diff --git a/docs/components/embedders/models/huggingface.mdx b/docs/components/embedders/models/huggingface.mdx index d2f0a5efb..c99920f1e 100644 --- a/docs/components/embedders/models/huggingface.mdx +++ b/docs/components/embedders/models/huggingface.mdx @@ -5,6 +5,10 @@ description: "Configure Hugging Face as an embedding provider in Mem0 for local You can use embedding models from Huggingface to run Mem0 locally. + +The TypeScript SDK supports Hugging Face only through a hosted [Text Embeddings Inference (TEI)](#using-text-embeddings-inference-tei) endpoint, or any OpenAI-compatible Hugging Face endpoint. The local `sentence-transformers` mode shown first is Python-only. + + ### Usage ```python @@ -34,9 +38,10 @@ m.add(messages, user_id="john") ### Using Text Embeddings Inference (TEI) -You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings: +You can also use Hugging Face's Text Embeddings Inference service for faster and more efficient embeddings. This is the mode the TypeScript SDK uses. -```python + +```python Python import os from mem0 import Memory @@ -56,6 +61,24 @@ m = Memory.from_config(config) m.add("This text will be embedded using the TEI service.", user_id="john") ``` +```typescript TypeScript +import { Memory } from 'mem0ai/oss'; + +// Point at a running TEI server, or any OpenAI-compatible HF endpoint +const config = { + embedder: { + provider: 'huggingface', + config: { + huggingfaceBaseUrl: 'http://localhost:3000/v1', + }, + }, +}; + +const memory = new Memory(config); +await memory.add("This text will be embedded using the TEI service.", { userId: "john" }); +``` + + To run the TEI service, you can use Docker: ```bash @@ -66,11 +89,22 @@ docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \ ### Config -Here are the parameters available for configuring Huggingface embedder: +Here are the parameters available for configuring the Hugging Face embedder: + + | Parameter | Description | Default Value | | --- | --- | --- | | `model` | The name of the model to use | `multi-qa-MiniLM-L6-cos-v1` | | `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` | | `model_kwargs` | Additional arguments for the model | `None` | -| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` | \ No newline at end of file +| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` | + + +| Parameter | Description | Default Value | +| --- | --- | --- | +| `huggingfaceBaseUrl` | TEI or OpenAI-compatible endpoint URL. Required; falls back to `baseURL`, `url`, then the `HUGGINGFACE_BASE_URL` env var | `None` | +| `model` | Model name sent to the endpoint (TEI ignores it) | `tei` | +| `apiKey` | API key for the endpoint; falls back to the `HUGGINGFACE_API_KEY` env var | `"hf"` | + + \ No newline at end of file diff --git a/mem0-ts/src/oss/src/embeddings/huggingface.ts b/mem0-ts/src/oss/src/embeddings/huggingface.ts new file mode 100644 index 000000000..6307220fa --- /dev/null +++ b/mem0-ts/src/oss/src/embeddings/huggingface.ts @@ -0,0 +1,78 @@ +import OpenAI from "openai"; +import { Embedder } from "./base"; +import { EmbeddingConfig } from "../types"; + +/** + * HuggingFace embedding provider (hosted inference mode). + * + * Mirrors the `huggingface_base_url` branch of the Python provider + * (`mem0/embeddings/huggingface.py`): a HuggingFace Text Embeddings Inference + * (TEI) server, or any HuggingFace OpenAI-compatible inference endpoint, + * exposes a `/v1/embeddings` route, so this embedder reuses the existing + * `openai` client pointed at that base URL. No new dependency is required. + * + * A base URL is required. The Python provider's alternative local + * `sentence-transformers` path has no lightweight TypeScript equivalent, so + * hosted inference is the supported TS mode. + */ +export class HuggingFaceEmbedder implements Embedder { + private openai: OpenAI; + private model: string; + + constructor(config: EmbeddingConfig) { + const baseURL = + config.huggingfaceBaseUrl || + config.baseURL || + config.url || + process.env.HUGGINGFACE_BASE_URL; + + if (!baseURL) { + throw new Error( + "HuggingFace embedder requires an inference endpoint. Set " + + "`huggingfaceBaseUrl` (or `baseURL`) in the embedder config, or the " + + "HUGGINGFACE_BASE_URL environment variable (e.g. a TEI server at " + + "http://localhost:8080/v1).", + ); + } + + this.openai = new OpenAI({ + apiKey: config.apiKey || process.env.HUGGINGFACE_API_KEY || "hf", + baseURL, + }); + // TEI ignores the model field; default mirrors the Python provider. + this.model = config.model || "tei"; + } + + async embed(text: string): Promise { + const response = await this.openai.embeddings.create({ + model: this.model, + input: text, + }); + if (!response.data || response.data.length === 0) { + throw new Error( + `HuggingFace embed() returned no embeddings for model '${this.model}'`, + ); + } + return response.data[0].embedding; + } + + async embedBatch(texts: string[]): Promise { + if (texts.length === 0) { + return []; + } + const response = await this.openai.embeddings.create({ + model: this.model, + input: texts, + }); + const embeddings = response.data + .sort((a, b) => a.index - b.index) + .map((item) => item.embedding); + if (embeddings.length !== texts.length) { + throw new Error( + `HuggingFace embedBatch() returned ${embeddings.length} embeddings ` + + `for ${texts.length} texts using model '${this.model}'`, + ); + } + return embeddings; + } +} diff --git a/mem0-ts/src/oss/src/index.ts b/mem0-ts/src/oss/src/index.ts index 49742ac53..44cb5fd5d 100644 --- a/mem0-ts/src/oss/src/index.ts +++ b/mem0-ts/src/oss/src/index.ts @@ -2,6 +2,7 @@ export * from "./memory"; export * from "./memory/memory.types"; export * from "./types"; export * from "./embeddings/base"; +export * from "./embeddings/huggingface"; export * from "./embeddings/openai"; export * from "./embeddings/ollama"; export * from "./embeddings/lmstudio"; diff --git a/mem0-ts/src/oss/src/types/index.ts b/mem0-ts/src/oss/src/types/index.ts index 99c59a489..82f7bcc17 100644 --- a/mem0-ts/src/oss/src/types/index.ts +++ b/mem0-ts/src/oss/src/types/index.ts @@ -19,6 +19,8 @@ export interface EmbeddingConfig { url?: string; embeddingDims?: number; modelProperties?: Record; + // HuggingFace TEI / OpenAI-compatible inference endpoint base URL. + huggingfaceBaseUrl?: string; } export type { ValkeyConfig } from "./valkey"; diff --git a/mem0-ts/src/oss/src/utils/factory.ts b/mem0-ts/src/oss/src/utils/factory.ts index 8577f341a..fa604bf76 100644 --- a/mem0-ts/src/oss/src/utils/factory.ts +++ b/mem0-ts/src/oss/src/utils/factory.ts @@ -43,6 +43,7 @@ import { AzureOpenAIEmbedder } from "../embeddings/azure"; import { FastEmbedEmbedder } from "../embeddings/fastembed"; import { LangchainLLM } from "../llms/langchain"; import { LangchainEmbedder } from "../embeddings/langchain"; +import { HuggingFaceEmbedder } from "../embeddings/huggingface"; import { LangchainVectorStore } from "../vector_stores/langchain"; import { AzureAISearch } from "../vector_stores/azure_ai_search"; import { PGVector } from "../vector_stores/pgvector"; @@ -77,6 +78,8 @@ export class EmbedderFactory { return new FastEmbedEmbedder(config); case "langchain": return new LangchainEmbedder(config); + case "huggingface": + return new HuggingFaceEmbedder(config); default: throw new Error(`Unsupported embedder provider: ${provider}`); } diff --git a/mem0-ts/src/oss/tests/huggingface-embedder.test.ts b/mem0-ts/src/oss/tests/huggingface-embedder.test.ts new file mode 100644 index 000000000..926bb8e3b --- /dev/null +++ b/mem0-ts/src/oss/tests/huggingface-embedder.test.ts @@ -0,0 +1,149 @@ +/// +/** + * HuggingFace Embedder unit tests (mocked OpenAI client). + * The TS provider targets a HuggingFace TEI / OpenAI-compatible inference + * endpoint, so it reuses the `openai` client with a HuggingFace baseURL. + * These tests verify the required base URL, request shape, and batch ordering. + */ + +const mockEmbeddingsCreate = jest.fn(); +const mockOpenAICtor = jest.fn(); + +jest.mock("openai", () => { + return { + __esModule: true, + default: jest.fn().mockImplementation((opts: any) => { + mockOpenAICtor(opts); + return { embeddings: { create: mockEmbeddingsCreate } }; + }), + }; +}); + +import { HuggingFaceEmbedder } from "../src/embeddings/huggingface"; + +const mockEmbedding = [0.1, 0.2, 0.3, 0.4, 0.5]; + +describe("HuggingFaceEmbedder (unit)", () => { + const OLD_ENV = process.env; + + beforeEach(() => { + mockEmbeddingsCreate.mockReset(); + mockOpenAICtor.mockReset(); + mockEmbeddingsCreate.mockResolvedValue({ + data: [{ index: 0, embedding: mockEmbedding }], + }); + process.env = { ...OLD_ENV }; + delete process.env.HUGGINGFACE_BASE_URL; + }); + + afterAll(() => { + process.env = OLD_ENV; + }); + + describe("configuration", () => { + it("throws when no inference endpoint is configured", () => { + expect(() => new HuggingFaceEmbedder({ apiKey: "test-key" })).toThrow( + /requires an inference endpoint/, + ); + }); + + it("uses huggingfaceBaseUrl and the default model", async () => { + const embedder = new HuggingFaceEmbedder({ + apiKey: "test-key", + huggingfaceBaseUrl: "http://localhost:8080/v1", + }); + await embedder.embed("hello"); + + expect(mockOpenAICtor.mock.calls[0][0]).toMatchObject({ + apiKey: "test-key", + baseURL: "http://localhost:8080/v1", + }); + const callArgs = mockEmbeddingsCreate.mock.calls[0][0]; + expect(callArgs).toEqual({ model: "tei", input: "hello" }); + }); + + it("falls back to baseURL and honors a custom model", async () => { + const embedder = new HuggingFaceEmbedder({ + baseURL: "https://tei.example.com/v1", + model: "BAAI/bge-small-en-v1.5", + }); + await embedder.embed("hello"); + + expect(mockOpenAICtor.mock.calls[0][0]).toMatchObject({ + baseURL: "https://tei.example.com/v1", + }); + expect(mockEmbeddingsCreate.mock.calls[0][0].model).toBe( + "BAAI/bge-small-en-v1.5", + ); + }); + + it("reads HUGGINGFACE_BASE_URL from the environment", async () => { + process.env.HUGGINGFACE_BASE_URL = "http://env-host:8080/v1"; + const embedder = new HuggingFaceEmbedder({ apiKey: "test-key" }); + await embedder.embed("hello"); + + expect(mockOpenAICtor.mock.calls[0][0]).toMatchObject({ + baseURL: "http://env-host:8080/v1", + }); + }); + + it("never forwards a dimensions parameter", async () => { + const embedder = new HuggingFaceEmbedder({ + huggingfaceBaseUrl: "http://localhost:8080/v1", + embeddingDims: 384, + }); + await embedder.embed("hello"); + + expect(mockEmbeddingsCreate.mock.calls[0][0]).not.toHaveProperty( + "dimensions", + ); + }); + }); + + describe("basic functionality", () => { + const cfg = { huggingfaceBaseUrl: "http://localhost:8080/v1" }; + + it("embed() returns the embedding vector", async () => { + const embedder = new HuggingFaceEmbedder(cfg); + expect(await embedder.embed("hello")).toEqual(mockEmbedding); + }); + + it("embedBatch() returns [] for empty input without calling the API", async () => { + const embedder = new HuggingFaceEmbedder(cfg); + expect(await embedder.embedBatch([])).toEqual([]); + expect(mockEmbeddingsCreate).not.toHaveBeenCalled(); + }); + + it("embedBatch() sorts results by index", async () => { + mockEmbeddingsCreate.mockResolvedValue({ + data: [ + { index: 1, embedding: [0.3, 0.4] }, + { index: 0, embedding: [0.1, 0.2] }, + ], + }); + const embedder = new HuggingFaceEmbedder(cfg); + expect(await embedder.embedBatch(["a", "b"])).toEqual([ + [0.1, 0.2], + [0.3, 0.4], + ]); + }); + + it("embedBatch() throws when the count mismatches the input", async () => { + mockEmbeddingsCreate.mockResolvedValue({ + data: [{ index: 0, embedding: [0.1, 0.2] }], + }); + const embedder = new HuggingFaceEmbedder(cfg); + await expect(embedder.embedBatch(["a", "b"])).rejects.toThrow( + /returned 1 embeddings for 2 texts/, + ); + }); + + it("embed() throws when the endpoint returns no embeddings", async () => { + mockEmbeddingsCreate.mockResolvedValue({ data: [] }); + const embedder = new HuggingFaceEmbedder(cfg); + await expect(embedder.embed("hello")).rejects.toThrow( + /returned no embeddings/, + ); + }); + }); +});