# 🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation
---
## 🎉 News
- [X] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing.
- [X] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.
- [X] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution.
- [X] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
- [X] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
- [X] [2025.01.06]🎯📢You can now [use PostgreSQL for Storage](#using-postgresql-for-storage).
- [X] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
- [X] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
- [X] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
- [X] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
- [X] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
- [X] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
- [X] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
- [X] [2024.10.20]🎯📢We've added a new feature to LightRAG: Graph Visualization.
- [X] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
- [X] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
- [X] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
- [X] [2024.10.15]🎯📢LightRAG now supports [Hugging Face models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
Algorithm Flowchart
*Figure 1: LightRAG Indexing Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
*Figure 2: LightRAG Retrieval and Querying Flowchart - Img Caption : [Source](https://learnopencv.com/lightrag/)*
## Installation
### 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
pip install "lightrag-hku[api]"
```
* Installation from Source
```bash
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG
# create a Python virtual enviroment if neccesary
# Install in editable mode with API support
pip install -e ".[api]"
```
* Launching the LightRAG Server with Docker Compose
```
git clone https://github.com/HKUDS/LightRAG.git
cd LightRAG
cp env.example .env
# 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)
### Install LightRAG Core
* Install from source (Recommend)
```bash
cd LightRAG
pip install -e .
```
* Install from PyPI
```bash
pip install lightrag-hku
```
## Quick Start
### Quick Start for LightRAG Server
* For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).
### 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](https://www.youtube.com/watch?v=g21royNJ4fw) 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
### provide your API-KEY for OpenAI
export OPENAI_API_KEY="sk-...your_opeai_key..."
### 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.
## Programing with LightRAG Core
> If you would like to integrate LightRAG into your project, we recommend utilizing the REST API provided by the LightRAG Server. LightRAG Core is typically intended for embedded applications or for researchers who wish to conduct studies and evaluations.
### ⚠️ Important: Initialization Requirements
**LightRAG requires explicit initialization before use.** You must call both `await rag.initialize_storages()` and `await initialize_pipeline_status()` after creating a LightRAG instance, otherwise you will encounter errors like:
- `AttributeError: __aenter__` - if storages are not initialized
- `KeyError: 'history_messages'` - if pipeline status is not initialized
### A Simple Program
Use the below Python snippet to initialize LightRAG, insert text to it, and perform queries:
```python
import os
import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.utils import setup_logger
setup_logger("lightrag", level="INFO")
WORKING_DIR = "./rag_storage"
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
async def initialize_rag():
rag = LightRAG(
working_dir=WORKING_DIR,
embedding_func=openai_embed,
llm_model_func=gpt_4o_mini_complete,
)
# IMPORTANT: Both initialization calls are required!
await rag.initialize_storages() # Initialize storage backends
await initialize_pipeline_status() # Initialize processing pipeline
return rag
async def main():
try:
# Initialize RAG instance
rag = await initialize_rag()
rag.insert("Your text")
# Perform hybrid search
mode = "hybrid"
print(
await rag.query(
"What are the top themes in this story?",
param=QueryParam(mode=mode)
)
)
except Exception as e:
print(f"An error occurred: {e}")
finally:
if rag:
await rag.finalize_storages()
if __name__ == "__main__":
asyncio.run(main())
```
Important notes for the above snippet:
- Export your OPENAI_API_KEY environment variable before running the script.
- This program uses the default storage settings for LightRAG, so all data will be persisted to WORKING_DIR/rag_storage.
- This program demonstrates only the simplest way to initialize a LightRAG object: Injecting the embedding and LLM functions, and initializing storage and pipeline status after creating the LightRAG object.
### LightRAG init parameters
A full list of LightRAG init parameters:
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`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage` | `JsonKVStorage` |
| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` |
| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage` | `NetworkXStorage` |
| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` |
| **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` |
| **tokenizer** | `Tokenizer` | The function used to convert text into tokens (numbers) and back using .encode() and .decode() functions following `TokenizerInterface` protocol. If you don't specify one, it will use the default Tiktoken tokenizer. | `TiktokenTokenizer` |
| **tiktoken_model_name** | `str` | If you're using the default Tiktoken tokenizer, this is the name of the specific Tiktoken model to use. This setting is ignored if you provide your own tokenizer. | `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_embed` |
| **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`（default value changed by env var MAX_TOKENS) |
| **llm_model_max_async** | `int` | Maximum number of concurrent asynchronous LLM processes | `4`（default value changed by env var MAX_ASYNC) |
| **llm_model_kwargs** | `dict` | Additional parameters for LLM generation | |
| **vector_db_storage_cls_kwargs** | `dict` | Additional parameters for vector database, like setting the threshold for nodes and relations retrieval | cosine_better_than_threshold: 0.2（default value changed by env var COSINE_THRESHOLD) |
| **enable_llm_cache** | `bool` | If `TRUE`, stores LLM results in cache; repeated prompts return cached responses | `TRUE` |
| **enable_llm_cache_for_entity_extract** | `bool` | If `TRUE`, stores LLM results in cache for entity extraction; Good for beginners to debug your application | `TRUE` |
| **addon_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"]}`: sets example limit, entiy/relation extraction output language | `example_number: all examples, language: English` |
| **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}` |
### Query Param
Use QueryParam to control the behavior your query:
```python
class QueryParam:
"""Configuration parameters for query execution in LightRAG."""
mode: Literal["local", "global", "hybrid", "naive", "mix", "bypass"] = "global"
"""Specifies the retrieval mode:
- "local": Focuses on context-dependent information.
- "global": Utilizes global knowledge.
- "hybrid": Combines local and global retrieval methods.
- "naive": Performs a basic search without advanced techniques.
- "mix": Integrates knowledge graph and vector retrieval.
"""
only_need_context: bool = False
"""If True, only returns the retrieved context without generating a response."""
only_need_prompt: bool = False
"""If True, only returns the generated prompt without producing a response."""
response_type: str = "Multiple Paragraphs"
"""Defines the response format. Examples: 'Multiple Paragraphs', 'Single Paragraph', 'Bullet Points'."""
stream: bool = False
"""If True, enables streaming output for real-time responses."""
top_k: int = int(os.getenv("TOP_K", "60"))
"""Number of top items to retrieve. Represents entities in 'local' mode and relationships in 'global' mode."""
max_token_for_text_unit: int = int(os.getenv("MAX_TOKEN_TEXT_CHUNK", "4000"))
"""Maximum number of tokens allowed for each retrieved text chunk."""
max_token_for_global_context: int = int(
os.getenv("MAX_TOKEN_RELATION_DESC", "4000")
)
"""Maximum number of tokens allocated for relationship descriptions in global retrieval."""
max_token_for_local_context: int = int(os.getenv("MAX_TOKEN_ENTITY_DESC", "4000"))
"""Maximum number of tokens allocated for entity descriptions in local retrieval."""
conversation_history: list[dict[str, str]] = field(default_factory=list)
"""Stores past conversation history to maintain context.
Format: [{"role": "user/assistant", "content": "message"}].
"""
history_turns: int = 3
"""Number of complete conversation turns (user-assistant pairs) to consider in the response context."""
ids: list[str] | None = None
"""List of ids to filter the results."""
model_func: Callable[..., object] | None = None
"""Optional override for the LLM model function to use for this specific query.
If provided, this will be used instead of the global model function.
This allows using different models for different query modes.
"""
user_prompt: str | None = None
"""User-provided prompt for the query.
If proivded, this will be use instead of the default vaulue from prompt template.
"""
```
> default value of Top_k can be change by environment variables TOP_K.
### LLM and Embedding Injection
LightRAG requires the utilization of LLM and Embedding models to accomplish document indexing and querying tasks. During the initialization phase, it is necessary to inject the invocation methods of the relevant models into LightRAG：
Using Open AI-like APIs
* LightRAG also supports Open AI-like chat/embeddings APIs:
```python
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_embed(
texts,
model="solar-embedding-1-large-query",
api_key=os.getenv("UPSTAGE_API_KEY"),
base_url="https://api.upstage.ai/v1/solar"
)
async def initialize_rag():
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
)
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
```
Using Hugging Face Models
* If you want to use Hugging Face models, you only need to set LightRAG as follows:
See `lightrag_hf_demo.py`
```python
# 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_embed(
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:
```python
# 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_embed(
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**
1. Pull the model:
```bash
ollama pull qwen2
```
2. Display the model file:
```bash
ollama show --modelfile qwen2 > Modelfile
```
3. Edit the Modelfile by adding the following line:
```bash
PARAMETER num_ctx 32768
```
4. Create the modified model:
```bash
ollama create -f Modelfile qwen2m
```
* **Setup `num_ctx` via Ollama API**
Tiy can use `llm_model_kwargs` param to configure ollama:
```python
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_embed(
texts,
embed_model="nomic-embed-text"
)
),
)
```
* **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`.
LlamaIndex
LightRAG supports integration with LlamaIndex (`llm/llama_index_impl.py`):
- Integrates with OpenAI and other providers through LlamaIndex
- See [LlamaIndex Documentation](lightrag/llm/Readme.md) for detailed setup and examples
**Example Usage**
```python
# Using LlamaIndex with direct OpenAI access
import asyncio
from lightrag import LightRAG
from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_index_embed
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.utils import setup_logger
# Setup log handler for LightRAG
setup_logger("lightrag", level="INFO")
async def initialize_rag():
rag = LightRAG(
working_dir="your/path",
llm_model_func=llama_index_complete_if_cache, # LlamaIndex-compatible completion function
embedding_func=EmbeddingFunc( # LlamaIndex-compatible embedding function
embedding_dim=1536,
max_token_size=8192,
func=lambda texts: llama_index_embed(texts, embed_model=embed_model)
),
)
await rag.initialize_storages()
await initialize_pipeline_status()
return rag
def main():
# Initialize RAG instance
rag = asyncio.run(initialize_rag())
with open("./book.txt", "r", encoding="utf-8") 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"))
)
if __name__ == "__main__":
main()
```
**For detailed documentation and examples, see:**
- [LlamaIndex Documentation](lightrag/llm/Readme.md)
- [Direct OpenAI Example](examples/lightrag_llamaindex_direct_demo.py)
- [LiteLLM Proxy Example](examples/lightrag_llamaindex_litellm_demo.py)
### Conversation History Support
LightRAG now supports multi-turn dialogue through the conversation history feature. Here's how to use it:
Usage Example
```python
# Create conversation history
conversation_history = [
{"role": "user", "content": "What is the main character's attitude towards Christmas?"},
{"role": "assistant", "content": "At the beginning of the story, Ebenezer Scrooge has a very negative attitude towards Christmas..."},
{"role": "user", "content": "How does his attitude change?"}
]
# Create query parameters with conversation history
query_param = QueryParam(
mode="mix", # or any other mode: "local", "global", "hybrid"
conversation_history=conversation_history, # Add the conversation history
history_turns=3 # Number of recent conversation turns to consider
)
# Make a query that takes into account the conversation history
response = rag.query(
"What causes this change in his character?",
param=query_param
)
```
### User Prompt vs. Query
When using LightRAG for content queries, avoid combining the search process with unrelated output processing, as this significantly impacts query effectiveness. The `user_prompt` parameter in Query Param is specifically designed to address this issue — it does not participate in the RAG retrieval phase, but rather guides the LLM on how to process the retrieved results after the query is completed. Here's how to use it:
```python
# Create query parameters
query_param = QueryParam(
mode = "hybrid", # Other modes：local, global, hybrid, mix, naive
user_prompt = "For diagrams, use mermaid format with English/Pinyin node names and Chinese display labels",
)
# Query and process
response_default = rag.query(
"Please draw a character relationship diagram for Scrooge",
param=query_param
)
print(response_default)
```
### Insert
Basic Insert
```python
# Basic Insert
rag.insert("Text")
```
Batch Insert
```python
# 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,
max_parallel_insert = 4
)
rag.insert(["TEXT1", "TEXT2", "TEXT3", ...]) # Documents will be processed in batches of 4
```
The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.The `max_parallel_insert` parameter determines the number of documents processed concurrently in the document indexing pipeline. If unspecified, the default value is **2**. We recommend keeping this setting **below 10**, as the performance bottleneck typically lies with the LLM (Large Language Model) processing.
Insert with ID
If you want to provide your own IDs for your documents, number of documents and number of IDs must be the same.
```python
# Insert single text, and provide ID for it
rag.insert("TEXT1", ids=["ID_FOR_TEXT1"])
# Insert multiple texts, and provide IDs for them
rag.insert(["TEXT1", "TEXT2",...], ids=["ID_FOR_TEXT1", "ID_FOR_TEXT2"])
```
Insert using Pipeline
The `apipeline_enqueue_documents` and `apipeline_process_enqueue_documents` functions allow you to perform incremental insertion of documents into the graph.
This is useful for scenarios where you want to process documents in the background while still allowing the main thread to continue executing.
And using a routine to process new documents.
```python
rag = LightRAG(..)
await rag.apipeline_enqueue_documents(input)
# Your routine in loop
await rag.apipeline_process_enqueue_documents(input)
```
Insert Multi-file Type Support
The `textract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.
```python
import textract
file_path = 'TEXT.pdf'
text_content = textract.process(file_path)
rag.insert(text_content.decode('utf-8'))
```
Citation Functionality
By providing file paths, the system ensures that sources can be traced back to their original documents.
```python
# Define documents and their file paths
documents = ["Document content 1", "Document content 2"]
file_paths = ["path/to/doc1.txt", "path/to/doc2.txt"]
# Insert documents with file paths
rag.insert(documents, file_paths=file_paths)
```
### Storage
LightRAG uses four types of storage, each of which has multiple implementation options. When initializing LightRAG, the implementation schemes for these four types of storage can be set through parameters. For details, please refer to the previous LightRAG initialization parameters.
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
```python
export NEO4J_URI="neo4j://localhost:7687"
export NEO4J_USERNAME="neo4j"
export NEO4J_PASSWORD="password"
# Setup logger for LightRAG
setup_logger("lightrag", level="INFO")
# 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.
async def initialize_rag():
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
)
# Initialize database connections
await rag.initialize_storages()
# Initialize pipeline status for document processing
await initialize_pipeline_status()
return rag
```
see test_neo4j.py for a working example.
Using PostgreSQL for Storage
For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE).
* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
* Create index for AGE example: (Change below `dickens` to your graph name if necessary)
```sql
load 'age';
SET search_path = ag_catalog, "$user", public;
CREATE INDEX CONCURRENTLY entity_p_idx ON dickens."Entity" (id);
CREATE INDEX CONCURRENTLY vertex_p_idx ON dickens."_ag_label_vertex" (id);
CREATE INDEX CONCURRENTLY directed_p_idx ON dickens."DIRECTED" (id);
CREATE INDEX CONCURRENTLY directed_eid_idx ON dickens."DIRECTED" (end_id);
CREATE INDEX CONCURRENTLY directed_sid_idx ON dickens."DIRECTED" (start_id);
CREATE INDEX CONCURRENTLY directed_seid_idx ON dickens."DIRECTED" (start_id,end_id);
CREATE INDEX CONCURRENTLY edge_p_idx ON dickens."_ag_label_edge" (id);
CREATE INDEX CONCURRENTLY edge_sid_idx ON dickens."_ag_label_edge" (start_id);
CREATE INDEX CONCURRENTLY edge_eid_idx ON dickens."_ag_label_edge" (end_id);
CREATE INDEX CONCURRENTLY edge_seid_idx ON dickens."_ag_label_edge" (start_id,end_id);
create INDEX CONCURRENTLY vertex_idx_node_id ON dickens."_ag_label_vertex" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
create INDEX CONCURRENTLY entity_idx_node_id ON dickens."Entity" (ag_catalog.agtype_access_operator(properties, '"node_id"'::agtype));
CREATE INDEX CONCURRENTLY entity_node_id_gin_idx ON dickens."Entity" using gin(properties);
ALTER TABLE dickens."DIRECTED" CLUSTER ON directed_sid_idx;
-- drop if necessary
drop INDEX entity_p_idx;
drop INDEX vertex_p_idx;
drop INDEX directed_p_idx;
drop INDEX directed_eid_idx;
drop INDEX directed_sid_idx;
drop INDEX directed_seid_idx;
drop INDEX edge_p_idx;
drop INDEX edge_sid_idx;
drop INDEX edge_eid_idx;
drop INDEX edge_seid_idx;
drop INDEX vertex_idx_node_id;
drop INDEX entity_idx_node_id;
drop INDEX entity_node_id_gin_idx;
```
* Known issue of the Apache AGE: The released versions got below issue:
> You might find that the properties of the nodes/edges are empty.
> It is a known issue of the release version: https://github.com/apache/age/pull/1721
>
> You can Compile the AGE from source code and fix it.
>
Using Faiss for Storage
- Install the required dependencies:
```
pip install faiss-cpu
```
You can also install `faiss-gpu` if you have GPU support.
- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
```python
async def embedding_func(texts: list[str]) -> np.ndarray:
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(texts, convert_to_numpy=True)
return embeddings
# Initialize LightRAG with the LLM model function and embedding function
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=llm_model_func,
embedding_func=EmbeddingFunc(
embedding_dim=384,
max_token_size=8192,
func=embedding_func,
),
vector_storage="FaissVectorDBStorage",
vector_db_storage_cls_kwargs={
"cosine_better_than_threshold": 0.3 # Your desired threshold
}
)
```
## Edit Entities and Relations
LightRAG now supports comprehensive knowledge graph management capabilities, allowing you to create, edit, and delete entities and relationships within your knowledge graph.
Create Entities and Relations
```python
# Create new entity
entity = rag.create_entity("Google", {
"description": "Google is a multinational technology company specializing in internet-related services and products.",
"entity_type": "company"
})
# Create another entity
product = rag.create_entity("Gmail", {
"description": "Gmail is an email service developed by Google.",
"entity_type": "product"
})
# Create relation between entities
relation = rag.create_relation("Google", "Gmail", {
"description": "Google develops and operates Gmail.",
"keywords": "develops operates service",
"weight": 2.0
})
```
Edit Entities and Relations
```python
# Edit an existing entity
updated_entity = rag.edit_entity("Google", {
"description": "Google is a subsidiary of Alphabet Inc., founded in 1998.",
"entity_type": "tech_company"
})
# Rename an entity (with all its relationships properly migrated)
renamed_entity = rag.edit_entity("Gmail", {
"entity_name": "Google Mail",
"description": "Google Mail (formerly Gmail) is an email service."
})
# Edit a relation between entities
updated_relation = rag.edit_relation("Google", "Google Mail", {
"description": "Google created and maintains Google Mail service.",
"keywords": "creates maintains email service",
"weight": 3.0
})
```
All operations are available in both synchronous and asynchronous versions. The asynchronous versions have the prefix "a" (e.g., `acreate_entity`, `aedit_relation`).
Insert Custom KG
```python
custom_kg = {
"chunks": [
{
"content": "Alice and Bob are collaborating on quantum computing research.",
"source_id": "doc-1",
"file_path": "test_file",
}
],
"entities": [
{
"entity_name": "Alice",
"entity_type": "person",
"description": "Alice is a researcher specializing in quantum physics.",
"source_id": "doc-1",
"file_path": "test_file"
},
{
"entity_name": "Bob",
"entity_type": "person",
"description": "Bob is a mathematician.",
"source_id": "doc-1",
"file_path": "test_file"
},
{
"entity_name": "Quantum Computing",
"entity_type": "technology",
"description": "Quantum computing utilizes quantum mechanical phenomena for computation.",
"source_id": "doc-1",
"file_path": "test_file"
}
],
"relationships": [
{
"src_id": "Alice",
"tgt_id": "Bob",
"description": "Alice and Bob are research partners.",
"keywords": "collaboration research",
"weight": 1.0,
"source_id": "doc-1",
"file_path": "test_file"
},
{
"src_id": "Alice",
"tgt_id": "Quantum Computing",
"description": "Alice conducts research on quantum computing.",
"keywords": "research expertise",
"weight": 1.0,
"source_id": "doc-1",
"file_path": "test_file"
},
{
"src_id": "Bob",
"tgt_id": "Quantum Computing",
"description": "Bob researches quantum computing.",
"keywords": "research application",
"weight": 1.0,
"source_id": "doc-1",
"file_path": "test_file"
}
]
}
rag.insert_custom_kg(custom_kg)
```
Other Entity and Relation Operations
- **create_entity**: Creates a new entity with specified attributes
- **edit_entity**: Updates an existing entity's attributes or renames it
- **create_relation**: Creates a new relation between existing entities
- **edit_relation**: Updates an existing relation's attributes
These operations maintain data consistency across both the graph database and vector database components, ensuring your knowledge graph remains coherent.
## Delete Functions
LightRAG provides comprehensive deletion capabilities, allowing you to delete documents, entities, and relationships.
Delete Entities
You can delete entities by their name along with all associated relationships:
```python
# Delete entity and all its relationships (synchronous version)
rag.delete_by_entity("Google")
# Asynchronous version
await rag.adelete_by_entity("Google")
```
When deleting an entity:
- Removes the entity node from the knowledge graph
- Deletes all associated relationships
- Removes related embedding vectors from the vector database
- Maintains knowledge graph integrity
Delete Relations
You can delete relationships between two specific entities:
```python
# Delete relationship between two entities (synchronous version)
rag.delete_by_relation("Google", "Gmail")
# Asynchronous version
await rag.adelete_by_relation("Google", "Gmail")
```
When deleting a relationship:
- Removes the specified relationship edge
- Deletes the relationship's embedding vector from the vector database
- Preserves both entity nodes and their other relationships
Delete by Document ID
You can delete an entire document and all its related knowledge through document ID:
```python
# Delete by document ID (asynchronous version)
await rag.adelete_by_doc_id("doc-12345")
```
Optimized processing when deleting by document ID:
- **Smart Cleanup**: Automatically identifies and removes entities and relationships that belong only to this document
- **Preserve Shared Knowledge**: If entities or relationships exist in other documents, they are preserved and their descriptions are rebuilt
- **Cache Optimization**: Clears related LLM cache to reduce storage overhead
- **Incremental Rebuilding**: Reconstructs affected entity and relationship descriptions from remaining documents
The deletion process includes:
1. Delete all text chunks related to the document
2. Identify and delete entities and relationships that belong only to this document
3. Rebuild entities and relationships that still exist in other documents
4. Update all related vector indexes
5. Clean up document status records
Note: Deletion by document ID is an asynchronous operation as it involves complex knowledge graph reconstruction processes.
**Important Reminders:**
1. **Irreversible Operations**: All deletion operations are irreversible, please use with caution
2. **Performance Considerations**: Deleting large amounts of data may take some time, especially deletion by document ID
3. **Data Consistency**: Deletion operations automatically maintain consistency between the knowledge graph and vector database
4. **Backup Recommendations**: Consider backing up data before performing important deletion operations
**Batch Deletion Recommendations:**
- For batch deletion operations, consider using asynchronous methods for better performance
- For large-scale deletions, consider processing in batches to avoid excessive system load
## Entity Merging
Merge Entities and Their Relationships
LightRAG now supports merging multiple entities into a single entity, automatically handling all relationships:
```python
# Basic entity merging
rag.merge_entities(
source_entities=["Artificial Intelligence", "AI", "Machine Intelligence"],
target_entity="AI Technology"
)
```
With custom merge strategy:
```python
# Define custom merge strategy for different fields
rag.merge_entities(
source_entities=["John Smith", "Dr. Smith", "J. Smith"],
target_entity="John Smith",
merge_strategy={
"description": "concatenate", # Combine all descriptions
"entity_type": "keep_first", # Keep the entity type from the first entity
"source_id": "join_unique" # Combine all unique source IDs
}
)
```
With custom target entity data:
```python
# Specify exact values for the merged entity
rag.merge_entities(
source_entities=["New York", "NYC", "Big Apple"],
target_entity="New York City",
target_entity_data={
"entity_type": "LOCATION",
"description": "New York City is the most populous city in the United States.",
}
)
```
Advanced usage combining both approaches:
```python
# Merge company entities with both strategy and custom data
rag.merge_entities(
source_entities=["Microsoft Corp", "Microsoft Corporation", "MSFT"],
target_entity="Microsoft",
merge_strategy={
"description": "concatenate", # Combine all descriptions
"source_id": "join_unique" # Combine source IDs
},
target_entity_data={
"entity_type": "ORGANIZATION",
}
)
```
When merging entities:
* All relationships from source entities are redirected to the target entity
* Duplicate relationships are intelligently merged
* Self-relationships (loops) are prevented
* Source entities are removed after merging
* Relationship weights and attributes are preserved
## Multimodal Document Processing (RAG-Anything Integration)
LightRAG now seamlessly integrates with [RAG-Anything](https://github.com/HKUDS/RAG-Anything), a comprehensive **All-in-One Multimodal Document Processing RAG system** built specifically for LightRAG. RAG-Anything enables advanced parsing and retrieval-augmented generation (RAG) capabilities, allowing you to handle multimodal documents seamlessly and extract structured content—including text, images, tables, and formulas—from various document formats for integration into your RAG pipeline.
**Key Features:**
- **End-to-End Multimodal Pipeline**: Complete workflow from document ingestion and parsing to intelligent multimodal query answering
- **Universal Document Support**: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats
- **Specialized Content Analysis**: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types
- **Multimodal Knowledge Graph**: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding
- **Hybrid Intelligent Retrieval**: Advanced search capabilities spanning textual and multimodal content with contextual understanding
**Quick Start:**
1. Install RAG-Anything:
```bash
pip install raganything
```
2. Process multimodal documents:
RAGAnything Usage Example
```python
import asyncio
from raganything import RAGAnything
from lightrag import LightRAG
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
from lightrag.utils import EmbeddingFunc
import os
async def load_existing_lightrag():
# First, create or load an existing LightRAG instance
lightrag_working_dir = "./existing_lightrag_storage"
# Check if previous LightRAG instance exists
if os.path.exists(lightrag_working_dir) and os.listdir(lightrag_working_dir):
print("✅ Found existing LightRAG instance, loading...")
else:
print("❌ No existing LightRAG instance found, will create new one")
# Create/Load LightRAG instance with your configurations
lightrag_instance = LightRAG(
working_dir=lightrag_working_dir,
llm_model_func=lambda prompt, system_prompt=None, history_messages=[], **kwargs: openai_complete_if_cache(
"gpt-4o-mini",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
api_key="your-api-key",
**kwargs,
),
embedding_func=EmbeddingFunc(
embedding_dim=3072,
max_token_size=8192,
func=lambda texts: openai_embed(
texts,
model="text-embedding-3-large",
api_key=api_key,
base_url=base_url,
),
)
)
# Initialize storage (this will load existing data if available)
await lightrag_instance.initialize_storages()
# Now initialize RAGAnything with the existing LightRAG instance
rag = RAGAnything(
lightrag=lightrag_instance, # Pass the existing LightRAG instance
# Only need vision model for multimodal processing
vision_model_func=lambda prompt, system_prompt=None, history_messages=[], image_data=None, **kwargs: openai_complete_if_cache(
"gpt-4o",
"",
system_prompt=None,
history_messages=[],
messages=[
{"role": "system", "content": system_prompt} if system_prompt else None,
{"role": "user", "content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}}
]} if image_data else {"role": "user", "content": prompt}
],
api_key="your-api-key",
**kwargs,
) if image_data else openai_complete_if_cache(
"gpt-4o-mini",
prompt,
system_prompt=system_prompt,
history_messages=history_messages,
api_key="your-api-key",
**kwargs,
)
# Note: working_dir, llm_model_func, embedding_func, etc. are inherited from lightrag_instance
)
# Query the existing knowledge base
result = await rag.query_with_multimodal(
"What data has been processed in this LightRAG instance?",
mode="hybrid"
)
print("Query result:", result)
# Add new multimodal documents to the existing LightRAG instance
await rag.process_document_complete(
file_path="path/to/new/multimodal_document.pdf",
output_dir="./output"
)
if __name__ == "__main__":
asyncio.run(load_existing_lightrag())
```
For detailed documentation and advanced usage, please refer to the [RAG-Anything repository](https://github.com/HKUDS/RAG-Anything).
## Token Usage Tracking
Overview and Usage
LightRAG provides a TokenTracker tool to monitor and manage token consumption by large language models. This feature is particularly useful for controlling API costs and optimizing performance.
### Usage
```python
from lightrag.utils import TokenTracker
# Create TokenTracker instance
token_tracker = TokenTracker()
# Method 1: Using context manager (Recommended)
# Suitable for scenarios requiring automatic token usage tracking
with token_tracker:
result1 = await llm_model_func("your question 1")
result2 = await llm_model_func("your question 2")
# Method 2: Manually adding token usage records
# Suitable for scenarios requiring more granular control over token statistics
token_tracker.reset()
rag.insert()
rag.query("your question 1", param=QueryParam(mode="naive"))
rag.query("your question 2", param=QueryParam(mode="mix"))
# Display total token usage (including insert and query operations)
print("Token usage:", token_tracker.get_usage())
```
### Usage Tips
- Use context managers for long sessions or batch operations to automatically track all token consumption
- For scenarios requiring segmented statistics, use manual mode and call reset() when appropriate
- Regular checking of token usage helps detect abnormal consumption early
- Actively use this feature during development and testing to optimize production costs
### Practical Examples
You can refer to these examples for implementing token tracking:
- `examples/lightrag_gemini_track_token_demo.py`: Token tracking example using Google Gemini model
- `examples/lightrag_siliconcloud_track_token_demo.py`: Token tracking example using SiliconCloud model
These examples demonstrate how to effectively use the TokenTracker feature with different models and scenarios.
## Data Export Functions
### Overview
LightRAG allows you to export your knowledge graph data in various formats for analysis, sharing, and backup purposes. The system supports exporting entities, relations, and relationship data.
### Export Functions
Basic Usage
```python
# Basic CSV export (default format)
rag.export_data("knowledge_graph.csv")
# Specify any format
rag.export_data("output.xlsx", file_format="excel")
```
Different File Formats supported
```python
#Export data in CSV format
rag.export_data("graph_data.csv", file_format="csv")
# Export data in Excel sheet
rag.export_data("graph_data.xlsx", file_format="excel")
# Export data in markdown format
rag.export_data("graph_data.md", file_format="md")
# Export data in Text
rag.export_data("graph_data.txt", file_format="txt")
```
Additional Options
Include vector embeddings in the export (optional):
```python
rag.export_data("complete_data.csv", include_vector_data=True)
```
### Data Included in Export
All exports include:
* Entity information (names, IDs, metadata)
* Relation data (connections between entities)
* Relationship information from vector database
## Cache
Clear Cache
You can clear the LLM response cache with different modes:
```python
# Clear all cache
await rag.aclear_cache()
# Clear local mode cache
await rag.aclear_cache(modes=["local"])
# Clear extraction cache
await rag.aclear_cache(modes=["default"])
# Clear multiple modes
await rag.aclear_cache(modes=["local", "global", "hybrid"])
# Synchronous version
rag.clear_cache(modes=["local"])
```
Valid modes are:
- `"default"`: Extraction cache
- `"naive"`: Naive search cache
- `"local"`: Local search cache
- `"global"`: Global search cache
- `"hybrid"`: Hybrid search cache
- `"mix"`: Mix search cache
## Troubleshooting
### Common Initialization Errors
If you encounter these errors when using LightRAG:
1. **`AttributeError: __aenter__`**
- **Cause**: Storage backends not initialized
- **Solution**: Call `await rag.initialize_storages()` after creating the LightRAG instance
2. **`KeyError: 'history_messages'`**
- **Cause**: Pipeline status not initialized
- **Solution**: Call `await initialize_pipeline_status()` after initializing storages
3. **Both errors in sequence**
- **Cause**: Neither initialization method was called
- **Solution**: Always follow this pattern:
```python
rag = LightRAG(...)
await rag.initialize_storages()
await initialize_pipeline_status()
```
### Model Switching Issues
When switching between different embedding models, you must clear the data directory to avoid errors. The only file you may want to preserve is `kv_store_llm_response_cache.json` if you wish to retain the LLM cache.
## LightRAG API
The LightRAG Server is designed to provide Web UI and API support. **For more information about LightRAG Server, please refer to [LightRAG Server](./lightrag/api/README.md).**
## Graph Visualization
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](./lightrag/api/README.md).**
## Evaluation
### Dataset
The dataset used in LightRAG can be downloaded from [TommyChien/UltraDomain](https://huggingface.co/datasets/TommyChien/UltraDomain).
### Generate Query
LightRAG uses the following prompt to generate high-level queries, with the corresponding code in `example/generate_query.py`.
Prompt
```python
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 `reproduce/batch_eval.py`.
Prompt
```python
---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
```python
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
```python
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
```python
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
```python
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
```
## 🔗 Related Projects
*Ecosystem & Extensions*
We thank all our contributors for their valuable contributions.
---
## 📖 Citation
```python
@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}
}
```
---
