Generative AI

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.

Gemini, a multimodal model, processes text, images, videos, and audio. Multimodal prompts combine different data types (e.g., an image and a question) in a single request. The API accepts an array of "parts," each a text string or inline data (like a base64-encoded image). A Python example demonstrates sending an image and text prompt to the Gemini API to analyze the image. Best practices include specific instructions, placing images before text in the `contents` array, and using the correct MIME type for image data.

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.