Update README.md to enhance documentation with detailed features, architecture, and usage instructions

README.md

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 # PDF Insight Pro
+
+An advanced PDF document analysis tool that combines RAG (Retrieval Augmented Generation) with agentic search capabilities to provide intelligent answers to queries about PDF documents.
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Features](#features)
+- [Architecture](#architecture)
+- [Technical Stack](#technical-stack)
+- [Installation](#installation)
+- [Usage](#usage)
+- [API Endpoints](#api-endpoints)
+- [Deployment](#deployment)
+- [Android App](#android-app)
+- [License](#license)
+
+## Overview
+
+PDF Insight Pro is a sophisticated document analysis tool that allows users to upload PDF documents and ask questions about their content. The system uses state-of-the-art RAG techniques, combining document chunking, embedding generation, similarity search, and LLM processing to provide accurate and contextually relevant answers.
+
+The application employs an agentic approach that can augment the document's information with web search capabilities when needed, ensuring comprehensive and up-to-date responses.
+
+## Features
+
+- **PDF Document Processing**: Upload and process PDF documents with automated text extraction and chunking
+- **Agentic RAG System**: Combines document retrieval with powerful LLM reasoning
+- **Web Search Integration**: Verifies document information with Tavily search API integration
+- **Session Management**: Persistent session handling for chat history and document context
+- **Multiple LLM Support**: Choose from different language models (Llama 4 Scout, Llama 3.1, Llama 3.3)
+- **FastAPI Backend**: High-performance API with async support
+- **Responsive UI**: User-friendly interface adaptable to different screen sizes
+- **Docker Containerization**: Easy deployment with containerized application
+- **Hugging Face Integration**: Automatic deployment to Hugging Face Spaces
+- **Android Application**: Native mobile client
+
+## Architecture
+
+The application follows a modular architecture with these main components:
+
+### Backend Components
+
+1. **PDF Processing Module** (`preprocessing.py`):
+   - Document loading and text extraction using PyMuPDF
+   - Intelligent chunking with metadata preservation
+   - Embedding generation with sentence transformers
+   - FAISS vector index for similarity search
+
+2. **RAG Engine**:
+   - Context retrieval based on semantic similarity
+   - LLM integration using Groq API
+   - Agentic processing with tool-calling capabilities
+   - Web search augmentation with Tavily API
+
+3. **API Layer** (`app.py`):
+   - FastAPI framework for REST endpoints
+   - Session management and persistence
+   - File upload and processing
+   - Chat interface and history management
+
+### Workflow
+
+1. **Document Processing**:
+   - User uploads a PDF document
+   - System extracts text using PyMuPDF
+   - Text is chunked into semantically meaningful segments
+   - Embeddings are generated for each chunk
+   - A FAISS index is built for efficient similarity search
+
+2. **Query Processing**:
+   - User submits a question about the document
+   - System retrieves relevant chunks using semantic similarity
+   - Relevant chunks are combined into a context window
+   - Context and query are sent to the LLM for processing
+   - Optional: Web search integration for fact verification
+
+3. **Response Generation**:
+   - LLM generates a response based on the provided context
+   - If web search is enabled, additional information may be incorporated
+   - Response is returned to the user
+   - Chat history is updated and persisted
+
+## Technical Stack
+
+### Backend
+- **Python 3.12**: Core programming language
+- **FastAPI**: API framework with async support
+- **PyMuPDF**: PDF processing library
+- **LangChain**: Framework for LLM application development
+- **FAISS**: Vector similarity search library from Facebook AI
+- **Sentence Transformers**: Text embedding generation
+- **Groq API**: LLM inference service
+- **Tavily API**: Web search integration
+- **Uvicorn**: ASGI server
+
+### Frontend
+- **HTML/CSS/JavaScript**: Core web technologies
+- **Font Awesome**: Icon library
+- **Highlight.js**: Code syntax highlighting
+- **Marked.js**: Markdown rendering
+- **Responsive Design**: Mobile-friendly interface
+
+*Note: The frontend was developed with assistance from Claude 3.7 AI.*
+
+### DevOps
+- **Docker**: Containerization
+- **GitHub Actions**: CI/CD pipeline
+- **Hugging Face Spaces**: Deployment platform
+
+## Installation
+
+### Prerequisites
+- Python 3.12+
+- API keys for Groq and Tavily
+
+### Local Setup
+
+1. Clone the repository:
+```bash
+git clone https://github.com/yourusername/PDF-Insight-Beta.git
+cd PDF-Insight-Beta
+```
+
+2. Create and activate a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Create a `.env` file with your API keys:
+```
+GROQ_API_KEY=your_groq_api_key
+TAVILY_API_KEY=your_tavily_api_key
+```
+
+5. Run the application:
+```bash
+uvicorn app:app --host 0.0.0.0 --port 8000 --reload
+```
+
+### Docker Deployment
+
+1. Build the Docker image:
+```bash
+docker build -t pdf-insight-pro .
+```
+
+2. Run the container:
+```bash
+docker run -p 7860:7860 \
+  --mount type=secret,id=GROQ_API_KEY,dst=/run/secrets/GROQ_API_KEY \
+  --mount type=secret,id=TAVILY_API_KEY,dst=/run/secrets/TAVILY_API_KEY \
+  pdf-insight-pro
+```
+
+## Usage
+
+1. Open the application in your browser at `http://localhost:8000`
+2. Upload a PDF document using the interface
+3. Wait for processing to complete
+4. Ask questions about the document in the chat interface
+5. Toggle the "Use web search" option for enhanced responses
+
+## API Endpoints
+
+- **GET `/`**: Redirect to static HTML interface
+- **POST `/upload-pdf`**: Upload and process a PDF document
+  - Returns a session ID for subsequent queries
+- **POST `/chat`**: Send a query about the uploaded document
+  - Requires session ID from previous upload
+  - Optional parameter to enable web search
+- **POST `/chat-history`**: Retrieve chat history for a session
+- **POST `/clear-history`**: Clear chat history for a session
+- **POST `/remove-pdf`**: Remove PDF and session data
+- **GET `/models`**: List available language models
+
+## Deployment
+
+### Hugging Face Spaces
+
+This project is configured for automatic deployment to Hugging Face Spaces using GitHub Actions. The workflow in `.github/workflows/sync_to_hf.yml` handles the deployment process.
+
+To deploy to your own space:
+
+1. Fork this repository
+2. Create a Hugging Face Space
+3. Add your Hugging Face token as a GitHub secret named `HF_TOKEN`
+4. Update the username and space name in the workflow file
+5. Push to the main branch to trigger deployment
+
+## Android App
+
+The repository includes an Android application that serves as a mobile interface to the web application. Rather than implementing a native client with direct API integration, the Android app utilizes a WebView component to load the deployed web interface from Hugging Face Spaces. This approach ensures consistency between the web and mobile experiences while reducing maintenance overhead.
+
+### Android App Features
+
+- WebView integration to the deployed web application
+- Splash screen with app branding
+- Responsive design that adapts to the mobile interface
+- Native Android navigation and user experience
+- Direct access to the full functionality of the web application
+
+### Implementation Details
+
+The Android app is implemented using Java and consists of:
+- SplashActivity: Displays the app logo and transitions to the main activity
+- MainActivity: Contains a WebView component that loads the deployed web application
+- WebView configuration: Enables JavaScript, DOM storage, and handles file uploads
+
+## License
+
+MIT