Zing Forum

Reading

Full-Stack LLM Chatbot: AI Conversation App Built with Spring Boot and React

This project is a full-stack chatbot application based on large language models (LLMs). The backend uses Spring Boot to build REST APIs, while the frontend employs React.js and Tailwind CSS. It implements real-time AI responses and a scalable architecture, providing developers with a complete reference for LLM application development.

全栈开发聊天机器人Spring BootReactTailwind CSSLLM应用流式响应AI对话
Published 2026-06-08 16:13Recent activity 2026-06-08 16:31Estimated read 5 min
Full-Stack LLM Chatbot: AI Conversation App Built with Spring Boot and React
1

Section 01

[Introduction] Full-Stack LLM Chatbot Project: Reference for AI Conversation App Built with Spring Boot + React

This project is an open-source ChatBot application released by GitHub user Gitanjan123 on June 8, 2026. It is built with a Spring Boot backend, React frontend, and Tailwind CSS styling. It implements real-time AI streaming responses and a scalable architecture, providing developers with a complete reference for LLM application development.

2

Section 02

Background: Challenges and Needs in Full-Stack LLM Application Development

As LLM technology matures, developers face multi-layered challenges when integrating AI conversation capabilities: the backend needs to handle LLM calls, session management, and streaming responses; the frontend needs to build a smooth chat interface; the system needs to solve integration, deployment, and operation issues. This project provides a well-structured reference for developers learning LLM application development.

3

Section 03

Technology Stack and Implementation Approach: Spring Boot + React + Tailwind Combination

Backend: Spring Boot

  • Mature and stable with a rich ecosystem, supporting asynchronous streaming responses and scalable microservice architecture

Frontend: React.js

  • Component-based development, flexible state management, supporting real-time updates and a rich ecosystem

Styling: Tailwind CSS

  • Atomic class names improve development efficiency, supporting responsive design and custom themes
4

Section 04

Core Features and Key Technical Implementation Points

Core Features

  • Real-time AI streaming response: Word-by-word display via SSE/WebSocket
  • Clean chat interface: Message bubbles, history scrolling, loading indicators, etc.
  • Scalable architecture: Modular layering, configurable LLM integration, session isolation

Implementation Points

  • LLM integration: Supports OpenAI/open-source model calls, including key management and retry mechanisms
  • Session management: Maintains context and message history
  • Frontend state: Manages message list, input state, and streaming updates
  • Security: API key protection, input validation, rate limiting, etc.
5

Section 05

Learning Value for Developers and Application Scenario Expansion

Learning Value

  • Complete runnable example covering project structure to code implementation
  • Reference for technology selection and engineering best practices
  • Scalable basic framework

Application Scenarios

  • Enterprise knowledge assistant, customer service robot, educational tutoring tool, personal AI assistant, etc.
6

Section 06

Project Summary and Technical Trends of LLM Applications

Project Summary

This project uses a modern technology stack and has a clear architecture, providing a practical reference for LLM application development. It is suitable for learning or as a project foundation.

Technical Trends

  • Streaming response becomes a standard feature
  • Component-based UI and hybrid technology stacks are common
  • Support for open-source models is increasingly important