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

src/lib/server/search/hybrid.search.service.test.ts

@@ -15,6 +15,8 @@ import { HybridSearchService } from './hybrid.search.service.js';
 import { VectorSearch, cosineSimilarity } from './vector.search.js';
 import { reciprocalRankFusion } from './rrf.js';
 import type { EmbeddingProvider, EmbeddingVector } from '../embeddings/provider.js';
+import { loadSqliteVec } from '../db/sqlite-vec.js';
+import { SqliteVecStore } from './sqlite-vec.store.js';
 
 // ---------------------------------------------------------------------------
 // In-memory DB factory
@@ -23,6 +25,7 @@ import type { EmbeddingProvider, EmbeddingVector } from '../embeddings/provider.
 function createTestDb(): Database.Database {
 	const client = new Database(':memory:');
 	client.pragma('foreign_keys = ON');
+	loadSqliteVec(client);
 
 	const migrationsFolder = join(import.meta.dirname, '../db/migrations');
 
@@ -30,7 +33,11 @@ function createTestDb(): Database.Database {
 	const migrations = [
 		'0000_large_master_chief.sql',
 		'0001_quick_nighthawk.sql',
-		'0002_silky_stellaris.sql'
+		'0002_silky_stellaris.sql',
+		'0003_multiversion_config.sql',
+		'0004_complete_sentry.sql',
+		'0005_fix_stage_defaults.sql',
+		'0006_yielding_centennial.sql'
 	];
 	for (const migrationFile of migrations) {
 		const migrationSql = readFileSync(join(migrationsFolder, migrationFile), 'utf-8');
@@ -121,6 +128,7 @@ function seedEmbedding(
        VALUES (?, ?, ?, ?, ?, ?)`
 		)
 		.run(snippetId, profileId, model, values.length, Buffer.from(f32.buffer), NOW_S);
+	new SqliteVecStore(client).upsertEmbedding(profileId, snippetId, f32);
 }
 
 // ---------------------------------------------------------------------------
@@ -368,6 +376,42 @@ describe('VectorSearch', () => {
 		const results = vs.vectorSearch(new Float32Array([-0.5, 0.5]), { repositoryId: repoId });
 		expect(results[0].score).toBeCloseTo(1.0, 4);
 	});
+
+	it('filters by profileId using per-profile vec tables', () => {
+		client
+			.prepare(
+				`INSERT INTO embedding_profiles (id, provider_kind, title, enabled, is_default, model, dimensions, config, created_at, updated_at)
+				 VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)`
+			)
+			.run('secondary-profile', 'local-transformers', 'Secondary', 1, 0, 'test-model', 2, '{}', NOW_S, NOW_S);
+
+		const defaultSnippet = seedSnippet(client, {
+			repositoryId: repoId,
+			documentId: docId,
+			content: 'default profile snippet'
+		});
+		const secondarySnippet = seedSnippet(client, {
+			repositoryId: repoId,
+			documentId: docId,
+			content: 'secondary profile snippet'
+		});
+
+		seedEmbedding(client, defaultSnippet, [1, 0], 'local-default');
+		seedEmbedding(client, secondarySnippet, [1, 0], 'secondary-profile');
+
+		const vs = new VectorSearch(client);
+		const defaultResults = vs.vectorSearch(new Float32Array([1, 0]), {
+			repositoryId: repoId,
+			profileId: 'local-default'
+		});
+		const secondaryResults = vs.vectorSearch(new Float32Array([1, 0]), {
+			repositoryId: repoId,
+			profileId: 'secondary-profile'
+		});
+
+		expect(defaultResults.map((result) => result.snippetId)).toEqual([defaultSnippet]);
+		expect(secondaryResults.map((result) => result.snippetId)).toEqual([secondarySnippet]);
+	});
 });
 
 // ===========================================================================