yu-ai-agent

# AI Super Intelligent Agent Project > Author: [Programmer Yu Pi](https://yuyuanweb.feishu.cn/wiki/Abldw5WkjidySxkKxU2cQdAtnah) > > This project is an educational initiative that provides complete video tutorials + written guides + resume writing tips + interview question solutions + Q&A services, helping you enhance your project skills and add highlights to your resume! > > ⭐️ Join the project series for learning: [Join Programming Navigation](https://www.codefather.cn/vip) ## Project Introduction > Video introduction: https://www.bilibili.com/video/BV1UoLezKEbm This is a project tutorial centered around **AI Development Practice**, where we will develop the **AI Love Master Application + Super Intelligent Agent with Autonomous Planning Capability**, helping you master essential AI core concepts, practical tools, programming techniques, framework principles, and optimization skills that every programmer in the new era should know, significantly increasing your competitiveness in the job market!  The `AI Love Master Application` can rely on AI large models to solve users' emotional problems, supporting multi-turn conversations, Q&A based on a custom knowledge base, and autonomously calling tools and MCP services to complete tasks, such as using map services to find nearby locations and plan dates.  Additionally, we will guide you step-by-step to complete the `Autonomous Planning Intelligent Agent YuManus` based on the ReAct model, which can utilize web searches, resource downloads, and PDF generation tools to help users create complete date plans and generate documents:  Of course, after mastering this project, you will not only be able to develop the AI Love Master but also flexibly create various complex AI applications, unleashing your imagination! ## Why Should You Work on This Project? This project features a novel topic and real business scenarios, distinguishing it from the "common" CRUD projects. Yu Pi will guide you through practical applications of numerous new technologies and enterprise application scenarios, using a comprehensive tutorial to cover all the **AI technologies that every programmer must know**, helping you become a sought-after talent in the AI era and significantly enhancing your resume and job competitiveness. Yu Pi will teach you **universal project development methods and architectural design patterns**, and from this project, you will learn: - Usage of mainstream AI application platforms - 4 methods to access AI large models - AI development frameworks (Spring AI + LangChain4j) - Local deployment of AI large models - Prompt engineering and optimization techniques - Core features of Spring AI: such as custom Advisors, conversation memory, structured output - RAG (Retrieval-Augmented Generation) knowledge base practical application, principles, and tuning techniques - PgVector vector database + cloud database services - Tool Calling practical application and principles - MCP (Model Context Protocol) service development - Principles and autonomous development of AI agents Manus - AI service-oriented architecture and Serverless deployment - Various new concepts: such as multimodal, agent workflows, A2A protocols, large model evaluation, etc. For example, practical application and full-link optimization of RAG core features:  The project also has other advantages: - Practical experience with both AI cloud platforms and programming; not only will you use AI services, but you will also write your own! - Detailed explanations of the latest AI technologies based on official documentation, with thorough document and source code analysis! - Sharing a wealth of AI extension knowledge and programming skills, mastering best practices! Yu Pi will guide you through the source code of the open-source framework OpenManus:  Additionally, you will learn many methods for diagramming, problem-solving, and comparing solutions, enhancing your ability to troubleshoot and independently resolve bugs. ### Advantages of Yu Pi's Project Series Yu Pi's original projects focus on **practical experience**, guiding you **from 0 to 1** through **live broadcasts**, covering every aspect from requirement analysis, technology selection, project design, project initialization, demo writing, front-end and back-end development, project optimization, to deployment. I will explain everything clearly from theory to practice, leaving no detail overlooked! Compared to learning from online tutorials, the advantages of Yu Pi's project series include a comprehensive service from knowledge acquisition => practical projects => review notes => project Q&A => resume writing => interview question solutions:  Programming Navigation already has **over 10 project tutorials!** Each project focuses on different learning points, almost all of which are full-stack projects involving both front-end and back-end development. You can check out real feedback from participants; many have improved their skills and received job offers by working on projects with me!  Previous project introduction videos: [https://bilibili.com/video/BV1YvmbYbEgS](https://www.bilibili.com/video/BV1YvmbYbEgS/) ## Project Function Overview In this project, we will develop an AI Love Master Application, a super intelligent agent with autonomous planning capabilities, and a series of tools and MCP services. Specific requirements are as follows: - AI Love Master Application: Users inevitably encounter various difficulties in their romantic relationships, and AI will provide thoughtful emotional guidance. Supports multi-turn conversations, conversation memory persistence, RAG knowledge base retrieval, tool invocation, and MCP service invocation. - AI Super Intelligent Agent: Can autonomously reason and act based on user needs until the goal is achieved. - Tools provided to AI: Including web search, file operations, web scraping, resource downloading, terminal operations, PDF generation. - AI MCP Service: Can search for images from specific websites.  ## What Technologies Are Used? The project focuses on practical experience with the Spring AI development framework, involving various mainstream AI clients and tool libraries. - Java 21 + Spring Boot 3 framework - ⭐️ Spring AI + LangChain4j - ⭐️ RAG knowledge base - ⭐️ PGvector vector database - ⭐ Tool Calling - ⭐️ MCP model context protocol - ⭐️ ReAct Agent construction - ⭐️ Serverless computing services - ⭐️ AI large model development platform Bai Lian - ⭐️ Cursor AI code generation - ⭐️ SSE asynchronous push - Third-party APIs: such as SearchAPI / Pexels API - Ollama large model deployment - Tool libraries such as: Kryo high-performance serialization + Jsoup web scraping + iText PDF generation + Knife4j API documentation Practical application of RAG core features:  Project architecture design diagram:  ## First Phase Free Viewing The first phase is a public explanation, introducing the project background, project functions, technology selection, architecture design, tutorial plan, etc. Video link: https://www.bilibili.com/video/BV1Eq5DzcE9o ## Join Project Learning Programming Navigation already has **over 10 project tutorials!** Each project focuses on different learning points, almost all of which are full-stack projects involving both front-end and back-end development.  Welcome to join [Programming Navigation](https://mp.weixin.qq.com/s/I1oD6pAaWBvGLyFDT9AgvA?token=1925632390&lang=zh_CN). After joining, you can not only follow this project throughout but also have unlimited access to previous [10+ original project tutorials](https://mp.weixin.qq.com/s/omIazLMQlTo9M3jFFH7NzQ?token=70787607&lang=zh_CN). You will also enjoy more original technical materials, learning and job-seeking guidance, and hundreds of interview replay videos, starting your programming journey! 🧧 To support new project learning, we are offering **limited-time Programming Navigation discount coupons**. Scan the code to receive your coupon and join. If you are not satisfied within three days of joining, you can get a full refund. Welcome to join and experience; spots are limited, so hurry to learn! <img width="404" alt="image" src="https://github.com/user-attachments/assets/56411098-b60e-4267-8ba2-4ebc5d416afc" /> Less than 1 dollar a day, it is definitely the best investment in yourself! After becoming a member of Programming Navigation, you can unlock tutorials and materials for over 10 projects, available for learning on both PC websites and apps, as shown:  ## Preparations ### Basic Knowledge of AI Please watch the "Programmer Yu Pi AI Guide" first to understand the basic knowledge of AI and the learning path. This will provide a general impression during the practical application in the project, facilitating learning and understanding. ⭐️ Recommended video version: [https://www.bilibili.com/video/BV1i9Z8YhEja](https://www.bilibili.com/video/BV1i9Z8YhEja/) Text version: https://www.codefather.cn/course/1907378983347892226 ### Create a Code Repository Set up an open-source code repository using GitHub; those who star it are spiritual shareholders. Code repository: https://github.com/liyupi/yu-ai-agent ### AI Learning Resources It is recommended that you continuously read interview questions related to AI large models while learning the AI project to reinforce your knowledge. Yu Pi has already prepared this for you; our programmer interview practice tool, Interview Duck, has created an [AI Large Model Interview Question Bank](https://www.mianshiya.com/bank/1906189461556076546). It is advisable to read some questions regularly for learning.  Moreover, due to the rapid evolution of AI technology, it is recommended that you pay attention to AI-related news and updates regularly. For example, [Yu Pi's open-source AI knowledge base](https://github.com/liyupi/ai-guide) aggregates popular AI large models and tools, such as Deepseek usage guides, prompt engineering tips, knowledge nuggets, application scenarios, AI monetization, industry news, tutorial resources, and more, helping you quickly master AI technology and stay at the forefront of the times.  ## Learning Outline ### Phase 1: Project Overview - Project introduction - Project advantages - Project function overview - Technology selection - Architecture design - AI learning path - - Usage of AI application platforms (Dify) - Common AI tools - AI programming skills - AI programming techniques - Learning outline ### Phase 2: Accessing AI Large Models - Concept of AI large models - Accessing AI large models (3 methods) - Backend project initialization - Program invocation of AI large models (4 methods) - Local deployment of AI large models - Spring AI calling local large models ### Phase 3: AI Application Development - Concept of prompt engineering - Prompt optimization techniques - Requirement analysis for AI Love Master Application - Design scheme for AI Love Master Application - Features of Spring AI ChatClient / Advisor / ChatMemory - Multi-turn conversation AI application development - Custom Advisor in Spring AI - Structured output in Spring AI - Love report feature - Conversation memory persistence in Spring AI - Prompt template features in Spring AI - Concepts and development of multimodal applications ### Phase 4: Basics of RAG Knowledge Base - Requirement analysis for AI love knowledge Q&A - Concept of RAG (focus on understanding core steps) - RAG practical application: Spring AI + local knowledge base - RAG practical application: Spring AI + cloud knowledge base service ### Phase 5: Advanced RAG Knowledge Base - Core features of RAG - - Document collection and segmentation (ETL) - Vector transformation and storage (vector database) - Document filtering and retrieval (document retriever) - Query enhancement and association (context query enhancer) - Best practices and tuning for RAG - Retrieval strategies - Large model hallucinations ### Phase 6: Tool Invocation - Concept of tools - Spring AI tool development - Development of mainstream tools - - File operations - Web search - Web scraping - Terminal operations - Resource downloading - PDF generation - Advanced knowledge of tools (principles and advanced features) ### Phase 7: MCP - Concept of MCP - Using MCP (3 methods) - Spring AI MCP development model - Practical development of Spring AI MCP - Image search MCP - Best practices for MCP development - Deploying MCP - Security issues with MCP ### Phase 8: Building AI Agents - Concept of AI agents - Key implementations for agents - Using AI agents (2 methods) - Introduction to autonomous planning agents - Implementation principles of OpenManus - Autonomous implementation of Manus agents - Agent workflows ### Phase 9: AI Service-Oriented Architecture - Development of AI application interfaces (SSE) - Development of AI agent interfaces - AI-generated front-end code - AI service Serverless deployment