surrealdb/agent-skills/surrealdb-vector

Claude Code and agent skill by surrealdb/agent-skills

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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.

Install

npx vskill@latest install surrealdb/agent-skills/surrealdb-vector

SKILL.md

SurrealDB Vector Search

HNSW Index

Create a basic HNSW index:

surql
DEFINE INDEX hnsw_idx ON pts FIELDS point HNSW DIMENSION 4;

With specific distance function and type:

surql
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
surql
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
ParameterDescription
DIMENSIONVector dimensionality (must match your embeddings)
DISTDistance function: COSINE, EUCLIDEAN, etc.
TYPENumeric type: F64, F32, I64, I32, I16
EFCConstruction search effort (higher = better index)
MMax connections per node
M0Max 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 accuracy
  • 17 — fast but may miss some results
Basic KNN Query
surql
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
surql
SELECT *, score
FROM (
    SELECT *, (1 - vector::distance::knn()) AS score
    FROM document
    WHERE embedding <|20, 40|> $vector
)
WHERE score >= $threshold
ORDER BY score DESC;

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