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

# Nebius

> Large Language Model services using Nebius Token Factory's OpenAI-compatible API

## Overview

`NebiusLLMService` provides chat completion capabilities using Nebius Token Factory's API with OpenAI-compatible interface. It supports streaming responses and function calling.

<CardGroup cols={2}>
  <Card title="Nebius LLM API Reference" icon="code" href="https://reference-server.pipecat.ai/en/latest/api/pipecat.services.nebius.llm.html">
    Pipecat's API methods for Nebius integration
  </Card>

  <Card title="Example Implementation" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/14v-function-calling-nebius.py">
    Function calling example with Nebius
  </Card>

  <Card title="Nebius Documentation" icon="book" href="https://nebius.com/">
    Official Nebius documentation
  </Card>

  <Card title="Nebius Token Factory" icon="globe" href="https://api.tokenfactory.nebius.com/">
    Access models and manage API keys
  </Card>
</CardGroup>

## Installation

To use Nebius LLM services, install the required dependencies:

```bash theme={null}
uv add "pipecat-ai[nebius]"
```

## Prerequisites

### Nebius Account Setup

Before using Nebius LLM services, you need:

1. **Nebius Account**: Sign up at [Nebius](https://nebius.com/)
2. **API Key**: Generate an API key from the Token Factory dashboard
3. **Model Selection**: Choose from available models (default: `openai/gpt-oss-120b`)

### Required Environment Variables

* `NEBIUS_API_KEY`: Your Nebius API key for authentication

## Configuration

<ParamField path="api_key" type="str" required>
  The API key for accessing Nebius's API.
</ParamField>

<ParamField path="base_url" type="str" default="https://api.tokenfactory.nebius.com/v1/">
  The base URL for the Nebius API. Override if using a different endpoint.
</ParamField>

<ParamField path="settings" type="NebiusLLMService.Settings" default="None">
  Runtime-configurable model settings. See [Settings](#settings) below.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `NebiusLLMService.Settings(...)`. These can be updated mid-conversation with `LLMUpdateSettingsFrame`. See [Service Settings](/guides/fundamentals/service-settings) for details.

| Parameter           | Type    | Default                 | Description                                                                            |
| ------------------- | ------- | ----------------------- | -------------------------------------------------------------------------------------- |
| `model`             | `str`   | `"openai/gpt-oss-120b"` | Nebius model identifier. Check Nebius Token Factory for available models.              |
| `temperature`       | `float` | `NOT_GIVEN`             | Sampling temperature (0.0 to 2.0). Lower values are more focused, higher are creative. |
| `max_tokens`        | `int`   | `NOT_GIVEN`             | Maximum tokens to generate.                                                            |
| `top_p`             | `float` | `NOT_GIVEN`             | Top-p (nucleus) sampling (0.0 to 1.0). Controls diversity of output.                   |
| `frequency_penalty` | `float` | `NOT_GIVEN`             | Penalty for frequent tokens (-2.0 to 2.0). Positive values discourage repetition.      |
| `presence_penalty`  | `float` | `NOT_GIVEN`             | Penalty for new topics (-2.0 to 2.0). Positive values encourage new topics.            |

<Note>
  `NOT_GIVEN` values are omitted from the API request entirely, letting the
  Nebius API use its own defaults. This is different from `None`, which would be
  sent explicitly.
</Note>

## Usage

### Basic Setup

```python theme={null}
import os
from pipecat.services.nebius import NebiusLLMService

llm = NebiusLLMService(
    api_key=os.getenv("NEBIUS_API_KEY"),
)
```

### With Custom Settings

```python theme={null}
import os
from pipecat.services.nebius import NebiusLLMService

llm = NebiusLLMService(
    api_key=os.getenv("NEBIUS_API_KEY"),
    settings=NebiusLLMService.Settings(
        model="openai/gpt-oss-120b",
        temperature=0.7,
        max_tokens=1000,
    ),
)
```

### Updating Settings at Runtime

Model settings can be changed mid-conversation using `LLMUpdateSettingsFrame`:

```python theme={null}
from pipecat.frames.frames import LLMUpdateSettingsFrame
from pipecat.services.nebius.llm import NebiusLLMSettings

await task.queue_frame(
    LLMUpdateSettingsFrame(
        delta=NebiusLLMSettings(
            temperature=0.3,
        )
    )
)
```

## Notes

* **OpenAI Compatibility**: Nebius's API is OpenAI-compatible, allowing use of familiar patterns and parameters.
* **Function Calling**: Supports OpenAI-style tool/function calling format.
* **Streaming**: Supports streaming responses for real-time interaction.

## Event Handlers

`NebiusLLMService` supports the following event handlers, inherited from [LLMService](/server/events/service-events):

| Event                       | Description                                                             |
| --------------------------- | ----------------------------------------------------------------------- |
| `on_completion_timeout`     | Called when an LLM completion request times out                         |
| `on_function_calls_started` | Called when function calls are received and execution is about to start |

```python theme={null}
@llm.event_handler("on_completion_timeout")
async def on_completion_timeout(service):
    print("LLM completion timed out")
```