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Create new READMEs for top level directories
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‎.cloud-build/README.md‎

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# Cloud Build
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This directory contains files related to Google Cloud Build, including build configurations, scripts, and related resources.
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Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. You can use it to build, test, and deploy applications.
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The files in this directory are used to define and customize the build process for projects within this repository.

‎.github/README.md‎

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# GitHub Configuration
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This directory contains GitHub-specific configuration files for the repository.
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These files include:
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- **Issue templates**: Standard templates for reporting bugs or requesting features.
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- **Workflow definitions**: Automated processes using GitHub Actions for tasks like linting, testing, and deploying.
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- **CODEOWNERS**: Specifies individuals or teams responsible for code in different parts of the repository.
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- Other GitHub-related settings and configurations.

‎audio/README.md‎

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# Audio Processing
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This directory contains resources and examples related to audio processing on Google Cloud.
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It includes materials for various audio tasks, such as:
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- **Speech Recognition**: Converting spoken language into text.
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- **Speech Synthesis**: Generating human-like speech from text.
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- Other audio analysis and manipulation tools and techniques.
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Explore the subdirectories for specific examples and use cases.

‎genkit/README.md‎

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# Genkit
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# Genkit with Google Cloud
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This repository contains solutions using [Firebase Genkit](https://firebase.google.com/docs/genkit).
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This directory contains examples and solutions demonstrating the use of [Firebase Genkit](https://firebase.google.com/docs/genkit) integrated with Google Cloud services.
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Genkit is an open-source framework that helps developers build, deploy, and manage AI-powered applications. The examples here showcase how Genkit can be leveraged for various generative AI tasks on Google Cloud.
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## Directory Structure
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- **generate-synthetic-database/**: This directory contains the source code for a Google Cloud Function that generates a synthetic database.
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- **postcard-generator/**: This directory contains the source code for a Next.js app that generates postcard images based on a journey
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- **generate-synthetic-database/**: Contains the source code for a Google Cloud Function that uses Genkit to generate a synthetic database.
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- **postcard-generator/**: Features a Next.js application that utilizes Genkit to generate postcard images based on a user's journey.
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Explore these examples to understand how to build robust AI applications with Genkit and Google Cloud.

‎multimodal-dataset/README.md‎

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# Multimodal Datasets
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This directory contains resources and examples related to multimodal datasets on Google Cloud.
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Multimodal datasets are collections of data that involve multiple types of information, such as images, text, audio, and video. The materials in this directory may include:
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- Notebooks demonstrating how to work with specific multimodal datasets.
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- Information on accessing and utilizing public or sample multimodal datasets.
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- Examples of preprocessing and preparing multimodal data for use in machine learning models.
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Explore the contents to learn more about leveraging multimodal datasets for your generative AI projects.

‎partner-models/README.md‎

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# Partner Models
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This directory contains models and resources provided by Google Cloud partners.
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These models can be integrated with Vertex AI and other Google Cloud services to enhance your machine learning projects. Each subdirectory typically corresponds to a specific partner or model.

‎setup-env/README.md‎

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# Setup instructions to use Generative AI on Google Cloud
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# Environment Setup for Generative AI on Google Cloud
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This folder contains instructions on:
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This directory provides comprehensive instructions for setting up your environment to work with the generative AI examples and resources in this repository.
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- Setting up your Google Cloud project
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- Notebook environments
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- Setting up Colab
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- Setting up Vertex AI Workbench
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- Python SDK for Vertex AI
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The primary content of this directory is this README file, which guides you through:
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## Setting up your Google Cloud project
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- **Google Cloud Project Configuration**: Steps to select or create a Google Cloud project, enable billing, and activate necessary APIs like Vertex AI and Google Cloud Storage.
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- **Notebook Environments**: Detailed instructions for setting up popular notebook environments:
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- **Google Colab**: How to use notebooks from this repository in Colab, including authentication.
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- **Vertex AI Workbench**: How to create and use user-managed notebook instances on Vertex AI Workbench, and clone this repository.
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- **Local Development**: Information on installing the Google Cloud SDK and authenticating for local development.
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- **Python SDK for Vertex AI**: How to install the `google-cloud-aiplatform` library and initialize it for use with your project.
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1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager).
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When you first create an account, you get a $300 free credit towards your compute/storage costs.
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2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).
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3. [Enable the Vertex AI API and Google Cloud Storage API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,storage.googleapis.com).
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## Notebook environments
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### Colab
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[Google Colab](https://colab.research.google.com/) allows you to write and execute Python in your browser with minimal setup.
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To use Colab with this repo, please click on the "Open in Colab" link at the top of any notebook file in this repo to launch it in Colab. Then follow the instructions within.
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For Colab you will need to authenticate so that you can use Google Cloud from Colab:
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```py
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from google.colab import auth
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auth.authenticate_user()
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```
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When using the vertexai Python SDK, you will also need to initialize it with your Google Cloud `project_id` and `location`:
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```py
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PROJECT_ID = "your-project-id"
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LOCATION = "" #e.g. us-central1
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import vertexai
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vertexai.init(project=PROJECT_ID, location=LOCATION)
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```
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### Vertex AI Workbench
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[Vertex AI Workbench](https://cloud.google.com/vertex-ai-workbench) is the JupyterLab notebook environment on Google Cloud, which enables you to create and customize notebook instances. You do not need extra authentication steps.
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#### Creating your notebook instance on Vertex AI Workbench
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To create a new JupyterLab instance on Vertex AI Workbench, follow the [instructions here to create a user-managed notebooks instance](https://cloud.google.com/vertex-ai/docs/workbench/user-managed/create-new).
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#### Using this repository on Vertex AI Workbench
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After launching the notebook instance, you can clone this repository in your JupyterLab environment. To do so, open a Terminal in JupyterLab. Then run the command below to clone the repository into your instance:
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```sh
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git clone https://github.com/GoogleCloudPlatform/generative-ai.git
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```
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#### Local development
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- Install the [Google Cloud SDK](https://cloud.google.com/sdk).
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- Obtain authentication credentials. Create local credentials by running the following command and following the oauth2 flow (read more about the command [here](https://cloud.google.com/sdk/gcloud/reference/beta/auth/application-default/login)):
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```bash
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gcloud auth application-default login
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```
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## Python library
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Install the latest Python SDK:
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```sh
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%pip install google-cloud-aiplatform --upgrade
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```
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You will need to initialize `vertexai` with your `project_id` and `location`:
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```py
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PROJECT_ID = "your-project-id"
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LOCATION = "" #e.g. us-central1
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import vertexai
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vertexai.init(project=PROJECT_ID, location=LOCATION)
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```
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Following these instructions will ensure your environment is correctly configured to run the generative AI examples provided throughout this repository.

‎translation/README.md‎

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# Translation
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This directory contains resources related to translation models and tools on Google Cloud.
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You can find examples, notebooks, and scripts demonstrating how to use Vertex AI Translation services and models for various translation tasks.

‎workshops/README.md‎

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# Workshops
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This directory contains materials and resources for various workshops related to Google Cloud generative AI.
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The subdirectories include hands-on labs, code examples, and presentations covering different topics and technologies.
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Explore the workshop materials to learn about:
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- AI Agents
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- Question Answering Operations (QA-Ops)
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- Retrieval Augmented Generation Operations (RAG-Ops)
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- And more.

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