> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# FastEmbed

> Configure FastEmbed as an embedding provider in Mem0 to generate embeddings locally using ONNX-based models without a GPU.

You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an ONNX-based embedding library that runs efficiently on CPU without requiring a GPU or an external API key.

### Installation

FastEmbed is an optional dependency, so install it alongside Mem0.

<CodeGroup>
  ```bash Python theme={null}
  pip install fastembed
  ```

  ```bash TypeScript theme={null}
  npm install fastembed
  ```
</CodeGroup>

### Usage

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM

  config = {
      "embedder": {
          "provider": "fastembed",
          "config": {
              "model": "thenlper/gte-large"
          }
      }
  }

  m = Memory.from_config(config)
  messages = [
      {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
      {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
      {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
      {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
  ]
  m.add(messages, user_id="john")
  ```

  ```typescript TypeScript theme={null}
  import { Memory } from "mem0ai/oss";

  // FastEmbed needs no API key. Leave the embedder config empty to use the
  // default model (fast-bge-small-en-v1.5), or set `model` to one of the
  // supported models listed below.
  const memory = new Memory({
    embedder: {
      provider: "fastembed",
      config: {
        model: "fast-bge-small-en-v1.5",
      },
    },
    llm: {
      provider: "openai",
      config: { apiKey: process.env.OPENAI_API_KEY }, // For fact extraction
    },
  });

  const messages = [
    { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
    { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
    { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
    { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
  ];
  await memory.add(messages, { userId: "john" });
  ```
</CodeGroup>

<Note>
  **The Python and TypeScript SDKs default to different models.** Python defaults to `thenlper/gte-large` (1024 dimensions), while TypeScript defaults to `fast-bge-small-en-v1.5` (384 dimensions). The TypeScript package (`fastembed` on npm) ships a fixed set of ONNX models and does not include `thenlper/gte-large`. Because the two defaults produce vectors of different dimensions, do not point both SDKs at the same vector store collection unless you configure them to use the same model.
</Note>

The TypeScript SDK supports these FastEmbed models. Pass the exact string as `model`:

* `fast-bge-small-en-v1.5` (default)
* `fast-bge-small-en`
* `fast-bge-base-en`
* `fast-bge-base-en-v1.5`
* `fast-bge-small-zh-v1.5`
* `fast-all-MiniLM-L6-v2`
* `fast-multilingual-e5-large`

### Config

Here are the parameters available for configuring the FastEmbed embedder:

<Tabs>
  <Tab title="Python">
    | Parameter        | Description                                                                | Default Value        |
    | ---------------- | -------------------------------------------------------------------------- | -------------------- |
    | `model`          | The name of the FastEmbed model to use                                     | `thenlper/gte-large` |
    | `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None`               |
  </Tab>

  <Tab title="TypeScript">
    | Parameter | Description                                               | Default Value            |
    | --------- | --------------------------------------------------------- | ------------------------ |
    | `model`   | The FastEmbed model to use (see the supported list above) | `fast-bge-small-en-v1.5` |

    The embedding dimension is detected automatically at startup, so you do not need to set it manually.
  </Tab>
</Tabs>