> ## 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.

# Configurations

> Reference for embedder configuration options in Mem0, including provider selection and model settings.

Config in mem0 is a dictionary that specifies the settings for your embedding models. It allows you to customize the behavior and connection details of your chosen embedder.

## How to define configurations?

The config is defined as an object (or dictionary) with two main keys:

* `embedder`: Specifies the embedder provider and its configuration
  * `provider`: The name of the embedder (e.g., "openai", "ollama")
  * `config`: A nested object or dictionary containing provider-specific settings

## How to use configurations?

Here's a general example of how to use the config with mem0:

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

  os.environ["OPENAI_API_KEY"] = "sk-xx"

  config = {
      "embedder": {
          "provider": "your_chosen_provider",
          "config": {
              # Provider-specific settings go here
          }
      }
  }

  m = Memory.from_config(config)
  m.add("Your text here", user_id="user", metadata={"category": "example"})
  ```

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

  const config = {
    embedder: {
      provider: 'openai',
      config: {
        apiKey: process.env.OPENAI_API_KEY || '',
        model: 'text-embedding-3-small',
        // Provider-specific settings go here
      },
    },
  };

  const memory = new Memory(config);
  await memory.add("Your text here", { userId: "user", metadata: { category: "example" } });
  ```
</CodeGroup>

## Why is Config Needed?

Config is essential for:

1. Specifying which embedding model to use.
2. Providing necessary connection details (e.g., model, api\_key, embedding\_dims).
3. Ensuring proper initialization and connection to your chosen embedder.

## Master List of All Params in Config

Here's a comprehensive list of all parameters that can be used across different embedders:

<Tabs>
  <Tab title="Python">
    | Parameter                      | Description                                                 | Provider     |
    | ------------------------------ | ----------------------------------------------------------- | ------------ |
    | `model`                        | Embedding model to use                                      | All          |
    | `api_key`                      | API key of the provider                                     | All          |
    | `embedding_dims`               | Dimensions of the embedding model                           | All          |
    | `http_client_proxies`          | Allow proxy server settings                                 | All          |
    | `ollama_base_url`              | Base URL for the Ollama embedding model                     | Ollama       |
    | `model_kwargs`                 | Key-Value arguments for the Huggingface embedding model     | Huggingface  |
    | `azure_kwargs`                 | Key-Value arguments for the AzureOpenAI embedding model     | Azure OpenAI |
    | `openai_base_url`              | Base URL for OpenAI API                                     | OpenAI       |
    | `vertex_credentials_json`      | Path to the Google Cloud credentials JSON file for VertexAI | VertexAI     |
    | `memory_add_embedding_type`    | The type of embedding to use for the add memory action      | VertexAI     |
    | `memory_update_embedding_type` | The type of embedding to use for the update memory action   | VertexAI     |
    | `memory_search_embedding_type` | The type of embedding to use for the search memory action   | VertexAI     |
    | `lmstudio_base_url`            | Base URL for LM Studio API                                  | LM Studio    |
  </Tab>

  <Tab title="TypeScript">
    | Parameter       | Description                       | Provider |
    | --------------- | --------------------------------- | -------- |
    | `model`         | Embedding model to use            | All      |
    | `apiKey`        | API key of the provider           | All      |
    | `embeddingDims` | Dimensions of the embedding model | All      |
  </Tab>
</Tabs>

## Supported Embedding Models

For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.