HKUDS/LightRAG - Githubissues

🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation

LightRAG Image

This repository hosts the code of LightRAG. The structure of this code is based on [nano-graphrag](https://github.com/gusye1234/nano-graphrag). ![LightRAG Diagram](https://i-blog.csdnimg.cn/direct/b2aaf634151b4706892693ffb43d9093.png)

🎉 News

[x] [2024.11.19]🎯📢We have added a detailed blog link introducing LightRAG on LearnOpenCV. Many thanks to the blog author!
[x] [2024.11.12]🎯📢You can use Oracle Database 23ai for all storage types (kv/vector/graph) now.
[x] [2024.11.11]🎯📢LightRAG now supports deleting entities by their names.
[x] [2024.11.09]🎯📢Introducing the LightRAG Gui, which allows you to insert, query, visualize, and download LightRAG knowledge.
[x] [2024.11.04]🎯📢You can now use 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. Thanks to the author!
[x] [2024.10.17]🎯📢We have created a Discord channel! Welcome to join for sharing and discussions! 🎉🎉
[x] [2024.10.16]🎯📢LightRAG now supports Ollama models!
[x] [2024.10.15]🎯📢LightRAG now supports Hugging Face models!

Algorithm Flowchart

Figure 1: LightRAG Indexing Flowchart Figure 2: LightRAG Retrieval and Querying Flowchart

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")))

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=[], **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: ```python 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: ```python 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" ) ), ) ``` ### 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" When you launch the project be sure to override the default KG: NetworkS 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 kg="Neo4JStorage", #<-----------override KG default log_level="DEBUG" #<-----------override log_level default ) ``` see test_neo4j.py for a working example. ### 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_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"] = "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

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

Incremental Insert

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

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` ```python 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` ```python 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() ```

API Server Implementation

LightRAG also provides a FastAPI-based server implementation for RESTful API access to RAG operations. This allows you to run LightRAG as a service and interact with it through HTTP requests.

Setting up the API Server

Click to expand setup instructions

1. First, ensure you have the required dependencies: ```bash pip install fastapi uvicorn pydantic ``` 2. Set up your environment variables: ```bash export RAG_DIR="your_index_directory" # Optional: Defaults to "index_default" export OPENAI_BASE_URL="Your OpenAI API base URL" # Optional: Defaults to "https://api.openai.com/v1" export OPENAI_API_KEY="Your OpenAI API key" # Required export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini" export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large" ``` 3. Run the API server: ```bash python examples/lightrag_api_openai_compatible_demo.py ``` The server will start on `http://0.0.0.0:8020`.

API Endpoints

The API server provides the following endpoints:

1. Query Endpoint

Click to view Query endpoint details

- **URL:** `/query` - **Method:** POST - **Body:** ```json { "query": "Your question here", "mode": "hybrid", // Can be "naive", "local", "global", or "hybrid" "only_need_context": true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned } ``` - **Example:** ```bash curl -X POST "http://127.0.0.1:8020/query" \ -H "Content-Type: application/json" \ -d '{"query": "What are the main themes?", "mode": "hybrid"}' ```

2. Insert Text Endpoint

Click to view Insert Text endpoint details

- **URL:** `/insert` - **Method:** POST - **Body:** ```json { "text": "Your text content here" } ``` - **Example:** ```bash curl -X POST "http://127.0.0.1:8020/insert" \ -H "Content-Type: application/json" \ -d '{"text": "Content to be inserted into RAG"}' ```

3. Insert File Endpoint

Click to view Insert File endpoint details

- **URL:** `/insert_file` - **Method:** POST - **Body:** ```json { "file_path": "path/to/your/file.txt" } ``` - **Example:** ```bash curl -X POST "http://127.0.0.1:8020/insert_file" \ -H "Content-Type: application/json" \ -d '{"file_path": "./book.txt"}' ```

4. Health Check Endpoint

Click to view Health Check endpoint details

- **URL:** `/health` - **Method:** GET - **Example:** ```bash curl -X GET "http://127.0.0.1:8020/health" ```

Configuration

The API server can be configured using environment variables:

RAG_DIR: Directory for storing the RAG index (default: "index_default")
API keys and base URLs should be configured in the code for your specific LLM and embedding model providers

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

```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 example/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 ```

Code Structure

.
├── examples
│   ├── batch_eval.py
│   ├── generate_query.py
│   ├── graph_visual_with_html.py
│   ├── graph_visual_with_neo4j.py
│   ├── lightrag_api_openai_compatible_demo.py
│   ├── lightrag_azure_openai_demo.py
│   ├── lightrag_bedrock_demo.py
│   ├── lightrag_hf_demo.py
│   ├── lightrag_lmdeploy_demo.py
│   ├── lightrag_ollama_demo.py
│   ├── lightrag_openai_compatible_demo.py
│   ├── lightrag_openai_demo.py
│   ├── lightrag_siliconcloud_demo.py
│   └── vram_management_demo.py
├── lightrag
│   ├── kg
│   │   ├── __init__.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
├── Dockerfile
├── get_all_edges_nx.py
├── LICENSE
├── README.md
├── requirements.txt
├── setup.py
├── test_neo4j.py
└── test.py

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}
}

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HKUDS / LightRAG

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🚀 LightRAG: Simple and Fast Retrieval-Augmented Generation

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Algorithm Flowchart

Install

Quick Start

Query Param

Batch Insert

Incremental Insert

Delete Entity

Multi-file Type Support

Graph Visualization

API Server Implementation

Setting up the API Server

API Endpoints

1. Query Endpoint

2. Insert Text Endpoint

3. Insert File Endpoint

4. Health Check Endpoint

Configuration

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

Star History

Contribution

🌟Citation