feat(ts-sdk): add HuggingFace embedding provider (#6027)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

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.
 
+<Note>
+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.
+</Note>
+
 ### 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
+<CodeGroup>
+```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" });
+```
+</CodeGroup>
+
 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:
 
+<Tabs>
+<Tab title="Python">
 | 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` |
+| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
+</Tab>
+<Tab title="TypeScript">
+| 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"` |
+</Tab>
+</Tabs>

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<number[]> {
+    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<number[][]> {
+    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;
+  }
+}

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";

mem0-ts/src/oss/src/types/index.ts

@@ -19,6 +19,8 @@ export interface EmbeddingConfig {
   url?: string;
   embeddingDims?: number;
   modelProperties?: Record<string, any>;
+  // HuggingFace TEI / OpenAI-compatible inference endpoint base URL.
+  huggingfaceBaseUrl?: string;
 }
 
 export type { ValkeyConfig } from "./valkey";

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}`);
     }

mem0-ts/src/oss/tests/huggingface-embedder.test.ts

@@ -0,0 +1,149 @@
+/// <reference types="jest" />
+/**
+ * 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/,
+      );
+    });
+  });
+});