Update README.md

README.md

@@ -1,310 +1,319 @@
-# RAG Personal Diary Chatbot
-
-## 📖 Project Description
-
-RAG Personal Diary Chatbot is an intelligent chatbot application that uses RAG (Retrieval-Augmented Generation) architecture to interact with users' personal diaries. The application allows users to ask questions about diary content and receive accurate answers based on actual data.
-
-## ✨ Key Features
-
-
-## 🏗️ System Architecture
-
-```
-┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
-│   Streamlit UI  │    │   FastAPI       │    │   Vector        │
-│   (Frontend)    │◄──►│   Backend       │◄──►│   Database     │
-└─────────────────┘    └─────────────────┘    └─────────────────┘
-                              │
-                              ▼
-                       ┌─────────────────┐
-                       │   RAG Engine    │
-                       │   (LLM +       │
-                       │   Retrieval)    │
-                       └─────────────────┘
-```
-
-## 🚀 Installation and Setup
-
-### System Requirements
-
-### Install Dependencies
-```bash
-# Create virtual environment
-python -m venv .venv
-
-# Activate virtual environment
-# Windows
-.venv\Scripts\activate
-# Linux/Mac
-source .venv/bin/activate
-
-# Install packages
-pip install -r requirements.txt
-```
-
-### Environment Configuration
-
-Create a `.env` file in the project root directory with the following structure:
-
-```env
-# API Keys
-OPENAI_API_KEY=your_openai_api_key_here
-GOOGLE_API_KEY=your_google_api_key_here
-ANTHROPIC_API_KEY=your_anthropic_api_key_here
-
-# Database Configuration
-DATABASE_URL=sqlite:///./user_database/auth.db
-VECTOR_DB_PATH=./VectorDB
-
-# Model Configuration
-EMBEDDING_MODEL=google-universal-sentence-encoder
-LLM_MODEL=gpt-3.5-turbo
-CHUNK_SIZE=1000
-CHUNK_OVERLAP=200
-
-# Server Configuration
-RAG_SERVICE_PORT=8001
-STREAMLIT_PORT=8501
-FASTAPI_PORT=8000
-
-# Security
-SECRET_KEY=your_secret_key_here
-JWT_SECRET=your_jwt_secret_here
-
-# Logging
-LOG_LEVEL=INFO
-LOG_FILE=./logs/app.log
-
-# Vector Database
-CHROMA_DB_PATH=./VectorDB
-PERSIST_DIRECTORY=./VectorDB
-
-# File Processing
-SUPPORTED_FORMATS=pdf,docx,txt,md
-MAX_FILE_SIZE=10485760
-TEMP_DIR=./temp
-
-# RAG Configuration
-TOP_K_RESULTS=5
-SIMILARITY_THRESHOLD=0.7
-MAX_TOKENS=4096
-TEMPERATURE=0.7
-```
-
-**Important Notes:**
-
-### Run the Application
-
-#### 1. Start RAG Service
-```bash
-python start_rag_service.py
-```
-Service will run at: http://127.0.0.1:8001
-
-#### 2. Start Streamlit UI
-```bash
-cd src/streamlit_app
-streamlit run interface.py
-```
-UI will run at: http://localhost:8501
-
-## 📁 Directory Structure
-
-```
-RAG-Personal-Diary-Chatbot/
-├── src/
-│   ├── Indexingstep/          # Data indexing pipeline
-│   ├── Retrivel_And_Generation/  # RAG engine
-│   ├── rag_service/           # FastAPI backend
-│   ├── streamlit_app/         # User interface
-│   └── VectorDB/              # Vector database
-├── notebook/                   # Jupyter notebooks
-├── tests/                      # Unit tests
-├── images/                     # Documentation images
-├── start_rag_service.py       # Service startup script
-├── .env                       # Environment variables (create from template)
-├── env_template.txt           # Environment variables template
-└── README.md
-```
-
-## 🔧 Configuration
-
-### Vector Database
-
-### AI Models
-
-## 📊 Performance
-
-
-## 🧪 Testing
-
-```bash
-# Run all tests
-python -m pytest tests/
-
-# Run specific test
-python -m pytest tests/test_rag_system.py
-```
-
-## 🤝 Contributing
-
-1. Fork the project
-2. Create feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to branch (`git push origin feature/AmazingFeature`)
-5. Open Pull Request
-
-## 📝 License
-
-This project is distributed under the MIT License. See the `LICENSE` file for more details.
-
-## 📞 Contact
-
-
-## 🙏 Acknowledgments
-
-
-## 📖 Project Description
-
-RAG Personal Diary Chatbot is an intelligent chatbot application that leverages Retrieval-Augmented Generation (RAG) architecture to interact with users' personal diaries. Users can ask questions about their diary content and receive accurate, context-based answers.
-
-## ✨ Key Features
-
-- **Diary Indexing**: Automatically processes and indexes diary files (PDF, DOCX, TXT)
-- **Semantic Search**: Uses a vector database for semantic search
-- **AI Chatbot**: Natural interaction with diary data
-- **User Isolation**: Each user has a separate vector database
-- **Web Interface**: Easy-to-use Streamlit UI
-- **REST API**: FastAPI backend for integration
-
-## 🏗️ System Architecture
-
-```
-┌───────────────┐    ┌───────────────┐    ┌───────────────┐
-│ Streamlit UI  │◄──►│   FastAPI     │◄──►│  Vector DB    │
-│ (Frontend)    │    │   Backend     │    │  (ChromaDB)   │
-└───────────────┘    └───────────────┘    └───────────────┘
-                              │
-                              ▼
-                       ┌───────────────┐
-                       │   RAG Engine  │
-                       │ (LLM +        │
-                       │  Retrieval)   │
-                       └───────────────┘
-```
-
-## 🚀 Installation and Setup
-
-### System Requirements
-
-- Python 3.8+
-
-### Install Dependencies
-
-```bash
-# Create virtual environment
-python -m venv .venv
-
-# Activate virtual environment
-# Windows
-.venv\Scripts\activate
-# Linux/Mac
-source .venv/bin/activate
-
-# Install packages
-pip install -r requirements.txt
-```
-
-### Environment Configuration
-
-Create a `.env` file in the project root directory with the following structure:
-
-```env
-# Google API Configuration for RAG System
-GOOGLE_API_KEY=[Google API key]
-
-# Database Configuration
-DATABASE_PATH=./src/streamlit_app/backend/diary.db
-
-# Vector Database Configuration
-VECTOR_DB_PATH=./src/Indexingstep/diary_vector_db_enhanced
-COLLECTION_NAME=diary_entries
-
-# RAG Configuration
-EMBEDDING_MODEL=models/embedding-001
-CHAT_MODEL=gemini-2.5-flash
-```
-
-**Important Notes:**
-- Replace all placeholder values with your actual API keys and configuration
-- Keep your `.env` file secure and never commit it to version control
-- The `.env` file is already included in `.gitignore`
-- Use `env_template.txt` as a reference to create your `.env` file
-
-### Run the Application
-
-```bash
-# Start the RAG backend service
-python start_rag_service.py
-
-# Start the Streamlit UI
-streamlit run src/streamlit_app/interface.py
-```
-
-## 📁 Directory Structure
-
-```
-RAG-Personal-Diary-Chatbot/
-├── src/
-│   ├── Indexingstep/          # Data indexing pipeline
-│   ├── Retrivel_And_Generation/  # RAG engine
-│   ├── rag_service/           # FastAPI backend
-│   ├── streamlit_app/         # User interface
-│   └── VectorDB/              # Vector database
-├── notebook/                  # Jupyter notebooks
-├── tests/                     # Unit tests
-├── images/                    # Documentation images
-├── start_rag_service.py       # Service startup script
-├── .env                       # Environment variables (create from template)
-├── env_template.txt           # Environment variables template
-└── README.md
-```
-
-## 🔧 Configuration
-
-### Vector Database
-- **ChromaDB**: Main database for vector embeddings
-- **Chunk size**: 1000 characters (customizable)
-- **Overlap**: 200 characters between chunks
-
-### AI Models
-- **Embedding**: Google's Universal Sentence Encoder
-- **LLM**: Google Gemini (can be replaced with other models)
-
-## 📊 Performance
-
-- **Processing time**: ~2-5 seconds per question
-- **Accuracy**: 85-95% depending on data quality
-- **Scalability**: Supports thousands of diaries
-
-
-## 🤝 Contributing
-
-1. Fork the project
-2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
-3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
-4. Push to the branch (`git push origin feature/AmazingFeature`)
-5. Open a Pull Request
-## 📞 Contact
-
-- **Author**: [DongAnh]
-- **Email**: [donganhng098@gmail.com]
-- **GitHub**: [github.com/DongAnh]
-
-## 🙏 Acknowledgments
-
-- Gemini for GPT models
-- Google for Universal Sentence Encoder
-- ChromaDB team for vector database
-- FastAPI and Streamlit communities
-- RAG architecture
+---
+title: FastAPI + Streamlit Demo
+emoji: 🐳
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+app_file: Dockerfile
+pinned: false
+---
+# RAG Personal Diary Chatbot
+
+## 📖 Project Description
+
+RAG Personal Diary Chatbot is an intelligent chatbot application that uses RAG (Retrieval-Augmented Generation) architecture to interact with users' personal diaries. The application allows users to ask questions about diary content and receive accurate answers based on actual data.
+
+## ✨ Key Features
+
+
+## 🏗️ System Architecture
+
+```
+┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+│   Streamlit UI  │    │   FastAPI       │    │   Vector        │
+│   (Frontend)    │◄──►│   Backend       │◄──►│   Database     │
+└─────────────────┘    └─────────────────┘    └─────────────────┘
+                              │
+                              ▼
+                       ┌─────────────────┐
+                       │   RAG Engine    │
+                       │   (LLM +       │
+                       │   Retrieval)    │
+                       └─────────────────┘
+```
+
+## 🚀 Installation and Setup
+
+### System Requirements
+
+### Install Dependencies
+```bash
+# Create virtual environment
+python -m venv .venv
+
+# Activate virtual environment
+# Windows
+.venv\Scripts\activate
+# Linux/Mac
+source .venv/bin/activate
+
+# Install packages
+pip install -r requirements.txt
+```
+
+### Environment Configuration
+
+Create a `.env` file in the project root directory with the following structure:
+
+```env
+# API Keys
+OPENAI_API_KEY=your_openai_api_key_here
+GOOGLE_API_KEY=your_google_api_key_here
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+
+# Database Configuration
+DATABASE_URL=sqlite:///./user_database/auth.db
+VECTOR_DB_PATH=./VectorDB
+
+# Model Configuration
+EMBEDDING_MODEL=google-universal-sentence-encoder
+LLM_MODEL=gpt-3.5-turbo
+CHUNK_SIZE=1000
+CHUNK_OVERLAP=200
+
+# Server Configuration
+RAG_SERVICE_PORT=8001
+STREAMLIT_PORT=8501
+FASTAPI_PORT=8000
+
+# Security
+SECRET_KEY=your_secret_key_here
+JWT_SECRET=your_jwt_secret_here
+
+# Logging
+LOG_LEVEL=INFO
+LOG_FILE=./logs/app.log
+
+# Vector Database
+CHROMA_DB_PATH=./VectorDB
+PERSIST_DIRECTORY=./VectorDB
+
+# File Processing
+SUPPORTED_FORMATS=pdf,docx,txt,md
+MAX_FILE_SIZE=10485760
+TEMP_DIR=./temp
+
+# RAG Configuration
+TOP_K_RESULTS=5
+SIMILARITY_THRESHOLD=0.7
+MAX_TOKENS=4096
+TEMPERATURE=0.7
+```
+
+**Important Notes:**
+
+### Run the Application
+
+#### 1. Start RAG Service
+```bash
+python start_rag_service.py
+```
+Service will run at: http://127.0.0.1:8001
+
+#### 2. Start Streamlit UI
+```bash
+cd src/streamlit_app
+streamlit run interface.py
+```
+UI will run at: http://localhost:8501
+
+## 📁 Directory Structure
+
+```
+RAG-Personal-Diary-Chatbot/
+├── src/
+│   ├── Indexingstep/          # Data indexing pipeline
+│   ├── Retrivel_And_Generation/  # RAG engine
+│   ├── rag_service/           # FastAPI backend
+│   ├── streamlit_app/         # User interface
+│   └── VectorDB/              # Vector database
+├── notebook/                   # Jupyter notebooks
+├── tests/                      # Unit tests
+├── images/                     # Documentation images
+├── start_rag_service.py       # Service startup script
+├── .env                       # Environment variables (create from template)
+├── env_template.txt           # Environment variables template
+└── README.md
+```
+
+## 🔧 Configuration
+
+### Vector Database
+
+### AI Models
+
+## 📊 Performance
+
+
+## 🧪 Testing
+
+```bash
+# Run all tests
+python -m pytest tests/
+
+# Run specific test
+python -m pytest tests/test_rag_system.py
+```
+
+## 🤝 Contributing
+
+1. Fork the project
+2. Create feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to branch (`git push origin feature/AmazingFeature`)
+5. Open Pull Request
+
+## 📝 License
+
+This project is distributed under the MIT License. See the `LICENSE` file for more details.
+
+## 📞 Contact
+
+
+## 🙏 Acknowledgments
+
+
+## 📖 Project Description
+
+RAG Personal Diary Chatbot is an intelligent chatbot application that leverages Retrieval-Augmented Generation (RAG) architecture to interact with users' personal diaries. Users can ask questions about their diary content and receive accurate, context-based answers.
+
+## ✨ Key Features
+
+- **Diary Indexing**: Automatically processes and indexes diary files (PDF, DOCX, TXT)
+- **Semantic Search**: Uses a vector database for semantic search
+- **AI Chatbot**: Natural interaction with diary data
+- **User Isolation**: Each user has a separate vector database
+- **Web Interface**: Easy-to-use Streamlit UI
+- **REST API**: FastAPI backend for integration
+
+## 🏗️ System Architecture
+
+```
+┌───────────────┐    ┌───────────────┐    ┌───────────────┐
+│ Streamlit UI  │◄──►│   FastAPI     │◄──►│  Vector DB    │
+│ (Frontend)    │    │   Backend     │    │  (ChromaDB)   │
+└───────────────┘    └───────────────┘    └───────────────┘
+                              │
+                              ▼
+                       ┌───────────────┐
+                       │   RAG Engine  │
+                       │ (LLM +        │
+                       │  Retrieval)   │
+                       └───────────────┘
+```
+
+## 🚀 Installation and Setup
+
+### System Requirements
+
+- Python 3.8+
+
+### Install Dependencies
+
+```bash
+# Create virtual environment
+python -m venv .venv
+
+# Activate virtual environment
+# Windows
+.venv\Scripts\activate
+# Linux/Mac
+source .venv/bin/activate
+
+# Install packages
+pip install -r requirements.txt
+```
+
+### Environment Configuration
+
+Create a `.env` file in the project root directory with the following structure:
+
+```env
+# Google API Configuration for RAG System
+GOOGLE_API_KEY=[Google API key]
+
+# Database Configuration
+DATABASE_PATH=./src/streamlit_app/backend/diary.db
+
+# Vector Database Configuration
+VECTOR_DB_PATH=./src/Indexingstep/diary_vector_db_enhanced
+COLLECTION_NAME=diary_entries
+
+# RAG Configuration
+EMBEDDING_MODEL=models/embedding-001
+CHAT_MODEL=gemini-2.5-flash
+```
+
+**Important Notes:**
+- Replace all placeholder values with your actual API keys and configuration
+- Keep your `.env` file secure and never commit it to version control
+- The `.env` file is already included in `.gitignore`
+- Use `env_template.txt` as a reference to create your `.env` file
+
+### Run the Application
+
+```bash
+# Start the RAG backend service
+python start_rag_service.py
+
+# Start the Streamlit UI
+streamlit run src/streamlit_app/interface.py
+```
+
+## 📁 Directory Structure
+
+```
+RAG-Personal-Diary-Chatbot/
+├── src/
+│   ├── Indexingstep/          # Data indexing pipeline
+│   ├── Retrivel_And_Generation/  # RAG engine
+│   ├── rag_service/           # FastAPI backend
+│   ├── streamlit_app/         # User interface
+│   └── VectorDB/              # Vector database
+├── notebook/                  # Jupyter notebooks
+├── tests/                     # Unit tests
+├── images/                    # Documentation images
+├── start_rag_service.py       # Service startup script
+├── .env                       # Environment variables (create from template)
+├── env_template.txt           # Environment variables template
+└── README.md
+```
+
+## 🔧 Configuration
+
+### Vector Database
+- **ChromaDB**: Main database for vector embeddings
+- **Chunk size**: 1000 characters (customizable)
+- **Overlap**: 200 characters between chunks
+
+### AI Models
+- **Embedding**: Google's Universal Sentence Encoder
+- **LLM**: Google Gemini (can be replaced with other models)
+
+## 📊 Performance
+
+- **Processing time**: ~2-5 seconds per question
+- **Accuracy**: 85-95% depending on data quality
+- **Scalability**: Supports thousands of diaries
+
+
+## 🤝 Contributing
+
+1. Fork the project
+2. Create a feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a Pull Request
+## 📞 Contact
+
+- **Author**: [DongAnh]
+- **Email**: [donganhng098@gmail.com]
+- **GitHub**: [github.com/DongAnh]
+
+## 🙏 Acknowledgments
+
+- Gemini for GPT models
+- Google for Universal Sentence Encoder
+- ChromaDB team for vector database
+- FastAPI and Streamlit communities
+- RAG architecture