update setup instructions

README.md

@@ -4,204 +4,419 @@ An AI-powered academic guidance system that provides personalized advice through
 
 ## Features
 
-- **Multiple AI Advisor Personas**: Chat with specialized advisors including Methodologist (methodology expert), Theorist (conceptual frameworks), and Pragmatist (action-focused guidance)
-- **Document Upload Support**: Upload PDFs, Word documents, and text files to provide context for your questions
-- **Multi-LLM Backend**: Supports both Gemini API and Ollama models with seamless provider switching
-- **Real-time Chat Interface**: Modern, responsive chat interface with advisor-specific styling
-- **Context-Aware Responses**: Maintains conversation history and document context across the session
-- **Sequential Advisor Responses**: Get input from all advisors in a structured sequence
-- **Individual Advisor Chat**: Have focused conversations with specific advisors
+- **Multiple AI Advisor Personas**: Chat with 10+ specialized advisors including Methodologist, Theorist, Pragmatist, and more
+- **Document Upload & Analysis**: Upload PDFs, Word documents, and text files for context-aware advice
+- **Intelligent Document Retrieval (RAG)**: Advanced semantic search through your uploaded documents
+- **Multi-LLM Backend**: Supports both Gemini API and local Ollama models
+- **User Authentication**: Secure user accounts with persistent chat sessions
+- **Chat Session Management**: Save, load, and manage multiple conversation threads
+- **Export Capabilities**: Export chats and summaries in TXT, PDF, and DOCX formats
+- **Real-time Chat Interface**: Modern, responsive UI with advisor-specific styling
 
 ## Architecture
 
 ### Frontend (React)
 - **Technology**: React 18 with modern hooks and functional components
 - **Styling**: CSS custom properties with dark/light theme support
-- **Components**: Modular component architecture with reusable UI elements
-- **State Management**: React hooks for local state management
-- **Icons**: Lucide React for consistent iconography
+- **State Management**: React Context and hooks
+- **Authentication**: JWT-based authentication with persistent sessions
 
 ### Backend (FastAPI)
 - **Framework**: FastAPI with automatic API documentation
-- **LLM Integration**: Support for multiple providers (Gemini, Ollama)
-- **Document Processing**: PDF, DOCX, and text file extraction
-- **Session Management**: Global session context with file upload tracking
-- **CORS Support**: Configured for React development server
+- **Database**: MongoDB for user data and chat sessions
+- **Vector Database**: ChromaDB for document storage and semantic search
+- **LLM Integration**: Support for Gemini API and Ollama models
+- **Document Processing**: PDF, DOCX, and text file extraction with intelligent chunking
+- **Authentication**: JWT tokens with bcrypt password hashing
 
-## Quick Start
+## Prerequisites
 
-### Prerequisites
-- Node.js 16+ and npm
-- Python 3.8+
-- (Optional) Gemini API key for Google's models
-- (Optional) Ollama installation for local models
+Before you begin, ensure you have the following installed:
 
-### Backend Setup
+- **Python 3.8+** (3.9+ recommended)
+- **Node.js 16+** and npm
+- **MongoDB** (Community Edition)
+- **Git**
 
-1. **Clone and navigate to backend directory**
+## Installation Guide
+
+### Step 1: Clone the Repository
+
+```bash
+git clone https://github.com/sohank-17/Neon-AI-Project.git
+cd Neon-AI-Project
+```
+
+### Step 2: MongoDB Setup
+
+#### Option A: Local MongoDB Installation
+
+**On Windows:**
+1. Download MongoDB Community Server from [mongodb.com](https://www.mongodb.com/try/download/community)
+2. Install with default settings
+3. MongoDB will run as a Windows Service automatically
+
+**On macOS:**
+```bash
+# Using Homebrew
+brew tap mongodb/brew
+brew install mongodb-community
+brew services start mongodb/brew/mongodb-community
+```
+
+**On Linux (Ubuntu/Debian):**
+```bash
+# Import MongoDB public GPG key
+wget -qO - https://www.mongodb.org/static/pgp/server-6.0.asc | sudo apt-key add -
+
+# Create list file
+echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/6.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-6.0.list
+
+# Install MongoDB
+sudo apt-get update
+sudo apt-get install -y mongodb-org
+
+# Start MongoDB
+sudo systemctl start mongod
+sudo systemctl enable mongod
+```
+
+#### Option B: MongoDB Atlas (Cloud)
+1. Create a free account at [MongoDB Atlas](https://www.mongodb.com/atlas)
+2. Create a new cluster
+3. Get your connection string
+4. Skip the local MongoDB setup
+
+### Step 3: Ollama Installation (for Local LLM Support)
+
+#### Install Ollama
+
+**On Windows:**
+1. Download Ollama from [ollama.ai](https://ollama.ai)
+2. Run the installer
+3. Ollama will start automatically
+
+**On macOS:**
+```bash
+# Using Homebrew
+brew install ollama
+
+# Or download from ollama.ai
+```
+
+**On Linux:**
+```bash
+# Install Ollama
+curl -fsSL https://ollama.ai/install.sh | sh
+
+# Start Ollama service
+sudo systemctl start ollama
+sudo systemctl enable ollama
+```
+
+#### Download Required Models
+
+Once Ollama is installed, download the recommended models:
+
+```bash
+# Download the default model (recommended for development)
+ollama pull llama3.2:1b
+
+# Optional: Download larger, more capable models
+ollama pull llama3.2:3b
+ollama pull mistral:7b
+
+# Verify installation
+ollama list
+```
+
+**Note**: The `llama3.2:1b` model is small (~1.3GB) and fast, perfect for development. For production, consider larger models for better quality.
+
+### Step 4: Backend Setup
+
+1. **Navigate to the backend directory:**
 ```bash
 cd multi_llm_chatbot_backend
 ```
 
-2. **Install dependencies**
+2. **Create a Python virtual environment:**
+```bash
+# Create virtual environment
+python -m venv venv
+
+# Activate virtual environment
+# On Windows:
+venv\Scripts\activate
+# On macOS/Linux:
+source venv/bin/activate
+```
+
+3. **Install Python dependencies:**
 ```bash
 pip install -r requirements.txt
 ```
 
-3. **Set up environment variables**
-Create a `.env` file:
+4. **Set up environment variables:**
+Create a `.env` file in the `multi_llm_chatbot_backend` directory:
+
 ```env
+# MongoDB Configuration
+MONGODB_CONNECTION_STRING=mongodb://localhost:27017
+MONGODB_DATABASE_NAME=phd_advisor
+
+# JWT Configuration
+JWT_SECRET_KEY=your-super-secret-jwt-key-change-this-in-production-please-make-it-long-and-random
+
+# Gemini API Configuration (Optional - for cloud LLM)
 GEMINI_API_KEY=your_gemini_api_key_here
 GEMINI_MODEL=gemini-2.0-flash
+
+# Ollama Configuration (for local LLM)
+OLLAMA_BASE_URL=http://localhost:11434
+
+# Application Settings
+CORS_ORIGINS=http://localhost:3000
 ```
 
-4. **Start the backend server**
+**Getting a Gemini API Key (Optional):**
+1. Go to [Google AI Studio](https://makersuite.google.com/app/apikey)
+2. Create a new API key
+3. Add it to your `.env` file
+
+5. **Start the backend server:**
 ```bash
 uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
 ```
 
 The API will be available at `http://localhost:8000` with interactive docs at `http://localhost:8000/docs`
 
-### Frontend Setup
+### Step 5: Frontend Setup
 
-1. **Navigate to frontend directory**
+1. **Navigate to the frontend directory:**
 ```bash
-cd phd-advisor-frontend
+cd ../phd-advisor-frontend
 ```
 
-2. **Install dependencies**
+2. **Install dependencies:**
 ```bash
 npm install
 ```
 
-3. **Start the development server**
+3. **Start the development server:**
 ```bash
 npm start
 ```
 
 The application will open at `http://localhost:3000`
 
-## Advisor Personas
+## Quick Start Guide
 
-### Methodologist - Research Methodology Expert
-- **Focus**: Research design, validity, sampling, methodological rigor
-- **Style**: Precise, analytical, methodology-focused
-- **Best for**: Research design questions, data collection methods, validity concerns
+### First Time Setup Checklist
 
-### Theorist - Conceptual Frameworks Expert  
-- **Focus**: Theoretical positioning, epistemological assumptions, conceptual clarity
-- **Style**: Thoughtful, intellectually rigorous, theory-oriented
-- **Best for**: Literature review, theoretical frameworks, conceptual development
+1. MongoDB is running (check with `mongosh` or MongoDB Compass)
+2. Ollama is running with models downloaded (`ollama list`)
+3. Backend is running on port 8000
+4. Frontend is running on port 3000
+5. Create your first user account
 
-### Pragmatist - Action-Focused Advisor
-- **Focus**: Practical next steps, immediate actions, progress over perfection
-- **Style**: Warm, motivational, results-oriented
-- **Best for**: Getting unstuck, prioritizing tasks, actionable advice
+### Basic Usage
 
-## API Endpoints
+1. **Create an Account:**
+   - Open `http://localhost:3000`
+   - Click "Sign Up"
+   - Fill in your details
 
-### Core Chat Endpoints
-- `POST /chat-sequential` - Get responses from all advisors in sequence
-- `POST /chat/{persona_id}` - Chat with a specific advisor
-- `POST /reply-to-advisor` - Reply to a specific advisor's message
+2. **Start Your First Chat:**
+   - Click "New Chat"
+   - Ask a question like "I need help with my research methodology"
+   - Get responses from multiple advisor personas
 
-### Document Management
-- `POST /upload-document` - Upload documents (PDF, DOCX, TXT)
-- `GET /uploaded-files` - List uploaded filenames
-- `GET /context` - View current session context
+3. **Upload Documents:**
+   - Click the upload button in the chat
+   - Upload a PDF, DOCX, or TXT file
+   - Ask questions about your document
 
-### Provider Management
-- `GET /current-provider` - Get current LLM provider info
-- `POST /switch-provider` - Switch between Gemini and Ollama
+4. **Manage Chats:**
+   - Save important conversations
+   - Switch between different chat sessions
+   - Export chats in various formats
 
-### System
-- `GET /debug/personas` - Debug persona configurations
-- `GET /` - API health check
+## 🔧 Configuration
 
-## Configuration
+### Environment Variables Reference
 
-### Supported LLM Providers
+| Variable | Description | Default | Required |
+|----------|-------------|---------|----------|
+| `MONGODB_CONNECTION_STRING` | MongoDB connection URL | `mongodb://localhost:27017` | Yes |
+| `MONGODB_DATABASE_NAME` | Database name | `phd_advisor` | Yes |
+| `JWT_SECRET_KEY` | Secret key for JWT tokens | - | Yes |
+| `GEMINI_API_KEY` | Google Gemini API key | - | No |
+| `GEMINI_MODEL` | Gemini model to use | `gemini-2.0-flash` | No |
+| `OLLAMA_BASE_URL` | Ollama server URL | `http://localhost:11434` | No |
 
-**Gemini (Default)**
-- Requires GEMINI_API_KEY environment variable
-- Uses gemini-2.0-flash model by default
-- Cloud-based, requires internet connection
+### Switching Between LLM Providers
 
-**Ollama (Local)**
-- Requires Ollama installation
-- Uses llama3.2:1b model by default
-- Runs locally, no internet required
+The application supports two LLM providers:
 
-### File Upload Limits
-- Maximum file size: 10MB per file
-- Total session limit: 50MB
-- Supported formats: PDF, DOCX, TXT
+1. **Ollama (Local, Free):**
+   - Ensure Ollama is running
+   - Models run locally on your machine
+   - No API costs, complete privacy
 
-### Environment Variables
-```env
-# Required for Gemini
-GEMINI_API_KEY=your_api_key
+2. **Gemini (Cloud, Paid):**
+   - Requires API key
+   - Higher quality responses
+   - Faster response times
 
-# Optional configurations
-GEMINI_MODEL=gemini-2.0-flash
-OLLAMA_BASE_URL=http://localhost:11434
+Switch providers using the API:
+```bash
+curl -X POST "http://localhost:8000/switch-provider" \
+  -H "Content-Type: application/json" \
+  -d '{"provider": "ollama"}'
 ```
 
-## Development
+## API Documentation
 
-### Project Structure
-```
-phd-advisor-frontend/
-├── public/
-├── src/
-│   ├── components/     # Reusable UI components
-│   ├── data/          # Advisor configurations
-│   ├── pages/         # Main page components
-│   ├── styles/        # CSS stylesheets
-│   └── utils/         # Helper functions
+### Authentication Endpoints
+- `POST /auth/signup` - Create new user account
+- `POST /auth/login` - Login with email/password
+- `GET /auth/me` - Get current user profile
+
+### Chat Endpoints
+- `POST /chat-sequential` - Get responses from all advisors
+- `POST /chat/{persona_id}` - Chat with specific advisor
+- `POST /reply-to-advisor` - Reply to specific advisor message
+
+### Document Management
+- `POST /upload-document` - Upload PDF, DOCX, or TXT files
+- `GET /uploaded-files` - List uploaded files
+- `GET /document-stats` - Get document statistics
 
-multi_llm_chatbot_backend/
-├── app/
-│   ├── api/           # API routes
-│   ├── core/          # Business logic
-│   ├── llm/           # LLM client implementations
-│   ├── models/        # Data models
-│   ├── tests/         # Test files
-│   └── utils/         # Utility functions
+### Session Management
+- `GET /context` - Get current session context
+- `POST /reset-session` - Reset current session
+- `GET /session-stats` - Get session statistics
+
+### Export & Summary
+- `GET /export-chat` - Export chat (txt, pdf, docx)
+- `GET /chat-summary` - Generate chat summary
+
+Full API documentation is available at `http://localhost:8000/docs` when the server is running.
+
+## Troubleshooting
+
+### Common Issues
+
+**Backend won't start:**
+```bash
+# Check if port 8000 is already in use
+netstat -an | grep :8000
+
+# Check Python virtual environment is activated
+which python  # Should point to your venv
+
+# Check all dependencies are installed
+pip list
 ```
 
-### Adding New Advisors
-1. Create persona in `app/api/routes.py` with system prompt
-2. Add advisor configuration in frontend `src/data/advisors.js`
-3. Update advisor styling in `src/styles/`
+**MongoDB connection issues:**
+```bash
+# Test MongoDB connection
+mongosh
+
+# Check if MongoDB service is running
+# Windows: Check Services app
+# macOS: brew services list | grep mongodb
+# Linux: systemctl status mongod
+```
 
-### Testing Document Upload
-Use the test script:
+**Ollama not working:**
 ```bash
-cd multi_llm_chatbot_backend/app/tests
-python test_document_upload.py
+# Check if Ollama is running
+curl http://localhost:11434/api/tags
+
+# Check downloaded models
+ollama list
+
+# Test model directly
+ollama run llama3.2:1b "Hello"
 ```
 
-## Usage Examples
+**Frontend won't connect to backend:**
+- Verify backend is running on port 8000
+- Check CORS settings in backend `.env`
+- Check browser developer console for errors
+
+### Performance Tips
+
+1. **For faster local LLM responses:**
+   - Use smaller models like `llama3.2:1b` for development
+   - Ensure sufficient RAM (8GB+ recommended)
+   - Use SSD storage for better model loading
 
-### Starting a Conversation
-1. Navigate to the home page
-2. Click "Start Conversation"
-3. Type your PhD-related question
-4. Receive responses from all three advisors
+2. **For better document search:**
+   - Upload focused, relevant documents
+   - Use clear, descriptive filenames
+   - Break large documents into smaller sections
+
+3. **For production deployment:**
+   - Use larger, more capable models
+   - Consider GPU acceleration for Ollama
+   - Use MongoDB Atlas for cloud database
+   - Set up proper authentication and HTTPS
+
+## Development
 
-### Uploading Documents
-1. In the chat interface, click the upload button
-2. Select your PDF, DOCX, or TXT file
-3. The document content will be added to the conversation context
-4. Ask questions about your uploaded documents
+### Running Tests
 
-### Switching LLM Providers
 ```bash
-curl -X POST "http://localhost:8000/switch-provider" \
-  -H "Content-Type: application/json" \
-  -d '{"provider": "ollama"}'
+# Backend tests
+cd multi_llm_chatbot_backend
+python -m pytest app/tests/
+
+# Test specific functionality
+python app/tests/test_rag_system.py
+python app/tests/debug_rag.py
+```
+
+### Project Structure
+
 ```
+phd-advisor-panel/
+├── multi_llm_chatbot_backend/
+│   ├── app/
+│   │   ├── api/routes/          # API route handlers
+│   │   ├── core/                # Core business logic
+│   │   ├── llm/                 # LLM client implementations
+│   │   ├── models/              # Data models and schemas
+│   │   ├── utils/               # Utility functions
+│   │   └── tests/               # Test files
+│   ├── requirements.txt
+│   └── .env
+├── phd-advisor-frontend/
+│   ├── src/
+│   │   ├── components/          # React components
+│   │   ├── pages/               # Page components
+│   │   ├── styles/              # CSS files
+│   │   └── utils/               # Frontend utilities
+│   ├── package.json
+│   └── public/
+└── README.md
+```
+
+### Adding New Advisor Personas
+
+1. Edit `app/models/default_personas.py`
+2. Add your persona configuration
+3. Restart the backend server
+4. The new persona will be available in chat
+
+### Extending Document Support
+
+1. Add new file type to `app/utils/document_extractor.py`
+2. Update the upload endpoint in `app/api/routes/documents.py`
+3. Test with sample files
+
 
 ## Contributing
 
@@ -211,10 +426,16 @@ curl -X POST "http://localhost:8000/switch-provider" \
 4. Push to the branch (`git push origin feature/amazing-feature`)
 5. Open a Pull Request
 
-
 ## Support
 
-- Check the API documentation at `http://localhost:8000/docs`
-- Review the debug endpoints for troubleshooting
-- Ensure all environment variables are properly configured
-- Verify that your LLM provider (Gemini/Ollama) is accessible
+- Check the [API Documentation](http://localhost:8000/docs)
+- Report bugs by opening an issue
+- Request features by opening an issue
+- Contact the development team
+
+## Acknowledgments
+
+- Built with [FastAPI](https://fastapi.tiangolo.com/) and [React](https://reactjs.org/)
+- Powered by [Ollama](https://ollama.ai/) for local LLM support
+- Uses [ChromaDB](https://www.trychroma.com/) for vector storage
+- Document processing with [PyPDF2](https://pypdf2.readthedocs.io/) and [python-docx](https://python-docx.readthedocs.io/)