dining-places

Dining-Places Skill

A personal dining knowledge base built from a user's Google Maps export, used to give calibrated eating-out advice.

Data source: a Google Takeout export (categories Saved + Maps) delivered to the user's Drive My Drive/Takeout folder, typically on a recurring schedule. The data arrives as one or more .zip archives (e.g. takeout-…-001.zip, …-002.zip) — the CSV/JSON files live inside the zips and the parser reads them directly (no manual unzip needed).

Reality check: Google has no API for personal saved places. Takeout is the

only route, and the saved data contains place name + list + note + URL, plus

coordinates/address only for individually-saved places. Cuisine, rating,

price, and hours are added at advice-time via a Places search tool.

The two data sources (read both)

A Takeout export contains two different structures, and they barely overlap — you must use both:

Critical consequence: a neighbourhood/geo filter ("places in a given neighbourhood") only works on rows that have coordinates. The List CSVs usually have none, so the large "Want to go" / "To visit" lists are invisible to a location search unless you enrich them first. For a location-scoped request:

1. Geo-filter the coordinate-bearing places directly.
2. For List places with no coordinates, resolve them via the Places search tool

(name + any note/address) to attach coordinates, then geo-filter — so both sources feed the result.

The included parser (scripts/parse_takeout_maps.py) reads both the GeoJSON and the List CSVs and supports both the current (lowercase) and legacy (Title-case) Takeout GeoJSON schemas.

Runtime — where this works best

Step-by-step workflow

Step 1 — Get the latest export (zips in Drive)

1. List the My Drive/Takeout folder, newest first, and read each file id.

(Newly-delivered files can lag in search; if a zip is missing, ask the user for its share link and read the id from …/file/d/<id>/view.)

1. Download every zip from the newest export (there may be -001, -002, …)

and write each to a working folder. Keep them together.

1. If a cached references/saved_places.json snapshot exists and no newer export

is present, use the snapshot (fast path).

Step 2 — Parse into the knowledge base

Point the parser at the folder of zips (it extracts each internally and merges them; no manual unzip, no Saved/ folder required):

python scripts/parse_takeout_maps.py <folder> --out references/saved_places.json --dedupe
# or a single archive:  python scripts/parse_takeout_maps.py <archive>.zip

This merges every List CSV + the saved-places GeoJSON into one normalized list: name, source_list, note, address, lat, lng, maps_url. Cache the result.

Step 3 — Match the request to saved places (use BOTH sources)

Filter the knowledge base by the location in the request (home neighbourhood, or the city being travelled to). Coordinate-bearing places (the starred GeoJSON) filter directly. List places have no coordinates and must be enriched, or they are silently excluded — this is the single most common failure mode, so do not skip it.

Procedure for a location-scoped request:

1. Geo-filter the coord-bearing places against the target area (fast, free).
2. Select coord-less List places worth resolving: pre-filter by name/note

keywords against the request (cuisine, vibe, list relevance) so you resolve a small candidate set, not all of them.

1. Resolve that candidate set via the Places search tool, **location-biased to

the target area**, to attach lat/lng (and rating/cuisine/price for free).

1. Keep the ones that fall inside the area; merge with step 1.
2. Cache every newly-resolved lat/lng back into references/saved_places.json

so each place is looked up at most once — the snapshot self-completes over time.

If, after enrichment, few/no saved places match (common when travelling), say so plainly and lean on Step 4.

Optional one-time backfill: if you want every List place searchable

immediately rather than lazily, run a build-time geocoding pass over all

coord-less rows (requires a Maps/Places API key) and write the coordinates into

the snapshot. The lazy approach above needs no key and converges to the same

place.

Step 4 — Enrich + supplement (saved-first, then widen, taste-calibrated)

1. Surface saved matches first, enriched with current detail via the Places

search tool (rating, cuisine, price, area, opening status, a maps link).

1. Infer a taste profile from the saved set (cuisines, neighbourhoods, vibe)

— keep it to ~1 line.

1. Widen with fresh recommendations that fit that profile, found via Places /

web search (recent, well-reviewed), excluding anything already saved.

1. Always label provenance: ⭐ Saved vs ✨ New.

Step 5 — Respond (default output: map + table + booking links)

Lead with the bottom line, then detail by importance. Default to:

• •A map of the candidates via the map-display tool, each pin tagged ⭐ Saved /

✨ New with a one-line note.

• •A comparison table: name, ⭐/✨, cuisine, current rating, price, why.
• •A booking route per place: prefer a direct reservation link (the venue's own

page, or a reservation platform that lists it). If a reservation connector is connected and lists the venue, offer to book — but always confirm place / date / party size before submitting, and never auto-submit silently.

Adapt the body to the ask:

Booking notes

• •Most independent / neighbourhood restaurants book through their own website

or a platform like Dish Cult, SevenRooms, Tock, OpenTable, or Resy. Coverage varies a lot by country and city — verify with a quick search before claiming a venue is on any given platform.

• •Reservation platforms with thin coverage in a given area (e.g. Resy outside its

core cities) frequently don't list neighbourhood spots — fall back to the venue's own booking page rather than forcing a platform widget.

• •Booking is a side-effecting action: confirm the exact details with the user and

let them complete any login / final submit.

references/

• •saved_places.json — cached normalized snapshot (rebuilt when a newer export

lands). Personal data — git-ignored; never commit.

• •known_ids.md — optional local cache of the Drive Takeout folder id.

Personal data — git-ignored; never commit. See *.example templates.

Script reference — scripts/parse_takeout_maps.py