Building a Custom GPT Lead Generation Chatbot

Aug 10, 2024

Lecture Notes: Creating a Custom GPT Lead Generation Chatbot

Introduction

  • Lecturer: Liam Otley
  • Company: Morningside AI
  • Topic: Building a lead generation custom GPT with custom actions for websites using the assistance API.
  • Purpose: Move GPTs from ChatGPT site to real-world applications and add powerful custom actions.
  • Audience: Those experienced with ChatGPT builder wanting to extend its capabilities.
  • Outcome: By the end of the lecture, you'll know how to build custom GPTs using the assistance API, plan and implement custom actions for real-world value, and add these GPTs to websites.
  • Giveaway: The template built during the lecture is available for use.

Demo of Finished Product

  • Demonstration of a live example on a template website.
  • Features of the chatbot:
    • Knowledge Base: Answers FAQs about solar panels.
    • Solar Savings Calculation: Calculates potential solar savings using the Google Solar API.
    • Lead Capture: Collects user information for follow-up.

Explanation of Key Concepts

GPT Creation Methods

  • ChatGPT Builder:
    • Limited to ChatGPT site.
    • Suitable for consumer-level personal tools.
  • Assistance API:
    • Integrates GPT functionality into any application or website.
    • Provides significant business value and flexibility.

Components of the Lead Generation Chatbot

  • Prompting: Sets the chatbot's role and capabilities.
  • Knowledge: Uploading documents as the knowledge base.
  • Actions: Custom functions like solar calculations and lead capturing.

Building the Chatbot: Step-by-Step

Tools and Technologies Used

  • Assistance API: For creating powerful GPTs.
  • Google APIs: Solar API and Geocoding API for solar savings calculations.
  • Replit: Hosting the backend of the application.
  • Voiceflow: Front-end for the chatbot.

Setting Up Replit

  • Fork the provided Replit template.
  • Modify the main.py and functions.py files for custom functionalities.
  • Knowledge Update: Replace knowledge.docx with your document.
  • Instructions Update: Modify prompts.py for custom prompting.
  • Function Update: Define custom actions in functions.py.
  • Assistant ID Management: Handle assistant creation and re-creation by managing assistant.json.

Google Cloud Setup

  • Set up a Google Cloud project and enable necessary APIs: Geocoding API and Solar API.
  • Copy and store the API key in Replit secrets.

Airtable Setup

  • Use Airtable as a temporary CRM for lead logging.
  • Copy the provided Airtable base and generate a personal access token.
  • Store the Airtable API key in Replit secrets.
  • Update the Airtable base URL in the create_lead function in functions.py.

OpenAI API Setup

  • Get OpenAI API key and store it in Replit secrets.

Running and Testing the Chatbot

  • Run the Replit Application: Ensure it's running correctly and check the assistant.json file.
  • Voiceflow Integration: Import the Voiceflow template and update with Replit URLs.
  • Testing: Use Voiceflow to interact with the chatbot, verify functionalities like knowledge queries and solar savings calculations.

Deployment

  • Deploy the application using Replit’s deployment options for a production-grade setup.

Embedding on a Website

  • Copy the integration script from Voiceflow and add it to the website's HTML.
  • Test the chatbot on the website to ensure it functions correctly.

Conclusion

  • The chatbot is an effective lead generation tool that handles customer inquiries and prompts for lead information.
  • Encourages exploring more advanced integrations and deployments.
  • Resources and templates are available for building and customizing your own chatbot.
  • Next steps include subscribing to the channel for more detailed tutorials and joining the AI Business Accelerator community for deeper insights.

Additional Resources

  • Resource Hub: Access to all templates and materials used in the lecture.
  • Free Telegram: Join for daily GPT updates.
  • AI Business Accelerator: Community for learning and sharing AI business strategies.

Note: The lecture also includes discussions on optimizing API usage and reducing costs by removing unnecessary AI tasks in Voiceflow.