_tokens = tokens[-(1000 + half_tokens):1000]

    summary_tokens = start_tokens + end_tokens
    summary = tokenizer.convert_tokens_to_string(summary_tokens)
    
    return summary

Step-3 Query

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

def extract_queries(file_path):
    with open(file_path, 'r') as f:
        data = f.read()
    
    data = data.replace('**', '')

    queries = re.findall(r'- Question \d+: (.+)', data)

    return queries

Code Structure

.
├── examples
│   ├── batch_eval.py
│   ├── generate_query.py
│   ├── lightrag_openai_demo.py
│   └── lightrag_hf_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
├── 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={2410.05779},
archivePrefix={arXiv},
primaryClass={cs.IR}
}