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chore(server): don't insert embeddings if the model has changed (#17885)

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* chore(server): don't insert embeddings if the model has changed

We're moving away from the heuristic of waiting for queues to complete. The job
which inserts embeddings can simply check if the model has changed before
inserting, rather than attempting to lock the queue.

* more robust dim size update

* use check constraint

* index command cleanup

* add create statement

* update medium test, create appropriate extension

* new line

* set dimension size when running on all assets

* why does it want braces smh

* take 2

---------

Co-authored-by: mertalev <101130780+mertalev@users.noreply.github.com>
This commit is contained in:
Thomas 2025-04-29 19:23:01 +01:00 • committed by GitHub
parent 0e4cf9ac57
commit 3ce353393a
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11 changed files with 82 additions and 136 deletions
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18
server/src/migrations/1718486162779-AddFaceSearchRelation.ts
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@ -1,5 +1,6 @@
import { DatabaseExtension } from 'src/enum';
import { ConfigRepository } from 'src/repositories/config.repository';
import { vectorIndexQuery } from 'src/utils/database';
import { MigrationInterface, QueryRunner } from 'typeorm';
const vectorExtension = new ConfigRepository().getEnv().database.vectorExtension;
@ -8,7 +9,6 @@ export class AddFaceSearchRelation1718486162779 implements MigrationInterface {
public async up(queryRunner: QueryRunner): Promise<void> {
if (vectorExtension === DatabaseExtension.VECTORS) {
await queryRunner.query(`SET search_path TO "$user", public, vectors`);
await queryRunner.query(`SET vectors.pgvector_compatibility=on`);
}
const hasEmbeddings = async (tableName: string): Promise<boolean> => {
@ -47,21 +47,14 @@ export class AddFaceSearchRelation1718486162779 implements MigrationInterface {
await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET DATA TYPE real[]`);
await queryRunner.query(`ALTER TABLE face_search ALTER COLUMN embedding SET DATA TYPE vector(512)`);
await queryRunner.query(`
CREATE INDEX IF NOT EXISTS clip_index ON smart_search
USING hnsw (embedding vector_cosine_ops)
WITH (ef_construction = 300, m = 16)`);
await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'smart_search', indexName: 'clip_index' }));
await queryRunner.query(`
CREATE INDEX face_index ON face_search
USING hnsw (embedding vector_cosine_ops)
WITH (ef_construction = 300, m = 16)`);
await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'face_search', indexName: 'face_index' }));
}
public async down(queryRunner: QueryRunner): Promise<void> {
if (vectorExtension === DatabaseExtension.VECTORS) {
await queryRunner.query(`SET search_path TO "$user", public, vectors`);
await queryRunner.query(`SET vectors.pgvector_compatibility=on`);
}
await queryRunner.query(`ALTER TABLE asset_faces ADD COLUMN "embedding" vector(512)`);
@ -74,9 +67,6 @@ export class AddFaceSearchRelation1718486162779 implements MigrationInterface {
WHERE id = fs."faceId"`);
await queryRunner.query(`DROP TABLE face_search`);
await queryRunner.query(`
CREATE INDEX face_index ON asset_faces
USING hnsw (embedding vector_cosine_ops)
WITH (ef_construction = 300, m = 16)`);
await queryRunner.query(vectorIndexQuery({ vectorExtension, table: 'asset_faces', indexName: 'face_index' }));
}
}