Using AI Assistants

I'm migrating my app from Google Maps Platform to Mapsi. Generate a complete migration guide and replacement code.

## My Current Stack
- Framework: [JavaScript / React / Vue / Next.js / Node.js]
- Google Maps APIs I'm using: [Geocoding API / Places Autocomplete / Directions API / Maps JavaScript API / Static Maps API]
- Language: [JavaScript / TypeScript / Python]

## Mapsi API Details
- Base URL: https://mapsi.dev
- Auth: X-API-Key header (server-side) or ?key= param (tiles only, browser-safe)
- All endpoints return JSON unless noted

## API Mapping — Google → Mapsi

### Geocoding
- Google: GET https://maps.googleapis.com/maps/api/geocode/json?address=...&key=KEY
- Mapsi:  GET https://mapsi.dev/v1/geocode?q=Eiffel+Tower+Paris&limit=1
  Headers: X-API-Key: YOUR_KEY
  Response: { "results": [{ "coordinates": { "lat": 48.8584, "lon": 2.2945 }, "formatted_address": "..." }] }

### Reverse Geocoding
- Google: GET .../geocode/json?latlng=48.8584,2.2945&key=KEY
- Mapsi:  GET https://mapsi.dev/v1/reverse?lat=48.8584&lon=2.2945
  Response: { "results": [{ "formatted_address": "...", "city": "Paris", "country": "France" }] }

### Places Autocomplete
- Google: GET .../place/autocomplete/json?input=main+str&key=KEY
- Mapsi:  GET https://mapsi.dev/v1/autocomplete?text=main+str&limit=5
  Response: { "suggestions": [{ "text": "Main Street, London", "lat": 51.5, "lon": -0.1 }] }
  Optional: &countries=US,GB to narrow results. &focus.lat=X&focus.lon=Y to bias locally.

### Places Search
- Google: GET .../place/nearbysearch/json?location=48.85,2.29&radius=1000&keyword=restaurant&key=KEY
- Mapsi:  GET https://mapsi.dev/v1/places?q=restaurant&lat=48.8584&lon=2.2945&radius=1000&limit=10
  Response: { "places": [{ "name": "...", "lat": ..., "lon": ..., "categories": [...] }] }

### Directions / Routing
- Google: GET .../directions/json?origin=A&destination=B&key=KEY
- Mapsi:  POST https://mapsi.dev/v1/route
  Body: { "waypoints": [{"lat":48.8584,"lon":2.2945}, {"lat":48.853,"lon":2.3499}], "mode": "auto", "units": "km" }
  Response: { "distance_km": 3.2, "duration_sec": 720, "legs": [...], "polyline": { "type": "LineString", "coordinates": [...] } }
  Travel modes: "auto" (car), "truck", "bicycle", "pedestrian", "motor_scooter"

### Map Tiles (MapLibre GL replaces Maps JavaScript API)
- Google: Maps JavaScript API with google.maps.Map
- Mapsi:  MapLibre GL JS with style URL:
  new maplibregl.Map({ container: 'map', style: 'https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_KEY' })
  Available styles: light, dark, white, grayscale, black, streets, topo, liberty

### Static Maps
- Google: GET https://maps.googleapis.com/maps/api/staticmap?center=48.85,2.29&zoom=15&size=600x400&key=KEY
- Mapsi:  GET https://mapsi.dev/v1/static-map?lat=48.8584&lon=2.2945&zoom=15&width=600&height=400
  Headers: X-API-Key: YOUR_KEY  → Returns PNG

## What to Generate
1. Side-by-side migration table (Google API call → Mapsi equivalent)
2. Full replacement code for every Google API call in my app
3. Auth migration: remove API key from URL params, move to X-API-Key header (except tiles — use ?key= param for MapLibre)
4. MapLibre GL setup replacing google.maps.Map — same center/zoom, same markers (maplibregl.Marker), same click handlers
5. Response field remapping: where Google returns "geometry.location.lat/lng", Mapsi returns "coordinates.lat/lon"
6. Backend proxy if any Mapsi calls need to be server-side (to keep X-API-Key out of browser)

## Code Requirements
- Show before/after for every replaced call — makes review easy
- Flag any Google feature that has no direct Mapsi equivalent (Street View, Traffic layer) — suggest workaround or note as out of scope
- Preserve all existing UI behaviour — same markers, same popups, same map interactions

Generate the migration code now.

I'm adding an interactive map to a React app using Mapsi. Generate a complete, production-ready implementation.

## App Context
- Framework: React [18+ / Next.js 14+ / Vite]
- Language: TypeScript
- Use Case: [e.g., show store locations, display delivery route, user picks a location]
- Map style preference: [light / dark / streets / topo — or leave as light default]

## Mapsi Map Tiles API
Style URL (pass directly to MapLibre — includes tiles, fonts, sprites):
  https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY

Note: The tiles API key goes in the URL as ?key= param (safe for browser).
All other Mapsi APIs (geocode, places, etc.) must use X-API-Key header — server-side only.

Available styles: light, dark, white, grayscale, black, streets, topo, liberty

## Mapsi Geocoding API (for search)
GET https://mapsi.dev/v1/geocode?q=Eiffel+Tower+Paris&limit=1
Headers: X-API-Key: YOUR_KEY  ← server-side proxy only
Response: { "results": [{ "coordinates": { "lat": 48.8584, "lon": 2.2945 }, "formatted_address": "..." }] }

## Mapsi Autocomplete API (for search-as-you-type)
GET https://mapsi.dev/v1/autocomplete?text=eiffel&limit=5
Headers: X-API-Key: YOUR_KEY  ← server-side proxy only
Response: { "suggestions": [{ "text": "Eiffel Tower, Paris, France", "lat": 48.8584, "lon": 2.2945 }] }

## What to Generate
1.  React component:
   - MapLibre GL JS map (npm: maplibre-gl)
   - Style from Mapsi tiles URL
   - Accepts: center, zoom, markers (array of {lat, lon, label}), optional onClick handler
   - maplibregl.Marker for each location, with popup on click
   - map.flyTo() for smooth navigation
   - Responsive: fill container div, handle resize

2.  component:
   - Input field → debounce 350ms → call backend proxy → show dropdown suggestions
   - On selection: place marker on map, fly to location
   - Clear button, keyboard navigation (arrow keys + Enter)

3. Next.js API route (or Express endpoint) as proxy:
   GET /api/autocomplete?text=... → calls Mapsi autocomplete with X-API-Key
   GET /api/geocode?q=... → calls Mapsi geocode with X-API-Key
   (Keeps API key server-side, never exposed to browser)

4. useMap() custom hook: exposes addMarker(), removeMarker(), flyTo(), getCurrentBounds()

5. TypeScript types: MapMarker, MapCenter, SearchResult interfaces

## Code Requirements
- Install instructions: npm install maplibre-gl @types/maplibre-gl
- Import maplibre-gl/dist/maplibre-gl.css in layout
- useEffect cleanup: map.remove() on unmount to prevent memory leaks
- SSR safe for Next.js: dynamic import with ssr: false, or useEffect guard
- No API key in any client-side code — only in .env.local on server

Generate the code now.

I need a secure backend proxy for Mapsi so my API key is never exposed in browser code. Generate a complete implementation.

## Context
- Runtime: [Node.js (Express) / Python (FastAPI) / Next.js API routes / PHP Laravel]
- Language: [TypeScript / JavaScript / Python / PHP]
- Caching: [Redis / in-memory / none]

## Mapsi Auth Rules
- All non-tile endpoints require: X-API-Key: YOUR_API_KEY header (server-side only)
- Tiles endpoint ONLY: key goes in URL as ?key= param — this is the one exception that can be browser-direct
- Never put X-API-Key in browser JavaScript — it will be visible in DevTools

## Mapsi Rate Limits by Plan
- Free: 2 req/sec, 1,000/day
- Growth: 15 req/sec, 35,000/day
- Business: 25 req/sec, 140,000/day
- On 429: { "error": "Rate limit exceeded" } — must back off and retry

## Endpoints to Proxy (all GET unless noted)
  /api/geocode?q=...          → https://mapsi.dev/v1/geocode?q=...
  /api/autocomplete?text=...  → https://mapsi.dev/v1/autocomplete?text=...
  /api/reverse?lat=&lon=      → https://mapsi.dev/v1/reverse?lat=&lon=
  /api/places?q=&lat=&lon=    → https://mapsi.dev/v1/places?q=&lat=&lon=
  /api/route                  → POST https://mapsi.dev/v1/route  (forward body)
  /api/isochrone?lat=&lon=&time= → https://mapsi.dev/v1/isochrone?...
  /api/timezone?lat=&lon=     → https://mapsi.dev/v1/timezone?...
  /api/pip?lat=&lon=          → https://mapsi.dev/v1/pip?lat=&lon=

## What to Generate
1. Generic proxy handler: validates incoming params, forwards to Mapsi with X-API-Key header, returns response
2. Input validation: lat must be -90 to 90, lon -180 to 180, q/text must be non-empty strings, max length 200
3. Response caching:
   - Geocode / reverse / PIP / timezone: cache 24 hours (stable data)
   - Autocomplete: cache 5 minutes (slightly dynamic)
   - Route / isochrone: cache 1 hour
   - Cache key: md5(endpoint + sorted params)
4. Rate limit handling: on 429 from Mapsi, return 429 to client with Retry-After header, log incident
5. Error normalisation: map Mapsi 400/401/404 to appropriate client-facing messages (don't leak internal errors)
6. Request logging: log endpoint, params (not full addresses for privacy), response_ms, cache hit/miss
7. CORS: allow your frontend origin only
8. .env.example with MAPSI_API_KEY, REDIS_URL, ALLOWED_ORIGIN

## Code Requirements
- Single proxy route handler — parametric path so adding new endpoints doesn't require new code
- Never log full coordinates or addresses in production (privacy)
- Return Mapsi response_ms in a X-Response-Time header for monitoring

Generate the code now.

I'm building a store locator for my website using Mapsi. Generate a complete implementation.

## Context
- Framework: [React / Vue / Vanilla JS / Next.js]
- Backend: [Node.js / Python / PHP]
- Use Case: User types their address or city, sees nearest store locations on a map and in a list

## Mapsi APIs Used (5 API calls total)

### 1. Autocomplete (as user types)
GET https://mapsi.dev/v1/autocomplete?text=high+str&limit=5
Headers: X-API-Key: YOUR_KEY  (server-side proxy)
Response: { "suggestions": [{ "text": "High Street, London", "lat": 51.509, "lon": -0.118 }] }

### 2. Geocode (when user selects a suggestion or submits)
GET https://mapsi.dev/v1/geocode?q=High+Street+London&limit=1
Response: { "results": [{ "coordinates": { "lat": 51.509, "lon": -0.118 }, "formatted_address": "..." }] }
Note: Use the lat/lon from the autocomplete suggestion directly if available — saves an extra geocode call.

### 3. Places search (find stores near geocoded point)
GET https://mapsi.dev/v1/places?q=[YOUR_STORE_CATEGORY]&lat=51.509&lon=-0.118&radius=10000&limit=10
Response: { "places": [{ "name": "...", "lat": ..., "lon": ..., "address": "..." }] }
Note: Replace [YOUR_STORE_CATEGORY] with your category keyword e.g. "pharmacy", "coffee", "electronics"
OR: Use your own store database — query by distance from geocoded lat/lon instead of calling /places.

### 4. Map tiles (interactive map with pins)
MapLibre style URL: https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY
(?key= in URL is safe for browser — tiles-only exception)
Add a maplibregl.Marker for each store result.

### 5. Static map (thumbnail per result in list view)
GET https://mapsi.dev/v1/static-map?lat=51.509&lon=-0.118&zoom=14&width=200&height=120
Headers: X-API-Key: YOUR_KEY (server-side) OR use as img src with key in URL for browser convenience
Returns: PNG image

## What to Generate
1. Search bar component: autocomplete dropdown with debounce (350ms), keyboard nav, "use my location" button (browser geolocation)
2. On search submit: geocode address → find stores nearby → render results
3. Results list: store name, address, distance from search point, static map thumbnail (200×120)
4. Interactive MapLibre map: all stores as markers, click marker → highlight list item → show popup with name + address
5. "Get directions" link per result: opens native maps (Google Maps / Apple Maps) with destination pre-filled
6. Backend routes (through your server proxy): /api/autocomplete, /api/geocode, /api/places, /api/static-map
7. Distance calculation: Haversine distance from user's search point to each store — display as "2.3 km away"
8. Empty state: "No stores found within 10km — showing nearest 3" fallback with expanded radius

## Code Requirements
- Never call Mapsi directly from browser except for tile styles URL
- Lazy-load MapLibre GL to not block initial page render
- Accessible: keyboard-navigable results list, aria-labels on map markers
- Mobile: list view default on small screens, map toggle button

Generate the code now.

I'm building an address autocomplete input for a checkout or form using Mapsi. Generate a complete implementation.

## Context
- Framework: [React / Vue / Vanilla JS]
- Backend: [Node.js / Python]
- Goal: Reduce address input errors — suggest valid addresses as user types, then parse into structured fields

## Mapsi APIs Used (3 API calls total)

### 1. Autocomplete (real-time suggestions)
GET https://mapsi.dev/v1/autocomplete?text=10+downing&limit=5
Headers: X-API-Key: YOUR_KEY  (server-side proxy — never in browser)
Optional: &countries=GB to restrict. &focus.lat=X&focus.lon=Y to bias toward user's region.
&layers=address to return address-level results only (filters out city/country suggestions)
Response: { "suggestions": [{ "text": "10 Downing Street, London, SW1A 2AA", "lat": 51.503, "lon": -0.127 }] }

### 2. Normalize (parse selected address into components)
POST https://mapsi.dev/v1/normalize
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: { "address": "10 Downing Street, London, SW1A 2AA" }
Response: {
  "parsed": {
    "house_number": "10",
    "road": "Downing Street",
    "city": "London",
    "postcode": "SW1A 2AA",
    "country": "United Kingdom"
  },
  "normalized": ["10 downing street london sw1a 2aa"]
}
Use case: auto-populate house_number, street, city, postcode form fields after user selects a suggestion.

### 3. Reverse geocode (confirm coordinates → address, optional quality check)
GET https://mapsi.dev/v1/reverse?lat=51.503&lon=-0.127
Response: { "results": [{ "formatted_address": "10 Downing St, London SW1A 2AA, UK" }] }
Use case: if user enables browser geolocation, auto-fill form with their current address.

## What to Generate
1. AddressAutocomplete component:
   - Single text input field
   - Debounce 300ms before calling autocomplete
   - Dropdown showing up to 5 suggestions (text from API)
   - Keyboard navigation: arrow keys, Enter to select, Escape to close
   - On selection: call normalize → populate street, city, postcode, country fields automatically
   - Show spinner during API calls, error state if API fails (fall back to plain text input)

2. "Use my location" button:
   - navigator.geolocation.getCurrentPosition → call reverse geocode → populate same fields
   - Handle permission denied gracefully

3. Backend proxy routes (server-side):
   GET /api/autocomplete?text=...&countries=...
   POST /api/normalize  (forward body)
   GET /api/reverse?lat=...&lon=...

4. Validation after population:
   - Warn if postcode is empty (normalize may not always return it)
   - Warn if country doesn't match expected shipping countries

## Code Requirements
- Minimum 2 characters before triggering autocomplete
- Debounce on input, not on API call — cancel in-flight requests if user keeps typing
- Accessible: role="combobox", aria-autocomplete="list", aria-expanded, proper keyboard handling
- Never call Mapsi directly from browser — all calls through your backend proxy
- Handle Mapsi 429 gracefully: show "Too many requests — please type your address manually"

Generate the code now.

I'm building a delivery routing feature using Mapsi. Generate a complete implementation.

## Context
- Framework: [React / Vanilla JS / Node.js backend]
- Use Case: Dispatcher enters pickup and drop-off addresses, sees the route on a map with distance and ETA

## Mapsi APIs Used (3 API calls — matches the Route a Delivery demo)

### 1. Geocode pickup address
GET https://mapsi.dev/v1/geocode?q=10+Downing+Street+London&limit=1
Headers: X-API-Key: YOUR_KEY
Response: { "results": [{ "coordinates": { "lat": 51.503, "lon": -0.127 }, "formatted_address": "..." }] }

### 2. Route API (compute route between geocoded coordinates)
POST https://mapsi.dev/v1/route
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body:
{
  "waypoints": [
    { "lat": 51.503, "lon": -0.127 },   // pickup
    { "lat": 51.509, "lon": -0.118 }    // drop-off
  ],
  "mode": "auto",   // auto (car), truck, bicycle, pedestrian, motor_scooter
  "units": "km"
}
Response:
{
  "distance_km": 2.3,
  "duration_sec": 480,
  "waypoint_count": 2,
  "legs": [{ "from": 0, "to": 1, "distance_km": 2.3, "duration_sec": 480 }],
  "polyline": { "type": "LineString", "coordinates": [[lon1,lat1], [lon2,lat2], ...] },
  "mode": "auto",
  "units": "km"
}
Note: polyline is a GeoJSON LineString — add directly as a MapLibre geojson source.

### 3. Map Tiles (render route on map)
MapLibre style URL: https://mapsi.dev/v1/tiles/styles?style=streets&key=YOUR_API_KEY
(?key= in URL is browser-safe for tiles only)
Add route polyline as a GeoJSON layer. Add markers for pickup and drop-off.

## What to Generate
1. Route form: two address inputs with autocomplete (use /v1/autocomplete), travel mode selector, "Calculate Route" button
2. On submit: geocode both addresses → POST /v1/route → display results
3. Results display:
   - Distance: "2.3 km"
   - ETA: format duration_sec as "8 min" or "1h 23m"
   - Per-leg breakdown if multi-stop (show from/to names, distance, time)
4. Map rendering (MapLibre):
   - Draw route polyline: { type: 'line', paint: { 'line-color': '#1a56db', 'line-width': 4 } }
   - Pickup marker (green), drop-off marker (red)
   - map.fitBounds() to show full route with padding
5. Multi-stop extension: allow adding intermediate stops (drag to reorder, max 25 waypoints)
6. Share link: encode waypoints in URL so route can be shared/bookmarked
7. Backend: geocode proxy (/api/geocode), route proxy (/api/route) — keep X-API-Key server-side

## Code Requirements
- GeoJSON coordinates from Mapsi are [lon, lat] order (GeoJSON standard) — MapLibre expects same
- Geocode both addresses in parallel (Promise.all) to halve latency
- Handle "no route found" (Mapsi returns 400 if coordinates are unreachable by chosen mode)
- Mode "truck" adds routing restrictions (height, weight limits) — useful for fleet apps

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I'm building a GPS ping locator using Mapsi to convert raw coordinates into human-readable addresses. Generate a complete implementation.

## Context
- Source: [Driver mobile app / IoT device webhook / Fleet tracking system]
- Framework: [React / Node.js / Python]
- Use Case: Show where a device is right now — address, timezone, on a live map

## Mapsi APIs Used (matches the Reverse Locate a GPS Ping demo)

### 1. Reverse Geocoding
GET https://mapsi.dev/v1/reverse?lat=48.8584&lon=2.2945
Headers: X-API-Key: YOUR_KEY
Response:
{
  "results": [{
    "formatted_address": "Champ de Mars, 5 Av. Anatole France, 75007 Paris, France",
    "city": "Paris",
    "state": "Île-de-France",
    "country": "France",
    "postcode": "75007"
  }]
}

### 2. Timezone (for accurate local time display)
GET https://mapsi.dev/v1/timezone?lat=48.8584&lon=2.2945
Headers: X-API-Key: YOUR_KEY
Response:
{
  "timezone": "Europe/Paris",
  "utc_offset": "+01:00",
  "dst": true,
  "current_time": "Thursday, May 8, 2025 at 3:14:00 PM GMT+1"
}

### 3. Map Tiles (pin on live map)
MapLibre style URL: https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY
Add maplibregl.Marker at the coordinate, popup with formatted_address.

## What to Generate
1. Webhook receiver (if GPS pings arrive via HTTP webhook):
   POST /webhook/gps-ping
   Body: { device_id, lat, lon, timestamp }
   Calls reverse geocode + timezone in parallel (Promise.all)
   Stores enriched record: { device_id, lat, lon, formatted_address, city, country, timezone, local_time, timestamp }

2. Live location UI:
   - MapLibre tile map with pin at current device coordinate
   - Popup on pin: formatted address + local time
   - Sidebar: device_id, coordinates, address, timezone, last seen timestamp
   - "Locate" button: enter lat/lon manually, or paste raw coordinate string (parse "48.8584, 2.2945")

3. History view: last N pings for a device as a line on the map (array of coordinates → GeoJSON LineString)

4. Batch enrichment for historical pings:
   POST https://mapsi.dev/v1/batch/reverse
   Body: { "points": [{ "lat": 48.8584, "lon": 2.2945, "id": "ping-001" }, ...] }
   Response: { "results": [{ "id": "ping-001", "address": "...", "lat": ..., "lon": ... }] }
   Batch limits: Free=10, Growth=250, Business=500 points per request.
   Credits: 1 credit per 2 points (rounded up).

## Code Requirements
- Call reverse geocode + timezone in parallel, not sequentially — saves ~100ms per ping
- Cache reverse geocode results for coordinates within 100m of each other (grid snap) for 1 hour
- Handle coordinates over water or unpopulated areas gracefully (reverse may return null formatted_address — show coordinates only)
- For batch: chunk large arrays into plan-limit-sized batches, process sequentially with 200ms delay between batches

Generate the code now.

I'm building a multi-stop route optimizer using Mapsi. Generate a complete implementation.

## Context
- Framework: [React / Node.js / Python]
- Use Case: Enter multiple delivery addresses, get the optimal visiting order, see the route on a map

## Mapsi APIs Used

### 1. Geocode all stops (batch geocode or parallel single geocodes)
GET https://mapsi.dev/v1/geocode?q=ADDRESS&limit=1
Headers: X-API-Key: YOUR_KEY
Or for large lists: POST https://mapsi.dev/v1/batch/geocode
Body: { "addresses": ["address 1", "address 2", ...], "limit": 1 }
Response: array of { "results": [{ "coordinates": { "lat": ..., "lon": ... }, "formatted_address": "..." }] }

### 2. Matrix API (travel times between all pairs — needed for optimisation)
POST https://mapsi.dev/v1/matrix
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body:
{
  "sources": [{"lat": 48.85, "lon": 2.29}, ...],  // all stops including depot
  "targets": [{"lat": 48.85, "lon": 2.29}, ...],  // same list
  "profile": "car",   // car, truck, bicycle, pedestrian
  "units": "metric"
}
Response:
{
  "durations": [[0, 720, 480], [720, 0, 360], [480, 360, 0]],  // seconds[i][j]
  "distances": [[0, 2300, 1800], ...],                          // metres[i][j]
  "durations_text": [["0s", "12m", "8m"], ...]
}
Matrix limits: Free=5×5 (25 pairs), Growth=10×10 (100), Business=25×25 (625)

### 3. Route API (render optimised sequence as polyline)
POST https://mapsi.dev/v1/route
Body: { "waypoints": [ordered stops as {lat,lon}], "mode": "auto", "units": "km" }
Response: { "distance_km": ..., "duration_sec": ..., "polyline": { GeoJSON LineString } }

### 4. Map Tiles
MapLibre: https://mapsi.dev/v1/tiles/styles?style=streets&key=YOUR_API_KEY

## What to Generate
1. Stop input UI: add/remove addresses, drag to manually reorder, max 25 stops (Mapsi route limit)
2. Geocode all addresses (parallel for ≤10, batch API for >10)
3. Matrix call: compute N×N travel time matrix for all stops
4. Optimisation: implement nearest-neighbour TSP heuristic using the duration matrix:
   - Start from depot (first stop)
   - Greedily visit nearest unvisited stop
   - Return to depot
   - This gives a good-enough route for ≤15 stops without complex solver
5. Route call: POST /v1/route with optimised stop order → get polyline
6. Map: draw polyline, numbered markers for each stop in visiting order
7. Summary: total distance, total time, stop-by-stop breakdown from legs[]
8. Export: "Copy optimised addresses" button, optional CSV download

## Code Requirements
- Run geocoding for all stops in parallel (Promise.all) before matrix call
- Matrix is symmetric for car profile — only need upper triangle, but Mapsi returns full matrix (use as-is)
- Handle geocode failures per stop (show error on that stop, proceed with others)
- For >25 stops: warn user, suggest splitting into multiple routes
- Nearest-neighbour is O(n²) — fine for n≤25 in browser, runs in <10ms

Generate the code now.

I'm building a proof-of-location / agent visit verification system using Mapsi. Generate a complete implementation.

## Context
- Use Case: Field agent captures a GPS coordinate on their device. System produces a verified location record — address, admin area, nearest road, local time — stored for audit.
- Backend: [Node.js / Python]
- Mobile: [React Native / Flutter / or just a browser app with geolocation]

## Mapsi APIs Used (4 API calls — all in parallel)

### 1. Reverse Geocoding (human-readable address)
GET https://mapsi.dev/v1/reverse?lat=48.8584&lon=2.2945
Response: { "results": [{ "formatted_address": "...", "city": "Paris", "country": "France", "postcode": "75007" }] }

### 2. Point-in-Polygon (administrative hierarchy)
GET https://mapsi.dev/v1/pip?lat=48.8584&lon=2.2945
Response:
{
  "country": "France",
  "country_code": "FRA",
  "state": "Île-de-France",
  "city": "Paris",
  "neighbourhood": "Gros Caillou",
  "hierarchy": { "continent": "...", "country": "...", "region": "...", "city": "...", "neighbourhood": "..." }
}
Use case: unambiguous admin-level location proof (not just an address string — structured and queryable).

### 3. Nearest Road (road-level verification)
GET https://mapsi.dev/v1/nearest-road?lat=48.8584&lon=2.2945&limit=1
Response:
{
  "results": [{
    "road_name": "Avenue Anatole France",
    "highway": "secondary",
    "speed_limit_kmh": 50,
    "snapped_point": { "lat": 48.8582, "lon": 2.2948 },
    "distance_m": 18,
    "osm_way_id": "12345678"
  }]
}
Use case: confirms agent was near a real road (distance_m > 500 = suspicious — agent indoors or GPS drift).

### 4. Timezone (authoritative local time)
GET https://mapsi.dev/v1/timezone?lat=48.8584&lon=2.2945
Response: { "timezone": "Europe/Paris", "utc_offset": "+01:00", "dst": true, "current_time": "..." }
Use case: prevents timestamp disputes (device clock may be wrong; timezone from coords is authoritative).

## What to Generate
1. captureVisit(agentId, lat, lon) function:
   - Run all 4 Mapsi calls in parallel (Promise.all)
   - Assemble a VisitRecord: {
       agent_id, lat, lon, accuracy_m,
       formatted_address, city, country, postcode,  // from reverse
       admin_hierarchy,                              // from PIP
       nearest_road, road_distance_m, osm_way_id,  // from nearest-road
       timezone, local_time, utc_offset,            // from timezone
       captured_at_utc,                             // server UTC timestamp
       confidence_flags                              // derived (see below)
     }
2. Confidence flags (derived from API responses):
   - road_too_far: true if nearest road distance_m > 300 (GPS drift or indoors)
   - address_mismatch: true if reverse city ≠ PIP city (data quality warning)
   - dst_active: from timezone.dst (useful for time-sensitive workflows)
3. Visit record storage: POST /api/visits → store in DB, return record ID
4. Verification UI: show map with pin, address, admin hierarchy table, road name, local time, confidence badge
5. PDF/export endpoint: GET /api/visits/:id/export → formatted visit certificate (agent name, location, timestamp, map static image via /v1/static-map)
6. Mobile capture flow: browser geolocation → send lat/lon/accuracy to backend → get VisitRecord back → display

## Code Requirements
- All 4 API calls MUST run in parallel — sequential would take 400ms+ per visit
- Store raw coordinates + all API responses in DB (not just derived fields) for future reprocessing
- Never accept coordinates from client as authoritative — compare with claimed address if provided
- road_distance_m in the hundreds of metres is normal in dense cities — tune threshold for your use case

Generate the code now.

I'm building a service area coverage map using Mapsi's Isochrone API. Generate a complete implementation.

## Context
- Framework: [React / Vanilla JS / Next.js]
- Use Case: Visualise which areas are reachable from a depot within 15, 30, 60 minutes. Show coverage zone for delivery, ambulance, or service radius.

## Mapsi APIs Used

### 1. Geocode depot address
GET https://mapsi.dev/v1/geocode?q=DEPOT_ADDRESS&limit=1
Response: { "results": [{ "coordinates": { "lat": 48.85, "lon": 2.29 }, "formatted_address": "..." }] }

### 2. Isochrone API (reachability polygons)
GET https://mapsi.dev/v1/isochrone?lat=48.85&lon=2.29&time=15,30,60&profile=car
Headers: X-API-Key: YOUR_KEY
Parameters:
  - time: comma-separated minutes. Max 5 contours. Example: "10,20,30"
  - OR distance_km: comma-separated distances (cannot combine with time)
  - profile: car (default), bicycle, pedestrian
  - denoise: 0.0–1.0 polygon smoothing (default 0.5)
Response: GeoJSON FeatureCollection — one Polygon Feature per contour.
  Each Feature has properties.contour_minutes (or contour_km).
  Polygons are nested — larger contours enclose smaller ones.

### 3. Map Tiles
MapLibre: https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY

## What to Generate
1. Depot input: address field with autocomplete → geocode → compute isochrone
2. Controls: travel time selector (preset buttons: 15m / 30m / 60m, or custom slider), travel mode (car/bicycle/pedestrian)
3. Render isochrone polygons on MapLibre:
   - Add as GeoJSON source: map.addSource('isochrone', { type: 'geojson', data: geojson })
   - 3 fill layers, one per contour, styled with decreasing opacity (60m=0.15, 30m=0.25, 15m=0.35)
   - Colours: innermost = strongest, outermost = lightest (e.g., blue scale)
   - Outline layer: 'line' type, 1px stroke per polygon boundary
4. Legend: colour-coded travel time key ("Within 15 min", "Within 30 min", "Within 60 min")
5. map.fitBounds() to outer isochrone polygon on load
6. Optional: customer pins — show which customers fall inside/outside coverage:
   - For each customer lat/lon: check if inside outermost polygon (use turf.js booleanPointInPolygon)
   - Green pin = within coverage, grey pin = outside
7. "Export Coverage" button: download the GeoJSON FeatureCollection for use in other tools

## Code Requirements
- GeoJSON from Mapsi is ready to pass to map.addSource() directly — no transformation needed
- Isochrone can take 1-3 seconds for complex polygons — show loading spinner on map
- Recompute isochrone when user changes travel mode or time (debounce 500ms)
- Backend proxy: GET /api/isochrone?lat=...&lon=...&time=... (X-API-Key server-side)

Generate the code now.

I'm building a bulk address cleansing and geocoding pipeline using Mapsi. Generate a complete implementation.

## Context
- Language: [Python / Node.js]
- Input: CSV file with a column of raw address strings (potentially messy, abbreviated, inconsistent)
- Output: Enriched CSV with structured components + coordinates per address
- Scale: [hundreds / thousands / tens of thousands] of addresses

## Mapsi APIs Used

### 1. Address Normalisation (pre-processing before geocoding)
POST https://mapsi.dev/v1/normalize
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: { "address": "123 main st new york ny 10001" }
Response:
{
  "input": "123 main st new york ny 10001",
  "parsed": {
    "house_number": "123",
    "road": "main st",
    "city": "new york",
    "state": "ny",
    "postcode": "10001"
  },
  "normalized": ["123 main street new york ny 10001", "123 main st new york new york 10001"]
}
Why normalize first: Libpostal expands abbreviations (St→Street), fixes casing, handles international formats.
Use normalized[0] as input to batch geocode for higher match rates.

### 2. Batch Geocoding
POST https://mapsi.dev/v1/batch/geocode
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: { "addresses": ["123 Main Street New York NY 10001", ...], "limit": 1 }
Response: array of geocode results, one per address input.
Each result: { "results": [{ "coordinates": { "lat": ..., "lon": ... }, "formatted_address": "..." }] } OR { "results": [] } on no match.

Batch size limits by plan:
  Free: 10 per batch · Growth: 5,000 per batch · Business: 30,000 per batch

## What to Generate
1. CSV reader: parse input file, extract address column (configurable column name/index)
2. Normalize phase:
   - Call POST /v1/normalize for each address individually (no batch endpoint — run with concurrency limit of 5)
   - Store normalized[0] as the geocoding input. If normalized is empty, use original address.
   - Track: original_address, normalized_address, parsed components
3. Batch geocode phase:
   - Chunk normalized addresses into plan-appropriate batch sizes (configurable: default 1,000)
   - POST each chunk to /v1/batch/geocode
   - Add 200ms delay between batches to respect rate limits (15 req/sec on Growth)
   - Retry failed batches once on 5xx with exponential backoff
4. Output CSV columns:
   original_address | normalized_address | house_number | road | city | state | postcode | country |
   lat | lon | formatted_address | match_status | geocode_confidence
   match_status: "matched" | "no_match" | "error"
5. Quality report (printed to stdout after run):
   - Total processed, matched count, match rate %, no_match count
   - List of top 20 unmatched addresses for manual review
6. Resumable: checkpoint every 1,000 records to a temp file — if run is interrupted, skip already-processed rows

## Code Requirements
- Normalize and geocode are separate phases — run normalize for all rows first, then geocode
- Concurrency for normalize: max 5 parallel requests (rate limit headroom)
- Never geocode without normalizing first — it meaningfully improves match rates on messy data
- Log progress: "Processed 500/10000 — match rate so far: 94.2%"

Generate the code now.

I'm building a GPS telemetry enrichment pipeline using Mapsi. Generate a complete implementation.

## Context
- Language: [Python / Node.js]
- Input: Raw GPS pings from IoT devices or fleet vehicles (device_id, lat, lon, timestamp, accuracy_m)
- Output: Enriched records with human-readable address, road info, elevation, timezone, map-matched coordinates
- Volume: [hundreds to tens of thousands of pings per batch]

## Mapsi APIs Used (4 APIs)

### 1. Batch Reverse Geocoding (address per coordinate)
POST https://mapsi.dev/v1/batch/reverse
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: {
  "points": [
    { "lat": 48.8584, "lon": 2.2945, "id": "ping-001" },
    { "lat": 51.5074, "lon": -0.1278, "id": "ping-002" }
  ]
}
Response: { "results": [{ "id": "ping-001", "address": "...", "lat": 48.8584, "lon": 2.2945 }] }
Credits: 1 credit per 2 points (rounded up)
Batch limits: Free=10, Growth=250, Business=500 points per request

### 2. Map Matching (snap noisy GPS to road network)
POST https://mapsi.dev/v1/match
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: {
  "points": [
    { "lat": 48.8584, "lon": 2.2945, "timestamp": 1741000000 },
    { "lat": 48.8591, "lon": 2.2962, "timestamp": 1741000015 }
  ],
  "profile": "car"   // car, bicycle, pedestrian
}
Response: {
  "confidence": 0.92,
  "geometry": { "type": "LineString", "coordinates": [...] },  // snapped road-aligned route
  "matched_points": [{ "lat": ..., "lon": ..., "distance_from_original_m": 8 }],
  "matched_roads": [{ "road_name": "Avenue Anatole France", "highway": "secondary" }],
  "distance_meters": 412
}
Max points per request: 500. Use for a contiguous GPS trace from one device trip.

### 3. Elevation (height above sea level)
POST https://mapsi.dev/v1/elevation
Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
Body: { "points": [{ "lat": 48.8584, "lon": 2.2945 }, ...] }
Response: { "results": [{ "lat": ..., "lon": ..., "elevation_m": 33, "source": "srtm30" }] }
Batch limits: Free=10, Growth=128, Business=512 points

### 4. Timezone (one lookup per unique timezone region, not per ping)
GET https://mapsi.dev/v1/timezone?lat=48.8584&lon=2.2945
Response: { "timezone": "Europe/Paris", "utc_offset": "+01:00", "dst": true, "current_time": "..." }
Optimisation: cache timezone by H3 cell (resolution 4 ≈ 1,000 km²) — timezone rarely changes within a region.

## What to Generate
1. Ingestion: read pings from [CSV / JSON / database / Kafka topic], group by device_id and time window (a "trip")
2. Phase 1 — Batch reverse geocode: chunk all pings into batch-size groups, call /v1/batch/reverse, map results back by id
3. Phase 2 — Map matching: per trip (contiguous pings from one device), call /v1/match. Chunk trips >500 pings into segments.
   Attach matched_roads and confidence to each trip record.
   Flag pings where distance_from_original_m > 50 as "high GPS drift"
4. Phase 3 — Elevation: batch POST /v1/elevation for all ping coordinates, attach elevation_m per ping
5. Phase 4 — Timezone: deduplicate coordinates to unique H3 cells (resolution 4), lookup timezone once per cell, assign to all pings in cell
6. Output schema per enriched ping:
   { device_id, original_lat, original_lon, matched_lat, matched_lon, gps_drift_m,
     address, road_name, highway_type, elevation_m, timezone, utc_offset,
     timestamp_utc, timestamp_local, trip_id, match_confidence }
7. Quality metrics: pings processed, match rate, avg GPS drift, high-drift count, unmatched address count

## Code Requirements
- All 4 phases run sequentially per batch — don't interleave (easier to debug, retry individual phases)
- Checkpoint after each phase to allow resuming failed runs
- H3 timezone cache prevents calling timezone API for every ping — huge credit savings on large datasets
- Map matching works on a trip trace, not individual pings — group by device + trip_id before calling /v1/match

Generate the code now.

I'm building a delivery zone check at checkout using Mapsi and Fencemaker together. Generate a complete implementation.

## Context
- Framework: [React / Next.js / Node.js]
- Use Case: Compute a drive-time delivery zone from our depot with Mapsi, register it as a territory in Fencemaker, then check whether a customer address at checkout falls inside it.
- This matches the Mapsi + Fencemaker zone-check demo: 5 API calls total.

## API Keys
- Mapsi key: X-API-Key header on all Mapsi calls (server-side)
- Fencemaker key: X-API-Key header on all Fencemaker calls (separate key, server-side)
- Tile URLs only: use ?key= param in URL (browser-safe, Mapsi tiles only)

## Step 1 — Geocode depot address (Mapsi)
GET https://mapsi.dev/v1/geocode?q=DEPOT_ADDRESS&limit=1
Headers: X-API-Key: MAPSI_KEY
Response: { "results": [{ "coordinates": { "lat": 48.85, "lon": 2.29 }, "formatted_address": "..." }] }

## Step 2 — Generate delivery zone polygon (Mapsi Isochrone)
GET https://mapsi.dev/v1/isochrone?lat=48.85&lon=2.29&time=30&profile=car
Headers: X-API-Key: MAPSI_KEY
Response: GeoJSON FeatureCollection — one Polygon for the 30-minute drive-time zone
  { "type": "FeatureCollection", "features": [{ "type": "Feature", "geometry": { "type": "Polygon", "coordinates": [[...]] } }] }

## Step 3 — Register polygon as a Fencemaker territory
POST https://fencemaker.app/api/v1/territories
Headers: X-API-Key: FENCEMAKER_KEY, Content-Type: application/json
Body:
{
  "name": "30-Minute Delivery Zone",
  "boundary": <GeoJSON Polygon geometry from Step 2>,
  "code": "DEPOT-30MIN"
}
Response:
{
  "id": "uuid",
  "name": "30-Minute Delivery Zone",
  "code": "DEPOT-30MIN",
  "color": "#ff5500",
  "location_id": "uuid",
  "created_at": "2026-05-06T10:00:00Z"
}
Note: Do this once on setup (or when depot or drive time changes) — not on every checkout.
Store the returned "id" — you'll need it to update the territory boundary later.

## Step 4 — Check customer address at checkout (Fencemaker PIP)

### Option A — By coordinates (fastest, use when you already have lat/lon)
GET https://fencemaker.app/api/v1/pip?lat=CUSTOMER_LAT&lon=CUSTOMER_LON
Headers: X-API-Key: FENCEMAKER_KEY
Response (matched):
{
  "matched": true,
  "territory": {
    "id": "uuid",
    "name": "30-Minute Delivery Zone",
    "code": "DEPOT-30MIN",
    "agent_id": null,
    "location_id": "uuid",
    "location_name": "Depot"
  },
  "lat": CUSTOMER_LAT, "lon": CUSTOMER_LON,
  "response_ms": 8
}
Response (no match):
{
  "matched": false,
  "territory": null,
  "lat": CUSTOMER_LAT, "lon": CUSTOMER_LON,
  "response_ms": 6
}
Use territory.code to map to your delivery config. territory.id for audit trail.

### Option B — By address string (Fencemaker geocodes internally)
POST https://fencemaker.app/api/v1/territory
Headers: X-API-Key: FENCEMAKER_KEY, Content-Type: application/json
Body: { "address": "CUSTOMER_ADDRESS" }
Response (matched):
{
  "matched": true,
  "territory_id": "uuid",
  "territory_code": "DEPOT-30MIN",
  "agent_id": null,
  "agent_name": null,
  "location_name": "Depot",
  "response_ms": 8
}
Response (no match):
{
  "matched": false,
  "territory_id": null,
  "response_ms": 7
}
Note: GET /pip and POST /territory return DIFFERENT response shapes — this is by design.
  GET /pip  → result is inside "territory": { id, name, code, agent_id, ... }
  POST /territory → result fields are at the top level: territory_id, territory_code, agent_id, ...
Your backend proxy must normalise these into one consistent shape before returning to your frontend.

## Step 5 — Show zone on map (Mapsi Tiles)
MapLibre: https://mapsi.dev/v1/tiles/styles?style=light&key=MAPSI_API_KEY
Add isochrone polygon as a GeoJSON fill layer. Show depot pin and customer pin.

## What to Generate
1. Setup script (run once): geocode depot → generate isochrone → POST to Fencemaker → log territory_id
2. Checkout eligibility check (per customer):
   - Accept address string or lat/lon
   - If lat/lon available: GET /pip → read result from territory.code, territory.id
   - If address string: POST /territory → read result from territory_code, territory_id (top-level)
   - Backend proxy normalises both shapes into: { eligible: bool, zone_code, zone_name, territory_id }
   - Return normalised shape to frontend — never expose raw Fencemaker response shape differences
3. Checkout UI component:
   - Address input with Mapsi autocomplete (via your backend proxy)
   - "Check delivery availability" button
   - Result: "✓ We deliver to your address — 30 min drive time" or "✗ Outside our delivery zone"
   - Show delivery zone map: isochrone polygon + customer pin (eligible = green, ineligible = red)
4. Backend proxy routes:
   GET /api/geocode?q=...           → Mapsi /v1/geocode
   GET /api/isochrone?...           → Mapsi /v1/isochrone
   POST /api/fencemaker/territories → Fencemaker POST /api/v1/territories
   GET /api/fencemaker/pip?lat=&lon= → Fencemaker GET /api/v1/pip
5. Admin panel endpoint: update drive time threshold (15/30/60 min) → regenerate isochrone → update Fencemaker territory

## Code Requirements
- Isochrone + Fencemaker territory registration happens at setup time, not per checkout
- Per-checkout: only ONE Fencemaker PIP call (fast — sub-20ms)
- Both API keys in environment variables: MAPSI_API_KEY and FENCEMAKER_API_KEY — never in client
- Handle Fencemaker 403 (territory limit reached on free plan) with clear error message
- Cache PIP result per customer address for 24 hours (delivery zones don't change per order)

## Where to Get Keys
- Mapsi key: https://mapsi.dev/console/api-keys
- Fencemaker key: https://fencemaker.app/keys

Generate the code now.

I'm building a nearest-location finder with two ranking modes using Mapsi. Generate a complete implementation.
    
    ## Context
    - Framework: [React / Vue / Vanilla JS]
    - Backend: [Node.js / Python]
    - Use Case: User searches their address or uses current location, sees a list of nearby locations (clinics / stores / depots) ranked by proximity. Two modes: straight-line distance (fast) and actual drive time (accurate).
    - Location data: I have a static list of locations as JSON: [{ id, name, address, lat, lon }, ...]
    
    ## Why Two Modes
    - Haversine (straight-line): instant, no API call, good enough for initial display
    - Matrix (drive-time): accurate, costs 1 API call per search, surfaced as "Sort by drive time"
    - Pattern: show Haversine results immediately, let user upgrade to drive-time ranking on demand
    
    ## Mapsi APIs Used
    
    ### 1. Autocomplete + Geocode (get user's search coordinate)
    GET https://mapsi.dev/v1/autocomplete?text=PARTIAL&limit=5
    Headers: X-API-Key: YOUR_KEY  (server-side proxy)
    Response: { "suggestions": [{ "text": "...", "lat": 51.509, "lon": -0.118 }] }
    
    GET https://mapsi.dev/v1/geocode?q=ADDRESS&limit=1
    Headers: X-API-Key: YOUR_KEY
    Response: { "results": [{ "coordinates": { "lat": 51.509, "lon": -0.118 }, "formatted_address": "..." }] }
    
    ### 2. Matrix API (drive-time from user to all locations)
    POST https://mapsi.dev/v1/matrix
    Headers: X-API-Key: YOUR_KEY, Content-Type: application/json
    Body:
    {
      "sources": [{ "lat": USER_LAT, "lon": USER_LON }],
      "targets": [
        { "lat": 51.50, "lon": -0.10 },   // location 1
        { "lat": 51.52, "lon": -0.09 },   // location 2
        ...up to plan limit
      ],
      "profile": "car",
      "units": "metric"
    }
    Response:
    {
      "durations": [[480, 720, ...]],       // seconds from user to each target — durations[0][i]
      "distances": [[2300, 4100, ...]],     // metres
      "durations_text": [["8m", "12m", ...]]
    }
    sources has 1 entry (user). Result is a 1×N matrix — durations[0][i] is travel time to location i.
    
    ## IMPORTANT — Matrix Plan Limits (sources × targets, both dimensions capped independently)
    The matrix limit is NOT total pairs — it is the max for EACH dimension separately.
    In this use case: sources = 1 (the user), targets = your locations.
    The targets dimension determines how many locations you can rank per call:
    
      Free:       5×5  → max 5 targets  → max 5 locations per Matrix call
      Growth:    10×10 → max 10 targets → max 10 locations per Matrix call
      Business:  25×25 → max 25 targets → max 25 locations per Matrix call
      Enterprise: Custom
    
    This means:
    - Free plan with 6+ locations: Matrix call will return a 400 error
    - Growth plan with 11+ locations: same
    - Always check location count against plan limit before calling Matrix
    
    Handling location counts that exceed your plan limit:
      Option A — Haversine pre-filter: rank all locations by Haversine first,
        take the nearest N (within your plan's target limit), then call Matrix
        on just those N. Result: accurate drive-time for the most plausible candidates.
      Option B — Haversine only: if location count exceeds limit, skip Matrix entirely
        and stay on Haversine mode. Show a note: "Drive-time ranking unavailable
        — too many locations for your current plan."
      Implement Option A — it gives the best user experience on all plans.
    
    ### 3. Map Tiles
    MapLibre: https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY
    
    ## What to Generate
    
    ### Haversine Mode (default — no API call)
    1. haversineDistance(lat1, lon1, lat2, lon2) function — returns kilometres
       Formula: uses Earth radius 6371km, converts degrees to radians, standard haversine
    2. On search: geocode user address → calculate haversine to every location → sort ascending
    3. Display: ranked list with "X.X km away", top 3 highlighted
    
    ### Matrix Mode (on demand — with plan-aware pre-filtering)
    4. "Sort by drive time" toggle button — triggers Matrix API call
    5. Pre-filter step: sort all locations by Haversine, take nearest N based on plan:
       const MATRIX_TARGET_LIMIT = 5;  // set to match your Mapsi plan (5/10/25)
       const candidates = allLocations
         .map(loc => ({ ...loc, hDist: haversine(userLat, userLon, loc.lat, loc.lon) }))
         .sort((a, b) => a.hDist - b.hDist)
         .slice(0, MATRIX_TARGET_LIMIT);
    6. POST /api/matrix through your backend proxy:
       - sources: [{ lat: userLat, lon: userLon }]
       - targets: candidate location coordinates (max MATRIX_TARGET_LIMIT)
    7. Re-rank candidates by durations[0][i] ascending
    8. Display: ranked list with "X min drive" from durations_text[0][i]
       Note: only the top N candidates are drive-time ranked — remaining locations show
       Haversine distance with a "(straight-line)" label
    9. Loading state on toggle button while Matrix call is in flight
    
    ### Map
    10. MapLibre map: all location markers, user location marker (different colour)
    11. On list item hover: highlight corresponding marker, show popup with name + distance/time
    12. On list item click: map.flyTo() to that location
    
    ### UI
    13. Search bar with autocomplete dropdown (call your proxy /api/autocomplete)
    14. "Use my location" button: browser geolocation → reverse geocode for display label
    15. Mode toggle: "📍 By distance" (default) | "🚗 By drive time" — clear visual state
    16. Result cards: rank number, location name, address, distance or drive time, "Get directions" link
    17. If Matrix unavailable (plan limit + no candidates): show toast "Showing straight-line distance"
    
    ## Code Requirements
    - MATRIX_TARGET_LIMIT must be a named constant — easy to update when plan upgrades
    - Run Haversine client-side (pure JS, no backend needed) — show results before any API call
    - Matrix call goes through backend proxy (X-API-Key must not be in browser)
    - Cache Matrix results per user coordinate (rounded to 3 decimal places) for 10 minutes
    - If Matrix call fails (400 target limit exceeded): fall back to Haversine mode silently,
      log the error server-side with the location count so you know when to upgrade plan
    - If Matrix call fails (429 or network error): stay on Haversine mode, show "Drive time unavailable" toast
    - "Get directions" link: https://www.google.com/maps/dir/?api=1&destination=LAT,LON
    
    Generate the code now.

I'm building a side-by-side comparison of fixed GeoJSON zones vs Mapsi isochrone zones so a stakeholder can choose which approach to use. Generate a complete implementation.
    
    ## Context
    - Framework: [React / Vanilla JS]
    - Use Case: We have two options for coverage zones — manually drawn postcode/boundary polygons (Fixed) and Mapsi drive-time isochrones (Dynamic). We need a comparison view to make the decision.
    - Decision criteria: Fixed = predictable, easy to explain to customers. Isochrone = accurate to actual drive time, updates if roads change.
    
    ## Zone Type A — Fixed GeoJSON Polygons
    Manually defined polygons stored as a GeoJSON FeatureCollection in your codebase.
    Each feature has properties: { name, tier, fee, color }
    Example structure:
    {
      "type": "FeatureCollection",
      "features": [
        {
          "type": "Feature",
          "properties": { "name": "Central Zone", "tier": 1, "fee": 50, "color": "#1a56db" },
          "geometry": { "type": "Polygon", "coordinates": [[[lon,lat], [lon,lat], ...]] }
        },
        {
          "type": "Feature",
          "properties": { "name": "Inner Zone", "tier": 2, "fee": 75, "color": "#7c3aed" },
          "geometry": { "type": "Polygon", "coordinates": [[[lon,lat], [lon,lat], ...]] }
        }
      ]
    }
    Note: Coordinates are [longitude, latitude] order in GeoJSON — not [lat, lon].
    
    ## Zone Type B — Mapsi Isochrone (Dynamic Drive-Time)
    GET https://mapsi.dev/v1/isochrone?lat=DEPOT_LAT&lon=DEPOT_LON&time=15,30&profile=car
    Headers: X-API-Key: YOUR_KEY  (server-side proxy)
    Parameters:
      time: comma-separated minutes — up to 5 contours e.g. "10,20,30"
      profile: car (default), bicycle, pedestrian
      denoise: 0.0–1.0 polygon smoothing (default 0.5)
    Response: GeoJSON FeatureCollection — one Polygon Feature per contour
      Each Feature has properties.contour_minutes
      Polygons are nested — 30-min polygon encloses 15-min polygon
    Pass directly to MapLibre as a GeoJSON source — no transformation needed.
    
    ## Map Tiles
    MapLibre: https://mapsi.dev/v1/tiles/styles?style=light&key=YOUR_API_KEY
    
    ## What to Generate
    
    ### Comparison Layout
    1. Two-panel layout OR single map with toggle:
       Option A (split-screen): two MapLibre maps side by side — left = Fixed, right = Isochrone
       Option B (single map toggle): one map, toggle button switches between zone layers
       Implement Option B (single map toggle) — simpler for mobile, same MapLibre instance
    
    2. Toggle control: "Fixed Zones" | "Drive-Time Zones" — pill toggle, clear active state
    
    ### Fixed Zone Panel
    3. Load GeoJSON FeatureCollection from local file or API endpoint
    4. Add to MapLibre as two sources (one per zone tier):
       map.addSource('fixed-zones', { type: 'geojson', data: fixedGeoJson })
    5. Fill layer per zone: 'fill' type, fill-color from feature properties.color, fill-opacity: 0.25
    6. Outline layer: 'line' type, 1.5px stroke matching feature color
    7. Click on zone: show popup with zone name and fee ("Central Zone — £50")
    8. Legend: colour-coded zone names with fee amounts
    
    ### Isochrone Panel
    9. On first switch to isochrone view: fetch from backend proxy GET /api/isochrone?lat=X&lon=Y&time=15,30
    10. Show loading spinner on map while fetching (isochrones can take 1-3 seconds)
    11. Add to MapLibre: same pattern — fill + outline layers, one per contour
    12. Colour by contour: 15-min = stronger opacity, 30-min = lighter
    13. Overlay depot marker (origin pin) on isochrone map
    14. map.fitBounds() to outer polygon on load
    
    ### Comparison UI
    15. Stats panel below map showing key differences:
        Fixed zones: "2 zones — defined by postcode boundaries — consistent shape"
        Isochrone:   "2 zones — defined by actual drive time — shape varies by traffic patterns"
    16. Pro/con list per approach (static content, not from API):
        Fixed: + Easy to explain, + Predictable boundaries, - Ignores road network
        Isochrone: + Accurate to real drive time, + Updates with road changes, - Shape less intuitive to customers
    17. "Choose this approach" button per mode — emits a decision event (onZoneTypeSelected('fixed' | 'isochrone'))
    
    ### Backend Proxy
    18. GET /api/isochrone?lat=X&lon=Y&time=X,Y — forwards to Mapsi with X-API-Key
    19. Cache isochrone result for 1 hour (same origin + time params = same result)
    
    ## Code Requirements
    - Both zone datasets loaded before map renders — no flicker on toggle
    - Toggle switches MapLibre layer visibility (setLayoutProperty 'visibility' 'visible'/'none') — faster than removing/adding layers
    - Fixed GeoJSON stored in /public/data/zones.geojson — easy for client to edit
    - Isochrone origin coordinate stored in env var (DEPOT_LAT, DEPOT_LON) — not hardcoded
    
    Generate the code now.

I'm building a coverage zone checker that returns a specific fee tier based on which zone a customer address falls in. Generate a complete implementation.
    
    ## Context
    - Framework: [React / Vue / Vanilla JS]
    - Backend: [Node.js / Python]
    - Use Case: Customer enters their address at checkout or on a landing page. System checks which coverage zone they're in and displays the corresponding fee (or "Not covered").
    - Zone data: [Fixed GeoJSON polygons stored locally] OR [Fencemaker territories via API]
    
    ## Fee Tier Configuration (your side — not from Mapsi)
    Map zone name/ID to a fee — store in your config:
      "Central Zone"  → { fee: 50, currency: "£", label: "Express", eta: "Same day" }
      "Inner Zone"    → { fee: 75, currency: "£", label: "Standard", eta: "Next day" }
      null (no match) → { fee: null, label: "Not covered", eta: null }
    
    ## Mapsi APIs Used
    
    ### 1. Autocomplete (as user types)
    GET https://mapsi.dev/v1/autocomplete?text=PARTIAL&limit=5&layers=address
    Headers: X-API-Key: YOUR_KEY  (server-side proxy)
    Response: { "suggestions": [{ "text": "14 MG Road, London", "lat": 51.509, "lon": -0.118 }] }
    Use &layers=address to return address-level suggestions only (not city/country level)
    
    ### 2. Geocode (on selection or form submit)
    GET https://mapsi.dev/v1/geocode?q=ADDRESS&limit=1
    Response: { "results": [{ "coordinates": { "lat": 51.509, "lon": -0.118 }, "formatted_address": "..." }] }
    Note: if user selected from autocomplete, the lat/lon is already in the suggestion — skip geocode.
    
    ### 3. Point-in-Polygon check
    Option A — Client-side with Turf.js (for local GeoJSON zones):
      import * as turf from '@turf/turf'
      const point = turf.point([lon, lat])   // GeoJSON is [lon, lat] order
      const matched = zones.features.find(f => turf.booleanPointInPolygon(point, f))
      const zone = matched ? matched.properties : null
    
    Option B — Fencemaker API (for managed territories, more accurate for complex polygons):
      GET https://fencemaker.app/api/v1/pip?lat=LAT&lon=LON
      Headers: X-API-Key: FENCEMAKER_KEY  (server-side)
      Response: { "matched": true, "territory": { "name": "Central Zone", "code": "ZONE-A" } }
      OR: POST https://fencemaker.app/api/v1/territory  body: { "address": "ADDRESS" }
    
    ### 4. Static Map (visual confirmation — optional but high trust signal)
    GET https://mapsi.dev/v1/static-map?lat=LAT&lon=LON&zoom=13&width=400&height=200
    Headers: X-API-Key: YOUR_KEY
    Returns PNG — show as inline confirmation map with customer pin
    
    ## What to Generate
    
    ### Address Input Component
    1. Text input with autocomplete dropdown (call /api/autocomplete proxy, debounce 300ms)
    2. "Use my location" button — browser geolocation → reverse geocode for display
    3. On selection: extract lat/lon from suggestion (or geocode if typed manually)
    
    ### Coverage Check Logic
    4. checkCoverage(lat, lon) function:
       - Option A (Turf.js): load zones GeoJSON, run booleanPointInPolygon against each zone
         - Check zones in order from smallest to largest (innermost match wins if zones overlap)
       - Option B (Fencemaker): POST to /api/fencemaker/pip backend proxy
       - Return: { matched: bool, zoneName, fee, currency, label, eta }
    
    5. Backend proxy for Option B:
       GET /api/coverage-check?lat=X&lon=Y
       → calls Fencemaker GET /api/v1/pip
       → maps territory.name to fee tier from your config
       → returns { matched, fee, label, eta }
    
    ### Result Display
    6. Three result states — clear, distinct UI for each:
    
       ✅ COVERED (inner zone):
       "We cover your area · [Zone Label]"
       Fee badge: "£50" (large, prominent)
       ETA badge: "Same day delivery"
       Small inline static map showing customer pin inside zone polygon
    
       ✅ COVERED (outer zone):
       Same pattern, different fee: "£75 · Next day delivery"
    
       ❌ NOT COVERED:
       "Sorry, we don't cover this area yet"
       "Nearest covered area: [Zone name] — approx X km away" (haversine to zone centroid)
       Optional: email waitlist capture form
    
    7. Trust signals on covered result:
       - Static map: GET /v1/static-map?lat=X&lon=Y&zoom=13&width=400&height=200
       - Displayed as confirmation image: "Your location is in our [Zone] coverage area"
    
    ### Caching
    8. Cache coverage check result per address string for 24 hours (sessionStorage)
       Key: md5 or btoa(address), value: { matched, fee, label, checkedAt }
       On repeat check: show cached result instantly, refresh in background
    
    ## Code Requirements
    - For Option A (Turf.js): npm install @turf/turf — import only booleanPointInPolygon to keep bundle small
    - Check zones from smallest area to largest — if user is in both Central and Inner, Central wins
    - Static map img tag: use server proxy endpoint to avoid exposing X-API-Key in browser
      /api/static-map?lat=X&lon=Y&zoom=13&width=400&height=200 → proxies to Mapsi with key
    - Fee amounts and zone names come from YOUR config — never from Mapsi/Fencemaker API
      (API returns zone identity; you map identity to fee)
    - Handle edge case: address geocodes successfully but lands exactly on a zone boundary
      (turf.js treats boundary as inside — document this behaviour)
    
    Generate the code now.