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Saket Aryan
2025-03-01 06:07:05 +05:30
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docs/components/llms/config.mdx
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@@ -6,26 +6,40 @@ iconType: "solid"
## How to define configurations?
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
<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):
1. Values explicitly set in the `config` dictionary
1. Values explicitly set in the `config` object/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.
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:
Here's a general example of how to use the config with Mem0:
```python
<CodeGroup>
```python Python
import os
from mem0 import Memory
@@ -44,39 +58,70 @@ 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:
1. Specifying which llm to use.
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.
3. 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:
Here's a comprehensive list of all parameters that can be used across different LLMs:
Here's the table based on the provided parameters:
| 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 |
<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 |
</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](./models) section. There you'll find information for each supported llm with provider-specific usage examples and configuration details.
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.