This repository provides sample code, notebooks, and resources for developing and managing generative AI workflows on Google Cloud, specifically leveraging Gemini and Vertex AI. It targets developers and researchers looking to implement solutions for text generation, search, retrieval-augmented generation (RAG), image generation, and speech processing.

How It Works

The repository is organized into distinct folders, each focusing on a specific area of Generative AI on Google Cloud. It utilizes Vertex AI, Google's managed machine learning platform, to provide access to models like Gemini and Imagen. The samples demonstrate various use cases, including starter notebooks, function calling, sample applications, and end-to-end solutions for tasks like building custom search engines and implementing RAG.

Quick Start & Requirements

• Setup: Follow instructions in the setup-env/ directory for Google Cloud setup, Vertex AI Python SDK, and notebook environments (Colab, Vertex AI Workbench).
• Dependencies: Google Cloud account, Vertex AI Python SDK.
• Resources: Links to official documentation, demos, and learning resources are available within the repository.

Highlighted Details

• Demonstrates Gemini 2.5 Pro capabilities.
• Includes samples for Vertex AI Search (formerly Enterprise Search on Generative AI App Builder).
• Features Retrieval Augmented Generation (RAG) and Grounding examples.
• Provides Imagen API samples for image generation, editing, and visual question answering.
• Offers Chirp API samples for speech processing.

Maintenance & Community

• Contributions are welcome via the Contributing Guide.
• Issues can be reported on the GitHub issues page for suggestions, feedback, or bug reports.

Licensing & Compatibility

• The repository contains sample code and is intended for demonstrative purposes.
• It is not an officially supported Google product.

Limitations & Caveats

The code in this repository is for demonstrative purposes only and is not an officially supported Google product.