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+# Production-Grade RAG Python Application
+
+A production-ready Retrieval-Augmented Generation (RAG) system that allows users to upload PDFs, ask questions, and get AI-powered answers based on the document content. Built with FastAPI, Streamlit, OpenAI, and Qdrant vector database.
+
+## 🌟 Features
+
+- 📄 PDF document ingestion and processing
+- 🔍 Semantic search using vector embeddings
+- 🤖 AI-powered question answering using OpenAI GPT
+- 🎯 Production-ready architecture with event-driven design
+- 📊 User-friendly Streamlit interface
+- 🔄 Asynchronous processing using Inngest
+- 📦 Vector storage with Qdrant
+
+## 🏗️ Architecture
+
+The application follows a modern, event-driven architecture:
+
+1. **Frontend (Streamlit)**
+   - Handles PDF uploads
+   - Provides question-answering interface
+   - Displays results with source citations
+
+2. **Backend (FastAPI + Inngest)**
+   - Processes PDF ingestion events
+   - Manages question-answering workflow
+   - Coordinates with vector database
+
+3. **Vector Database (Qdrant)**
+   - Stores document embeddings
+   - Enables semantic search
+   - Manages document chunks
+
+## 🚀 Getting Started
+
+### Prerequisites
+
+- Python 3.13 or higher
+- OpenAI API key
+- Inngest account
+- Qdrant instance (local or cloud)
+
+### Environment Setup
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/mahanteshimath/ProductionGradeRAGPythonApp.git
+   cd ProductionGradeRAGPythonApp
+   ```
+
+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 configuration:
+   ```
+   OPENAI_API_KEY=your_openai_api_key
+   INNGEST_EVENT_KEY=your_inngest_key
+   QDRANT_URL=your_qdrant_url  # Default: http://localhost:6333
+   ```
+
+### Running the Application
+
+1. Start the FastAPI backend:
+   ```bash
+   uvicorn main:app --reload
+   ```
+
+2. In a new terminal, start the Streamlit frontend:
+   ```bash
+   streamlit run streamlit_app.py
+   ```
+
+3. Access the application:
+   - Frontend UI: http://localhost:8501
+   - API Documentation: http://localhost:8000/docs
+
+## 🛠️ Tech Stack
+
+- **FastAPI**: Backend API framework
+- **Streamlit**: User interface
+- **OpenAI**: Embeddings and LLM
+- **Qdrant**: Vector database
+- **Inngest**: Event processing
+- **LlamaIndex**: Document processing
+- **Python-dotenv**: Environment management
+- **Uvicorn**: ASGI server
+
+## 📋 Usage
+
+1. **Upload a PDF**
+   - Click the "Upload PDF" button in the Streamlit interface
+   - Wait for the ingestion confirmation
+
+2. **Ask Questions**
+   - Type your question in the input field
+   - Click "Ask" or press Enter
+   - View the AI-generated answer and source citations
+
+## 🔧 Advanced Configuration
+
+- Adjust chunk size and overlap in `data_loader.py`
+- Modify vector search parameters in `vector_db.py`
+- Configure Inngest event settings in `main.py`
+- Customize UI components in `streamlit_app.py`
+
+## 🤝 Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Commit your changes
+4. Push to the branch
+5. Create a Pull Request
+
+## 📝 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.