> ## 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.

# Inception

> LLM service implementation using Inception's API with OpenAI-compatible interface

## Overview

`InceptionLLMService` provides access to Inception's Mercury-2 diffusion-based reasoning model through an OpenAI-compatible interface. It inherits from `OpenAILLMService` and supports streaming responses, function calling, and context management with advanced reasoning capabilities.

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

  <Card title="Example Implementation" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/function-calling/function-calling-inception.py">
    Complete example with function calling
  </Card>

  <Card title="Inception Labs" icon="microphone" href="https://inceptionlabs.ai/">
    Access models and manage API keys
  </Card>
</CardGroup>

## Installation

To use Inception services, install the required dependency:

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

## Prerequisites

### Inception Account Setup

Before using Inception LLM services, you need:

1. **Inception Account**: Sign up at [Inception Labs](https://inceptionlabs.ai/)
2. **API Key**: Generate an API key from your account dashboard
3. **Model Selection**: Access to Mercury-2, Inception's diffusion-based reasoning model

### Required Environment Variables

* `INCEPTION_API_KEY`: Your Inception API key for authentication

## Configuration

<ParamField path="api_key" type="str" required>
  Inception API key for authentication.
</ParamField>

<ParamField path="base_url" type="str" default="https://api.inceptionlabs.ai/v1">
  Base URL for Inception API endpoint.
</ParamField>

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

### Settings

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

This service extends `OpenAILLMService.Settings` with Inception-specific parameters:

<ParamField path="model" type="str" default="mercury-2">
  Model identifier to use. Defaults to "mercury-2", Inception's diffusion-based reasoning model.
</ParamField>

<ParamField path="reasoning_effort" type="Literal['instant', 'low', 'medium', 'high'] | None" default="None">
  Controls how much reasoning the model applies. Options are "instant", "low", "medium", or "high". When unset, the parameter is omitted and Inception's server-side default applies.
</ParamField>

<ParamField path="realtime" type="bool | None" default="None">
  When True, reduces time to first diffusion block (TTFT) for faster initial response times.
</ParamField>

For additional settings inherited from OpenAI, see [OpenAI LLM Settings](/api-reference/server/services/llm/openai#settings).

## Usage

### Basic Setup

```python theme={null}
import os
from pipecat.services.inception import InceptionLLMService

llm = InceptionLLMService(
    api_key=os.getenv("INCEPTION_API_KEY"),
)
```

### With Custom Settings

```python theme={null}
from pipecat.services.inception import InceptionLLMService

llm = InceptionLLMService(
    api_key=os.getenv("INCEPTION_API_KEY"),
    settings=InceptionLLMService.Settings(
        model="mercury-2",
        reasoning_effort="instant",
        realtime=True,
        temperature=0.7,
        max_tokens=2048,
    ),
)
```

### With Function Calling

```python theme={null}
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.services.inception import InceptionLLMService
from pipecat.services.llm_service import FunctionCallParams

# A direct function: schema is auto-derived from the signature and docstring
async def get_weather(params: FunctionCallParams, location: str):
    """Get the current weather.

    Args:
        location: The city and state, e.g. "San Francisco, CA".
    """
    await params.result_callback({"temperature": "75", "conditions": "sunny"})

llm = InceptionLLMService(
    api_key=os.getenv("INCEPTION_API_KEY"),
    settings=InceptionLLMService.Settings(
        reasoning_effort="low",
    ),
)

context = LLMContext(tools=[get_weather])
```

## Notes

* Inception does not support the `"developer"` message role. Use `"system"` instead.
* The Mercury-2 model uses a diffusion-based reasoning approach, which can be controlled via the `reasoning_effort` parameter.
* Setting `realtime=True` optimizes for lower time-to-first-token at the potential cost of reasoning depth.

<Tip>
  The `InputParams` / `params=` pattern is deprecated as of v0.0.105. Use
  `Settings` / `settings=` instead. See the [Service Settings
  guide](/pipecat/fundamentals/service-settings) for migration details.
</Tip>