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

# Chat

> API reference for client.chat

## `client.chat.complete()`

Send a chat completion request and return the full response.

```python theme={null}
response = client.chat.complete(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "Hello"}],
)
```

### Parameters

<ParamField path="model" type="str" required>
  Model ID. Example: `"kf-reasoning-10b"`.
</ParamField>

<ParamField path="messages" type="List[dict]" required>
  Conversation history as a list of `{"role": ..., "content": ...}` dicts.
  Roles: `"user"`, `"assistant"`, `"system"`.
</ParamField>

<ParamField path="system" type="str" optional>
  System prompt. Prepended automatically as a `system` message.
</ParamField>

<ParamField path="max_tokens" type="int" optional>
  Maximum tokens to generate. Default `1024`.
</ParamField>

<ParamField path="temperature" type="float" optional>
  Sampling temperature between `0.0` and `2.0`. Default `0.7`.
</ParamField>

<ParamField path="top_p" type="float" optional>
  Nucleus sampling probability. Default `1.0`.
</ParamField>

<ParamField path="extra" type="dict" optional>
  Any additional parameters passed through to the API.
</ParamField>

### Returns: `ChatCompletion`

| Field     | Type            | Description         |
| --------- | --------------- | ------------------- |
| `id`      | `str`           | Request ID          |
| `model`   | `str`           | Model used          |
| `choices` | `List[Choice]`  | Generated responses |
| `usage`   | `Usage` or None | Token usage         |

Each `Choice`:

| Field           | Type      | Description            |
| --------------- | --------- | ---------------------- |
| `index`         | `int`     | Index in choices list  |
| `message`       | `Message` | The generated message  |
| `finish_reason` | `str`     | Why generation stopped |

`Message` has `role` and `content` fields.

***

## `client.chat.stream()`

Send a streaming chat request. Returns a `ChatStream` context manager.

```python theme={null}
with client.chat.stream(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "Hello"}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)
```

### Parameters

Same as `complete()`.

### Returns: `ChatStream`

Use as a context manager and iterate over `StreamChunk` objects.

| Method             | Returns | Description                            |
| ------------------ | ------- | -------------------------------------- |
| `__iter__`         | chunks  | Yields `StreamChunk` objects           |
| `get_final_text()` | `str`   | Full concatenated text after iteration |

Each `StreamChunk`:

| Field           | Type          | Description                 |
| --------------- | ------------- | --------------------------- |
| `id`            | `str`         | Request ID                  |
| `model`         | `str`         | Model used                  |
| `delta`         | `str`         | Text content of this chunk  |
| `finish_reason` | `str` or None | `"stop"` on the final chunk |