> ## Documentation Index
> Fetch the complete documentation index at: https://docs.llmstudio.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Chat

Make chat calls using your LLM.

## Parameters

The llm.chat method can have the following parameters.

| Parameter   | Type | Description                                      |
| ----------- | ---- | ------------------------------------------------ |
| `input `    | str  | The input message to send to the chat model.     |
| `is_stream` | bool | The temperature parameter for the model.         |
| `**kwargs`  | dict | Additional parameters to pass to the chat model. |

<Check>Refer to your provider-specific documentation for additional kwargs you can use.</Check>

## Returns

| Output           | Type   | Description                                                                    |
| ---------------- | ------ | ------------------------------------------------------------------------------ |
| `ChatCompletion` | object | A chat completion object in the OpenAI format + metrics computed by LLMstudio. |

## Usage

Here's how to use `.chat()` to make calls to your LLM.

<Steps>
  <Step>
    Start by importing LLM.

    ```python theme={null}
    from llmstudio import LLM
    ```
  </Step>

  <Step>
    Set up an LLM from your desired provider.

    ```python theme={null}
    llm = LLM('openai/gpt-4o')
    ```
  </Step>

  <Step>
    Create your message. Your message can be a simple `string` or a message in the `OpenAI format`.

    <Tabs>
      <Tab title="String format">
        ```python theme={null}
        message = "Hello, how are you today?"
        ```
      </Tab>

      <Tab title="OpenAI format">
        ```python theme={null}
        message = [
        {"role": "system", "content": "You are a helpfull assistant."},
        {"role": "user", "content": "Hello, how are you today?"}
        ]
        ```
      </Tab>
    </Tabs>
  </Step>

  <Step>
    <Tabs>
      <Tab title="Non-stream response">
        Get your response.

        ```python theme={null}
        response = llm.chat(message)
        ```

        Vizualize your response.

        ```python theme={null}
        print(response)
        ```
      </Tab>

      <Tab title="Stream response">
        Get your response.

        ```python theme={null}
        response = llm.chat(message, is_stream = True)
        ```

        Vizualize your response.

        ```python theme={null}
        for chunk in response:
           print(chunk)
        ```
      </Tab>
    </Tabs>

    <Check>You are done chating with your **LLMstudio LLM**!</Check>
  </Step>
</Steps>