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It leverages Retrieval-Augmented Generation (RAG) techniques to provide accurate and context-aware answers.\n\n## **Table of Contents**\n\n- [Features](#features)\n- [Installation](#installation)\n\n## **Features**\n\n- **Upload and Process PDFs**: Users can upload multiple PDF documents for processing.\n- **Contextual Question Answering**: Ask questions related to the uploaded documents and receive detailed answers.\n- **Document Management**: View, reprocess, or delete uploaded documents within the app.\n- **Adjustable Parameters**: Customize the number of documents to retrieve for context.\n- **Feedback Mechanism**: Provide feedback on the helpfulness of the AI assistant's responses.\n- **Interactive Interface**: User-friendly interface built with Streamlit, featuring tabs and interactive elements.\n]\n\n## **Installation**\n\n### **Prerequisites**\n\n- Python 3.8 or higher\n- pip (Python package installer)\n\n### **Clone the Repository**\n\n```bash\ngit clone https://github.com/yourusername/DocAssist-QA.git\ncd RAG-Question-Answering-App\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilippe2023%2Frag-question-answering-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fphilippe2023%2Frag-question-answering-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fphilippe2023%2Frag-question-answering-app/lists"}