Here's a structured and professional README for your project, which you can use on GitHub. It covers the project's purpose, features, technology stack, installation, usage, and contribution guidelines.

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AI-Doctor RAG Platform

A cutting-edge AI-powered platform enabling patients to engage with an AI version of their doctor for post-consultation care, scheduling appointments, and managing healthcare information, all while ensuring secure data handling. This project was developed for the [Hackathon Name] Healthcare Track.

Table of Contents

• Introduction
• Features
• Tech Stack
• Installation
• Usage
• Financial Integration
• Security and Compliance
• Future Roadmap
• Contributing
• License

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Introduction

AI-Doctor RAG is a platform designed to improve post-consultation patient care through the use of advanced AI Retrieval-Augmented Generation (RAG). Patients can chat with an AI version of their doctor to ask questions about their medical reports, schedule appointments, and access healthcare-related financial assistance—all while ensuring security and privacy for sensitive medical information.

This project addresses the growing demand for more accessible healthcare, especially for underserved or remote communities, by leveraging AI to enhance care continuity and support doctors in managing their workload efficiently.

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Features

• AI-Powered Post-Consultation Care: Patients can ask follow-up questions about their medical reports and receive personalized, accurate responses from the AI, which analyzes data uploaded by the doctor.

• Document Upload: Only doctors can upload medical reports (PDF format), and patients can securely access them through their profile.

• NLP-Driven Appointment Scheduling: Patients can schedule or reschedule appointments with their doctor via simple, conversational requests through AI tool calling.

• Healthcare Assistant: The AI assists patients in understanding potential treatment, improving literacy in healthcare and financially.

• Mobile-Friendly, Accessible Design: The platform is optimized for mobile and includes accessibility features such as text-to-speech for visually impaired users.

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Tech Stack

This project is built using modern, scalable technologies to ensure performance, security, and ease of use.

• Frontend: Next.js 14, React.js, TypeScript
• Backend: Node.js, Next.js 14
• Database and Auth: Supabase (for user authentication and secure data storage)
• AI: GPT-4o-mini for conversational AI and NLP tasks
• Document Parsing: Langchainn PDF parsing and extraction
• Styling: Tailwind CSS
• Vector Database: Supabase
• Deployment: Vercel

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Installation

Requirements

• Node.js (v14+)
• Supabase account (for database and authentication)
• Python (for document parsing scripts)

Steps

1. Clone the repository:

   git clone https://github.com/yourusername/ai-doctor-rag.git
   cd ai-doctor-rag

2. Install dependencies:

   npm install

3. Configure environment variables:

   Create a .env.local file in the root directory with the following configuration:

   NEXT_PUBLIC_SUPABASE_URL=<your-supabase-url>
   NEXT_PUBLIC_SUPABASE_ANON_KEY=<your-supabase-anon-key>
   OPENAI_API_KEY=<your-api-key>
   

4. Start the development server:

   npm run dev

   The app will be running on http://localhost:3000.

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We hope this platform significantly improves the quality and accessibility of post-consultation healthcare while addressing financial challenges that patients may face. Thank you for checking out our project!

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