Knowledge Graph Integration

Example Workflow

1. Add Knowledge to the Graph

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   from src.utils.knowledge_graph import KnowledgeGraph
   
   # Initialize the Knowledge Graph
   knowledge_graph = KnowledgeGraph()
   
   # Add a concept
   knowledge_graph.add_concept("AI Agent", {"role": "worker", "status": "active"})
   
   # Add a relationship between concepts
   knowledge_graph.add_relationship("AI Agent", "Swarm", "belongs_to")

2. Query the Knowledge Graph

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   # Query a concept
   result = knowledge_graph.query_concept("AI Agent")
   print(f"Attributes of AI Agent: {result}")
   
   # Query relationships
   relationships = knowledge_graph.query_relationships("AI Agent")
   print(f"Relationships of AI Agent: {relationships}")

3. Visualize the Knowledge Graph

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   # Visualize the graph
   knowledge_graph.visualize_graph(output_path="knowledge_graph.png")
   print("Knowledge graph saved as knowledge_graph.png")

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Benefits of Knowledge Graphs in Wingman

1. Enhanced Reasoning Agents can use structured knowledge to make more informed decisions.

2. Collaboration Agents can share knowledge across the swarm, improving collective performance.

3. Persistent Memory Knowledge graphs serve as a long-term memory for agents.

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Best Practices

1. Use Attributes Effectively Add meaningful attributes to concepts for better querying and reasoning.

2. Structure Relationships Clearly Define relationships that reflect real-world connections (e.g., "belongs_to", "depends_on").

3. Regular Updates Periodically update the graph to reflect the latest knowledge and task history.