Config

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What is Config?

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

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How to Define Config

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

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Config Values Precedence

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

1. Values explicitly set in the config dictionary
2. Environment variables (e.g., OPENAI_API_KEY, OPENAI_API_BASE)
3. Default values defined in the LLM implementation

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

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How to Use Config

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

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"})

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Why is Config Needed?

Config is essential for:

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

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Master List of All Params in Config

Here’s a comprehensive list of all parameters that can be used across different llms:

Here’s the table based on the provided parameters:

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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.