Yu AI Agent 🤖

Welcome to the Yu AI Agent repository! This project serves as a comprehensive guide for AI development in 2025. Built on Spring Boot 3, Java 21, and Spring AI, this repository focuses on creating the AI Love Master application and the ReAct mode autonomous planning agent, YuManus.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Core Concepts
• Technologies Used
• Contributing
• License
• Contact

Introduction

The Yu AI Agent project aims to equip programmers with essential AI skills. With a structured tutorial, you will learn about:

• AI model integration
• Core features of Spring AI
• Prompt engineering and optimization
• RAG (Retrieval-Augmented Generation)
• Vector databases
• Tool calling
• MCP (Model Context Protocol)
• AI agent development
• Cursor AI tools

This knowledge will significantly enhance your resume and job prospects in the AI-driven job market.

Features

• AI Model Integration: Learn how to connect and utilize large AI models effectively.
• Spring AI Core Features: Understand the fundamental aspects of Spring AI.
• Prompt Engineering: Master the art of crafting effective prompts for AI models.
• RAG: Explore retrieval-augmented generation techniques.
• Vector Databases: Get hands-on experience with vector databases for efficient data retrieval.
• Tool Calling: Implement tool calling for enhanced functionality in your applications.
• MCP: Grasp the Model Context Protocol for better AI interaction.
• AI Agent Development: Build your own intelligent agents using the knowledge gained.
• Cursor AI Tools: Utilize Cursor AI tools to streamline your AI projects.

Installation

To get started with the Yu AI Agent, follow these steps:

1. Clone the Repository:

   git clone https://github.com/hfgwygey/yu-ai-agent.git

2. Navigate to the Directory:

   cd yu-ai-agent

3. Install Dependencies: Ensure you have Java 21 and Maven installed. Run the following command:

   mvn install

4. Run the Application: You can run the application using:

   mvn spring-boot:run

For more detailed installation instructions, visit the Releases section.

Usage

After setting up the project, you can start using the AI Love Master application. The application provides an interface for users to interact with the AI models.

1. Access the Application: Open your browser and navigate to http://localhost:8080.

2. Interact with the AI: Use the interface to ask questions or request information. The AI will respond based on the prompts you provide.

3. Explore Features: Experiment with different features such as tool calling and vector database queries.

For more examples and detailed usage instructions, refer to the documentation provided in the repository.

Core Concepts

AI Model Integration

Integrating AI models involves connecting the application to various large models. This allows the application to leverage the capabilities of these models for various tasks.

Prompt Engineering

Crafting effective prompts is crucial for getting the best responses from AI models. This involves understanding how to phrase questions and requests.

RAG

Retrieval-Augmented Generation combines retrieval methods with generation techniques. This allows for more accurate and context-aware responses from AI models.

Vector Databases

Vector databases store data in a way that allows for efficient similarity searches. This is essential for applications that require quick access to large datasets.

Tool Calling

Tool calling refers to the ability of the AI to invoke external tools or services. This enhances the functionality of the AI and allows it to perform more complex tasks.

MCP

The Model Context Protocol helps maintain context during interactions with AI models. This ensures that the AI can provide relevant responses based on previous interactions.

AI Agent Development

Developing AI agents involves creating intelligent systems that can perform tasks autonomously. This requires a solid understanding of the concepts mentioned above.

Cursor AI Tools

Cursor AI tools provide additional functionality for managing AI projects. These tools can streamline development and enhance productivity.

Technologies Used

• Java 21: The programming language used for building the application.
• Spring Boot 3: The framework that simplifies the development of Java applications.
• Spring AI: A library that provides features for building AI applications.
• Maven: A build automation tool used for managing project dependencies.
• Vector Databases: Used for efficient data storage and retrieval.
• ReAct: A framework for developing autonomous agents.

Contributing

We welcome contributions to the Yu AI Agent project. If you would like to contribute, please follow these steps:

1. Fork the Repository: Click the "Fork" button on the top right corner of the repository page.
2. Create a New Branch:

   git checkout -b feature/YourFeatureName

3. Make Your Changes: Implement your feature or fix.
4. Commit Your Changes:

   git commit -m "Add your message here"

5. Push to Your Fork:

   git push origin feature/YourFeatureName

6. Create a Pull Request: Go to the original repository and click "New Pull Request".

Thank you for considering contributing to this project!

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any inquiries or feedback, feel free to reach out:

• Email: your-email@example.com
• GitHub: hfgwygey

Explore the project and become proficient in AI development. Don’t forget to check the Releases section for updates and new features!