Gemini AI Chat Example App

This project demonstrates a simple implementation of the Vertex AI Gemini Chat model using Vaadin Flow. The application allows users to enter a prompt and receive a generated response from the AI model.

Getting Started

Prerequisites

Ensure you have the following installed:

• Java Development Kit (JDK) 11 or later
• Maven
• Google Cloud SDK

Environment Setup

1. Set Environment Variables:

   • Create a .env file in the root directory of your project or set the environment variables directly in your terminal.
   • Add the following lines to the .env file (replace with your actual project ID and location):

     GOOGLE_CLOUD_PROJECT_ID=your-project-id
     GOOGLE_CLOUD_LOCATION=your-location
     GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/service-account-file.json

2. Load Environment Variables:

   • For MacOS/Linux, run the following command in your terminal to load the environment variables:

     export $(cat .env | xargs)

Running the Application

This is a standard Maven project. To run it from the command line:

1. Build and Run:

   ./mvnw clean install
   ./mvnw spring-boot:run

2. Import to IDE:
   • You can also import the project to your IDE of choice as you would with any Maven project.

Deploying to Production

To create a production build:

1. Build:

   ./mvnw clean package -Pproduction

   • This will build a JAR file with all dependencies and front-end resources, ready to be deployed. The file can be found in the target folder after the build completes.
2. Run

   java -jar target/your-app-1.0-SNAPSHOT.jar
   
   

Project Structure

- GeminiView.java: The main view component where users can enter prompts and see responses.
- application.properties: Configuration file where you can set various properties like project ID and location.

Useful Links

• Vertex AI Documentation
• Vaadin Documentation
• Spring AI Documentation
• Google Cloud SDK Installation
• Vaadin Start Page
• Vaadin GitHub Repository