A modern AI-powered assistant built with Spring Boot and Spring AI, featuring a chat interface with an assistant avatar. The assistant can answer user queries using Open AI model.
- Modern chat interface with glassmorphism (transparent glass effect)
- Anime assistant avatar popping out of chat bubbles
- Smooth animations: floating messages, fade-in, and slide-in effects
- Fully responsive UI for desktop and mobile
- Simple and extensible backend for AI interaction
- Easily integrated with Spring Boot templates (Thymeleaf compatible)
- Ready for future AI improvements:
- RAG (Retrieval-Augmented Generation) for context-aware responses
- Vector databases for semantic search and knowledge retrieval
- Production-level architecture with clean file structure
🧠 Overview
Spring-AI-Assistant is a Java-based backend service that integrates Spring Boot with the Spring AI framework to provide conversational AI functionality 🤖
It exposes REST endpoints for client applications and uses Spring AI to interact with Large Language Models (LLMs) like OpenAI chat models.
This project is designed for ⚙️ easy backend extensibility, 🚀 production readiness, and 📈 future scaling with advanced AI techniques.
🛠️ Backend Architecture
📌 Core Technologies
| Layer | Technology |
|---|---|
| Framework | Spring Boot |
| AI Integration | Spring AI (ChatClient / Model Integration) |
| LLM Provider | OpenAI Models |
| API Pattern | REST |
| Dependency Management | Maven |
| Java Version | Java 17+ |
Spring AI is a Spring-friendly framework for AI model integration that provides unified abstractions for interacting with AI models, memory, advisors, and future retrieval workflows 🧩.
🚀 Key Backend Features
💬 Conversational AI
- Handles incoming chat requests over REST APIs
- Uses Spring AI
ChatClientto send prompts to OpenAI or other LLMs - Parses and returns AI-generated responses efficiently
🔌 Spring Boot Integration
- Full Spring Boot application with controllers, services, and configuration layers
- Easily testable using
spring-boot:run - Modular backend structure designed for security, scalability, and persistence
📦 Future Enhancements (Backend Focus)
Here’s a roadmap of backend-centric enhancements you can implement as you evolve the project:
🚀 AI & Model Improvements
🧠 RAG (Retrieval-Augmented Generation)
Integrate vector search with semantic embeddings (e.g., PGVector, Chroma) to enable context-aware responses using external knowledge bases.
💾 Conversational Memory / State Management
Add session memory using Spring AI’s ChatMemory to preserve context across multi-turn interactions.
🔄 Support Multiple Providers
Add support for Anthropic, Gemini, or local LLMs via Spring AI abstractions so consumers can easily swap AI providers.
🛡️ Backend Scalability & Reliability
⚡ Asynchronous Processing
Offload LLM calls to background workers or async executors to prevent blocking API threads.
🚦 Rate Limiting
Protect backend APIs from overload using rate limiting (Spring Cloud Gateway, filters, or interceptors).
🧯 Circuit Breakers / Resilience
Apply Spring Boot resilience patterns to safeguard external AI model calls and handle failures gracefully.
🧾 Monitoring & Observability
📜 Structured Logging
Integrate centralized logging frameworks (e.g., Logstash, ELK stack) to trace requests and AI responses.
📊 Metrics & Alerting
Add Micrometer + Prometheus to track latency, AI call counts, throughput, and error rates.
📚 Persistence & Knowledge
🗄️ Database Persistence
Persist chat history, sessions, or AI responses using H2 or PostgreSQL.
🧠 Knowledge Graph / Semantic Index
Build a knowledge store with vector embeddings to enable domain-specific semantic search.
🚀 API Evolution
🔗 GraphQL Support
Expose a GraphQL API alongside REST for flexible data querying.
📡 WebSocket / SSE Streaming
Enable real-time AI response streaming to clients using WebSockets or Server-Sent Events (SSE).