Guide
Vector indexes rank node or edge embeddings by distance. Every definition requires a
non-zero dimension and a distance metric.
Supported metrics
- Cosine
- Euclidean
- Manhattan
Create a vector index
Search
Start with the SDK’s vector-search operation for labelDoc, property embedding,
a query vector, and a result limit of 10. $distance is available on the hit
stream; project it before traversing away from the hit if it must remain in the
response.
Result limits
The server caps unrestricted vector search at 800 effective results per request. A narrower access bound from the surrounding plan can reduce this value. Traversal-scoped vector search checks the effective count after candidate intersection. It rejects the request whenmin(k, unique candidates) is greater than 800; it does not silently reduce k.
A request with k above 800 can succeed when the candidate stream contains 800 or fewer unique
entities. The candidate stream cannot contain more than 1,000,000 unique entities.
Operational notes
- Creation returns before the backfill necessarily finishes.
- Malformed source vectors can block the operation.
- A new generation remains hidden until validation and activation succeed.
- Dropping an index is also a durable lifecycle operation.
Next steps
Project search results
Preserve ranked hit metadata before continuing a traversal.
Vector prefiltering
Rank only an exact traversal-defined candidate set.
Troubleshoot index operations
Resolve blocked builds and lifecycle errors.