Groq Function Calling Llama 3 - How to Integrate Custom API in AI App?

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AI Summary

Summary: Building an AI Application with API Integration Using Llama 3

• Introduction
  • Excitement about demonstrating Gro function calling with Llama 3.
  • Step-by-step guide to create an API, integrate with AI app, and create a user interface.
  • Reminder to subscribe to the YouTube channel for more AI content.
• Creating the API
  • Use Flask to create a microservice API.
  • Write a function get_game_score to return scores based on team names.
  • Set up an endpoint /score to handle requests.
  • Run the Flask application to make the API live.
• Integrating API with AI Application
  • Activate a virtual environment and install necessary packages.
  • Export Gro API key.
  • Initialize Gro and set up the Llama 3 model.
  • Write a function get_game_score to call the API and return data.
  • Create a function run_conversation to handle user prompts and system messages.
  • Define a tool for the language model to get NBA game scores.
  • Use Grock API to handle tool calls and return responses.
• User Interface Creation
  • Modify code to import Gradio.
  • Use Gradio to create a simple user interface with text input and output.
  • Launch the interface to allow users to ask questions and receive responses.
• Testing and Conclusion
  • Run the AI application and test with a question about the Golden State Warriors’ score.
  • Demonstrate the quick and accurate response from the AI application.
  • Encourage viewers to like, share, subscribe, and stay tuned for more videos.