# 🤖 Advanced AI Chatbot A full-stack web application exploring the frontiers of AI-assisted communication, demonstrating modern software development practices and machine learning integration. ## ✨ Project Features ### 🌟 Core Functionality - **💬 Interactive Chatbot**: Basic conversational interface - **📜 Conversation Tracking**: Store and retrieve user chat history - **🔐 Secure Authentication**: JWT-based user management - **🌐 OpenAI API Integration**: Foundational AI-powered responses ### 🧠 AI Exploration - **🤖 OpenAI Language Model**: Basic natural language processing - **📊 Contextual Understanding**: Preliminary conversation context tracking - **🌱 Recommendation Engine**: Simple topic-based suggestions - **🔍 Learning Prototype**: Exploring AI interaction patterns ### 🎨 User Experience - **⚛️ Responsive Frontend**: Modern React.js interface - **🌓 Theme Flexibility**: Light and dark mode support - **📱 Adaptive Design**: Responsive across devices ## 🛠️ Technical Architecture ### Backend - **🐍 Framework**: Python with FastAPI - **💾 Database**: PostgreSQL - **🔑 Authentication**: JWT (JSON Web Tokens) - **🧠 AI Layer**: OpenAI GPT Integration - **🚀 Async Processing**: Background task management ### Frontend - **⚛️ Framework**: React.js with Hooks - **🎨 UI Components**: Modern, responsive design - **🌐 State Management**: React Context API - **📡 API Communication**: Axios ### DevOps & Infrastructure - **📦 Containerization**: Docker - **☸️ Orchestration**: Kubernetes configuration - **🌐 Web Server**: Nginx - **🔄 CI/CD**: GitHub Actions workflow ## 🚀 Project Vision This project represents a learning journey in: - Full-stack web development - AI and machine learning exploration - Modern software architecture - DevOps and cloud technologies ### 🔮 Future Exploration Goals - Enhanced AI context understanding - More sophisticated NLP capabilities - Advanced recommendation systems - Improved user personalization ## 🧪 Technical Challenges Addressed ### 🌐 Architectural Complexity - Microservices design - Scalable application architecture - Secure authentication mechanisms ### 🤖 AI Integration Exploration - OpenAI API interaction - Basic context tracking - Foundational recommendation engine ### 🔒 Security Considerations - JWT authentication - Environment-based configuration - Secure API interactions ## 🚀 Setup & Installation ### Prerequisites - Python 3.9+ - Node.js 16+ - PostgreSQL 13+ - Redis (optional, for caching) - OpenAI API key - Docker (optional, for containerization) ### Backend Installation 1. Clone the repository: ``` git clone https://github.com/muratyigitartuk/AdvancedAIChatbot.git cd advanced-ai-chatbot ``` 2. Set up a virtual environment: ``` python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate ``` 3. Install dependencies: ``` pip install -r requirements.txt ``` 4. Set up environment variables: ``` cp .env.example .env # Edit .env with your configuration ``` 5. Initialize the database: ``` alembic upgrade head ``` 6. Start the backend: ``` uvicorn app.main:app --reload --host 0.0.0.0 --port 8000 ``` ### Frontend Installation 1. Navigate to the frontend directory: ```bash cd frontend ``` 2. Install dependencies: ```bash npm install ``` 3. Start the development server: ```bash npm start ``` ## 🔧 Environment Variables The application uses the following environment variables: ``` # Database Configuration DB_HOST=localhost DB_PORT=5432 DB_NAME=chatbot_db DB_USER=postgres DB_PASSWORD=your_password # OpenAI Configuration OPENAI_API_KEY=your_openai_api_key DEFAULT_MODEL=gpt-3.5-turbo # Authentication SECRET_KEY=your_secret_key ALGORITHM=HS256 ACCESS_TOKEN_EXPIRE_MINUTES=30 ``` ## 🚢 Deployment ### Docker Deployment 1. Build the Docker images: ``` docker-compose build ``` 2. Start the containers: ``` docker-compose up -d ``` ### Kubernetes Deployment 1. Apply Kubernetes configurations: ``` kubectl apply -f k8s/ ``` ## 📚 API Documentation Once the application is running, visit `/docs` or `/redoc` for interactive API documentation. ### Key API Endpoints - **🔐 Authentication** - `POST /api/auth/register`: Register a new user - `POST /api/auth/token`: Login and get access token - `GET /api/auth/me`: Get current user profile - **💬 Chat** - `POST /api/chat`: Send a message to the chatbot - `GET /api/user/history`: Get user conversation history - `GET /api/recommendations`: Get proactive recommendations ## 📂 Project Structure ```markdown advanced-ai-chatbot/ ├── app/ # Backend application │ ├── api/ # API endpoints │ │ ├── auth.py # Authentication routes │ │ ├── chat.py # Chat functionality routes │ │ └── voice.py # Voice processing routes │ ├── core/ # Core functionality │ │ ├── ai_engine.py # Main AI processing engine │ │ ├── auth.py # Authentication logic │ │ ├── context.py # Context management │ │ └── proactive.py # Proactive recommendations │ ├── db/ # Database models & connections │ │ ├── database.py # Database connection │ │ └── models.py # SQLAlchemy models │ ├── services/ # External services integration │ │ ├── user_profile.py # User profile management │ │ └── voice_service.py # Voice processing service │ └── utils/ # Utility functions │ └── helpers.py # Helper functions ├── frontend/ # React frontend │ ├── public/ # Static files │ └── src/ # Source code │ ├── components/ # Reusable components │ ├── contexts/ # React contexts │ ├── pages/ # Page components │ └── App.js # Main application component ├── config/ # Configuration files ├── k8s/ # Kubernetes configurations ├── tests/ # Test suite ├── .env.example # Example environment variables ├── docker-compose.yml # Docker Compose configuration ├── Dockerfile # Docker configuration └── README.md # Project documentation ``` ## 🧪 Testing ### Running Backend Tests ```bash pytest ``` ### Running Frontend Tests ```bash cd frontend npm test ``` ## 👨‍💻 Development Workflow ### Code Style and Linting I followed PEP 8 guidelines for Python code. You can check your code with: ```bash flake8 . ``` ### Database Migrations When changing models, create a new migration: ```bash alembic revision --autogenerate -m "Description of changes" alembic upgrade head ``` ### Branch Strategy - `main`: Production-ready code - `develop`: Integration branch for features - `feature/*`: New features - `bugfix/*`: Bug fixes ### Pull Request Process 1. Create a feature/bugfix branch 2. Make your changes 3. Run tests and linting 4. Submit a pull request to the develop branch ## 🔍 Troubleshooting ### Common Issues #### Database Connection Errors If you encounter database connection issues: 1. Check that PostgreSQL is running 2. Verify your database credentials in `.env` 3. Ensure the database exists: `createdb chatbot_db` #### OpenAI API Issues If AI responses are not working: 1. Verify your OpenAI API key in `.env` 2. Check API usage limits on OpenAI dashboard 3. Ensure internet connectivity #### JWT Authentication Problems If authentication is failing: 1. Check that SECRET_KEY is properly set 2. Verify token expiration settings 3. Clear browser cookies and try again ## 🚧 Recent Changes ### 2025-02-28: SQLAlchemy Metadata Conflict Resolution - Renamed 'metadata' column to 'message_metadata' in Message model - Updated all references across the codebase - Fixed potential naming conflicts with SQLAlchemy's Declarative API - Maintained API contract for external interfaces ## 🔮 Future Enhancements - **🌐 Multi-language Support**: Add support for multiple languages - **😊 Sentiment Analysis**: Analyze user sentiment to adapt responses - **🔌 Integration with External APIs**: Weather, news, calendar, etc. - **📊 Advanced Analytics**: Dashboard for conversation insights - **📱 Mobile App**: React Native mobile application ## 📚 Learning Objectives - Practical full-stack development - AI integration techniques - Modern web technologies - DevOps and deployment strategies ## 📜 License MIT License - Open-source and educational project ## 👨‍💻 About the Developer **Murat Yigit Artuk** - 🎓 Aspiring Software Engineering Student - 🌟 Passionate about Artificial Intelligence, Cloud Computing - 🚀 Seeking opportunities in innovative software development - 📍 Targeting Duales Studium Informatik Program in Germany / Wants to be Computer Science Student working Student in America. ## 📬 Contact - GitHub: [muratyigitartuk](https://github.com/muratyigitartuk) - Email: [muratyigitartuk0@gmail.com](mailto:muratyigitartuk0@gmail.com) ## 📚 Detailed API Documentation ### Authentication Endpoints #### `POST /api/auth/register` Register a new user in the system. **Request Body:** ```json { "username": "newuser", "email": "user@example.com", "password": "securepassword123", "full_name": "New User" } ``` **Response (201 Created):** ```json { "id": "user_uuid", "username": "newuser", "email": "user@example.com", "full_name": "New User", "created_at": "2025-02-28T19:02:23" } ``` #### `POST /api/auth/token` Authenticate and receive an access token. **Request Body:** ```json { "username": "newuser", "password": "securepassword123" } ``` **Response (200 OK):** ```json { "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", "token_type": "bearer", "expires_in": 1800 } ``` #### `GET /api/auth/me` Get the current user's profile information. **Headers:** ``` Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` **Response (200 OK):** ```json { "id": "user_uuid", "username": "newuser", "email": "user@example.com", "full_name": "New User", "created_at": "2025-02-28T19:02:23" } ``` ### Chat Endpoints #### `POST /api/chat` Send a message to the chatbot and receive a response. **Headers:** ``` Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` **Request Body:** ```json { "message": "Tell me about artificial intelligence", "context_id": "optional_conversation_id", "metadata": { "source": "web", "device_info": "Chrome/Windows" } } ``` **Response (200 OK):** ```json { "id": "message_uuid", "content": "Artificial intelligence (AI) refers to...", "created_at": "2025-02-28T19:05:23", "context_id": "conversation_uuid", "metadata": { "source": "web", "device_info": "Chrome/Windows", "tokens_used": 150, "model": "gpt-3.5-turbo" } } ``` #### `GET /api/user/history` Retrieve the user's conversation history. **Headers:** ``` Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` **Query Parameters:** - `limit` (optional): Maximum number of conversations to return - `offset` (optional): Pagination offset - `start_date` (optional): Filter by start date - `end_date` (optional): Filter by end date **Response (200 OK):** ```json { "conversations": [ { "id": "conversation_uuid", "title": "Conversation about AI", "created_at": "2025-02-28T19:00:23", "updated_at": "2025-02-28T19:10:23", "messages": [ { "id": "message_uuid1", "role": "user", "content": "Tell me about artificial intelligence", "created_at": "2025-02-28T19:05:23" }, { "id": "message_uuid2", "role": "assistant", "content": "Artificial intelligence (AI) refers to...", "created_at": "2025-02-28T19:05:30" } ] } ], "total": 10, "limit": 10, "offset": 0 } ``` #### `GET /api/recommendations` Get proactive recommendations based on user's conversation history. **Headers:** ``` Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9... ``` **Response (200 OK):** ```json { "recommendations": [ { "id": "rec_uuid1", "title": "Learn more about Machine Learning", "description": "Based on your interest in AI, you might want to explore machine learning concepts.", "relevance_score": 0.92, "created_at": "2025-02-28T19:15:23" }, { "id": "rec_uuid2", "title": "Explore Natural Language Processing", "description": "NLP is a key component of modern AI systems like chatbots.", "relevance_score": 0.85, "created_at": "2025-02-28T19:15:23" } ] } ``` ## 💻 Code Examples ### Backend Examples #### Sending a Message to the Chatbot (Python) ```python import requests import json # Configuration API_URL = "http://localhost:8000" TOKEN = "your_access_token" # Headers headers = { "Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json" } # Send a message to the chatbot def send_message(message, context_id=None): endpoint = f"{API_URL}/api/chat" payload = { "message": message, "metadata": { "source": "python_client", "device_info": "Python/Script" } } if context_id: payload["context_id"] = context_id response = requests.post(endpoint, headers=headers, json=payload) if response.status_code == 200: return response.json() else: print(f"Error: {response.status_code}") print(response.text) return None # Example usage response = send_message("Tell me about machine learning") print(json.dumps(response, indent=2)) # Continue the conversation if response and "context_id" in response: follow_up = send_message("How is it different from deep learning?", response["context_id"]) print(json.dumps(follow_up, indent=2)) ``` #### User Authentication (Python) ```python import requests # Configuration API_URL = "http://localhost:8000" # Register a new user def register_user(username, email, password, full_name): endpoint = f"{API_URL}/api/auth/register" payload = { "username": username, "email": email, "password": password, "full_name": full_name } response = requests.post(endpoint, json=payload) if response.status_code == 201: print("User registered successfully!") return response.json() else: print(f"Error: {response.status_code}") print(response.text) return None # Login and get token def login(username, password): endpoint = f"{API_URL}/api/auth/token" payload = { "username": username, "password": password } response = requests.post(endpoint, data=payload) if response.status_code == 200: token_data = response.json() print("Login successful!") return token_data["access_token"] else: print(f"Error: {response.status_code}") print(response.text) return None # Example usage new_user = register_user("testuser", "test@example.com", "securepass123", "Test User") if new_user: token = login("testuser", "securepass123") print(f"Access Token: {token}") ``` ### Frontend Examples #### React Chat Component ```jsx import React, { useState, useEffect, useRef } from 'react'; import axios from 'axios'; import { useAuth } from '../contexts/AuthContext'; import ChatMessage from './ChatMessage'; import './Chat.css'; const Chat = () => { const [messages, setMessages] = useState([]); const [input, setInput] = useState(''); const [loading, setLoading] = useState(false); const [contextId, setContextId] = useState(null); const { token } = useAuth(); const messagesEndRef = useRef(null); // Scroll to bottom of messages const scrollToBottom = () => { messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' }); }; useEffect(() => { scrollToBottom(); }, [messages]); // Send message to API const sendMessage = async (e) => { e.preventDefault(); if (!input.trim()) return; // Add user message to chat const userMessage = { id: Date.now().toString(), role: 'user', content: input, created_at: new Date().toISOString() }; setMessages([...messages, userMessage]); setInput(''); setLoading(true); try { const response = await axios.post( '/api/chat', { message: input, context_id: contextId, metadata: { source: 'web', device_info: navigator.userAgent } }, { headers: { Authorization: `Bearer ${token}` } } ); // Add AI response to chat const aiMessage = { id: response.data.id, role: 'assistant', content: response.data.content, created_at: response.data.created_at }; setMessages(prev => [...prev, aiMessage]); // Save context ID for conversation continuity if (response.data.context_id) { setContextId(response.data.context_id); } } catch (error) { console.error('Error sending message:', error); // Add error message const errorMessage = { id: Date.now().toString(), role: 'system', content: 'Sorry, there was an error processing your request.', created_at: new Date().toISOString() }; setMessages(prev => [...prev, errorMessage]); } finally { setLoading(false); } }; return (

{messages.map(message => ( ))} {loading && (

)}

); }; export default Chat; ``` ## 🏗️ Architecture Diagrams ### System Architecture ``` ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │ │ │ │ │ │ React Frontend ├──────► FastAPI Backend├──────► OpenAI API │ │ │ │ │ │ │ └────────┬────────┘ └────────┬────────┘ └─────────────────┘ │ │ │ │ │ ┌───────▼────────┐ │ │ │ └────────────────► PostgreSQL DB │ │ │ └────────────────┘ ``` ### Data Flow Diagram ``` ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐ │ │ │ │ │ │ │ │ │ User Input ├────►│ Auth Service ├────►│ AI Engine ├────►│ Response │ │ │ │ │ │ │ │ Generation │ └───────┬───────┘ └───────────────┘ └───────┬───────┘ └───────┬───────┘ │ │ │ │ │ │ ┌───────▼───────┐ ┌────────▼──────┐ ┌───────▼───────┐ │ │ │ │ │ │ │ Context │◄─────────────────────────┤ User Profile │◄────┤ Metadata │ │ Management │ │ Service │ │ Processing │ │ │ │ │ │ │ └───────────────┘ └───────────────┘ └───────────────┘ ``` ### Database Schema ``` ┌─────────────────────┐ ┌─────────────────────┐ │ User │ │ Conversation │ ├─────────────────────┤ ├─────────────────────┤ │ id (PK) │ │ id (PK) │ │ username │ │ user_id (FK) │ │ email │ │ title │ │ hashed_password │ │ created_at │ │ full_name │ │ updated_at │ │ created_at │ └──────────┬──────────┘ └──────────┬──────────┘ │ │ │ │ 1 1 ┃ └─────────────────────────────┫ ┃ * ┌────────▼──────────┐ │ Message │ ├─────────────────────┤ │ id (PK) │ │ conversation_id (FK)│ │ role │ │ content │ │ message_metadata │ │ created_at │ └─────────────────────┘ ``` ## 🤝 Contributing Guide We welcome contributions to the Advanced AI Chatbot project! This guide will help you get started with the contribution process. ### Getting Started 1. **Fork the Repository** - Click the "Fork" button at the top right of the repository page - This creates a copy of the repository in your GitHub account 2. **Clone Your Fork** ```bash git clone https://github.com/YOUR-USERNAME/AdvancedAIChatbot.git cd AdvancedAIChatbot ``` 3. **Add the Original Repository as Upstream** ```bash git remote add upstream https://github.com/muratyigitartuk/AdvancedAIChatbot.git ``` ### Development Workflow 1. **Create a New Branch** ```bash git checkout -b feature/your-feature-name ``` 2. **Make Your Changes** - Write code that follows the project's coding standards - Add tests for new functionality - Update documentation as needed 3. **Commit Your Changes** ```bash git add . git commit -m "Feature: Brief description of your changes" ``` 4. **Pull Latest Changes from Upstream** ```bash git pull upstream main ``` 5. **Push to Your Fork** ```bash git push origin feature/your-feature-name ``` 6. **Create a Pull Request** - Go to your fork on GitHub - Click "New Pull Request" - Select your branch and submit the pull request with a clear description ### Code Standards - Follow PEP 8 for Python code - Use ESLint rules for JavaScript/React code - Write meaningful commit messages following conventional commits format - Include docstrings for all functions, classes, and modules - Maintain test coverage for new code ### Testing - Write unit tests for new functionality - Ensure all tests pass before submitting a pull request - Consider edge cases and error scenarios ### Documentation - Update README.md with new features or changes - Document new API endpoints - Add inline comments for complex logic ### Review Process 1. All pull requests require at least one review 2. Address all review comments 3. CI checks must pass before merging 4. Maintainers will merge approved pull requests ### Issue Reporting If you find a bug or have a feature request: 1. Check if the issue already exists 2. Create a new issue with a descriptive title 3. Include steps to reproduce for bugs 4. Add relevant labels Thank you for contributing to the Advanced AI Chatbot project! ![AI-Chatbot.webp](AI-Chatbot.webp)

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