Embedding Search (Index)¶
This page is the entry point to NornicDB's embedding and vector-search documentation.
Start here¶
- Embedding Search Overview — overview + key concepts.
- Embedding & Search Architecture — data model + execution paths.
- Embedding & Search Flow Diagrams — Mermaid diagrams.
- Embedding & Search Examples — end-to-end examples.
User-facing guides¶
- Vector Embeddings — embedding generation.
- Vector Search — hybrid search and RRF usage.
- Qdrant gRPC — Qdrant compatibility layer.
Agent-ready skills¶
- Managed Embeddings —
WITH EMBEDDING,db.index.vector.embed, provider config. - Vector & Full-Text Search —
CREATE/DROP VECTOR INDEX,db.index.vector.queryNodes. - RAG Procedures —
db.retrieve,db.rerank,db.infer. - gRPC (Qdrant + NornicSearch) — gRPC surface for vector ingestion and hybrid search.
Implementation references¶
pkg/search/search.go— unified search service + vector pipeline selection.pkg/qdrantgrpc/points_service.go— Qdrant Points API mapping.