🧠 Medical Chatbot – Flask + LangChain + Pinecone + OpenAI

An end-to-end medical assistant chatbot built using LLMs, LangChain, Pinecone, and Flask. It can answer health-related questions using vector-based semantic retrieval and GPT-powered reasoning. (This project is for educational purposes and does not provide medical advice.)

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🚀 Quick Start (Local Setup)

1️⃣ Clone the repository

git clone https://github.com/entbappy/Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS.git
cd Build-a-Complete-Medical-Chatbot-with-LLMs-LangChain-Pinecone-Flask-AWS

2️⃣ Create a Conda environment

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

3️⃣ Install the dependencies

pip install -r requirements.txt

4️⃣ Add your API keys in a .env file

In the root folder, create a .env file:

PINECONE_API_KEY=your_pinecone_api_key
OPENAI_API_KEY=your_openai_api_key

5️⃣ Create and store embeddings to Pinecone

python store_index.py

6️⃣ Run the Flask app

python app.py

Then open your browser and go to:

http://127.0.0.1:8080

You’ll see the chatbot interface (from templates/chat.html) running locally.

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🧩 Tech Stack

Category	Tools
Backend	Python, Flask
LLM Framework	LangChain
Embeddings & Storage	Pinecone Vector DB
LLM Provider	OpenAI (GPT models)
Frontend	HTML, CSS, jQuery, Bootstrap
Deployment	AWS EC2 + ECR + GitHub Actions (optional)

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☁️ AWS Deployment (Optional)

To deploy the chatbot using Docker + AWS + CI/CD:

1. Create required AWS resources

• IAM User with permissions:

  • AmazonEC2FullAccess
  • AmazonEC2ContainerRegistryFullAccess

• ECR Repository for your Docker image Example:

  873925794895.dkr.ecr.us-east-1.amazonaws.com/medicalbot
  

2. Launch an EC2 instance (Ubuntu)

Install Docker:

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

3. Configure GitHub self-hosted runner

In your repo → Settings → Actions → Runners → New self-hosted runner Follow the commands displayed for Ubuntu.

4. Add GitHub Secrets

Secret	Description
AWS_ACCESS_KEY_ID	IAM access key
AWS_SECRET_ACCESS_KEY	IAM secret key
AWS_DEFAULT_REGION	e.g. us-east-1
ECR_REPO	ECR image URI
PINECONE_API_KEY	Your Pinecone key
OPENAI_API_KEY	Your OpenAI key

5. Workflow

1. Code pushed → GitHub Actions triggers build
2. Docker image built and pushed to ECR
3. EC2 runner pulls and runs the latest container

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💡 Project Structure

Medical-Chatbot/
│
├── static/
│   └── style.css
├── templates/
│   └── chat.html
│
├── app.py                # Flask main app
├── store_index.py        # Pinecone index builder
├── requirements.txt
├── .env.example
└── README.md

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⚠️ Disclaimer

This chatbot is not a substitute for professional medical advice. Always consult a qualified healthcare provider for any medical condition or emergency.

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