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Starter Template: 'Dining Decision' Microapp with Map, Chat and Agent Hooks

aappcreators

2026-02-01

9 min read

Download a starter microapp that combines chat UI, Mapbox, an embeddings recommender and ChatGPT/Claude agent hooks for fast dining decisions.

Solve decision fatigue: a downloadable starter template that wires chat, maps, recommender and agent hooks

Pain point: your team needs to prototype a location-aware, chat-driven microapp fast — with real agent integration (ChatGPT/Claude), a mapping SDK, and a recommender that actually helps users pick a restaurant. This guide gives you a ready-to-run starter template, architecture, and step-by-step wiring so you can launch in hours, not weeks.

Why this matters in 2026

Microapps — small, focused single-purpose apps — became mainstream as developers and non-developers leverage modern LLM agents, mapping SDKs, and embeddable UI components to ship quick solutions. In late 2025 and early 2026 we've seen major momentum: Anthropic released the Cowork research preview (bringing agent-like file and desktop access) and providers expanded function-calling/agent APIs that let microapps take actions safely. That means you can now build a Dining Decision microapp that acts, not just responds.

What you'll get (downloadable starter template)

The repo in this template wires together a React frontend, a minimal Node/Serverless backend, a vector store (Mapbox by default), and a simple recommender using embeddings + vector store, and agent hooks for both ChatGPT-style and Claude-style providers.

React + Vite frontend with a chat UI and embedded map
Serverless API routes for LLM calls, embeddings and caching
Mapbox map component with pins and clustering
Lightweight recommender: embedding-based similarity + rule filters
Agent hook examples: function-calling, tool invocation, and click-to-action (open details, navigate to place, call phone)
Deployment-ready config (Vercel / Netlify / Cloud Run) and CI tips

Clone the template: https://github.com/appcreators-cloud/dining-decision-starter (repo contains README, sample data, and script to seed demo restaurants)

Architecture overview

Keep it minimal and production-minded. The template uses a split frontend/backend pattern so keys and agent logic never touch the browser.

Frontend (React + Vite) — Chat UI, Map component, local state, and event wiring.
Backend (Node/Serverless) — API routes for: LLM chat + function calls, embedding generation, vector DB queries, place details and caching.
Vector store — Pinecone/Weaviate/Milvus for fast similarity search of place embeddings.
Mapping SDK — Mapbox GL JS (or Google Maps) for rendering pins, popups and user location.
Agent hooks — Exposed server-side functions that the LLM can call (function-calling / tool invocation). Each hook is annotated and audited for safety/limits.

Quick setup (5–15 minutes)

git clone https://github.com/appcreators-cloud/dining-decision-starter
cd dining-decision-starter && cp .env.example .env
Fill .env with provider keys: OPENAI_API_KEY or ANTHROPIC_API_KEY, MAPBOX_TOKEN, VECTOR_DB_KEY
npm install && npm run dev (or deploy to Vercel with environment vars)
Run seed script: npm run seed:places (loads ~100 demo restaurants with sample embeddings)

Key components and code snippets

1) Chat UI (React)

Keep the chat UI simple but extensible. This starter uses a message list, composer, and a small middleware that transforms clicks on map pins into system messages the agent can consume.

// src/components/ChatWindow.jsx
import React from 'react';
export default function ChatWindow({messages, onSend}){
  return (
    <div className="chat-window">
      <ul className="messages">
        {messages.map(m => <li key={m.id} className={m.role}>{m.text}</li>)}
      </ul>
      <Composer onSend={onSend} />
    </div>
  )
}

Important: when the user clicks a suggested restaurant, the frontend should call the backend to add a structured event to the LLM context (example shown below under agent hooks).

2) Map component (Mapbox)

The map renders recommended places as pins. Clicking a pin sends a structured action into the chat context so the LLM can respond with next actions.

// src/components/MapView.jsx
import React, {useEffect} from 'react';
import mapboxgl from 'mapbox-gl';
mapboxgl.accessToken = process.env.VITE_MAPBOX_TOKEN;

export default function MapView({places, onPinClick}){
  useEffect(()=>{
    const map = new mapboxgl.Map({ container: 'map', style: 'mapbox://styles/mapbox/streets-v11' });
    places.forEach(p => {
      const el = document.createElement('div');
      el.className = 'pin';
      el.onclick = () => onPinClick(p);
      new mapboxgl.Marker(el).setLngLat([p.lng, p.lat]).addTo(map);
    });
    return ()=>map.remove();
  }, [places]);
  return <div id="map" style={{height: '400px'}} />
}

3) Embedding recommender (backend)

We recommend pairing each place with a small description + tags, generate embeddings, and store them in a vector DB. At query time, embed the user's short prompt and perform a similarity search. Add rule filters (price, distance) post-search.

// server/handlers/recommend.js (pseudo)
const embed = await embeddingsClient.embed({input: userPrompt});
const results = await vectorDB.query({vector: embed, topK: 10});
const filtered = results.filter(r => withinDistance(r, userLocation) && matchesPreferences(r, filters));
return filtered;

This approach offers fast, explainable recommendations and plays well with LLM-based personalization.

4) Agent hooks and function calling

Modern LLM providers let model responses call back to your code. Expose minimal, audited server-side functions that perform actions: retrieve place details, open map link, or initiate booking.

// server/handlers/agentFunctions.js
exports.getPlaceDetails = async (placeId) => {
  // fetch from DB or third-party Places API, return structured JSON
}

exports.openDirections = async ({from, to}) => {
  // return a map URL or a navigation payload
}

// In your LLM call wrapper, add these functions to the model's function-calling signature

Example: for OpenAI-style function-calling, include a functions array describing available actions. For Anthropic/Claude, follow the provider's tool/assistant pattern. Keep the functions narrow and validate inputs strictly.

Example chat-to-action flow

User: "I want Italian within 2 miles, price $$, good for groups"
Frontend sends a short prompt + user profile to backend
Backend: generate embedding for the prompt -> query vector DB -> apply filters -> return top 5 places
Frontend: display places on map + chat suggests the top choice with a CTA "Show on map"
User clicks "Show on map" -> frontend calls /agent/invoke with action {type: 'show_place', placeId}
Server-side agent logs event, optionally augments context, and calls the LLM for follow-up (e.g., "Do you want directions or to call the restaurant?")

Provider integration tips (ChatGPT & Claude)

2025–26 trends favor provider interoperability with agent hooks. Here are practical patterns:

Use server-side proxies: Never embed raw provider keys in the client.
Prefer function-calling/tool APIs: Expose only safe functions. For OpenAI-style APIs, supply a function schema. For Anthropic-style agents, expose controlled tools or use Claude Code-like tooling where available.
Graceful provider fallback: Implement an adapter layer so you can switch from ChatGPT to Claude or route high-cost operations to Claude if price/latency fits your needs.
Prompt templates + system messages: Maintain concise system prompts that define policy (e.g., content, allowed phone numbers, no personal data exfiltration).

Performance, cost control and scaling

Microapps should minimize expensive LLM ops. Use these strategies:

Cache embeddings and similarity results for repeated queries and hot regions.
Hybrid architecture: do similarity search with vector DBs, then use LLMs only to personalize or format messages.
Batch and throttle LLM calls: group multiple small requests when possible and limit concurrent calls per user.
Edge-render static UI: deliver the chat skeleton from the CDN and only initialize LLM interactions when users engage.
Estimate per-user cost: capture typical prompt + response sizes and use that to set usage guards in the server.

Privacy, security and compliance

This starter template includes default controls. When building for teams or customers, do the following:

Server-side key management: Store provider keys in secrets manager and rotate them regularly.
PII minimization: Do not store user messages unless required. If you must, redact or encrypt sensitive fields.
Request auditing: Log agent function calls and decisions for debugging and policy review.
Rate limiting & quotas: Apply per-user quotas and circuit breakers to prevent runaway costs or abuse.

Extending the recommender: practical enhancements

The starter template is intentionally lightweight — here are stepwise upgrades you can add in the next sprints:

Contextual embeddings: include user history, social signals or recent chat messages in embedding generation to create truly personalized results.
Multi-vector fusion: combine geographic vectors (lat/lng encoded) and semantic embeddings for geo-semantic ranking.
Feedback loop: capture thumbs up/down events and retrain weighting or re-rank results in-vivo.
Booking integration: add an action that sends a booking request to the restaurant or an external reservation provider via an API hook.

Devops, CI/CD and tests

Ship reliably:

Use environment-specific secrets and disabled dev keys in production.
Run unit tests for recommender logic and integration tests for agent function schemas.
Deploy with preview environments (Vercel/Netlify) to test provider fallbacks and webhook flows.
Use lightweight monitoring: request latency, LLM response size, and token consumption meters.

Real-world example: Where2Eat inspiration

Rebecca Yu's rapid creation of a dining app demonstrates the power of microapps when combined with LLMs and mapping. The starter template mirrors that approach: build fast, iterate quickly, and keep the app focused on one core user job — in this case, resolving where to eat.

“Microapps let you ship small, useful experiences tied to a single decision. With modern agents and mapping SDKs, your app can act on behalf of the user.” — appcreators.cloud

Troubleshooting: common pitfalls

Slow map loads: Use tiled vector sources and marker clustering. Defer loading non-essential layers.
LLM hallucinations for place details: Always verify third-party facts with API calls (Yelp/Google Places) instead of relying on the model.
Rate limits & 429s: Implement exponential backoff and graceful degradation UI (show cached results).
Unclear agent actions: Keep functions small and return human-readable confirmations after invocation.

Next steps and roadmap ideas

Once the starter is live for internal teams, prioritize these features:

Multi-user voting flows for group decision-making
Calendar integration for booking and availability coordination
Push notifications and ephemeral invites via secure links
Analytics dashboard tracking conversions (click-to-call, booking)

Download and run the template

Get started now — clone the repo and run the included seed script. The README includes full env variable examples, deployment instructions and provider adapter templates for OpenAI and Anthropic/Claude.

Repo: https://github.com/appcreators-cloud/dining-decision-starter

Actionable checklist (what to do in your first 2 hours)

Clone the starter and set environment vars.
Seed the example places and run the app locally.
Test the recommender with 3 persona prompts (budget eater, foodie, group-friendly).
Click map pins and confirm agent actions are recorded and LLM follow-ups make sense.
Deploy to a preview environment and validate provider key usage and costs.

Final notes: future-proofing for 2026+

Expect the agent landscape to keep evolving. In 2026, tool-enabled agents and desktop assistants will be more capable; your microapp should be ready to plug into those capabilities with a small, auditable set of actions and a provider-agnostic adapter layer. That preserves flexibility and reduces vendor lock-in while enabling richer experiences.

Call to action

Clone the starter template now, run the demo, and adapt the recommender to your dataset. If you want a guided integration or a production hardening checklist for enterprise use, reach out to appcreators.cloud — we deliver secure, scalable microapps and agent integrations that teams can deploy in production.

Download: https://github.com/appcreators-cloud/dining-decision-starter — start prototyping your Dining Decision microapp today.

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