Skip to main content
Glama

cognee-mcp

weighted_edges_example.py4.12 kB
import asyncio from os import path from typing import Any from pydantic import SkipValidation from cognee.api.v1.visualize.visualize import visualize_graph from cognee.infrastructure.engine import DataPoint from cognee.infrastructure.engine.models.Edge import Edge from cognee.tasks.storage import add_data_points import cognee class Clothes(DataPoint): name: str description: str class Object(DataPoint): name: str description: str has_clothes: list[Clothes] class Person(DataPoint): name: str description: str has_items: SkipValidation[Any] # (Edge, list[Clothes]) has_objects: SkipValidation[Any] # (Edge, list[Object]) knows: SkipValidation[Any] # (Edge, list["Person"]) async def main(): # Clear the database for a clean state await cognee.prune.prune_data() await cognee.prune.prune_system(metadata=True) # Create clothes items item1 = Clothes(name="Shirt", description="A blue shirt") item2 = Clothes(name="Pants", description="Black pants") item3 = Clothes(name="Jacket", description="Leather jacket") # Create object with simple relationship to clothes object1 = Object( name="Closet", description="A wooden closet", has_clothes=[item1, item2, item3] ) # Create people with various weighted relationships person1 = Person( name="John", description="A software engineer", # Single weight (backward compatible) has_items=(Edge(weight=0.8, relationship_type="owns"), [item1, item2]), # Simple relationship without weights has_objects=(Edge(relationship_type="stores_in"), [object1]), knows=[], ) person2 = Person( name="Alice", description="A designer", # Multiple weights on edge has_items=( Edge( weights={ "ownership": 0.9, "frequency_of_use": 0.7, "emotional_attachment": 0.8, "monetary_value": 0.6, }, relationship_type="owns", ), [item3], ), has_objects=(Edge(relationship_type="uses"), [object1]), knows=[], ) person3 = Person( name="Bob", description="A friend", # Mixed: single weight + multiple weights has_items=( Edge( weight=0.5, # Default weight weights={"trust_level": 0.9, "communication_frequency": 0.6}, relationship_type="borrows", ), [item1], ), has_objects=[], knows=[], ) # Create relationships between people with multiple weights person1.knows = ( Edge( weights={ "friendship_strength": 0.9, "trust_level": 0.8, "years_known": 0.7, "shared_interests": 0.6, }, relationship_type="friend", ), [person2, person3], ) person2.knows = ( Edge( weights={"professional_collaboration": 0.8, "personal_friendship": 0.6}, relationship_type="colleague", ), [person1], ) all_data_points = [item1, item2, item3, object1, person1, person2, person3] # Add data points to the graph await add_data_points(all_data_points) # Visualize the graph graph_visualization_path = path.join( path.dirname(__file__), "weighted_graph_visualization.html" ) await visualize_graph(graph_visualization_path) print("Graph with multiple weighted edges has been created and visualized!") print(f"Visualization saved to: {graph_visualization_path}") print("\nFeatures demonstrated:") print("- Single weight edges (backward compatible)") print("- Multiple weights on single edges") print("- Mixed single + multiple weights") print("- Hover over edges to see all weight information") print("- Different visual styling for single vs. multiple weighted edges") if __name__ == "__main__": asyncio.run(main())

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/topoteretes/cognee'

If you have feedback or need assistance with the MCP directory API, please join our Discord server