graphrag-expert

To build a biomedical GraphRAG pipeline, combine vector similarity search with Cypher querying to retrieve 1-3 hop subgraphs. You must preserve evidence properties like pmid and gdaScore during context assembly to ensure non-hallucination and proper citation formatting.

The best way to generate Cypher queries is through prompt engineering that includes the full Neo4j schema. This enforces multi-hop traversal constraints and ensures the LLM produces valid queries for retrieving grounded evidence from the knowledge graph.

To ground LLM responses and prevent hallucination, assemble subgraph traversal results into LLM-ready context while preserving provenance fields. Apply citation-focused response formatting to ensure every output links back to original evidence.

Yes, hybrid retrieval works for mechanism-of-action questions by using vector entry discovery alongside structured Cypher generation. This combination identifies relevant entities and paths within the knowledge graph for evidence-grounded answers.

Preserving evidence properties like gdaScore, pmid, and source during context assembly maintains data provenance. This strict adherence to the Neo4j schema supports non-hallucination by providing verifiable citations for biomedical drug repurposing answers.