https://github.com/luillyfe/gcp-exam
https://github.com/luillyfe/gcp-exam
Last synced: 9 months ago
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- Host: GitHub
- URL: https://github.com/luillyfe/gcp-exam
- Owner: luillyfe
- Created: 2023-12-31T09:05:42.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-11T15:08:51.000Z (over 2 years ago)
- Last Synced: 2025-03-15T07:22:09.409Z (over 1 year ago)
- Language: TypeScript
- Homepage: https://gcp-exam.vercel.app
- Size: 215 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Ignite Your GCP Cloud Architect Prep with AI-Powered Q&A!
**Harness the power of Gemini Pro for content generation.**
## Overview
This project leverages the power of Gemini Pro to generate realistic and challenging practice questions for the GCP Cloud Architect exam. By mimicking the actual exam format with multiple-choice answers, it provides an engaging and game-like experience that helps candidates solidify their knowledge and boost their confidence.
## Features
- **Content Generation power by Gemini Pro** for enhanced accuracy.
## Target Audience
Developers who want to:
- Integrate Gemini Pro's capabilities into their applications.
- Build creative content generation features applied to Cloud Computing.
## Project Structure
- **src/app/action:** This folder holds all the server actions, including the `getNextQuestion` function.
- **src/api/generative-ai:** This folder defines the API route for connecting with the Google generative AI SDK, validating the response, formatting it, and storing it in a Vercel/PostgreSQL database.
- **src/app/lib/database:** This folder defines all the functions to communicate with the database.
- **src/app/lib/utils:** This folder manages all text formatting functions used in the application.
This structure allows you to separate concerns and maintain a clean and modular codebase. It also makes it easier to scale the application as it grows. If you have any specific questions or need further guidance, feel free to ask.
## Getting Started
1. Clone the repository.
2. Install dependencies: `npm install`
3. Set environment variables: `MODEL_NAME` and `API_KEY`.
4. Run the application: `npm run start`
## Explore Further
- **Gemini Docs:** [https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini]
## Contributing
We welcome contributions! Please see the Contributing Guidelines: [Link to contributing guidelines] for more information.
**Let's unleash the potential of Gemini Pro together!**