Usage

import asyncio
import cognee
from cognee.infrastructure.databases.graph import get_graph_engine
import pathlib
import os
import pprint
import cognee_agensgraph

async def main():
    # Set up agensgraph credentials in .env file and get the values from environment variables
    agensgraph_url = os.getenv("GRAPH_DATABASE_URL")

    # Configure agensgraph as the graph database provider
    cognee.config.set_graph_db_config(
        {
            "graph_database_url": agensgraph_url,  # agensgraph connection DSN
            "graph_database_provider": "agensgraph",  # Specify agensgraph as provider
        }
    )
    
    # Optional: Set custom data and system directories
    system_path = pathlib.Path(__file__).parent
    cognee.config.system_root_directory(os.path.join(system_path, ".cognee_system"))
    cognee.config.data_root_directory(os.path.join(system_path, ".data_storage"))
    
    # Sample data to add to the knowledge graph
    sample_data = [
        "Artificial intelligence is a branch of computer science that aims to create intelligent machines.",
        "Machine learning is a subset of AI that focuses on algorithms that can learn from data.",
        "Deep learning is a subset of machine learning that uses neural networks with many layers.",
        "Natural language processing enables computers to understand and process human language.",
        "Computer vision allows machines to interpret and make decisions based on visual information."
    ]
    
    try:
        print("Adding data to Cognee...")
        await cognee.add(sample_data, "ai_knowledge")
        
        print("Processing data with Cognee...")
        await cognee.cognify(["ai_knowledge"])
        
        print("Searching for insights...")
        search_results = await cognee.search(
            query_type=cognee.SearchType.GRAPH_COMPLETION,
            query_text="artificial intelligence"
        )
        
        print(f"Found {len(search_results)} insights:")
        for i, result in enumerate(search_results, 1):
            print(f"{i}. {result}")
            
        print("\nSearching with Chain of Thought reasoning...")
        await cognee.search(
            query_type=cognee.SearchType.GRAPH_COMPLETION_COT,
            query_text="How does machine learning relate to artificial intelligence and what are its applications?"
        )

        print("\nYou can get the graph data directly, or visualize it in an HTML file like below:")
        
        # Get graph data directly
        graph_engine = await get_graph_engine()
        graph_data = await graph_engine.get_graph_data()
        
        print("\nDirect graph data:")
        pprint.pprint(graph_data)

        # Or visualize it in HTML
        print("\nVisualizing the graph...")
        await cognee.visualize_graph(system_path / "graph.html")
        print(f"Graph visualization saved to {system_path / 'graph.html'}")

    except Exception as e:
        print(f"Error: {e}")
        print("Make sure AgensGraph is running and your DSN is correct.")

if __name__ == "__main__":
    asyncio.run(main())

Configuration

The adapter requires the following configuration using the set_graph_db_config() method:

cognee.config.set_graph_db_config({
    "graph_database_url": "postgresql://username:password@host:port/dbname",
    "graph_database_provider": "agensgraph",
})

Environment Variables

Set the following environment variables or pass them directly in the config:

export GRAPH_DATABASE_URL="postgresql://username:password@host:port/dbname"

Alternative: You can also use the .env.template file from the main cognee repository. Copy it to your project directory, rename it to .env, and fill in your AgensGraph configuration values.

Optional Configuration

You can also set custom directories for system and data storage:

cognee.config.system_root_directory("/path/to/system")
cognee.config.data_root_directory("/path/to/data")

Example

See examples for a complete working example that demonstrates:

• Setting up the AgensGraph adapter

• Adding comprehensive AI/ML knowledge to the graph

• Processing data with cognee

• Searching with graph completion

• Chain of Thought reasoning searches

• Direct graph data access and inspection

• Comprehensive error handling