generative-ai

This repository is a collection of sample code, Jupyter notebooks, and small demo apps maintained by Google Cloud Platform to show developers how to use generative AI on Google's cloud. A Jupyter notebook is an interactive document that mixes code, written explanations, and the results of running that code in one place, which makes it a popular way to learn new techniques step by step.

The material is split into folders by topic. The gemini folder holds starter notebooks and use cases for Google's Gemini family of models, including function calling and sample applications. The search folder covers Agent Search, a Google-managed solution for quickly building search engines over websites or enterprise data. The rag-grounding folder gathers examples about retrieval augmented generation and grounding, which are techniques for letting a model pull in outside information so its answers stay tied to real data. There are also folders for vision (using Imagen and Veo for image and video work), audio (using Chirp, Google's universal speech model), and setup instructions covering the Google Cloud environment, the Gen AI Python SDK, and notebook hosting on Google Colab and Workbench.

The audience is developers and teams who want to build on Google Cloud's generative AI offering — particularly the Gemini Enterprise Agent Platform, described as the latest evolution of Vertex AI. The README also points to many related repositories, including the Agent Development Kit samples, the Agent Starter Pack of production-ready templates, and the Gemini Cookbook. The full README is longer than what was provided.