Gemini Flash: Superior Function Calling for AI Agents

In this video, the host from Prompt Engineering explores the latest updates to the Gemini models, specifically focusing on the Pro and Flash versions. The update includes improved rate limits and the upcoming ability to fine-tune the Flash version on custom datasets. The video delves into the enhanced Json mode and function calling capabilities, which are crucial for building practical applications like customer support agents. The host explains that the Gemini Flash model strikes a balance between quality, price, and throughput, making it a competitive choice against other models like GPT-3.5 and Claude.

The video provides a detailed explanation of function calling, where an LLM can interact with real-time data through external APIs wrapped around functions. The host demonstrates how to build a customer support agent capable of performing sequential and parallel function calls. The setup involves installing the Google generative AI Python package, setting up API keys, and implementing basic functions like getting order status and initiating returns. The video shows how the Gemini Flash model handles function calls, executes them, and integrates the responses back into the LLM to generate final outputs.

The host also covers more complex scenarios, including nested function calls and adding new functions like canceling orders. The video demonstrates how the model can handle multiple functions and provides a step-by-step guide on executing function calls manually. The Gemini Flash model’s performance is highlighted, showing its ability to manage complex prompts and multiple function calls efficiently. The video concludes by emphasizing the model’s capabilities and its suitability for building advanced AI agents.