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{
buildPgrxExtension,
cargo-pgrx_0_16_1,
postgresql,
fetchFromGitHub,
lib,
postgresqlTestExtension,
}:
buildPgrxExtension (finalAttrs: {
pname = "pgvectorscale";
version = "0.9.0";
src = fetchFromGitHub {
owner = "timescale";
repo = "pgvectorscale";
tag = finalAttrs.version;
hash = "sha256-whGTJI73wifYkleC+aAbDV4nhwls3uFs1xKcB0zLDRo=";
};
doCheck = false;
cargoHash = "sha256-uaRKUtsUdZPcrQLAixCiEphXQqdsRhi8nSfh9b3w0ao=";
cargoPatches = [
./add-Cargo.lock.patch
];
cargoPgrxFlags = [
"-p"
"vectorscale"
];
inherit postgresql;
cargo-pgrx = cargo-pgrx_0_16_1;
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 = {
# Upstream removed support for PostgreSQL 13 on 0.9.0: https://github.com/timescale/pgvectorscale/releases/tag/0.9.0
broken = lib.versionOlder postgresql.version "14";
homepage = "https://github.com/timescale/pgvectorscale";
maintainers = [
lib.maintainers.leona
lib.maintainers.osnyx
];
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}";
};
})
|
