## Config in mem0

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:

### Python

```python
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
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" } });
```

## 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:

| 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                  |

| Parameter                     | Description                                        | Provider                   |
|-------------------------------|----------------------------------------------------|----------------------------|
| `model`                       | Embedding model to use                             | All                        |
| `apiKey`                     | API key of the provider                             | All                        |
| `embeddingDims`              | Dimensions of the embedding model                   | All                        |

## Supported Embedding Models

For detailed information on configuring specific embedders, please visit the [Embedding Models](https://docs.mem0.ai/components/embedders/models) section. There you’ll find information for each supported embedder with provider-specific usage examples and configuration details.