{ buildPgrxExtension, postgresql, fetchFromGitHub, lib, postgresqlTestExtension, }: buildPgrxExtension (finalAttrs: { pname = "pgvectorscale"; version = "0.7.0"; src = fetchFromGitHub { owner = "timescale"; repo = "pgvectorscale"; tag = finalAttrs.version; hash = "sha256-dy481k2SvyYXwwcsyLZSl3XlhSk9C5+4LfEfciB1DK4="; }; doCheck = false; useFetchCargoVendor = true; cargoHash = "sha256-CeRyDn9VhxfjWFJ1/Z/XvOUQOSnDoHHZAqgfYTeKU0o="; cargoPatches = [ ./add-Cargo.lock.patch ]; cargoPgrxFlags = [ "-p" "vectorscale" ]; inherit postgresql; passthru.tests.extension = postgresqlTestExtension { inherit (finalAttrs) finalPackage; withPackages = [ "pgvector" ]; sql = '' CREATE EXTENSION vectorscale CASCADE; CREATE TABLE document_embedding ( id BIGINT PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY, embedding VECTOR(3) ); INSERT INTO document_embedding (id, embedding) VALUES (10, '[1,2,4]'), (20, '[1,2,5]'); CREATE INDEX document_embedding_idx ON document_embedding USING diskann (embedding vector_cosine_ops); ''; asserts = [ { query = "SELECT id FROM document_embedding WHERE embedding <-> '[1,2,3]' = 1"; expected = "10"; description = "Expected vector of row with ID=10 to have an euclidean distance from [1,2,3] of 1."; } { query = "SELECT id FROM document_embedding WHERE embedding <-> '[1,2,3]' = 2"; expected = "20"; description = "Expected vector of row with ID=20 to have an euclidean distance from [1,2,3] of 2."; } ]; }; meta = { homepage = "https://github.com/timescale/pgvectorscale"; teams = [ lib.teams.flyingcircus ]; description = "Complement to pgvector for high performance, cost efficient vector search on large workloads"; license = lib.licenses.postgresql; platforms = postgresql.meta.platforms; changelog = "https://github.com/timescale/pgvectorscale/releases/tag/${finalAttrs.version}"; }; })