> ## 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.

# Ollama

> Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models.

You can use embedding models from Ollama to run Mem0 locally.

### 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": "ollama",
          "config": {
              "model": "mxbai-embed-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';

  const config = {
    embedder: {
      provider: 'ollama',
      config: {
        model: 'nomic-embed-text:latest', // or any other Ollama embedding model
        url: 'http://localhost:11434', // Ollama server URL
      },
    },
  };

  const memory = new Memory(config);
  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>

### Config

Here are the parameters available for configuring Ollama embedder:

<Tabs>
  <Tab title="Python">
    | Parameter         | Description                         | Default Value      |
    | ----------------- | ----------------------------------- | ------------------ |
    | `model`           | The name of the Ollama model to use | `nomic-embed-text` |
    | `embedding_dims`  | Dimensions of the embedding model   | `512`              |
    | `ollama_base_url` | Base URL for ollama connection      | `None`             |
  </Tab>

  <Tab title="TypeScript">
    | Parameter       | Description                         | Default Value             |
    | --------------- | ----------------------------------- | ------------------------- |
    | `model`         | The name of the Ollama model to use | `nomic-embed-text:latest` |
    | `url`           | Base URL for Ollama server          | `http://localhost:11434`  |
    | `embeddingDims` | Dimensions of the embedding model   | 768                       |
  </Tab>
</Tabs>