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AI-Powered Intent Discovery for Magento

An end-to-end intent-driven product discovery system that augments Magento layered navigation with:

  • Real-time intent scoring (client-side)
  • Deterministic pre-scoring (server-side)
  • AI-powered ranking and explanation
  • Structured JSON schema enforcement
  • Pluggable infrastructure integrations

Architecture Overview

Frontend (Vite + React) ↓ IntentEngine (client-side weighted signals) ↓ Node Backend (/intent/suggest) ↓ preScoreProducts (deterministic filtering) ↓ OpenAI (gpt-4o-mini, JSON schema enforced) ↓ Top 5 ranked suggestions + explanation ↓ UI presentation

Project Structure

  • vite_project/ --- React frontend widget
  • node-backend/ --- Express API + OpenAI integration

Backend Setup

cd node-backend
npm install

Create a .env file:

OPENAI_API_KEY=your_key_here
PORT=3003

Run:

npm run dev

Frontend Setup

cd vite_project
npm install
npm run dev

Runtime Integration Configuration

The backend endpoint is configured via integrations (NOT hard-coded):

intentApi: {
  baseUrl: "http://localhost:3003"
}

In production, this should be:

intentApi: {
  baseUrl: "https://api.yourdomain.com"
}

AI Recommendation Flow

  1. IntentEngine accumulates weighted interaction signals.
  2. Top N products are pre-scored server-side.
  3. AI selects up to 5 products.
  4. AI generates:
    • message
    • suggestions[]
    • confidence
    • reason
  5. JSON schema is strictly enforced.
  6. Deterministic fallback used if AI fails.

To run the test suite:

npx playwright test --config=tests/playwright.dev.config.ts
npx playwright test --config=tests/playwright.dev.config.ts --debug

Production Considerations

  • Prefer relative /intent/suggest behind reverse proxy
  • Enable HTTPS
  • Add TTL caching for identical requests
  • Monitor OpenAI token usage
  • Implement rate limiting
  • Add circuit breaker for repeated AI failures