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* wip auto-detect available extensions auto-recovery, fix reindexing check use original image for ml * set probes * update image for sql checker update images for gha * cascade * fix new instance * accurate dummy vector * simplify dummy * preexisiting pg docs * handle different db name * maybe fix sql generation * revert refreshfaces sql change * redundant switch * outdated message * update docker compose files * Update docs/docs/administration/postgres-standalone.md Co-authored-by: Daniel Dietzler <36593685+danieldietzler@users.noreply.github.com> * tighten range * avoid always printing "vector reindexing complete" * remove nesting * use new images * add vchord to unit tests * debug e2e image * mention 1.107.2 in startup error * support new vchord versions --------- Co-authored-by: Daniel Dietzler <36593685+danieldietzler@users.noreply.github.com>
71 lines
3.2 KiB
TypeScript
71 lines
3.2 KiB
TypeScript
import { DatabaseExtension } from 'src/enum';
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import { getVectorExtension } from 'src/repositories/database.repository';
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import { vectorIndexQuery } from 'src/utils/database';
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import { MigrationInterface, QueryRunner } from 'typeorm';
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export class AddFaceSearchRelation1718486162779 implements MigrationInterface {
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public async up(queryRunner: QueryRunner): Promise<void> {
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const vectorExtension = await getVectorExtension(queryRunner);
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if (vectorExtension === DatabaseExtension.VECTORS) {
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await queryRunner.query(`SET search_path TO "$user", public, vectors`);
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}
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const hasEmbeddings = async (tableName: string): Promise<boolean> => {
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const columns = await queryRunner.query(
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`SELECT column_name as name
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FROM information_schema.columns
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WHERE table_name = '${tableName}'`,
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);
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return columns.some((column: { name: string }) => column.name === 'embedding');
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};
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const hasAssetEmbeddings = await hasEmbeddings('smart_search');
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if (!hasAssetEmbeddings) {
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await queryRunner.query(`TRUNCATE smart_search`);
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await queryRunner.query(`ALTER TABLE smart_search ADD COLUMN IF NOT EXISTS embedding vector(512) NOT NULL`);
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}
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await queryRunner.query(`
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CREATE TABLE face_search (
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"faceId" uuid PRIMARY KEY REFERENCES asset_faces(id) ON DELETE CASCADE,
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embedding vector(512) NOT NULL )`);
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await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET STORAGE EXTERNAL`);
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await queryRunner.query(`ALTER TABLE smart_search ALTER COLUMN embedding SET STORAGE EXTERNAL`);
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const hasFaceEmbeddings = await hasEmbeddings('asset_faces');
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if (hasFaceEmbeddings) {
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await queryRunner.query(`
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INSERT INTO face_search("faceId", embedding)
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SELECT id, embedding
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FROM asset_faces faces`);
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}
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await queryRunner.query(`ALTER TABLE asset_faces DROP COLUMN IF EXISTS embedding`);
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await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET DATA TYPE real[]`);
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await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET DATA TYPE vector(512)`);
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await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'smart_search', indexName: 'clip_index' }));
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await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'face_search', indexName: 'face_index' }));
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}
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public async down(queryRunner: QueryRunner): Promise<void> {
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const vectorExtension = await getVectorExtension(queryRunner);
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if (vectorExtension === DatabaseExtension.VECTORS) {
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await queryRunner.query(`SET search_path TO "$user", public, vectors`);
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}
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await queryRunner.query(`ALTER TABLE asset_faces ADD COLUMN "embedding" vector(512)`);
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await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET STORAGE DEFAULT`);
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await queryRunner.query(`ALTER TABLE smart_search ALTER COLUMN embedding SET STORAGE DEFAULT`);
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await queryRunner.query(`
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UPDATE asset_faces
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SET embedding = fs.embedding
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FROM face_search fs
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WHERE id = fs."faceId"`);
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await queryRunner.query(`DROP TABLE face_search`);
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await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'asset_faces', indexName: 'face_index' }));
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}
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}
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