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Member Assistant: An Open-Source AI Helper for Mall Mini-Programs

This is a submission for the Hacktoberfest x Open Source AI Challenge — Side Hustle. What I Built Member Assistant is a production-grade AI assistant for shopping-mall mini-programs. Members ask questions in natural language — "How many points do I have?", "How does parking work?" — and get instant

dev.toMontag 03:55 · 19.10.2 Min. Lesezeit
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Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

This is a submission for the Hacktoberfest x Open Source AI Challenge — Side Hustle.

Member Assistant is a production-grade AI assistant for shopping-mall mini-programs. Members ask questions in natural language — "How many points do I have?", "How does parking work?" — and get instant, cited answers over an SSE stream.

Live demo: https://8001-agqutsuuudc2eonxfv2xrllzpq.instabox.tech

Try it (replace with any header value; M10001 has sample data):

curl -N -X POST https://8001-agqutsuuudc2eonxfv2xrllzpq.instabox.tech/chat \ -H "X-Member-Id: M10001" \ -H "Content-Type: application/json" \ -d '{"question": "我的积分有多少?"}' Enter fullscreen mode Exit fullscreen mode Code https://github.com/497900963/member-assistant

The whole stack is open source — no closed model APIs anywhere in the loop:

The companion piece in the same project, Ops Assistant ( ops-assistant/ ), is a CLI for on-call engineers: ask "why did coupon redemptions fail yesterday?" produces a structured incident report (symptoms / root cause / stop-the-bleed + permanent fix / risk / verification), and monitor does P0/P1 health checks with WeCom alerts. It follows the same read-only philosophy: a command filter rejects anything resembling a write, and analysis is pure Python — no shell to escape from.

A mall's member data is sensitive — points balances, coupons, order history. With a closed API:

Open isn't just cheaper here. For member data, it's the only architecture I'd sign off on.

Me. I'm a backend engineer at a shopping-mall company, and our mini-program's member support is currently humans answering the same five questions about points and parking, all day.

I dogfooded this against our actual rule docs. The moment it answered "前2小时免费" with a citation to the parking rules doc — correctly, in Chinese — I knew the RAG pipeline was real. Next step: point the LLM client at our internally deployed model (it's already env-configurable) and wire the X-Member-Id header to our existing *-api auth gateway. The Java 8 system doesn't change at all; this service sits beside it.

Would I pay for it? I'm already paying in the form of support hours it saves. If this saves our CS team even 30 minutes a day, it's paid for itself many times over.

Quelle: dev.to (dev.to). Original öffnen