The AMG-RAG pipeline follows a specific procedure: (1) Question parsing, where an LLM agent extracts medical terms from the user query; (2) Node exploration, where the system queries the knowledge graph for each term using a confidence threshold to filter relationships; (3) Knowledge traversal, supporting both breadth-first and depth-first strategies until a cumulative confidence threshold or document limit is reached; (4) Chain-of-thought generation, which synthesizes reasoning traces for each entity by integrating information from connected nodes; (5) Answer synthesis, which aggregates reasoning traces to produce a final output with an associated confidence score.

Authors

• Person: Not available Organization: arXiv
  Bridging the Gap Between LLMs and Evolving Medical Knowledge

Sources

• Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org arXiv via serper

Referenced by nodes (1)

• AMG-RAG concept