Secure medical document analysis and processing application.

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Features

• Document Processing: Automated parsing of PDF and text-based medical records.
• Entity Extraction: Identifies and profiles medications, dosages, and relevant medical history.
• Contextual Analysis: Interactive interface for querying uploaded document data.
• Data Security: Localized file storage and tracking within the deployment environment.

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System Architecture

HealthLoom utilizes a microservices-oriented architecture designed for accurate and performant health data extraction.

Backend Infrastructure

The backend is engineered for reliability and high performance, leveraging FastAPI for concurrent request handling and efficient API routing. The core logic is powered by LangGraph, which orchestrates a specialized processing pipeline:

• Ingestion Engine: Implements advanced document parsing with cryptographic hashing to prevent redundant processing of identical files, saving system resources.
• Extraction Pipeline: Dedicated processing nodes designed specifically for medical entity recognition, extracting critical data such as prescriptions, dosages, and contraindications.
• Dynamic Routing: An intelligent routing mechanism that evaluates user queries and directs them to the optimal subsystem (e.g., retrieving specific document context, accessing patient history, or querying general medical knowledge).
• Data Persistence: Utilizes PostgreSQL with async SQLAlchemy for reliable, high-performance tracking of document metadata, health records, and conversation history.

Tech Stack

Component	Technology
Frontend	React (Vite), CSS
Backend	FastAPI (Python)
Database	PostgreSQL
AI / LLM	Google Gemini API
Processing Pipeline	LangGraph, LangChain
Observability	LangFuse
Deployment	Docker, Docker Compose

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Installation and Deployment

Docker Deployment (Recommended)

1. Clone the repository.
2. Configure .env variables if required.
3. Build and start the containers:

   docker-compose up --build

4. Access the application at http://localhost:5173.

Local Development

Backend:

1. Navigate to the backend/ directory.
2. Install dependencies:

   pip install -r requirements.txt

3. Start the server:

   uvicorn main:app --reload

Frontend:

1. Navigate to the frontend/ directory.
2. Install dependencies and start the development server:

   npm install
   npm run dev

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Project Structure

HealthLoom/
├─ backend/           # FastAPI application, LangGraph pipelines, Database schemas
├─ frontend/          # React application, UI components
├─ docker-compose.yml # Container orchestration configuration
├─ README.md          # Project documentation

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Roadmap

• Improve mobile interface responsiveness.
• Implement multi-user authentication and session management.
• Add data visualization for extracted health metrics.

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License

This project is licensed under the MIT License. See the LICENSE file for details.