The GraphRAG pipeline operates in four steps: (1) Natural Language Processing and Hybrid Retrieval Strategy, where the system analyzes a user's natural language question to determine if a structured knowledge graph query is required; (2) Formal Query Code Generation, where the LLM reads the graph schema (ontology, entity types, and relationships) and generates the precise formal query code (e.g., Cypher or Gremlin) based on system prompts; (3) Query Execution and Result Return, where the knowledge graph engine performs structured traversal and multi-hop pathfinding to retrieve connected data points; and (4) Synthesis and Final Answer Generation, where the LLM uses the retrieved, verified, and structured results to formulate a coherent, context-rich, and grounded final answer.

Authors

• Person: Not available Organization: PuppyGraph
  LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph

Sources

• LLM Knowledge Graph: Merging AI with Structured Data - PuppyGraph www.puppygraph.com PuppyGraph via serper

Referenced by nodes (4)

• natural language processing concept
• ontology concept
• Cypher concept
• synthesis concept