ShopMindAI

ShopMindAI is an automotive diagnostic assistant that combines structured repair context, VIN-aware validation, and LLM reasoning to generate ranked repair guidance for technicians and small shops.

Screenshots

Value Proposition

• Converts unstructured symptom notes into ranked, actionable diagnostic paths.
• Reduces wasted troubleshooting time with likely causes and confirmatory tests.
• Exposes health/metrics endpoints for production monitoring and reliability checks.

Architecture Snapshot

• FastAPI backend with typed schemas and modular service layer.
• /api/diagnose endpoint for diagnosis workflow.
• /health and /metrics endpoints for uptime/observability.
• Static web UI served from app/static.
• SQLAlchemy persistence and retrieval/ranking utilities.

Detailed docs:

• Architecture
• Setup
• Impact

Quickstart

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
# fill required vars in .env
uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Open http://localhost:8000 and test:

curl -s http://localhost:8000/health

Deployment (Azure)

Production target: https://shopmindai-backend.azurewebsites.net.

Recommended Azure App Service startup command:

uvicorn app.main:app --host 0.0.0.0 --port 8000

Set secrets as app settings (do not commit secrets):

• DEFAULT_PROVIDER
• GCP_MODEL_URL or (SILICONEFLOW_API_KEY + SILICONEFLOW_URL)
• DATABASE_URL
• ALLOWED_ORIGINS

API

• POST /api/diagnose - submit VIN, OBD codes, and symptoms for ranked recommendations.
• GET /health - service health.
• GET /metrics - Prometheus metrics output.

Impact

• Designed for faster first-pass diagnostics and clearer technician decision support.
• Built for production readiness with typed contracts, error handling, rate limiting, and observability.