Update README.md

README.md

@@ -10,59 +10,91 @@ pinned: false
 license: mit
 short_description: Chat with your docs very Easy
 ---
-# Easy RAG 🤖
+# DocumentGPT - Advanced Document Analysis with RAG
 
-Easy RAG is a powerful and user-friendly Retrieval Augmented Generation (RAG) system that allows users to upload their own documents and query them using state-of-the-art language models.
+## Overview
 
-## Features
+DocumentGPT is a cutting-edge document analysis system that leverages the power of Retrieval Augmented Generation (RAG) to provide intelligent responses based on your documents. Built with advanced AI technologies, it allows users to upload multiple document types and get accurate, context-aware responses to their questions.
 
-- 📄 Support for multiple document formats (PDF, TXT, DOCX)
-- 📚 Upload up to 5 documents (max 10MB each)
-- 🔍 Advanced document processing and chunking
-- 💡 Intelligent question answering using Llama-2
-- 🌐 Multilingual support
-- 🚀 GPU-accelerated inference
-- 📊 Source tracking and citation
+## Key Features
+
+- Multi-Format Support: Process PDF, DOCX, CSV, and TXT files seamlessly
+- Advanced RAG Implementation: Using state-of-the-art LLM technology with Llama-2
+- GPU-Accelerated: Optimized performance with GPU acceleration
+- Real-Time Processing: Dynamic document processing and instant responses
+- Source Attribution: Every response includes references to source documents
+- Interactive Interface: User-friendly Gradio interface for easy interaction
 
 ## Technical Stack
 
-- **Language Model**: Meta-llama/Llama-2-7b-chat-hf
-- **Embeddings**: intfloat/multilingual-e5-large
-- **Vector Store**: FAISS
-- **UI Framework**: Gradio
-- **Document Processing**: LangChain
+- Large Language Model: Llama-2-7b-chat-hf
+- Embeddings: multilingual-e5-large
+- Vector Store: FAISS
+- Framework: Gradio
+- Processing: Langchain
+- Acceleration: HuggingFace Accelerate
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.8 or higher
+- GPU support (recommended)
+- HuggingFace account with access to Llama-2
 
-## Installation
+### Installation
 
-1. Clone the repository
-2. Install dependencies:
 ```bash
+# Clone the repository
+git clone https://github.com/yourusername/documentgpt.git
+cd documentgpt
+
+# Install dependencies
 pip install -r requirements.txt
-```
-3. Set up your HuggingFace token as an environment variable:
-```bash
+
+# Set up environment variables
 export HUGGINGFACE_TOKEN=your_token_here
 ```
-4. Run the application:
+
+### Running the Application
+
 ```bash
 python app.py
 ```
 
 ## Usage
 
-1. Upload your documents using the file upload interface
-2. Wait for the system to process and index your documents
-3. Start asking questions about your documents
-4. View answers with source citations
+1. Launch the application
+2. Upload your documents (PDF, DOCX, CSV, or TXT)
+3. Wait for the processing to complete
+4. Start asking questions about your documents
+5. View responses with source attributions
+
+## Advanced Features
 
-## Requirements
+- Dynamic Knowledge Base: Updates in real-time as new documents are added
+- Memory Management: Efficient handling of document processing
+- Source Tracking: Transparent attribution of information sources
+- Optimized Performance: GPU acceleration for faster processing
 
-See `requirements.txt` for a complete list of dependencies.
+## Author
 
-## Credits
+**Camilo Vega**
+- AI Professor and Solutions Consultant
+- LinkedIn: [Camilo Vega](https://www.linkedin.com/in/camilo-vega-169084b1/)
+- GitHub: [CamiloVega](https://github.com/camilovegag)
 
-Based on original work by [Camilo Vega](https://www.linkedin.com/in/camilo-vega-169084b1/), AI Professor and Solutions Consultant.
+## Acknowledgments
 
-## License
+Special thanks to:
+- HuggingFace for providing GPU acceleration support
+- Meta AI for the Llama-2 model
+- The Langchain community for their excellent tools
 
-MIT License
+## Contact
+
+For questions and support, please reach out through:
+- LinkedIn: [Camilo Vega](https://www.linkedin.com/in/camilo-vega-169084b1/)
+
+---
+Made with by Camilo Vega