78 lines
2.2 KiB
Nix
78 lines
2.2 KiB
Nix
{
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buildPgrxExtension,
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cargo-pgrx_0_16_1,
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postgresql,
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fetchFromGitHub,
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lib,
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postgresqlTestExtension,
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}:
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buildPgrxExtension (finalAttrs: {
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pname = "pgvectorscale";
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version = "0.9.0";
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src = fetchFromGitHub {
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owner = "timescale";
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repo = "pgvectorscale";
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tag = finalAttrs.version;
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hash = "sha256-whGTJI73wifYkleC+aAbDV4nhwls3uFs1xKcB0zLDRo=";
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};
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doCheck = false;
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cargoHash = "sha256-uaRKUtsUdZPcrQLAixCiEphXQqdsRhi8nSfh9b3w0ao=";
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cargoPatches = [
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./add-Cargo.lock.patch
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];
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cargoPgrxFlags = [
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"-p"
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"vectorscale"
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];
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inherit postgresql;
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cargo-pgrx = cargo-pgrx_0_16_1;
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passthru.tests.extension = postgresqlTestExtension {
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inherit (finalAttrs) finalPackage;
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withPackages = [ "pgvector" ];
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sql = ''
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CREATE EXTENSION vectorscale CASCADE;
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CREATE TABLE document_embedding (
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id BIGINT PRIMARY KEY GENERATED BY DEFAULT AS IDENTITY,
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embedding VECTOR(3)
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);
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INSERT INTO document_embedding (id, embedding) VALUES
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(10, '[1,2,4]'),
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(20, '[1,2,5]');
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CREATE INDEX document_embedding_idx ON document_embedding
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USING diskann (embedding vector_cosine_ops);
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'';
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asserts = [
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{
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query = "SELECT id FROM document_embedding WHERE embedding <-> '[1,2,3]' = 1";
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expected = "10";
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description = "Expected vector of row with ID=10 to have an euclidean distance from [1,2,3] of 1.";
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}
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{
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query = "SELECT id FROM document_embedding WHERE embedding <-> '[1,2,3]' = 2";
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expected = "20";
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description = "Expected vector of row with ID=20 to have an euclidean distance from [1,2,3] of 2.";
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}
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];
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};
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meta = {
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# Upstream removed support for PostgreSQL 13 on 0.9.0: https://github.com/timescale/pgvectorscale/releases/tag/0.9.0
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broken = lib.versionOlder postgresql.version "14";
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homepage = "https://github.com/timescale/pgvectorscale";
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teams = [ lib.teams.flyingcircus ];
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description = "Complement to pgvector for high performance, cost efficient vector search on large workloads";
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license = lib.licenses.postgresql;
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platforms = postgresql.meta.platforms;
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changelog = "https://github.com/timescale/pgvectorscale/releases/tag/${finalAttrs.version}";
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};
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})
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