AI applicationsTools and integrationsCognee

Cognee

Cognee is a framework for AI memory and knowledge graphs. It uses an LLM to extract concepts and relationships from text, stores them as a knowledge graph and searches them. The Memgraph adapter, from the Cognee community packages, stores the graph in Memgraph. You can also follow the Jupyter notebook example.

Installation

Start Memgraph, for example in Docker:

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

Install Cognee with the Memgraph adapter:

pip install cognee cognee-community-graph-adapter-memgraph

Cognee calls an LLM to extract the graph. Set your LLM API key, for example LLM_API_KEY for OpenAI, as described in the Cognee docs.

Use Cognee with Memgraph

Register the adapter, point Cognee at Memgraph, then add text, build the graph and search it:

import asyncio
import cognee
from cognee_community_graph_adapter_memgraph import register
 
 
async def main():
    # Register the Memgraph adapter and use it as the graph database
    register()
    cognee.config.set_graph_database_provider("memgraph")
    cognee.config.set_graph_db_config(
        {
            "graph_database_url": "bolt://localhost:7687",
            "graph_database_username": "",
            "graph_database_password": "",
        }
    )
 
    text = "Natural language processing (NLP) is a subfield of computer science focused on the interaction between computers and human language."
 
    # Store the text
    await cognee.add(text)
 
    # Extract a knowledge graph with the LLM and store it in Memgraph
    await cognee.cognify()
 
    # Search the graph
    results = await cognee.search(
        query_type=cognee.SearchType.GRAPH_COMPLETION, query_text="What is NLP?"
    )
    for result in results:
        print(result)
 
 
asyncio.run(main())

How it works

  • cognee.add() stores the input text.
  • cognee.cognify() extracts concepts and relationships with the LLM and stores them in Memgraph as a graph.
  • cognee.search() answers the question from the graph, with GRAPH_COMPLETION combining graph context and the LLM.

For more examples, see the adapter README.

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