From 0d30330be3e92809f8d8456a11d190e4fd086e49 Mon Sep 17 00:00:00 2001 From: Vineeth N K Date: Sat, 13 Jun 2026 23:15:52 +0530 Subject: [PATCH] feat(memory): add local embedding provider via transformers.js Wire EMBEDDING_PROVIDER=local to an in-process embedding adapter so PR-memory works without an external embedding API. LocalEmbeddingAdapter runs a sentence-transformers model (default Xenova/all-MiniLM-L6-v2, 384-dim) through @huggingface/transformers, forcing the WASM backend and a single thread because the Alpine runner image cannot load the native onnxruntime-node binary. The memory module now selects the embedding adapter from EMBEDDING_PROVIDER instead of hard-wiring Voyage, and EMBEDDING_MODEL becomes optional with a per-adapter default. A migration aligns pr_memory.embedding with EMBEDDING_DIMENSIONS, acting only when the dimension actually changes and truncating the (model-specific, now-incompatible) vectors since the column is NOT NULL. A persistent model-cache volume keeps the downloaded model across restarts. --- .env.example | 12 +- docker-compose.yml | 3 + package-lock.json | 851 +++++++++++++++++- package.json | 1 + src/config/app.config.ts | 6 +- .../adapters/local-embedding.adapter.spec.ts | 65 ++ .../adapters/local-embedding.adapter.ts | 54 ++ .../adapters/voyage-embedding.adapter.ts | 2 +- src/memory/memory.module.ts | 8 +- .../1780000000000-SyncEmbeddingDimension.ts | 45 + 10 files changed, 1037 insertions(+), 10 deletions(-) create mode 100644 src/memory/infrastructure/adapters/local-embedding.adapter.spec.ts create mode 100644 src/memory/infrastructure/adapters/local-embedding.adapter.ts create mode 100644 src/shared/infrastructure/database/migrations/1780000000000-SyncEmbeddingDimension.ts diff --git a/.env.example b/.env.example index ec16274..ce511a7 100644 --- a/.env.example +++ b/.env.example @@ -29,9 +29,17 @@ LLM_API_KEY= # Provider: voyage | local EMBEDDING_PROVIDER=voyage VOYAGE_API_KEY= -# EMBEDDING_MODEL=voyage-3-lite -# Must match the embedding model (voyage-3-lite=512, voyage-3=1024). +# EMBEDDING_MODEL defaults per provider (voyage: voyage-3-lite, local: Xenova/all-MiniLM-L6-v2) +# EMBEDDING_MODEL= +# Must match the embedding model (voyage-3-lite=512, voyage-3=1024, all-MiniLM-L6-v2=384). # EMBEDDING_DIMENSIONS=512 +# +# Local embeddings: runs a sentence-transformers model in-process via +# transformers.js (WASM), no API key. Downloads the model once to the cache dir. +# EMBEDDING_PROVIDER=local +# EMBEDDING_MODEL=Xenova/all-MiniLM-L6-v2 +# EMBEDDING_DIMENSIONS=384 +# EMBEDDING_CACHE_DIR=/app/models # =========================================================================== # Database & Redis (required) diff --git a/docker-compose.yml b/docker-compose.yml index 4d92f1e..b1604c2 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -10,6 +10,8 @@ services: - NODE_ENV=production extra_hosts: - 'host.docker.internal:host-gateway' + volumes: + - modelcache:/app/models depends_on: db: condition: service_healthy @@ -55,3 +57,4 @@ services: volumes: pgdata: redisdata: + modelcache: diff --git a/package-lock.json b/package-lock.json index 741bc26..1427fbd 100644 --- a/package-lock.json +++ b/package-lock.json @@ -11,6 +11,7 @@ "dependencies": { "@anthropic-ai/sdk": "^0.104.1", "@google/generative-ai": "^0.24.1", + "@huggingface/transformers": "^4.2.0", "@mistralai/mistralai": "^2.2.5", 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"node_modules/semver-compare": { + "version": "1.0.0", + "resolved": "https://registry.npmjs.org/semver-compare/-/semver-compare-1.0.0.tgz", + "integrity": "sha512-YM3/ITh2MJ5MtzaM429anh+x2jiLVjqILF4m4oyQB18W7Ggea7BfqdH/wGMK7dDiMghv/6WG7znWMwUDzJiXow==", + "license": "MIT" + }, "node_modules/send": { "version": "1.2.1", "resolved": "https://registry.npmjs.org/send/-/send-1.2.1.tgz", @@ -13342,6 +14113,33 @@ "url": "https://opencollective.com/express" } }, + "node_modules/serialize-error": { + "version": "7.0.1", + "resolved": "https://registry.npmjs.org/serialize-error/-/serialize-error-7.0.1.tgz", + "integrity": "sha512-8I8TjW5KMOKsZQTvoxjuSIa7foAwPWGOts+6o7sgjz41/qMD9VQHEDxi6PBvK2l0MXUmqZyNpUK+T2tQaaElvw==", + "license": "MIT", + "dependencies": { + "type-fest": "^0.13.1" + }, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/serialize-error/node_modules/type-fest": { + "version": "0.13.1", + "resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.13.1.tgz", + "integrity": "sha512-34R7HTnG0XIJcBSn5XhDd7nNFPRcXYRZrBB2O2jdKqYODldSzBAqzsWoZYYvduky73toYS/ESqxPvkDf/F0XMg==", + "license": "(MIT OR CC0-1.0)", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/serve-static": { "version": "2.2.1", "resolved": "https://registry.npmjs.org/serve-static/-/serve-static-2.2.1.tgz", @@ -13404,6 +14202,50 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/sharp": { + "version": "0.34.5", + "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.34.5.tgz", + "integrity": "sha512-Ou9I5Ft9WNcCbXrU9cMgPBcCK8LiwLqcbywW3t4oDV37n1pzpuNLsYiAV8eODnjbtQlSDwZ2cUEeQz4E54Hltg==", + "hasInstallScript": true, + "license": "Apache-2.0", + "dependencies": { + "@img/colour": "^1.0.0", + "detect-libc": "^2.1.2", + "semver": "^7.7.3" + }, + "engines": { + "node": "^18.17.0 || ^20.3.0 || >=21.0.0" + }, + "funding": { + "url": "https://opencollective.com/libvips" + }, + "optionalDependencies": { + "@img/sharp-darwin-arm64": "0.34.5", + "@img/sharp-darwin-x64": "0.34.5", + "@img/sharp-libvips-darwin-arm64": "1.2.4", + "@img/sharp-libvips-darwin-x64": "1.2.4", + "@img/sharp-libvips-linux-arm": "1.2.4", + "@img/sharp-libvips-linux-arm64": "1.2.4", + "@img/sharp-libvips-linux-ppc64": "1.2.4", + "@img/sharp-libvips-linux-riscv64": "1.2.4", + "@img/sharp-libvips-linux-s390x": "1.2.4", + "@img/sharp-libvips-linux-x64": "1.2.4", + "@img/sharp-libvips-linuxmusl-arm64": "1.2.4", + "@img/sharp-libvips-linuxmusl-x64": "1.2.4", + "@img/sharp-linux-arm": "0.34.5", + "@img/sharp-linux-arm64": "0.34.5", + "@img/sharp-linux-ppc64": "0.34.5", + "@img/sharp-linux-riscv64": "0.34.5", + "@img/sharp-linux-s390x": "0.34.5", + "@img/sharp-linux-x64": "0.34.5", + "@img/sharp-linuxmusl-arm64": "0.34.5", + "@img/sharp-linuxmusl-x64": "0.34.5", + "@img/sharp-wasm32": "0.34.5", + "@img/sharp-win32-arm64": "0.34.5", + "@img/sharp-win32-ia32": "0.34.5", + "@img/sharp-win32-x64": "0.34.5" + } + }, "node_modules/shebang-command": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz", @@ -14885,7 +15727,6 @@ "version": "7.24.6", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.24.6.tgz", "integrity": "sha512-WRNW+sJgj5OBN4/0JpHFqtqzhpbnV0GuB+OozA9gCL7a993SmU+1JBZCzLNxYsbMfIeDL+lTsphD5jN5N+n0zg==", - "devOptional": true, "license": "MIT" }, "node_modules/unist-util-is": { diff --git a/package.json b/package.json index 3a05fc3..39763d5 100644 --- a/package.json +++ b/package.json @@ -35,6 +35,7 @@ "dependencies": { "@anthropic-ai/sdk": "^0.104.1", "@google/generative-ai": "^0.24.1", + "@huggingface/transformers": "^4.2.0", "@mistralai/mistralai": "^2.2.5", "@nest-lab/throttler-storage-redis": "^1.2.0", "@nestjs/bullmq": "^11.0.4", diff --git a/src/config/app.config.ts b/src/config/app.config.ts index 3094775..c4a1cf1 100644 --- a/src/config/app.config.ts +++ b/src/config/app.config.ts @@ -88,7 +88,11 @@ export class AppConfig { @IsString() @IsOptional() - EMBEDDING_MODEL: string = 'voyage-3-lite'; + EMBEDDING_MODEL?: string; + + @IsString() + @IsOptional() + EMBEDDING_CACHE_DIR?: string; @Transform(({ value }: { value: string }) => parseInt(value, 10)) @IsInt() diff --git a/src/memory/infrastructure/adapters/local-embedding.adapter.spec.ts b/src/memory/infrastructure/adapters/local-embedding.adapter.spec.ts new file mode 100644 index 0000000..3442859 --- /dev/null +++ b/src/memory/infrastructure/adapters/local-embedding.adapter.spec.ts @@ -0,0 +1,65 @@ +const pipelineMock = jest.fn, unknown[]>(); + +jest.mock('@huggingface/transformers', () => ({ + pipeline: (...args: unknown[]): Promise => pipelineMock(...args), + env: { backends: { onnx: { wasm: {} } }, cacheDir: '' }, +})); + +import { LocalEmbeddingAdapter } from './local-embedding.adapter.js'; +import { AppConfig, EmbeddingProvider } from '../../../config/app.config.js'; +import { EmbeddingApiError } from '../../domain/errors/memory.errors.js'; + +function buildConfig(overrides: Partial = {}): AppConfig { + const config = new AppConfig(); + config.EMBEDDING_PROVIDER = EmbeddingProvider.LOCAL; + return Object.assign(config, overrides); +} + +describe('LocalEmbeddingAdapter', () => { + afterEach(() => jest.clearAllMocks()); + + it('loads the default model and maps the tensor to number[][]', async () => { + const extractor = jest.fn().mockResolvedValue({ tolist: () => [[0.1, 0.2, 0.3]] }); + pipelineMock.mockResolvedValue(extractor); + + const result = await new LocalEmbeddingAdapter(buildConfig()).embedBatch(['hello']); + + expect(result).toEqual([[0.1, 0.2, 0.3]]); + expect(pipelineMock).toHaveBeenCalledWith('feature-extraction', 'Xenova/all-MiniLM-L6-v2', { + device: 'wasm', + }); + expect(extractor).toHaveBeenCalledWith(['hello'], { pooling: 'mean', normalize: true }); + }); + + it('honours an EMBEDDING_MODEL override and caches the pipeline across calls', async () => { + const extractor = jest.fn().mockResolvedValue({ tolist: () => [[1]] }); + pipelineMock.mockResolvedValue(extractor); + + const adapter = new LocalEmbeddingAdapter( + buildConfig({ EMBEDDING_MODEL: 'Xenova/bge-small-en' }), + ); + await adapter.embed('a'); + await adapter.embed('b'); + + expect(pipelineMock).toHaveBeenCalledTimes(1); + expect(pipelineMock).toHaveBeenCalledWith('feature-extraction', 'Xenova/bge-small-en', { + device: 'wasm', + }); + }); + + it('returns the single vector from embed()', async () => { + pipelineMock.mockResolvedValue(jest.fn().mockResolvedValue({ tolist: () => [[0.5, 0.6]] })); + + const vector = await new LocalEmbeddingAdapter(buildConfig()).embed('x'); + + expect(vector).toEqual([0.5, 0.6]); + }); + + it('wraps inference failures in EmbeddingApiError', async () => { + pipelineMock.mockRejectedValue(new Error('model load failed')); + + await expect(new LocalEmbeddingAdapter(buildConfig()).embed('x')).rejects.toBeInstanceOf( + EmbeddingApiError, + ); + }); +}); diff --git a/src/memory/infrastructure/adapters/local-embedding.adapter.ts b/src/memory/infrastructure/adapters/local-embedding.adapter.ts new file mode 100644 index 0000000..685dedb --- /dev/null +++ b/src/memory/infrastructure/adapters/local-embedding.adapter.ts @@ -0,0 +1,54 @@ +import { Injectable, Logger } from '@nestjs/common'; +import { pipeline, env, type FeatureExtractionPipeline } from '@huggingface/transformers'; +import { EmbeddingProviderPort } from '../../domain/ports/embedding-provider.port.js'; +import { EmbeddingApiError } from '../../domain/errors/memory.errors.js'; +import { AppConfig } from '../../../config/app.config.js'; + +const DEFAULT_LOCAL_MODEL = 'Xenova/all-MiniLM-L6-v2'; + +// The runner image is Alpine (musl), where the native onnxruntime-node binary +// will not load. Force the WASM backend and a single thread so embeddings work +// without SharedArrayBuffer. +const wasmBackend = env.backends?.onnx?.wasm; +if (wasmBackend) { + wasmBackend.numThreads = 1; +} + +@Injectable() +export class LocalEmbeddingAdapter extends EmbeddingProviderPort { + private readonly logger = new Logger(LocalEmbeddingAdapter.name); + private readonly model: string; + private extractor?: Promise; + + constructor(config: AppConfig) { + super(); + this.model = config.EMBEDDING_MODEL ?? DEFAULT_LOCAL_MODEL; + if (config.EMBEDDING_CACHE_DIR) { + env.cacheDir = config.EMBEDDING_CACHE_DIR; + } + } + + async embed(text: string): Promise { + const [vector] = await this.embedBatch([text]); + if (!vector) throw new EmbeddingApiError('Empty embedding result'); + return vector; + } + + async embedBatch(texts: string[]): Promise { + try { + const extractor = await this.loadExtractor(); + const output = await extractor(texts, { pooling: 'mean', normalize: true }); + return output.tolist() as number[][]; + } catch (error) { + this.logger.error('Local embedding failed', error instanceof Error ? error.message : ''); + throw new EmbeddingApiError(error instanceof Error ? error.message : 'Unknown error'); + } + } + + private loadExtractor(): Promise { + if (!this.extractor) { + this.extractor = pipeline('feature-extraction', this.model, { device: 'wasm' }); + } + return this.extractor; + } +} diff --git a/src/memory/infrastructure/adapters/voyage-embedding.adapter.ts b/src/memory/infrastructure/adapters/voyage-embedding.adapter.ts index 412a2d6..f78f3b1 100644 --- a/src/memory/infrastructure/adapters/voyage-embedding.adapter.ts +++ b/src/memory/infrastructure/adapters/voyage-embedding.adapter.ts @@ -12,7 +12,7 @@ export class VoyageEmbeddingAdapter extends EmbeddingProviderPort { constructor(config: AppConfig) { super(); this.apiKey = config.VOYAGE_API_KEY ?? ''; - this.model = config.EMBEDDING_MODEL; + this.model = config.EMBEDDING_MODEL ?? 'voyage-3-lite'; } async embed(text: string): Promise { diff --git a/src/memory/memory.module.ts b/src/memory/memory.module.ts index b9c776c..d7d85bb 100644 --- a/src/memory/memory.module.ts +++ b/src/memory/memory.module.ts @@ -1,10 +1,12 @@ import { Module } from '@nestjs/common'; import { TypeOrmModule } from '@nestjs/typeorm'; import { PrMemoryRecord } from './infrastructure/repositories/memory.record.js'; +import { AppConfig, EmbeddingProvider } from '../config/app.config.js'; import { EmbeddingProviderPort } from './domain/ports/embedding-provider.port.js'; import { MemoryRepositoryPort } from './domain/ports/memory-repository.port.js'; import { PrHistoryProviderPort } from './application/ports/pr-history-provider.port.js'; import { VoyageEmbeddingAdapter } from './infrastructure/adapters/voyage-embedding.adapter.js'; +import { LocalEmbeddingAdapter } from './infrastructure/adapters/local-embedding.adapter.js'; import { GithubPrHistoryAdapter } from './infrastructure/adapters/github-pr-history.adapter.js'; import { TypeOrmMemoryRepository } from './infrastructure/repositories/typeorm-memory.repository.js'; import { IndexMemoryUseCase } from './application/use-cases/index-memory.use-case.js'; @@ -18,7 +20,11 @@ import { GitHubModule } from '../github/github.module.js'; providers: [ { provide: EmbeddingProviderPort, - useClass: VoyageEmbeddingAdapter, + inject: [AppConfig], + useFactory: (config: AppConfig): EmbeddingProviderPort => + config.EMBEDDING_PROVIDER === EmbeddingProvider.LOCAL + ? new LocalEmbeddingAdapter(config) + : new VoyageEmbeddingAdapter(config), }, { provide: MemoryRepositoryPort, diff --git a/src/shared/infrastructure/database/migrations/1780000000000-SyncEmbeddingDimension.ts b/src/shared/infrastructure/database/migrations/1780000000000-SyncEmbeddingDimension.ts new file mode 100644 index 0000000..20357c8 --- /dev/null +++ b/src/shared/infrastructure/database/migrations/1780000000000-SyncEmbeddingDimension.ts @@ -0,0 +1,45 @@ +import { MigrationInterface, QueryRunner } from 'typeorm'; + +// Aligns pr_memory.embedding with EMBEDDING_DIMENSIONS so switching embedding +// providers (e.g. Voyage 512-dim to a local model at 384-dim) keeps the pgvector +// column dimension correct. Only acts when the dimension actually changes; the +// existing vectors are model-specific and cannot survive a dimension change, so +// the table is truncated rather than nulled (which the NOT NULL column forbids). +export class SyncEmbeddingDimension1780000000000 implements MigrationInterface { + name = 'SyncEmbeddingDimension1780000000000'; + + public async up(queryRunner: QueryRunner): Promise { + const targetDimension = parseInt(process.env['EMBEDDING_DIMENSIONS'] ?? '512', 10); + if (!Number.isInteger(targetDimension) || targetDimension < 64) { + throw new Error('EMBEDDING_DIMENSIONS must be an integer >= 64'); + } + + const currentDimension = await this.readEmbeddingDimension(queryRunner); + if (currentDimension === targetDimension) return; + + await queryRunner.query('DROP INDEX IF EXISTS idx_pr_memory_embedding'); + await queryRunner.query('TRUNCATE TABLE pr_memory'); + await queryRunner.query( + `ALTER TABLE pr_memory ALTER COLUMN embedding TYPE vector(${targetDimension})`, + ); + await queryRunner.query( + 'CREATE INDEX idx_pr_memory_embedding ON pr_memory USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100)', + ); + } + + public async down(): Promise { + // No-op: the dimension is derived from EMBEDDING_DIMENSIONS, so reverting is + // a forward re-run of this migration with the previous value set. + } + + private async readEmbeddingDimension(queryRunner: QueryRunner): Promise { + const rows = (await queryRunner.query( + `SELECT format_type(atttypid, atttypmod) AS type + FROM pg_attribute + WHERE attrelid = 'pr_memory'::regclass AND attname = 'embedding'`, + )) as Array<{ type: string }>; + + const dimension = rows[0]?.type.match(/vector\((\d+)\)/)?.[1]; + return dimension ? parseInt(dimension, 10) : null; + } +}