## Create

`chat.completions.create(CompletionCreateParams**kwargs)  -> CreateChatCompletionResponse`

**post** `/chat/completions`

Generate a chat completion for the given messages using the specified model.

### Parameters

- **messages:** `Iterable[MessageParam]`

  List of messages in the conversation.

  - `UserMessage`

  - `SystemMessage`

  - `ToolResponseMessage`

  - `CompletionMessage`

- **model:** `str`

  The identifier of the model to use.

- **max\_completion\_tokens:** `int`

  The maximum number of tokens to generate.

- **repetition\_penalty:** `float`

  Controls the likelyhood and generating repetitive responses.

- **response\_format:** `ResponseFormat`

  An object specifying the format that the model must output.
  Setting to `{ "type": "json_schema", "json_schema": {...} }` enables Structured Outputs which ensures the model will match your supplied JSON schema.
  If not specified, the default is {"type": "text"}, and model will return a free-form text response.

  - `class ResponseFormatJsonSchemaResponseFormat`

    Configuration for JSON schema-guided response generation.

    - **json\_schema:** `ResponseFormatJsonSchemaResponseFormatJsonSchema`

      The JSON schema the response should conform to.

      - **name:** `str`

        The name of the response format.

      - **schema:** `object`

        The JSON schema the response should conform to. In a Python SDK, this is often a `pydantic` model.

    - **type:** `Literal["json_schema"]`

      The type of response format being defined. Always `json_schema`.

      - `"json_schema"`

  - `class ResponseFormatTextResponseFormat`

    Configuration for text-guided response generation.

    - **type:** `Literal["text"]`

      The type of response format being defined. Always `text`.

      - `"text"`

- **stream:** `Literal[false]`

  If True, generate an SSE event stream of the response. Defaults to False.

  - `false`

- **temperature:** `float`

  Controls randomness of the response by setting a temperature. Higher value leads to more creative responses. Lower values will make the response more focused and deterministic.

- **tool\_choice:** `ToolChoice`

  Controls which (if any) tool is called by the model.
  `none` means the model will not call any tool and instead generates a message.
  `auto` means the model can pick between generating a message or calling one or more tools.
  `required` means the model must call one or more tools.
  Specifying a particular tool via `{"type": "function", "function": {"name": "my_function"}}` forces the model to call that tool.

  `none` is the default when no tools are present. `auto` is the default if tools are present.

  - **ToolChoiceUnionMember0:** `Literal["none", "auto", "required"]`

    `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means the model must call one or more tools.

    - `"none"`

    - `"auto"`

    - `"required"`

  - `class ToolChoiceChatCompletionNamedToolChoice`

    Specifies a tool the model should use. Use to force the model to call a specific function.

    - **function:** `ToolChoiceChatCompletionNamedToolChoiceFunction`

      - **name:** `str`

        The name of the function to call.

    - **type:** `Literal["function"]`

      The type of the tool. Currently, only `function` is supported.

      - `"function"`

- **tools:** `Iterable[Tool]`

  List of tool definitions available to the model

  - **function:** `ToolFunction`

    - **name:** `str`

      The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.

    - **description:** `str`

      A description of what the function does, used by the model to choose when and how to call the function.

    - **parameters:** `Dict[str, object]`

      The parameters the functions accepts, described as a JSON Schema object. Omitting `parameters` defines a function with an empty parameter list.

    - **strict:** `bool`

      Whether to enable strict schema adherence when generating the function call. If set to true, the model will follow the exact schema defined in the `parameters` field. Only a subset of JSON Schema is supported when `strict` is `true`. Learn more about Structured Outputs in the [function calling guide](docs/guides/function-calling).

  - **type:** `Literal["function"]`

    The type of the tool. Currently, only `function` is supported.

    - `"function"`

- **top\_k:** `int`

  Only sample from the top K options for each subsequent token.

- **top\_p:** `float`

  Controls diversity of the response by setting a probability threshold when choosing the next token.

- **user:** `str`

  A unique identifier representing your application end-user for monitoring abuse.

### Returns

- `CreateChatCompletionResponse`

### Example

```python
from llama_api_client import LlamaAPIClient

client = LlamaAPIClient(
    api_key="My API Key",
)
create_chat_completion_response = client.chat.completions.create(
    messages=[{
        "content": "string",
        "role": "user",
    }],
    model="model",
)
print(create_chat_completion_response.id)
```