lightrag-memgraph 0.1.4

LightRAG integration with Memgraph

🔗 lightrag-memgraph

lightrag-memgraph is an integration that connects lightrag and memgraph. The library began as a small wrapper designed to specifically configure Memgraph within a pipeline that processes unstructured data (various texts) and transforms it into an ontology/entity schema graph. In other words, it enables you to extract and enhance entities from unstructured documents, storing them in a graph for powerful querying and analysis. Ideal for building knowledge graphs, improving data discovery, and leveraging advanced AI techniques on top of your domain data.

General Notes

• Entity/relationship extraction is high-quality, but also high-cost and relatively slow.
• The goal over time is to expose time and cost metrics (e.g., $ per your specific document page or chunk).

Quick start

Prerequisites: Memgraph running *(default bolt://localhost:7687), and an LLM API key (e.g. OPENAI_API_KEY or *ANTHROPIC_API_KEY).

Install:

pip install lightrag-memgraph

Minimal example (async): create the wrapper, initialize with a working *directory, insert text, then finalize.

import asyncio
from lightrag_memgraph import MemgraphLightRAGWrapper

async def main():
    wrapper = MemgraphLightRAGWrapper(disable_embeddings=True)
    await wrapper.initialize(working_dir="./lightrag_storage")
    await wrapper.ainsert(input="Your document text here.", file_paths=["doc1"])
    # optional: rag = wrapper.get_lightrag(); print(await rag.get_graph_labels())
    await wrapper.afinalize()

asyncio.run(main())

See example.py in this repo for a full run with sample texts and graph output.

Using Anthropic (Claude) as the LLM

LightRAG supports Claude via the lightrag.llm.anthropic module. Set your API key and pass the LLM function and model name when initializing the wrapper. The list of Anthropic models is available under https://platform.claude.com/docs/en/about-claude/models.

1. Set the API key (required for Claude):

   export ANTHROPIC_API_KEY="your-anthropic-api-key"
   

2. Use Anthropic in code by passing llm_model_func and llm_model_name to initialize():

   from lightrag.llm.anthropic import anthropic_complete
   from lightrag_memgraph import MemgraphLightRAGWrapper
   
   wrapper = MemgraphLightRAGWrapper(disable_embeddings=True)  # or set embedding_func
   await wrapper.initialize(
       working_dir="./lightrag_storage",
       llm_model_func=anthropic_complete,
       llm_model_name="claude-3-5-sonnet-20241022",  # or claude-3-haiku-20240307, etc.
   )
   

   Preset functions are also available: claude_3_opus_complete, claude_3_sonnet_complete, claude_3_haiku_complete (fixed older model IDs). For current models, use anthropic_complete with the desired llm_model_name.

3. Embeddings: Anthropic does not provide embeddings. Either use disable_embeddings=True (as above), or set embedding_func to another provider (e.g. openai_embed from lightrag.llm.openai with OPENAI_API_KEY, or Voyage AI via lightrag.llm.anthropic.anthropic_embed with VOYAGE_API_KEY).

Using OpenAI as the LLM

Set your API key and optionally choose a model.

1. Set the API key:

   export OPENAI_API_KEY="your-openai-api-key"
   

2. Use a specific OpenAI model by passing llm_model_func and optionally llm_model_name:

   from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed
   from lightrag_memgraph import MemgraphLightRAGWrapper
   
   wrapper = MemgraphLightRAGWrapper()
   await wrapper.initialize(
       working_dir="./lightrag_storage",
       llm_model_func=gpt_4o_mini_complete,
       embedding_func=openai_embed,
   )