🩺 MediQuery.ai: Advanced Medical RAG Chatbot

MediQuery.ai is a professional-grade medical assistant built using a Retrieval-Augmented Generation (RAG) architecture. It answers questions based on your indexed medical documents instead of raw LLM hallucinations, improving accuracy and trustworthiness.

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📊 Live Project Dashboard

Real-time status of the MediQuery.ai ecosystem:

Service	Status	Description
CI/CD Pipeline		Automated Build & Deploy via GitHub Actions
Backend API		Flask Server running on AWS EC2
Vector DB		Medical Index: medical-chatbot
Inference Engine		Real-time Neural Inference
Deployment		Live Production Environment

Live Production URL: http://13.60.62.104:8080

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📸 Demo Showcase

Modern, responsive UI with Dark Mode toggle for optimal readability.

🌙 Dark Mode (Default)	☀️ Light Mode

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🚀 Key Features

• Context‑aware answers:
  Powered by LangChain and Groq (Llama‑3.3‑70B) for fast, accurate medical reasoning grounded in your documents.
• Semantic search:
  Uses Pinecone for real‑time similarity search over embedded medical text.
• Modern UI:
  Clean, responsive Flask frontend with Dark Mode, glassmorphism, and real‑time thinking indicators.
• Automated deployment:
  Fully integrated CI/CD pipeline (GitHub Actions → AWS ECR → EC2/Docker).

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🛠️ Tech Stack

Category	Technology
LLM Framework	LangChain
Large Language Model	Groq (llama3-70b-8192)
Vector Database	Pinecone
Embeddings	HuggingFace (all-MiniLM-L6-v2)
Backend	Flask (Python 3.10)
Frontend	HTML5, CSS3, JavaScript (AJAX)
Cloud / DevOps	AWS (EC2, ECR), Docker, GitHub Actions

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📂 Project Structure

.
├── app.py              # Flask app (routes & RAG logic)
├── store_index.py      # Script to populate Pinecone index
├── src/
│   ├── helper.py       # Helper functions (embeddings, utils)
│   └── prompt.py       # System / RAG prompts
├── assets/             # Screenshots (linked above)
├── static/             # CSS / JS, images (Dark Mode styles)
├── templates/          # HTML templates (chat.html)
├── requirements.txt    # Python dependencies
├── Dockerfile          # Docker build config
└── .github/workflows   # GitHub Actions CI/CD workflows

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🚀 How to Run Locally

1. Clone the repository

   git clone https://github.com/salonyranjan/MediQuery.ai.git
   cd MediQuery.ai

2. Create a conda environment

   conda create -n medibot python=3.10 -y
   conda activate medibot

3. Install dependencies

   pip install -r requirements.txt

4. Create .env file

   Create .env in the root directory and add:

   PINECONE_API_KEY=xxxxxxxxxxxxxxxx
   OPENAI_API_KEY=xxxxxxxxxxxxxxxx
   GROQ_API_KEY=xxxxxxxxxxxxxxxx
   

   Replace values with your actual API keys.

5. Build the vector index

   python store_index.py

6. Run the app

   python app.py

   Open your browser:http://localhost:8080

text

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🐳 Docker Quick Start (no conda)

If you prefer running via Docker:

# 1. Build the image
docker build -t mediquery .

# 2. Run the container
docker run -d -p 8080:8080 \
-e PINECONE_API_KEY="xxxxxxxxxxxxxxxx" \
-e GROQ_API_KEY="xxxxxxxxxxxxxxxx" \
--name mediquery_app \
mediquery

Then open:http://localhost:8080

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🚀 AWS Deployment Overview (EC2 + Docker + GitHub Actions)

1. Login to AWS Console
   Go to https://aws.amazon.com and sign in.

2. Create an IAM user for deployment

   • Attach policies:
     • AmazonEC2ContainerRegistryFullAccess
     • AmazonEC2FullAccess
   • Save the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

3. Create an ECR repository

   Example (replace with your account and region):

   315865595366.dkr.ecr.us-east-1.amazonaws.com/medicalbot
   

4. Launch an EC2 instance (Ubuntu)

   • Choose an Ubuntu AMI (e.g., ubuntu/focal).
   • Attach a key pair and security group allowing port 8080.

5. Install Docker on EC2

   sudo apt-get update -y
   sudo apt-get upgrade -y
   
   curl -fsSL https://get.docker.com -o get-docker.sh
   sudo sh get-docker.sh
   sudo usermod -aG docker ubuntu
   newgrp docker

6. Configure EC2 as a self‑hosted GitHub Actions runner

   • In GitHub: Settings → Actions → Runners → New self‑hosted runner.
   • Choose Linux and run the provided commands on your EC2 machine.

7. Add GitHub Secrets

   In your repo: Settings → Secrets and variables → Actions.

   Add:

   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
   • AWS_DEFAULT_REGION (e.g., eu-north-1)
   • ECR_REPO (e.g., 577435557871.dkr.ecr.eu-north-1.amazonaws.com/medical_chatbot)
   • PINECONE_API_KEY
   • OPENAI_API_KEY
   • GROQ_API_KEY

Once GitHub Actions runs, it will:

• Build and push the Docker image to ECR.
• Deploy it to EC2 via docker pull and docker run.
• Expose the app on port 8080.