# Get started

This guide explains how to integrate the `ojin/oris-1.0` persona model into your applications using either Pipecat or Websockets

## Prerequisites

1. An Ojin account with an active API key, if you don't have one [get your API key](https://github.com/journee-live/ojin/blob/main/docs/public/models/ojin/oris/authentication.md)
2. [Create a Persona](https://docs.ojin.ai/models/oris/creating-persona) or use a [Persona Template](https://docs.ojin.ai/models/oris/using-persona-template)
3. Save the Persona Configuration ID from the dashboard
4. Integrate with your application using either [Pipecat](#pipecat-integration) or [Websockets](#websocket-integration)

{% hint style="info" %}
**Production deployments:** For secure, low-latency video applications, connect to the real-time WebSocket API from a backend server rather than a front-end client (to keep your API key secure and leverage a network transport appropriate for real-time video media delivery under varying network conditions). Typically, WebRTC is used to deliver the final media stream to end users for smooth, reliable, low-latency playback.
{% endhint %}

{% tabs %}
{% tab title="Pipecat" %}

### Pipecat Integration

[Pipecat](https://github.com/pipecat-ai/pipecat) is a powerful open source framework for building conversational AI pipelines. The `ojin/oris-1.0` model integrates seamlessly with Pipecat through our dedicated `OjinVideoService`.

#### Option 1: Clone pipecat repository and checkout the ready to use [ojin-chatbot example](https://github.com/journee-live/pipecat-ojin/tree/main/examples/ojin-chatbot)

To start using it, create a python virtual environment on it and install requirements

```bash
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

Create a `.env` file and add your Ojin API key and persona ID

```bash
OJIN_API_KEY="your_api_key_here"
OJIN_CONFIG_ID="your_persona_id_here"
```

Then just run the [mock\_bot.py](https://github.com/journee-live/pipecat-ojin/blob/main/examples/ojin-chatbot/mock/mock_bot.py) to check that your ojin setup is correct and see a generation out of a wav file:

```bash
python mock/mock_bot.py
```

Alternatively, you can configure all required environment variables for the services used in this example (such as Hume) by referring to env.example. Once configured, you can interact with a conversational, human-like bot using your local audio input/output

```bash
python bot.py
```

#### How It Works

1. The microphone listens for speech input
2. Voice Activity Detection identifies speech segments
3. User audio is sent to Hume to get an LLM response using their Speech-To-Speech service.
4. The OjinVideoService animates your persona based on the STS audio.
5. Video frames are received and displayed in real-time together with the audio.

{% hint style="info" %}
You can customize the pipeline by adding or removing components, or by adjusting their parameters to suit your needs.
{% endhint %}
{% endtab %}

{% tab title="WebSocket" %}

### WebSocket Integration

For Websocket integration check our [API Reference →](https://docs.ojin.ai/models/oris/api)
{% endtab %}
{% endtabs %}

## Next Steps

#### API Reference

Dive deeper into the model API for custom integrations

[API Reference →](https://docs.ojin.ai/models/oris/api)