Integrate vector search with knowledge graph traversal for hybrid retrieval.
npx skills add https://github.com/lofibrainwav/AFO_Kingdom --skill hybrid-graphrag
Please help me install this Agent Skill. Skill: hybrid-graphrag Source: https://github.com/lofibrainwav/AFO_Kingdom/tree/main/skills/hybrid-graphrag Command: npx skills add https://github.com/lofibrainwav/AFO_Kingdom --skill hybrid-graphrag
This Skill combines vector search with knowledge graph traversal to deliver richer, more accurate context for AI answers.
result = hybrid_graphrag.query("Explain Trinity Score pillars with entities from the knowledge graph.")
High-intent search queries and answers about installing and using this skill.
Hybrid vector and graph search merges semantic similarity from vector embeddings with structured relationship traversal through knowledge graphs, delivering richer context by capturing both unstructured meaning and explicit entity connections for multi-hop retrieval.
Yes. This Skill integrates vector search engines like Qdrant with graph databases like Neo4j to execute hybrid queries, enabling simultaneous semantic and relational lookups across enterprise data, research literature, and product catalogs.
Entity extraction identifies key concepts while relationship mapping documents their connections within a knowledge graph, enabling structured reasoning and improving answer accuracy when combined with vector-based semantic search.
Use hybrid RAG when your data includes both unstructured text and structured relationships—such as enterprise systems with linked entities, research networks, or product hierarchies—where multi-hop reasoning and explicit relationship context improve retrieval quality.
Enterprise data systems, research literature networks, and product catalogs benefit most because they contain interconnected entities with semantic meaning; hybrid search captures both implicit similarity and explicit relationships for comprehensive context synthesis.
neo4j-contribIngest PDFs and text into Neo4j knowledge graphs via SimpleKGPipeline.
pax-kLocate wiki pages and frontmatter via index-based retrieval and answer with citations.
neo4j-contribCreate Neo4j vector indexes and query nearest neighbors via SEARCH clause.
mwathibenDesign hybrid retrieval fusing vector similarity with keyword matching.
