AI applicationsTools and integrationsLightRAG

LightRAG

LightRAG is a retrieval-augmented generation (RAG) framework that extracts entities and relationships from your documents into a knowledge graph and retrieves from it to answer questions. It supports Memgraph as its graph storage. Unstructured2Graph uses LightRAG to build knowledge graphs in Memgraph.

Installation

Install LightRAG:

pip install lightrag-hku

Environment setup

Start Memgraph, for example in Docker:

docker run -p 7687:7687 memgraph/memgraph:latest

Set the connection details. These are the defaults for a local Memgraph without authentication:

export MEMGRAPH_URI="bolt://localhost:7687"
export MEMGRAPH_USERNAME=""
export MEMGRAPH_PASSWORD=""
export MEMGRAPH_DATABASE="memgraph"

The example uses OpenAI, so also set OPENAI_API_KEY.

Basic usage

This script inserts text, builds the graph in Memgraph and runs a hybrid query. graph_storage="MemgraphStorage" tells LightRAG to store the graph in Memgraph:

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,
        graph_storage="MemgraphStorage",  # store the graph in Memgraph
    )
    # Both initialization calls are required
    await rag.initialize_storages()
    await initialize_pipeline_status()
    return rag
 
async def main():
    rag = None
    try:
        rag = await initialize_rag()
        rag.insert("Your text")
 
        print(
          await rag.query(
              "What are the top themes in this story?",
              param=QueryParam(mode="hybrid")
          )
        )
 
    except Exception as e:
        print(f"An error occurred: {e}")
    finally:
        if rag:
            await rag.finalize_storages()
 
if __name__ == "__main__":
    asyncio.run(main())

Configuration

Workspace isolation: to keep the data of several LightRAG instances apart, set the MEMGRAPH_WORKSPACE environment variable:

export MEMGRAPH_WORKSPACE="project_a"

Configuration file: instead of environment variables, you can set the connection in a config.ini file:

[memgraph]
uri = bolt://localhost:7687
username =
password =
database = memgraph

For more, see the LightRAG integration in the Memgraph AI Toolkit, the Jupyter notebook example and the LightRAG repository.

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