| name | surrealdb-vector |
| description | Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance. |
| metadata | {"author":"surrealdb","version":"0.1.0"} |
SurrealDB Vector Search
HNSW Index
Create a basic HNSW index:
DEFINE INDEX hnsw_idx ON pts FIELDS point HNSW DIMENSION 4;
With specific distance function and type:
DEFINE INDEX hnsw_idx ON pts FIELDS point HNSW DIMENSION 4 DIST EUCLIDEAN TYPE F64;
Available types: F64, F32, I64, I32, I16.
Full Table Example
DEFINE TABLE OVERWRITE document SCHEMALESS;
DEFINE FIELD OVERWRITE embedding ON document TYPE array<float>;
DEFINE INDEX OVERWRITE hnsw_idx_document ON document
FIELDS embedding
HNSW DIMENSION 384
DIST COSINE
TYPE F32
EFC 150 M 12 M0 24;
HNSW Parameters
| Parameter | Description |
|---|
| DIMENSION | Vector dimensionality (must match your embeddings) |
| DIST | Distance function: COSINE, EUCLIDEAN, etc. |
| TYPE | Numeric type: F64, F32, I64, I32, I16 |
| EFC | Construction search effort (higher = better index) |
| M | Max connections per node |
| M0 | Max connections at layer 0 |
Querying Vectors
The <|K, EF|> operator performs KNN search. K is the number of results,
EF is the search effort (higher = more accurate, slower).
Recommended effort values:
40 — default, good accuracy17 — fast but may miss some results
Basic KNN Query
SELECT
*,
vector::distance::knn() AS dist
FROM document
WHERE embedding <|10, 40|> $vector;
vector::distance::knn() uses the distance function defined by the index.
Scored Results with Threshold
SELECT *, score
FROM (
SELECT *, (1 - vector::distance::knn()) AS score
FROM document
WHERE embedding <|20, 40|> $vector
)
WHERE score >= $threshold
ORDER BY score DESC;
