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+# RAG with Binary Quantization
+
+A high-performance Retrieval-Augmented Generation (RAG) system that uses binary quantization for efficient vector storage and similarity search. This project implements a document Q&A system with optimized memory usage and fast retrieval capabilities.
+
+## 🚀 Features
+
+- **Binary Quantization**: Converts high-dimensional embeddings to binary vectors for memory efficiency
+- **Milvus Vector Database**: Uses Milvus for scalable vector storage and similarity search
+- **Gradio Web Interface**: User-friendly web UI for document upload and chat
+- **BGE Embeddings**: Leverages BAAI/bge-large-en-v1.5 for high-quality text embeddings
+- **OpenAI Integration**: Uses GPT-4.1 for intelligent question answering
+- **Batch Processing**: Efficient document processing with configurable batch sizes
+
+## 🏗️ Architecture
+
+```
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   Documents     │───▶│  BGE Embeddings  │───▶│ Binary Vectors  │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+                                                         │
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   User Query    │───▶│  Query Embedding │───▶│  Milvus Search  │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+                                                         │
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│  Retrieved Docs │◀───│  Context Fusion  │◀───│  LLM Answer     │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+```
+
+## 🛠️ Installation
+
+1. **Clone the repository**:
+   ```bash
+   git clone <repository-url>
+   cd rag-w-binary-quant
+   ```
+
+2. **Install dependencies**:
+   ```bash
+   uv sync
+   ```
+
+3. **Set up environment variables**:
+   Create a `.env` file with your OpenAI API key:
+   ```env
+   OPENAI_API_KEY=your_openai_api_key_here
+   ```
+
+## 🚀 Usage
+
+### Starting the Application
+
+Run the Gradio web interface:
+```bash
+uv run app.py
+```
+
+The application will be available at `http://localhost:7860`
+
+### Using the Interface
+
+1. **Upload Documents**: 
+   - Go to the "Upload & Index" tab
+   - Upload your documents (supports multiple file formats)
+   - Click "Update Index" to process and index the documents
+
+2. **Chat with Documents**:
+   - Switch to the "Chat" tab
+   - Ask questions about your uploaded documents
+   - Get intelligent answers based on the document content
+
+## 🔧 Configuration
+
+Key configuration parameters in `src/config.py`:
+
+- `EMBEDDING_MODEL_NAME`: BAAI/bge-large-en-v1.5
+- `COLLECTION_NAME`: "fast_rag"
+- `MILVUS_DB_PATH`: "milvus_binary_quantized.db"
+- `MODEL_NAME`: "gpt-4.1"
+- `TEMPERATURE`: 0.2
+
+## 📊 Performance Benefits
+
+- **Memory Efficiency**: Binary vectors use 8x less memory than float32 embeddings
+- **Fast Search**: Hamming distance computation is highly optimized
+- **Scalable**: Milvus provides enterprise-grade vector database capabilities
+- **Accurate**: BGE embeddings provide high-quality semantic representations
+
+## 🏛️ Project Structure
+
+```
+rag-w-binary-quant/
+├── app.py                 # Gradio web interface
+├── main.py               # Main application entry point
+├── src/
+│   ├── config.py         # Configuration settings
+│   ├── data_loader.py    # Document loading utilities
+│   ├── embedding_generator.py  # Binary embedding generation
+│   ├── vector_store.py   # Milvus vector database operations
+│   └── rag_pipeline.py   # RAG question answering pipeline
+├── documents/            # Uploaded document storage
+└── README.md
+```
+
+## 🔍 Technical Details
+
+### Binary Quantization Process
+
+1. **Float32 Embeddings**: Generate embeddings using BGE model
+2. **Binary Conversion**: Convert to binary using threshold (positive values → 1, negative → 0)
+3. **Packing**: Pack binary vectors into bytes for efficient storage
+4. **Hamming Distance**: Use Hamming distance for similarity search
+
+### Vector Search
+
+- **Index Type**: BIN_FLAT (exact search for binary vectors)
+- **Metric**: Hamming distance
+- **Retrieval**: Top-k most similar documents
+
+## 🙏 Acknowledgments
+
+- [BAAI](https://github.com/FlagOpen/FlagEmbedding) for the BGE embedding model
+- [Milvus](https://milvus.io/) for the vector database
+- [Gradio](https://gradio.app/) for the web interface
+- [OpenAI](https://openai.com/) for the language model