Conversational AI

This tutorial demonstrates building a stateful chat application using React and the Gemini API. It leverages React's state management to maintain conversation history (`messages`, `input`, `isLoading`), automatically scrolling to new messages using `useRef` and `useEffect`. The core functionality lies in `callGeminiAPI`, which sends the entire conversation history to the Gemini API for context-aware responses, incorporating exponential backoff for error handling. The UI, built with JSX and Tailwind CSS, displays messages differently based on sender (user/model) and includes a simple input form. The complete code is provided for a functional application.

This text explains Gemini API's Function Calling, enabling the model to interact with external tools. Instead of only generating text, the model identifies needed functions, provides parameters, and receives results to formulate responses. This allows for real-time information retrieval, action execution on the user's behalf, and private data access. A four-step process is detailed: defining tools via function declarations, user requests, model function calls, and application execution of the function and returning results to the model. A Python example demonstrates building an e-commerce tool using `google.generativeai`, retrieving product price and stock. Best practices include descriptive function declarations, strong typing, error handling, result filtering/summarization, and multi-turn conversation management.

This guide details advanced Gemini API features beyond basic text input/output. It covers multimodal prompts (using text, images, video, audio), function calling (connecting Gemini to external tools and APIs), building stateful chat applications, and utilizing Gemini's "Deep Research" capabilities for analyzing large datasets and generating comprehensive reports. These features enable the creation of sophisticated and interactive applications leveraging Gemini's powerful reasoning abilities.