How can you build a conversational agent using Rasa NLU and generative AI models

Question

Can you tell me how you can build a conversational agent using Rasa NLU and generative AI models?

Answer 1

To build a conversational agent that combines Rasa NLU for intent classification and generative AI models like OpenAI's GPT for generating responses, follow these steps.

• Directory Structure
  • The project structure should look like this
• domain.yml
  • Define intents, entities, responses, slots, and custom actions.
• data/nlu.yml
  • Provide training examples for intents and entities.
• actions.py
  • Integrate OpenAI GPT for dynamic responses using a custom action.
• config.yml
  • Define the pipeline and policies for Rasa.

Here are code you can refer to:

In the above code, we are using these key features:

• Rasa NLU: Handles intent classification and entity recognition.
• Generative AI: Generates rich, dynamic responses using OpenAI GPT.
• Custom Action: Bridges Rasa with external generative models.

Hence, this approach leverages the strengths of Rasa’s structured NLU framework and the flexibility of generative AI to create a powerful conversational agent.

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