Rasa Chatbot — Spring Boot

A full-stack AI-powered chatbot. The Spring Boot backend acts as middleware between the HTML/JS chat frontend and the Rasa NLP server, forwarding user messages and returning AI-generated responses in real time.

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🏗️ Architecture

[ Browser — index.html + script.js ]
        ↓  POST /api/chat  { "message": "..." }
[ Spring Boot — chatController.java ]
        ↓  HTTP POST to localhost:5005
[ Rasa NLP Server — /webhooks/rest/webhook ]
        ↓  returns [ { "text": "bot reply" } ]
[ rasaService.java — extracts reply text ]
        ↓
[ ChatResponse returned to frontend ]

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📁 Project Structure

src/main/java/com/chatBot/chatBotApp/
├── controller/
│   └── chatController.java     → POST /api/chat endpoint
│                                 Accepts ChatMessage, returns ChatResponse
├── model/
│   └── userMessage.java        → User message model
├── service/
│   └── rasaService.java        → Calls Rasa webhook via RestTemplate
│                                 Sends sender + message, extracts reply
├── frontEnd/
│   ├── index.html              → Chat UI
│   └── script.js               → Sends messages, displays bot replies
└── ChatBotAppApplication.java  → Spring Boot entry point

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🔌 API

Send a message

POST /api/chat
Content-Type: application/json

{ "message": "Hello" }

Response

{ "reply": "Hi! How can I help you?" }

Internally, rasaService forwards the message to Rasa:

POST http://localhost:5005/webhooks/rest/webhook
{ "sender": "user1", "message": "Hello" }

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🛠️ Tech Stack

• Java / Spring Boot — REST API and middleware layer
• RestTemplate — HTTP client to communicate with Rasa
• Rasa — Open source NLP and conversational AI server
• HTML & JavaScript — Frontend chat interface
• Maven — Build and dependency management

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▶️ How to Run

Prerequisites: Java 8+, Maven, Rasa installed

1. Start the Rasa server:

rasa run --enable-api --cors "*"

2. Run the Spring Boot app:

cd chatBotApp
./mvnw spring-boot:run

3. Open in browser:

http://localhost:8080

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💡 Key Concepts Demonstrated

• REST API design with Spring Boot (@RestController, @PostMapping)
• HTTP client integration using RestTemplate
• Service layer communication with external AI server (Rasa)
• JSON request/response handling with inner model classes
• Full-stack development — Spring Boot backend + HTML/JS frontend
• Layered architecture — Controller → Service → NLP integration
• Graceful fallback — returns default reply if Rasa has no response