GitHub - HKUDS/LightRAG: "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

Algorithm Flowchart

Figure 1: LightRAG Indexing Flowchart - Img Caption : Source Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : Source

Install

Install from source (Recommend)

cd LightRAG
pip install -e .

Install from PyPI

pip install lightrag-hku

Quick Start

Video demo of running LightRAG locally.
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:

import os
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"


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")))

# Perform mix search (Knowledge Graph + Vector Retrieval)
# Mix mode combines knowledge graph and vector search:
# - Uses both structured (KG) and unstructured (vector) information
# - Provides comprehensive answers by analyzing relationships and context
# - Supports image content through HTML img tags
# - Allows control over retrieval depth via top_k parameter
print(rag.query("What are the top themes in this story?", param=QueryParam(
    mode="mix")))

Using Open AI-like APIs

LightRAG also supports Open AI-like chat/embeddings APIs:

async def llm_model_func(
    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
) -> str:
    return await openai_complete_if_cache(
        "solar-mini",
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        api_key=os.getenv("UPSTAGE_API_KEY"),
        base_url="https://api.upstage.ai/v1/solar",
        **kwargs
    )

async def embedding_func(texts: list[str]) -> np.ndarray:
    return await openai_embedding(
        texts,
        model="solar-embedding-1-large-query",
        api_key=os.getenv("UPSTAGE_API_KEY"),
        base_url="https://api.upstage.ai/v1/solar"
    )

rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=llm_model_func,
    embedding_func=EmbeddingFunc(
        embedding_dim=4096,
        max_token_size=8192,
        func=embedding_func
    )
)

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
from lightrag.utils import EmbeddingFunc

# Initialize LightRAG with Hugging Face model
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=hf_model_complete,  # Use Hugging Face 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")
        )
    ),
)

Using Ollama Models

Overview

If you want to use Ollama models, you need to pull model you plan to use and embedding model, for example nomic-embed-text.

Then you only need to set LightRAG as follows:

from lightrag.llm import ollama_model_complete, ollama_embedding
from lightrag.utils import EmbeddingFunc

# Initialize LightRAG with Ollama model
rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=ollama_model_complete,  # Use Ollama model for text generation
    llm_model_name='your_model_name', # Your model name
    # Use Ollama embedding function
    embedding_func=EmbeddingFunc(
        embedding_dim=768,
        max_token_size=8192,
        func=lambda texts: ollama_embedding(
            texts,
            embed_model="nomic-embed-text"
        )
    ),
)

Increasing context size

In order for LightRAG to work context should be at least 32k tokens. By default Ollama models have context size of 8k. You can achieve this using one of two ways:

Increasing the `num_ctx` parameter in Modelfile.

Pull the model:

ollama pull qwen2

Display the model file:

ollama show --modelfile qwen2 > Modelfile

Edit the Modelfile by adding the following line:

PARAMETER num_ctx 32768

Create the modified model:

ollama create -f Modelfile qwen2m

Setup `num_ctx` via Ollama API.

Tiy can use llm_model_kwargs param to configure ollama:

rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=ollama_model_complete,  # Use Ollama model for text generation
    llm_model_name='your_model_name', # Your model name
    llm_model_kwargs={"options": {"num_ctx": 32768}},
    # Use Ollama embedding function
    embedding_func=EmbeddingFunc(
        embedding_dim=768,
        max_token_size=8192,
        func=lambda texts: ollama_embedding(
            texts,
            embed_model="nomic-embed-text"
        )
    ),
)

Fully functional example

There fully functional example examples/lightrag_ollama_demo.py that utilizes gemma2:2b model, runs only 4 requests in parallel and set context size to 32k.

Low RAM GPUs

In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using gemma2:2b. It was able to find 197 entities and 19 relations on book.txt.

Query Param

class QueryParam:
    mode: Literal["local", "global", "hybrid", "naive", "mix"] = "global"
    only_need_context: bool = False
    response_type: str = "Multiple Paragraphs"
    # Number of top-k items to retrieve; corresponds to entities in "local" mode and relationships in "global" mode.
    top_k: int = 60
    # Number of tokens for the original chunks.
    max_token_for_text_unit: int = 4000
    # Number of tokens for the relationship descriptions
    max_token_for_global_context: int = 4000
    # Number of tokens for the entity descriptions
    max_token_for_local_context: int = 4000

Batch Insert

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

# Batch Insert with custom batch size configuration
rag = LightRAG(
    working_dir=WORKING_DIR,
    addon_params={
        "insert_batch_size": 20  # Process 20 documents per batch
    }
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...])  # Documents will be processed in batches of 20

The insert_batch_size parameter in addon_params controls how many documents are processed in each batch during insertion. This is useful for:

Managing memory usage with large document collections
Optimizing processing speed
Providing better progress tracking
Default value is 10 if not specified

Incremental Insert

# Incremental Insert: Insert new documents into an existing LightRAG instance
rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
     embedding_func=EmbeddingFunc(
          embedding_dim=embedding_dimension,
          max_token_size=8192,
          func=embedding_func,
     ),
)

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

Using Neo4J for Storage

For production level scenarios you will most likely want to leverage an enterprise solution
for KG storage. Running Neo4J in Docker is recommended for seamless local testing.
See: https://hub.docker.com/_/neo4j

export NEO4J_URI="neo4j://localhost:7687"
export NEO4J_USERNAME="neo4j"
export NEO4J_PASSWORD="password"

# When you launch the project be sure to override the default KG: NetworkX
# by specifying kg="Neo4JStorage".

# Note: Default settings use NetworkX
# Initialize LightRAG with Neo4J implementation.
WORKING_DIR = "./local_neo4jWorkDir"

rag = LightRAG(
    working_dir=WORKING_DIR,
    llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
    graph_storage="Neo4JStorage", #<-----------override KG default
    log_level="DEBUG"  #<-----------override log_level default
)

see test_neo4j.py for a working example.

Insert Custom KG

rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
     embedding_func=EmbeddingFunc(
          embedding_dim=embedding_dimension,
          max_token_size=8192,
          func=embedding_func,
     ),
)

custom_kg = {
    "entities": [
        {
            "entity_name": "CompanyA",
            "entity_type": "Organization",
            "description": "A major technology company",
            "source_id": "Source1"
        },
        {
            "entity_name": "ProductX",
            "entity_type": "Product",
            "description": "A popular product developed by CompanyA",
            "source_id": "Source1"
        }
    ],
    "relationships": [
        {
            "src_id": "CompanyA",
            "tgt_id": "ProductX",
            "description": "CompanyA develops ProductX",
            "keywords": "develop, produce",
            "weight": 1.0,
            "source_id": "Source1"
        }
    ],
    "chunks": [
        {
            "content": "ProductX, developed by CompanyA, has revolutionized the market with its cutting-edge features.",
            "source_id": "Source1",
        },
        {
            "content": "PersonA is a prominent researcher at UniversityB, focusing on artificial intelligence and machine learning.",
            "source_id": "Source2",
        },
        {
            "content": "None",
            "source_id": "UNKNOWN",
        },
    ],
}

rag.insert_custom_kg(custom_kg)

Delete Entity

#  Delete Entity: Deleting entities by their names
rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
     embedding_func=EmbeddingFunc(
          embedding_dim=embedding_dimension,
          max_token_size=8192,
          func=embedding_func,
     ),
)

rag.delete_by_entity("Project Gutenberg")

Multi-file Type Support

The textract supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.

import textract

file_path = 'TEXT.pdf'
text_content = textract.process(file_path)

rag.insert(text_content.decode('utf-8'))

Graph Visualization

Graph visualization with html

The following code can be found in examples/graph_visual_with_html.py

import networkx as nx
from pyvis.network import Network

# Load the GraphML file
G = nx.read_graphml('./dickens/graph_chunk_entity_relation.graphml')

# Create a Pyvis network
net = Network(notebook=True)

# Convert NetworkX graph to Pyvis network
net.from_nx(G)

# Save and display the network
net.show('knowledge_graph.html')

Graph visualization with Neo4j

The following code can be found in examples/graph_visual_with_neo4j.py

import os
import json
from lightrag.utils import xml_to_json
from neo4j import GraphDatabase

# Constants
WORKING_DIR = "./dickens"
BATCH_SIZE_NODES = 500
BATCH_SIZE_EDGES = 100

# Neo4j connection credentials
NEO4J_URI = "bolt://localhost:7687"
NEO4J_USERNAME = "neo4j"
NEO4J_PASSWORD = "your_password"

def convert_xml_to_json(xml_path, output_path):
    """Converts XML file to JSON and saves the output."""
    if not os.path.exists(xml_path):
        print(f"Error: File not found - {xml_path}")
        return None

    json_data = xml_to_json(xml_path)
    if json_data:
        with open(output_path, 'w', encoding='utf-8') as f:
            json.dump(json_data, f, ensure_ascii=False, indent=2)
        print(f"JSON file created: {output_path}")
        return json_data
    else:
        print("Failed to create JSON data")
        return None

def process_in_batches(tx, query, data, batch_size):
    """Process data in batches and execute the given query."""
    for i in range(0, len(data), batch_size):
        batch = data[i:i + batch_size]
        tx.run(query, {"nodes": batch} if "nodes" in query else {"edges": batch})

def main():
    # Paths
    xml_file = os.path.join(WORKING_DIR, 'graph_chunk_entity_relation.graphml')
    json_file = os.path.join(WORKING_DIR, 'graph_data.json')

    # Convert XML to JSON
    json_data = convert_xml_to_json(xml_file, json_file)
    if json_data is None:
        return

    # Load nodes and edges
    nodes = json_data.get('nodes', [])
    edges = json_data.get('edges', [])

    # Neo4j queries
    create_nodes_query = """
    UNWIND $nodes AS node
    MERGE (e:Entity {id: node.id})
    SET e.entity_type = node.entity_type,
        e.description = node.description,
        e.source_id = node.source_id,
        e.displayName = node.id
    REMOVE e:Entity
    WITH e, node
    CALL apoc.create.addLabels(e, [node.entity_type]) YIELD node AS labeledNode
    RETURN count(*)
    """

    create_edges_query = """
    UNWIND $edges AS edge
    MATCH (source {id: edge.source})
    MATCH (target {id: edge.target})
    WITH source, target, edge,
         CASE
            WHEN edge.keywords CONTAINS 'lead' THEN 'lead'
            WHEN edge.keywords CONTAINS 'participate' THEN 'participate'
            WHEN edge.keywords CONTAINS 'uses' THEN 'uses'
            WHEN edge.keywords CONTAINS 'located' THEN 'located'
            WHEN edge.keywords CONTAINS 'occurs' THEN 'occurs'
           ELSE REPLACE(SPLIT(edge.keywords, ',')[0], '\"', '')
         END AS relType
    CALL apoc.create.relationship(source, relType, {
      weight: edge.weight,
      description: edge.description,
      keywords: edge.keywords,
      source_id: edge.source_id
    }, target) YIELD rel
    RETURN count(*)
    """

    set_displayname_and_labels_query = """
    MATCH (n)
    SET n.displayName = n.id
    WITH n
    CALL apoc.create.setLabels(n, [n.entity_type]) YIELD node
    RETURN count(*)
    """

    # Create a Neo4j driver
    driver = GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USERNAME, NEO4J_PASSWORD))

    try:
        # Execute queries in batches
        with driver.session() as session:
            # Insert nodes in batches
            session.execute_write(process_in_batches, create_nodes_query, nodes, BATCH_SIZE_NODES)

            # Insert edges in batches
            session.execute_write(process_in_batches, create_edges_query, edges, BATCH_SIZE_EDGES)

            # Set displayName and labels
            session.run(set_displayname_and_labels_query)

    except Exception as e:
        print(f"Error occurred: {e}")

    finally:
        driver.close()

if __name__ == "__main__":
    main()

LightRAG init parameters

Parameter	Type	Explanation	Default
working_dir	`str`	Directory where the cache will be stored	`lightrag_cache+timestamp`
kv_storage	`str`	Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage`	`JsonKVStorage`
vector_storage	`str`	Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage`	`NanoVectorDBStorage`
graph_storage	`str`	Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage`	`NetworkXStorage`
log_level		Log level for application runtime	`logging.DEBUG`
chunk_token_size	`int`	Maximum token size per chunk when splitting documents	`1200`
chunk_overlap_token_size	`int`	Overlap token size between two chunks when splitting documents	`100`
tiktoken_model_name	`str`	Model name for the Tiktoken encoder used to calculate token numbers	`gpt-4o-mini`
entity_extract_max_gleaning	`int`	Number of loops in the entity extraction process, appending history messages	`1`
entity_summary_to_max_tokens	`int`	Maximum token size for each entity summary	`500`
node_embedding_algorithm	`str`	Algorithm for node embedding (currently not used)	`node2vec`
node2vec_params	`dict`	Parameters for node embedding	`{"dimensions": 1536,"num_walks": 10,"walk_length": 40,"window_size": 2,"iterations": 3,"random_seed": 3,}`
embedding_func	`EmbeddingFunc`	Function to generate embedding vectors from text	`openai_embedding`
embedding_batch_num	`int`	Maximum batch size for embedding processes (multiple texts sent per batch)	`32`
embedding_func_max_async	`int`	Maximum number of concurrent asynchronous embedding processes	`16`
llm_model_func	`callable`	Function for LLM generation	`gpt_4o_mini_complete`
llm_model_name	`str`	LLM model name for generation	`meta-llama/Llama-3.2-1B-Instruct`
llm_model_max_token_size	`int`	Maximum token size for LLM generation (affects entity relation summaries)	`32768`
llm_model_max_async	`int`	Maximum number of concurrent asynchronous LLM processes	`16`
llm_model_kwargs	`dict`	Additional parameters for LLM generation
vector_db_storage_cls_kwargs	`dict`	Additional parameters for vector database (currently not used)
enable_llm_cache	`bool`	If `TRUE`, stores LLM results in cache; repeated prompts return cached responses	`TRUE`
addon_params	`dict`	Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing	`example_number: all examples, language: English, insert_batch_size: 10`
convert_response_to_json_func	`callable`	Not used	`convert_response_to_json`
embedding_cache_config	`dict`	Configuration for question-answer caching. Contains three parameters: - `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers. - `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM. - `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers.	Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}`

Error Handling

Click to view error handling details

The API includes comprehensive error handling:

File not found errors (404)
Processing errors (500)
Supports multiple file encodings (UTF-8 and GBK)

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.

Prompt

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.

Prompt

---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	32.4%	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%

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.

Code

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.

Code

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

Code

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.

Code

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

.
├── .github/
│   ├── workflows/
│   │   └── linting.yaml
├── examples/
│   ├── batch_eval.py
│   ├── generate_query.py
│   ├── graph_visual_with_html.py
│   ├── graph_visual_with_neo4j.py
│   ├── insert_custom_kg.py
│   ├── lightrag_api_openai_compatible_demo.py
│   ├── lightrag_api_oracle_demo..py
│   ├── lightrag_azure_openai_demo.py
│   ├── lightrag_bedrock_demo.py
│   ├── lightrag_hf_demo.py
│   ├── lightrag_lmdeploy_demo.py
│   ├── lightrag_nvidia_demo.py
│   ├── lightrag_ollama_demo.py
│   ├── lightrag_openai_compatible_demo.py
│   ├── lightrag_openai_demo.py
│   ├── lightrag_oracle_demo.py
│   ├── lightrag_siliconcloud_demo.py
│   └── vram_management_demo.py
├── lightrag/
│   ├── api/
│   │   ├── lollms_lightrag_server.py
│   │   ├── ollama_lightrag_server.py
│   │   ├── openai_lightrag_server.py
│   │   ├── azure_openai_lightrag_server.py
│   │   └── requirements.txt
│   ├── kg/
│   │   ├── __init__.py
│   │   ├── oracle_impl.py
│   │   └── neo4j_impl.py
│   ├── __init__.py
│   ├── base.py
│   ├── lightrag.py
│   ├── llm.py
│   ├── operate.py
│   ├── prompt.py
│   ├── storage.py
│   └── utils.py
├── reproduce/
│   ├── Step_0.py
│   ├── Step_1_openai_compatible.py
│   ├── Step_1.py
│   ├── Step_2.py
│   ├── Step_3_openai_compatible.py
│   └── Step_3.py
├── .gitignore
├── .pre-commit-config.yaml
├── get_all_edges_nx.py
├── LICENSE
├── README.md
├── requirements.txt
├── setup.py
├── test_neo4j.py
└── test.py

Install with API Support

LightRAG provides optional API support through FastAPI servers that add RAG capabilities to existing LLM services. You can install LightRAG with API support in two ways:

1. Installation from PyPI

pip install "lightrag-hku[api]"

2. Installation from Source (Development)

# Clone the repository
git clone https://github.com/HKUDS/lightrag.git

# Change to the repository directory
cd lightrag

# Install in editable mode with API support
pip install -e ".[api]"

Prerequisites

Before running any of the servers, ensure you have the corresponding backend service running:

For LoLLMs Server

LoLLMs must be running and accessible
Default connection: http://localhost:9600
Configure using --lollms-host if running on a different host/port

For Ollama Server

Ollama must be running and accessible
Default connection: http://localhost:11434
Configure using --ollama-host if running on a different host/port

For OpenAI Server

Requires valid OpenAI API credentials set in environment variables
OPENAI_API_KEY must be set

For Azure OpenAI Server

Azure OpenAI API can be created using the following commands in Azure CLI (you need to install Azure CLI first from https://docs.microsoft.com/en-us/cli/azure/install-azure-cli):

# Change the resource group name, location and OpenAI resource name as needed
RESOURCE_GROUP_NAME=LightRAG
LOCATION=swedencentral
RESOURCE_NAME=LightRAG-OpenAI

az login
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION
az cognitiveservices account create --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME  --kind OpenAI --sku S0 --location swedencentral
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name gpt-4o --model-name gpt-4o --model-version "2024-08-06"  --sku-capacity 100 --sku-name "Standard"
az cognitiveservices account deployment create --resource-group $RESOURCE_GROUP_NAME  --model-format OpenAI --name $RESOURCE_NAME --deployment-name text-embedding-3-large --model-name text-embedding-3-large --model-version "1"  --sku-capacity 80 --sku-name "Standard"
az cognitiveservices account show --name $RESOURCE_NAME --resource-group $RESOURCE_GROUP_NAME --query "properties.endpoint"
az cognitiveservices account keys list --name $RESOURCE_NAME -g $RESOURCE_GROUP_NAME

The output of the last command will give you the endpoint and the key for the OpenAI API. You can use these values to set the environment variables in the .env file.

Configuration Options

Each server has its own specific configuration options:

LoLLMs Server Options

Parameter	Default	Description
--host	0.0.0.0	RAG server host
--port	9621	RAG server port
--model	mistral-nemo:latest	LLM model name
--embedding-model	bge-m3:latest	Embedding model name
--lollms-host	http://localhost:9600	LoLLMS backend URL
--working-dir	./rag_storage	Working directory for RAG
--max-async	4	Maximum async operations
--max-tokens	32768	Maximum token size
--embedding-dim	1024	Embedding dimensions
--max-embed-tokens	8192	Maximum embedding token size
--input-file	./book.txt	Initial input file
--log-level	INFO	Logging level

Ollama Server Options

Parameter	Default	Description
--host	0.0.0.0	RAG server host
--port	9621	RAG server port
--model	mistral-nemo:latest	LLM model name
--embedding-model	bge-m3:latest	Embedding model name
--ollama-host	http://localhost:11434	Ollama backend URL
--working-dir	./rag_storage	Working directory for RAG
--max-async	4	Maximum async operations
--max-tokens	32768	Maximum token size
--embedding-dim	1024	Embedding dimensions
--max-embed-tokens	8192	Maximum embedding token size
--input-file	./book.txt	Initial input file
--log-level	INFO	Logging level

OpenAI Server Options

Parameter	Default	Description
--host	0.0.0.0	RAG server host
--port	9621	RAG server port
--model	gpt-4	OpenAI model name
--embedding-model	text-embedding-3-large	OpenAI embedding model
--working-dir	./rag_storage	Working directory for RAG
--max-tokens	32768	Maximum token size
--max-embed-tokens	8192	Maximum embedding token size
--input-dir	./inputs	Input directory for documents
--log-level	INFO	Logging level

OpenAI AZURE Server Options

Parameter	Default	Description
--host	0.0.0.0	Server host
--port	9621	Server port
--model	gpt-4	OpenAI model name
--embedding-model	text-embedding-3-large	OpenAI embedding model
--working-dir	./rag_storage	Working directory for RAG
--max-tokens	32768	Maximum token size
--max-embed-tokens	8192	Maximum embedding token size
--input-dir	./inputs	Input directory for documents
--enable-cache	True	Enable response cache
--log-level	INFO	Logging level

Example Usage

LoLLMs RAG Server

# Custom configuration with specific model and working directory
lollms-lightrag-server --model mistral-nemo --port 8080 --working-dir ./custom_rag

# Using specific models (ensure they are installed in your LoLLMs instance)
lollms-lightrag-server --model mistral-nemo:latest --embedding-model bge-m3 --embedding-dim 1024

Ollama RAG Server

# Custom configuration with specific model and working directory
ollama-lightrag-server --model mistral-nemo:latest --port 8080 --working-dir ./custom_rag

# Using specific models (ensure they are installed in your Ollama instance)
ollama-lightrag-server --model mistral-nemo:latest --embedding-model bge-m3 --embedding-dim 1024

OpenAI RAG Server

# Using GPT-4 with text-embedding-3-large
openai-lightrag-server --port 9624 --model gpt-4 --embedding-model text-embedding-3-large

Azure OpenAI RAG Server

# Using GPT-4 with text-embedding-3-large
azure-openai-lightrag-server --model gpt-4o --port 8080 --working-dir ./custom_rag --embedding-model text-embedding-3-large

Important Notes:

For LoLLMs: Make sure the specified models are installed in your LoLLMs instance
For Ollama: Make sure the specified models are installed in your Ollama instance
For OpenAI: Ensure you have set up your OPENAI_API_KEY environment variable
For Azure OpenAI: Build and configure your server as stated in the Prequisites section

For help on any server, use the --help flag:

lollms-lightrag-server --help
ollama-lightrag-server --help
openai-lightrag-server --help
azure-openai-lightrag-server --help

Note: If you don't need the API functionality, you can install the base package without API support using:

pip install lightrag-hku

API Endpoints

All servers (LoLLMs, Ollama, OpenAI and Azure OpenAI) provide the same REST API endpoints for RAG functionality.

Query Endpoints

POST /query

Query the RAG system with options for different search modes.

curl -X POST "http://localhost:9621/query" \
    -H "Content-Type: application/json" \
    -d '{"query": "Your question here", "mode": "hybrid"}'

POST /query/stream

Stream responses from the RAG system.

curl -X POST "http://localhost:9621/query/stream" \
    -H "Content-Type: application/json" \
    -d '{"query": "Your question here", "mode": "hybrid"}'

Document Management Endpoints

POST /documents/text

Insert text directly into the RAG system.

curl -X POST "http://localhost:9621/documents/text" \
    -H "Content-Type: application/json" \
    -d '{"text": "Your text content here", "description": "Optional description"}'

POST /documents/file

Upload a single file to the RAG system.

curl -X POST "http://localhost:9621/documents/file" \
    -F "file=@/path/to/your/document.txt" \
    -F "description=Optional description"

POST /documents/batch

Upload multiple files at once.

curl -X POST "http://localhost:9621/documents/batch" \
    -F "files=@/path/to/doc1.txt" \
    -F "files=@/path/to/doc2.txt"

DELETE /documents

Clear all documents from the RAG system.

curl -X DELETE "http://localhost:9621/documents"

Utility Endpoints

GET /health

Check server health and configuration.

curl "http://localhost:9621/health"

Development

Running in Development Mode

For LoLLMs:

uvicorn lollms_lightrag_server:app --reload --port 9621

For Ollama:

uvicorn ollama_lightrag_server:app --reload --port 9621

For OpenAI:

uvicorn openai_lightrag_server:app --reload --port 9621

For Azure OpenAI:

uvicorn azure_openai_lightrag_server:app --reload --port 9621

API Documentation

When any server is running, visit:

Swagger UI: http://localhost:9621/docs
ReDoc: http://localhost:9621/redoc

Testing API Endpoints

You can test the API endpoints using the provided curl commands or through the Swagger UI interface. Make sure to:

Start the appropriate backend service (LoLLMs, Ollama, or OpenAI)
Start the RAG server
Upload some documents using the document management endpoints
Query the system using the query endpoints

Important Features

Automatic Document Vectorization

When starting any of the servers with the --input-dir parameter, the system will automatically:

Scan the specified directory for documents
Check for existing vectorized content in the database
Only vectorize new documents that aren't already in the database
Make all content immediately available for RAG queries

This intelligent caching mechanism:

Prevents unnecessary re-vectorization of existing documents
Reduces startup time for subsequent runs
Preserves system resources
Maintains consistency across restarts

Example Usage

LoLLMs RAG Server

# Start server with automatic document vectorization
# Only new documents will be vectorized, existing ones will be loaded from cache
lollms-lightrag-server --input-dir ./my_documents --port 8080

Ollama RAG Server

# Start server with automatic document vectorization
# Previously vectorized documents will be loaded from the database
ollama-lightrag-server --input-dir ./my_documents --port 8080

OpenAI RAG Server

# Start server with automatic document vectorization
# Existing documents are retrieved from cache, only new ones are processed
openai-lightrag-server --input-dir ./my_documents --port 9624

Azure OpenAI RAG Server

# Start server with automatic document vectorization
# Existing documents are retrieved from cache, only new ones are processed
azure-openai-lightrag-server --input-dir ./my_documents --port 9624

Important Notes:

The --input-dir parameter enables automatic document processing at startup
Documents already in the database are not re-vectorized
Only new documents in the input directory will be processed
This optimization significantly reduces startup time for subsequent runs
The working directory (--working-dir) stores the vectorized documents database

Star History

Contribution

Thank you to all our contributors!

🌟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}
}

Thank you for your interest in our work!

Name		Name	Last commit message	Last commit date
Latest commit History 874 Commits
.github/workflows		.github/workflows
examples		examples
lightrag		lightrag
reproduce		reproduce
.DS_Store		.DS_Store
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
LICENSE		LICENSE
README.md		README.md
get_all_edges_nx.py		get_all_edges_nx.py
requirements.txt		requirements.txt
setup.py		setup.py
test.py		test.py
test_chromadb.py		test_chromadb.py
test_neo4j.py		test_neo4j.py

License

HKUDS/LightRAG

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🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation

🎉 News

Algorithm Flowchart

Install

Quick Start

Overview

Increasing context size

Increasing the num_ctx parameter in Modelfile.

Setup num_ctx via Ollama API.

Fully functional example

Low RAM GPUs

Query Param

Batch Insert

Incremental Insert

Using Neo4J for Storage

Insert Custom KG

Delete Entity

Multi-file Type Support

Graph Visualization

LightRAG init parameters

Error Handling

Evaluation

Dataset

Generate Query

Batch Eval

Overall Performance Table

Reproduce

Step-0 Extract Unique Contexts

Step-1 Insert Contexts

Step-2 Generate Queries

Step-3 Query

Code Structure

Install with API Support

1. Installation from PyPI

2. Installation from Source (Development)

Prerequisites

For LoLLMs Server

For Ollama Server

For OpenAI Server

For Azure OpenAI Server

Configuration Options

LoLLMs Server Options

Ollama Server Options

OpenAI Server Options

OpenAI AZURE Server Options

Example Usage

LoLLMs RAG Server

Ollama RAG Server

OpenAI RAG Server

Azure OpenAI RAG Server

API Endpoints

Query Endpoints

POST /query

POST /query/stream

Document Management Endpoints

POST /documents/text

POST /documents/file

POST /documents/batch

DELETE /documents

Utility Endpoints

GET /health

Development

Running in Development Mode

API Documentation

Testing API Endpoints

Important Features

Automatic Document Vectorization

Example Usage

LoLLMs RAG Server

Ollama RAG Server

OpenAI RAG Server

Azure OpenAI RAG Server

Star History

Contribution

Increasing the `num_ctx` parameter in Modelfile.

Setup `num_ctx` via Ollama API.

Packages