OpenHealth is an AI-powered personal health assistant designed to help users understand and manage their health data privately. It targets individuals seeking to centralize, parse, and interact with their health information using AI models, offering a local-first approach for enhanced privacy.

How It Works

The system centralizes various health data sources, including clinical records, health platforms (Apple Health, Google Fit), wearables, and personal history. A data parser standardizes this information into a unified format. This structured data is then used as context for Large Language Models (LLMs) via techniques like Retrieval-Augmented Generation (RAG), enabling contextual and personalized health conversations.

Quick Start & Requirements

• Install/Run: Clone the repository and use docker/podman compose --env-file .env up.
• Prerequisites: Docker or Podman. Ollama is recommended for local LLM execution.
• Access: Navigate to http://localhost:3000 in your browser.
• Docs: https://qna.open-health.me/, https://www.open-health.me/

Highlighted Details

• Supports multiple data sources and LLMs (LLaMA, DeepSeek-V3, GPT, Claude, Gemini).
• Offers both a quick "Clinic" consultation web interface and a "Full Platform" for comprehensive management.
• Emphasizes local execution for maximum data privacy.
• Project diagram illustrates data flow from sources to AI integration.

Maintenance & Community

• Active community channels include a subreddit (r/AIDoctor) and Discord server.
• Direct contact with the team is available via Calendly and email.

Licensing & Compatibility

• The repository does not explicitly state a license in the README.

Limitations & Caveats

• The data parsing functionality is currently implemented in a separate Python server and is planned for migration to TypeScript.
• Ollama API endpoint configuration may require specific adjustments for Docker on Mac or Windows.