NornicDB

Embedding Search Flow Diagrams ​

Visual representations of NornicDB's embedding search architecture.

Complete Search Flow ​

Vector Strategy Selection (GPU brute → HNSW) ​

Embedding Storage Model ​

Search Priority Order ​

Indexing Flow ​

API Request Flow ​

Collection Architecture ​

Hybrid Search (RRF) Flow ​

Cypher Vector Query Flow ​

Complete Embedding Search Coverage ​

NornicDB vs Pure Solutions ​

Key Takeaways ​

  1. All vector search entrypoints route through search.Service (including Cypher db.index.vector.queryNodes)
  2. Qdrant gRPC vectors live in NamedEmbeddings only; ChunkEmbeddings is reserved for chunked/managed embeddings
  3. The vector pipeline auto-selects GPU brute / CPU brute / global HNSW / K-means routing / IVF-HNSW depending on runtime + dataset state
  4. Cypher queryNodes uses indexed candidate generation + in-memory metadata to preserve embedding precedence (no storage scans in steady-state)
  5. Hybrid search combines vector similarity with BM25 keyword matching (RRF), then enriches results from storage
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