Employee Learning Chatbot

A Spring Boot + React chatbot application with gamification features for helping employees stay updated with technology advancements.

Features

• 🤖 AI Chatbot: Powered by Ollama (llama2) running locally
• 💬 Chat History: Full conversation history tracking
• 🎮 Gamification: Points, levels, and achievements system
• 🔒 Privacy-First: All data stays local with Ollama
• 📚 RAG Ready: Architecture prepared for Retrieval-Augmented Generation

Tech Stack

Backend

• Spring Boot 3.2.0
• Java 17
• H2 Database (in-memory, easily switchable to PostgreSQL)
• WebFlux for Ollama API calls
• JPA/Hibernate

AI

• Ollama (llama2:latest)
• RAG architecture (ready for implementation)

Prerequisites

1. Java 17 or higher
2. Maven 3.6+
3. Docker (for Ollama)
4. Ollama container running:

   docker start fullstackollama

Quick Start

1. Start Ollama

# Your container is already set up
docker start fullstackollama

# Verify it's running
curl http://localhost:11434/api/tags

2. Run the Spring Boot Application

# From the project root directory
mvn clean install
mvn spring-boot:run

The backend will start on http://localhost:8080

3. Access H2 Console (Optional)

• URL: http://localhost:8080/h2-console
• JDBC URL: jdbc:h2:mem:chatbotdb
• Username: sa
• Password: (leave empty)

API Endpoints

Chat Endpoints

• POST /api/chat/message - Send a message to the chatbot
• GET /api/chat/history/{userId} - Get full chat history
• GET /api/chat/history/{userId}/recent - Get recent messages

User Endpoints

• GET /api/users - Get all users
• GET /api/users/{id} - Get user by ID
• POST /api/users - Create new user
• POST /api/users/{id}/login - Record user login

Gamification Endpoints

• GET /api/gamification/users/{userId}/achievements - Get user achievements
• GET /api/gamification/users/{userId}/stats - Get user stats
• GET /api/gamification/leaderboard - Get top 10 users

Testing the API

Create a Test User

curl -X POST http://localhost:8080/api/users \
  -H "Content-Type: application/json" \
  -d '{
    "username": "john.doe",
    "email": "john@company.com",
    "password": "password123",
    "fullName": "John Doe",
    "department": "Engineering"
  }'

Send a Chat Message

curl -X POST http://localhost:8080/api/chat/message \
  -H "Content-Type: application/json" \
  -d '{
    "userId": 1,
    "message": "What is Spring Boot?"
  }'

Gamification System

Points System

• 10 points per message sent
• Level up every 100 points
• Achievement completion awards bonus points

Default Achievements

• 🌟 First Steps (1 message)
• 💬 Conversationalist (50 messages)
• 🎓 Expert Learner (100 messages)
• ⭐ Point Master (500 points)
• 🚀 Rising Star (Level 5)
• 👨‍💻 Tech Guru (Level 10)

RAG Implementation (TODO)

The architecture is ready for RAG. To implement:

1. Choose a vector database (Chroma, Weaviate, Qdrant)
2. Add embedding model (sentence-transformers)
3. Implement document chunking and indexing
4. Complete the getContextForQuery() method in ChatService.java

Database Configuration

Switch to PostgreSQL

Update application.properties:

spring.datasource.url=jdbc:postgresql://localhost:5432/chatbot
spring.datasource.username=your_username
spring.datasource.password=your_password
spring.jpa.database-platform=org.hibernate.dialect.PostgreSQLDialect

Project Structure

src/main/java/com/company/chatbot/
├── config/          # Configuration classes
├── controller/      # REST controllers
├── dto/             # Data Transfer Objects
├── entity/          # JPA entities
├── repository/      # Data repositories
└── service/         # Business logic

Next Steps

1. ✅ Set up React frontend
2. ⬜ Implement RAG with vector database
3. ⬜ Add user authentication (Spring Security)
4. ⬜ Add WebSocket for real-time chat
5. ⬜ Deploy to production

Troubleshooting

Ollama not responding

# Check if container is running
docker ps | grep fullstackollama

# Restart if needed
docker restart fullstackollama

Port 8080 already in use

Change the port in application.properties:

server.port=8081

License

MIT