Knowledge Graph Integration

Knowledge Graph Integration

SynaptiQ Systems supports the integration of knowledge graphs to enable agents to store, query, and reason about structured information. This feature enhances the decision-making capabilities of agents by providing a persistent and queryable knowledge base.

Key Features

• Concept Storage: Agents can store concepts, attributes, and relationships in a graph structure.

• Reasoning and Querying: Agents can query the knowledge graph to retrieve relevant information or relationships.

• Visualization: The knowledge graph can be visualized for better understanding and debugging.

---

Example Workflow

1. Add Knowledge to the Graph

Code Example:

pythonCopy codefrom 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

Code Example:

pythonCopy code# 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

Code Example:

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

---

Benefits of Knowledge Graphs in SynaptiQ Systems

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

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

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

---

Best Practices

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

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

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