🧠 LangChain Knowledge Graph HealthTech Application

A HealthTech Application built using FastAPI, LangChain, NetworkX, and OpenAI GPT-4 to create and interact with a medical knowledge graph. The frontend is built with Streamlit for an interactive UI, and the backend uses FastAPI to serve the knowledge graph and AI-powered insights.

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🚀 Features

• 📌 FastAPI-based backend to manage a medical knowledge graph.
• 🤖 OpenAI-powered insights via LangChain for summarizing medical knowledge.
• 📊 Interactive visualization of the knowledge graph using PyVis.
• 🎨 Streamlit frontend for querying the knowledge graph and generating AI insights.
• 🔐 Uses .env file to store API keys securely.

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Project Structure

langchain-kgraph-healthtech-app/
│── frontend/
│   ├── app.py  # Streamlit UI
│── backend/
│   ├── main.py  # FastAPI
│   ├── .env
│── requirements.txt  # Project dependencies
│── README.md    # Project documentation

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Technologies Used

• FastAPI for backend API
• Streamlit for frontend UI
• LangChain for AI-based insights
• OpenAI GPT-4 for summarizing medical knowledge
• NetworkX for creating and managing the knowledge graph
• PyVis for interactive graph visualization

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📌 Prerequisites

Ensure you have the following installed on your system:

• Python 3.13+
• pip (Python package manager)

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🛠 Installation & Setup

1️⃣ Clone the Repository

git clone /~https://github.com/HaseebAhmed49/langchain-kgraph-healthtech-app.git
cd langchain-kgraph-healthtech-app

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Up Environment Variables

Create a .env file in the root directory and add your OpenAI API key:

echo "OPENAI_API_KEY=your_openai_api_key_here" > .env

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🖥 Backend (FastAPI)

📌 Start the FastAPI Server

Run the backend using:

uvicorn backend.main:app --reload

The API will be available at http://127.0.0.1:8000.

📌 API Endpoints

• GET /query/{entity} → Fetches medical knowledge related to an entity (e.g., "Diabetes").
• GET /llm_query/{entity} → Generates AI-powered insights for a medical entity.
• GET /visualize_graph → Generates an interactive HTML visualization of the knowledge graph.

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🎨 Frontend (Streamlit)

📌 Run the Streamlit App

streamlit run frontend/app.py

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🔄 How It Works

1. Query the Knowledge Graph: Users can input a medical entity (e.g., "Diabetes") in the Streamlit app. The app sends a request to the FastAPI backend (/query/{entity}) to fetch related medical knowledge.
2. Generate AI Insights: Users can request AI-generated insights for a medical entity. The app sends a request to the FastAPI backend (/llm_query/{entity}), which uses LangChain and OpenAI GPT-4 to summarize the knowledge.
3. Visualize the Graph: Users can trigger the backend to generate an interactive visualization of the knowledge graph. The app sends a request to the FastAPI backend (/visualize_graph), which creates an HTML file using PyVis.

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📜 License

This project is licensed under the MIT License.

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📩 Contact

For issues or suggestions, feel free to open an issue or reach out!

• Email: haseebahmed02@gmail.com
• LinkedIn/GitHub: /HaseebAhmed49

💡 Happy Coding! 🚀