feat(TRUEREF-0023): add sqlite-vec search pipeline

src/lib/server/embeddings/embedding.service.test.ts

@@ -12,6 +12,12 @@ import { migrate } from 'drizzle-orm/better-sqlite3/migrator';
 import { readFileSync } from 'node:fs';
 import { join } from 'node:path';
 import * as schema from '../db/schema.js';
+import {
+	loadSqliteVec,
+	sqliteVecRowidTableName,
+	sqliteVecTableName
+} from '../db/sqlite-vec.js';
+import { SqliteVecStore } from '../search/sqlite-vec.store.js';
 
 import { NoopEmbeddingProvider, EmbeddingError, type EmbeddingVector } from './provider.js';
 import { OpenAIEmbeddingProvider } from './openai.provider.js';
@@ -31,6 +37,7 @@ import { createProviderFromProfile } from './registry.js';
 function createTestDb() {
 	const client = new Database(':memory:');
 	client.pragma('foreign_keys = ON');
+	loadSqliteVec(client);
 
 	const db = drizzle(client, { schema });
 	const migrationsFolder = join(import.meta.dirname, '../db/migrations');
@@ -387,10 +394,19 @@ describe('EmbeddingService', () => {
 			embedding: Buffer;
 			profile_id: string;
 		};
+		expect((row as Record<string, unknown>).vec_embedding).toBeUndefined();
 		expect(row.model).toBe('test-model');
 		expect(row.dimensions).toBe(4);
 		expect(row.profile_id).toBe('local-default');
 		expect(row.embedding).toBeInstanceOf(Buffer);
+
+		const queryEmbedding = service.getEmbedding(snippetId, 'local-default');
+		const matches = new SqliteVecStore(client).queryNearestNeighbors(queryEmbedding!, {
+			repositoryId: '/test/embed-repo',
+			profileId: 'local-default',
+			limit: 5
+		});
+		expect(matches[0]?.snippetId).toBe(snippetId);
 	});
 
 	it('stores embeddings as retrievable Float32Array blobs', async () => {
@@ -436,6 +452,22 @@ describe('EmbeddingService', () => {
 			.prepare('SELECT profile_id FROM snippet_embeddings WHERE snippet_id = ?')
 			.get(snippetId) as { profile_id: string };
 		expect(row.profile_id).toBe('openai-custom');
+
+		const queryEmbedding = service.getEmbedding(snippetId, 'openai-custom');
+		const store = new SqliteVecStore(client);
+		const customMatches = store.queryNearestNeighbors(queryEmbedding!, {
+			repositoryId: '/test/embed-repo',
+			profileId: 'openai-custom',
+			limit: 5
+		});
+		const defaultMatches = store.queryNearestNeighbors(new Float32Array([1, 0, 0, 0]), {
+			repositoryId: '/test/embed-repo',
+			profileId: 'local-default',
+			limit: 5
+		});
+
+		expect(customMatches[0]?.snippetId).toBe(snippetId);
+		expect(defaultMatches).toHaveLength(0);
 	});
 
 	it('is idempotent — re-embedding replaces the existing row', async () => {
@@ -450,6 +482,17 @@ describe('EmbeddingService', () => {
 			.prepare('SELECT COUNT(*) as cnt FROM snippet_embeddings WHERE snippet_id = ?')
 			.get(snippetId) as { cnt: number };
 		expect(rows.cnt).toBe(1);
+
+		const vecTable = sqliteVecTableName('local-default');
+		const rowidTable = sqliteVecRowidTableName('local-default');
+		const vecRows = client.prepare(`SELECT COUNT(*) as cnt FROM "${vecTable}"`).get() as {
+			cnt: number;
+		};
+		const rowidRows = client.prepare(`SELECT COUNT(*) as cnt FROM "${rowidTable}"`).get() as {
+			cnt: number;
+		};
+		expect(vecRows.cnt).toBe(1);
+		expect(rowidRows.cnt).toBe(1);
 	});
 
 	it('calls onProgress after each batch', async () => {