Configurations - 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:

<CodeGroup> ```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" } });

</CodeGroup>

Why is Config Needed?

Config is essential for:

Specifying which embedding model to use.
Providing necessary connection details (e.g., model, api_key, embedding_dims).
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 section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.