Configurations - Mem0

How to define configurations?

<Tabs> <Tab title="Python"> The `config` is defined as a Python dictionary with two main keys: - `llm`: Specifies the llm provider and its configuration - `provider`: The name of the llm (e.g., "openai", "groq") - `config`: A nested dictionary containing provider-specific settings </Tab> <Tab title="TypeScript"> The `config` is defined as a TypeScript object with these keys: - `llm`: Specifies the LLM provider and its configuration (required) - `provider`: The name of the LLM (e.g., "openai", "groq") - `config`: A nested object containing provider-specific settings - `embedder`: Specifies the embedder provider and its configuration (optional) - `vectorStore`: Specifies the vector store provider and its configuration (optional) - `historyDbPath`: Path to the history database file (optional) </Tab> </Tabs>

Config Values Precedence

Config values are applied in the following order of precedence (from highest to lowest):

Values explicitly set in the config object/dictionary
Environment variables (e.g., OPENAI_API_KEY, OPENAI_BASE_URL)
Default values defined in the LLM implementation

This means that values specified in the config will override corresponding environment variables, which in turn override default values.

How to Use Config

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" # for embedder

config = { "llm": { "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';

// Minimal configuration with just the LLM settings
const config = {
  llm: {
    provider: 'your_chosen_provider',
    config: {
      // Provider-specific settings go here
    }
  }
};

const memory = new Memory(config);
await memory.add("Your text here", { userId: "user123", metadata: { category: "example" } });

</CodeGroup>

Why is Config Needed?

Config is essential for:

Specifying which LLM to use.
Providing necessary connection details (e.g., model, api_key, temperature).
Ensuring proper initialization and connection to your chosen LLM.

Master List of All Params in Config

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

<Tabs> <Tab title="Python"> | Parameter | Description | Provider | |----------------------|-----------------------------------------------|-------------------| | `model` | Embedding model to use | All | | `temperature` | Temperature of the model | All | | `api_key` | API key to use | All | | `max_tokens` | Tokens to generate | All | | `top_p` | Probability threshold for nucleus sampling | All | | `top_k` | Number of highest probability tokens to keep | All | | `http_client_proxies`| Allow proxy server settings | AzureOpenAI | | `models` | List of models | Openrouter | | `route` | Routing strategy | Openrouter | | `openrouter_base_url`| Base URL for Openrouter API | Openrouter | | `site_url` | Site URL | Openrouter | | `app_name` | Application name | Openrouter | | `ollama_base_url` | Base URL for Ollama API | Ollama | | `openai_base_url` | Base URL for OpenAI API | OpenAI | | `azure_kwargs` | Azure LLM args for initialization | AzureOpenAI | | `deepseek_base_url` | Base URL for DeepSeek API | DeepSeek | | `xai_base_url` | Base URL for XAI API | XAI | | `sarvam_base_url` | Base URL for Sarvam API | Sarvam | | `reasoning_effort` | Reasoning level (low, medium, high) | Sarvam | | `frequency_penalty` | Penalize frequent tokens (-2.0 to 2.0) | Sarvam | | `presence_penalty` | Penalize existing tokens (-2.0 to 2.0) | Sarvam | | `seed` | Seed for deterministic sampling | Sarvam | | `stop` | Stop sequences (max 4) | Sarvam | | `lmstudio_base_url` | Base URL for LM Studio API | LM Studio | | `response_callback` | LLM response callback function | OpenAI | </Tab> <Tab title="TypeScript"> | Parameter | Description | Provider | |----------------------|-----------------------------------------------|-------------------| | `model` | Embedding model to use | All | | `temperature` | Temperature of the model | All | | `apiKey` | API key to use | All | | `maxTokens` | Tokens to generate | All | | `topP` | Probability threshold for nucleus sampling | All | | `topK` | Number of highest probability tokens to keep | All | | `openaiBaseUrl` | Base URL for OpenAI API | OpenAI | </Tab> </Tabs>

Supported LLMs

For detailed information on configuring specific LLMs, please visit the LLMs section. There you'll find information for each supported LLM with provider-specific usage examples and configuration details.