🤖 AI/ML Advanced ⏱️ 11 min

Hybrid Retrieval with Graph Filters: FalkorDB + Qdrant in GraphRAG

A production blueprint for combining graph traversal and vector search to improve recall and precision in medical document retrieval.

By Victor Robin • • Updated:
graphragqdrantfalkordbhybrid-searchensembling

Introduction

Vector search alone can miss structural relationships, while graph search alone can miss semantic nuance. BlueRobin’s GraphRAG path combines both to retrieve context that is both meaningful and connected.

What Changed in the Codebase

  • 35a98e7: Introduced GraphRAG hybrid retriever and graph orchestration nodes.
  • 64a1c53 (infra): Fixed Qdrant endpoint to REST port 6333 for stable runtime integration.
  • f05595f (infra): Added full GraphRAG deployment overlays.

Retrieval Flow

  1. Parse the user query into semantic + entity-focused intents.
  2. Use graph traversal to discover related entities and neighborhoods.
  3. Query Qdrant with enriched terms.
  4. Fuse candidate lists and rank final contexts.

Practical Benefits

  • Better retrieval when documents reference the same concept using different vocabulary.
  • Reduced hallucination risk because the answer context is graph-constrained.
  • Cleaner explainability: you can show both vector score and graph path.

Conclusion

Graph-filtered vector retrieval is a strong middle ground between pure vector RAG and expensive symbolic-only reasoning. It improves context quality without requiring a full architecture rewrite.

Related reading:

  • /hybrid-search-semantic-keyword/
  • /falkordb-graph-database-knowledge-graph/