80 lines
3.5 KiB
Plaintext
80 lines
3.5 KiB
Plaintext
## What is Config?
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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
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The config is defined as a Python dictionary with two main keys:
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- `llm`: Specifies the llm provider and its configuration
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- `provider`: The name of the llm (e.g., "openai", "groq")
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- `config`: A nested dictionary containing provider-specific settings
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### Config Values Precedence
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Config values are applied in the following order of precedence (from highest to lowest):
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1. Values explicitly set in the `config` dictionary
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2. Environment variables (e.g., `OPENAI_API_KEY`, `OPENAI_API_BASE`)
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3. Default values defined in the LLM implementation
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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
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Here's a general example of how to use the config with mem0:
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "sk-xx" # for embedder
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config = {
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"llm": {
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"provider": "your_chosen_provider",
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"config": {
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# Provider-specific settings go here
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}
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}
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}
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m = Memory.from_config(config)
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m.add("Your text here", user_id="user", metadata={"category": "example"})
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```
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## Why is Config Needed?
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Config is essential for:
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1. Specifying which llm to use.
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2. Providing necessary connection details (e.g., model, api_key, temperature).
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3. Ensuring proper initialization and connection to your chosen llm.
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## Master List of All Params in Config
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Here's a comprehensive list of all parameters that can be used across different llms:
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Here's the table based on the provided parameters:
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| Parameter | Description | Provider |
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|----------------------|-----------------------------------------------|-------------------|
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| `model` | Embedding model to use | All |
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| `temperature` | Temperature of the model | All |
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| `api_key` | API key to use | All |
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| `max_tokens` | Tokens to generate | All |
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| `top_p` | Probability threshold for nucleus sampling | All |
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| `top_k` | Number of highest probability tokens to keep | All |
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| `http_client_proxies`| Allow proxy server settings | AzureOpenAI |
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| `models` | List of models | Openrouter |
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| `route` | Routing strategy | Openrouter |
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| `openrouter_base_url`| Base URL for Openrouter API | Openrouter |
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| `site_url` | Site URL | Openrouter |
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| `app_name` | Application name | Openrouter |
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| `ollama_base_url` | Base URL for Ollama API | Ollama |
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| `openai_base_url` | Base URL for OpenAI API | OpenAI |
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| `azure_kwargs` | Azure LLM args for initialization | AzureOpenAI |
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## Supported LLMs
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For detailed information on configuring specific llms, please visit the [LLMs](./models) section. There you'll find information for each supported llm with provider-specific usage examples and configuration details.
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