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 recommendatio

SurrealDB Vector Search HNSW Index Create a basic HNSW index: With specific distance function and type: Available types: F64 , F32 , I64 , I32 , I16 . Full Table Example 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 accuracy 17 — fast but may miss some results Basic KNN Query vector::distance::knn() uses the distance function defined by the index. Scored Results with Threshold

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