NornicDB

Embedding Search Examples ​

Examples that show how embedding search works end-to-end, and which embedding field each API uses.

For full end-user docs, see:

  • Vector search guide: docs/user-guides/vector-search.md
  • Qdrant gRPC guide: docs/user-guides/qdrant-grpc.md

1) HTTP hybrid search (search.Service) ​

The HTTP search endpoint runs hybrid search (vector + BM25 with RRF fusion):

bash
curl -X POST http://localhost:7474/nornicdb/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "machine learning algorithms",
    "limit": 10
  }'

Notes:

  • Query text is embedded server-side (when embeddings are enabled).
  • Results come from the indexed search path in pkg/search/search.go.

2) Cypher vector search (db.index.vector.queryNodes) ​

Cypher’s Neo4j-compatible vector procedure currently scans nodes in storage and computes similarity:

  • Implementation: pkg/cypher/call_vector.go
  • Storage access: storage.Engine.AllNodes()

String query (server-side embedding) ​

cypher
CALL db.index.vector.queryNodes('embeddings', 10, 'machine learning algorithms')
YIELD node, score
RETURN node, score
ORDER BY score DESC

Vector array query (client-side embedding) ​

cypher
CALL db.index.vector.queryNodes('embeddings', 10, [0.1, 0.2, 0.3, 0.4])
YIELD node, score
RETURN node, score
ORDER BY score DESC

Property vectors vs. internal embeddings ​

db.index.vector.queryNodes chooses embeddings in this order:

  1. node.NamedEmbeddings[index.property] (or "default" when no property is configured)
  2. node.Properties[index.property] (if it contains a vector array)
  3. node.ChunkEmbeddings[0..N] (best score across chunks)

That means Cypher can always work with property vectors, even if you never use NamedEmbeddings.

3) Qdrant gRPC named vectors (NamedEmbeddings) ​

The Qdrant gRPC endpoint stores vectors in Node.NamedEmbeddings:

  • Unnamed vector → NamedEmbeddings["default"]
  • Named vectors → NamedEmbeddings[name]

Example (Python qdrant-client, gRPC):

python
from qdrant_client import QdrantClient
from qdrant_client.http import models as m

client = QdrantClient(host="127.0.0.1", grpc_port=6334, prefer_grpc=True)

client.create_collection(
    collection_name="docs",
    vectors_config={
        "title": m.VectorParams(size=128, distance=m.Distance.COSINE),
        "content": m.VectorParams(size=128, distance=m.Distance.COSINE),
    },
)

client.upsert(
    collection_name="docs",
    points=[
        m.PointStruct(
            id="doc1",
            vector={"title": [1.0] * 128, "content": [0.2] * 128},
            payload={"kind": "article"},
        )
    ],
)

results = client.search(
    collection_name="docs",
    query_vector=("title", [1.0] * 128),
    limit=5,
)
print([r.id for r in results])

4) Graph + vector together (Cypher) ​

Vector similarity plus graph traversal is the “hybrid” capability you don’t get in a pure vector DB:

cypher
CALL db.index.vector.queryNodes('embeddings', 10, 'neural networks')
YIELD node, score
MATCH (node)-[:CITES]->(cited:Paper)
RETURN node, cited, score
ORDER BY score DESC

5) Mapping summary ​

  • Managed embeddings / chunked docs → Node.ChunkEmbeddings
  • Qdrant vectors / named vectors → Node.NamedEmbeddings
  • Cypher property vectors → node.Properties[index.property]
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