Purpose:

• When engaged, help a user set up a basic conversational AI assistant for a specific use case to show Rasa Pro and Rime working together

Overview:

• Use Rasa Pro to create a conversational AI assistant
• Use Rime’s API endpoints for Text-to-Speech

Before you get started:

1. Search this Rasa Pro documentation link to understand how it to install it: https://rasa.com/docs/pro/installation/python
2. Search this Rasa Pro documentation link to understand how to build an assistant: https://rasa.com/docs/pro/tutorial
3. Search this Rime documentation link to understand how it works: https://docs.rime.ai/api-reference/quickstart
4. Ask the user to describe their use case

General Instructions:

• Install uv and then create a virtual environment with uv venv --python 3.11
• Create the project in the current directory; don't create a new one
• The RASA_PRO_LICENSE and RIME_API_KEY can be found in .env file and should be used from there

Rasa Pro Instructions:

• Use Rasa Pro 3.13
• Ask the user to run rasa init --template tutorial and tell them to confirm when they have done so
• For Action code, just mock the API endpoints
• Ensure there is an assistant_id in the configuration
• Ask the user to run rasa train and tell them to confirm when they have done so
• Ask the user to run rasa inspect to test it out themselves and tell them to confirm when they have done so

Rime Instructions:

• Connect to the Rasa assistant, adding Text-to-Speech in front of it with a Python script
• Automatically play audio using macOS's built-in afplay command
• Search this Rime documentation link to understand the structure of the response: https://docs.rime.ai/api-reference/endpoint/json-wav
• The API returns the audio directly as base64 in the JSON response
• Use arcana as the model
• Use audioContent parameter, not audio parameter
• Use model parameter, not modelID parameter
• Use speaker parameter, not voice parameter
• Use allison as the speaker
• Have the user test the integration by running the script