5.8 KiB
5.8 KiB
- [X] t1.1: harden giant receipt fetch cli (2-4 commits)
- [X] t1.2: define grocery data model and file layout (1-2 commits)
- [X] t1.3: build giant parser/enricher from raw json (2-4 commits)
- [ ] t1.4: generate observed-product layer from enriched items (2-3 commits)
- [ ] t1.5: build review queue for unresolved or low-confidence products (1-3 commits)
- [ ] t1.6: create canonical product layer and observed→canonical links (2-4 commits)
- [ ] t1.7: implement auto-link rules for easy matches (2-3 commits)
- [ ] t1.8: support costco raw ingest path (2-5 commits)
- [ ] t1.9: compute normalized comparison metrics (2-3 commits)
- [ ] t1.10: add optional llm-assisted suggestion workflow for unresolved products (2-4 commits)
[X] t1.1: harden giant receipt fetch cli (2-4 commits)
acceptance criteria
- giant scraper runs from cli with prompts or env-backed defaults for `user_id` and `loyalty`
- script reuses current browser session via firefox cookies + `curl_cffi`
- script only fetches unseen orders
- script appends to `orders.csv` and `items.csv` without duplicating prior visits
- script prints a note that giant only exposes the most recent 50 visits
notes
- keep this giant-specific
- no canonical product logic here
- raw json archive remains source of truth
evidence
- commit: `d57b9cf` on branch `cx`
- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python scraper.py –help`; verified `.env` loading via `scraper.load_config()`
- date: 2026-03-14
[X] t1.2: define grocery data model and file layout (1-2 commits)
acceptance criteria
-
decide and document the files/directories for:
- retailer raw exports
- enriched line items
- observed products
- canonical products
- product links
- define stable column schemas for each file
- explicitly separate retailer-specific parsing from cross-retailer canonicalization
notes
- this is the guardrail task so we don't make giant-specific hacks the system of record
- keep schema minimal but extensible
evidence
- commit: `42dbae1` on branch `cx`
- tests: reviewed `giant_output/raw/history.json`, one sample raw order json, `giant_output/orders.csv`, `giant_output/items.csv`; documented schemas in `pm/data-model.org`
- date: 2026-03-15
[X] t1.3: build giant parser/enricher from raw json (2-4 commits)
acceptance criteria
- parser reads giant raw order json files
- outputs `items_enriched.csv`
-
preserves core raw values plus parsed fields such as:
- normalized item name
- image url
- size value/unit guesses
- pack/count guesses
- fee/store-brand flags
- per-unit/per-weight derived price where possible
- parser is deterministic and rerunnable
notes
- do not attempt canonical cross-store matching yet
- parser should preserve ambiguity rather than hallucinating precision
evidence
- commit:
- tests: `./venv/bin/python -m unittest discover -s tests`; `./venv/bin/python enrich_giant.py`; verified `giant_output/items_enriched.csv` on real raw data
- date: 2026-03-16
[ ] t1.4: generate observed-product layer from enriched items (2-3 commits)
acceptance criteria
- distinct observed products are generated from enriched giant items
- each observed product has a stable `observed_product_id`
-
observed products aggregate:
- first seen / last seen
- times seen
- representative upc
- representative image url
- representative normalized name
- outputs `products_observed.csv`
notes
- observed product is retailer-facing, not yet canonical
- likely key is some combo of retailer + upc + normalized name
evidence
- commit:
- tests:
- date:
[ ] t1.5: build review queue for unresolved or low-confidence products (1-3 commits)
acceptance criteria
- produce a review file containing observed products needing manual review
-
include enough context to review quickly:
- raw names
- parsed names
- upc
- image url
- example prices
- seen count
- reviewed status can be stored and reused
notes
- this is where human-in-the-loop starts
- optimize for “approve once, remember forever”
evidence
- commit:
- tests:
- date:
[ ] t1.6: create canonical product layer and observed→canonical links (2-4 commits)
acceptance criteria
- define and create `products_canonical.csv`
- define and create `product_links.csv`
- support linking one or more observed products to one canonical product
-
canonical product schema supports food-cost comparison fields such as:
- product type
- variant
- size
- measure type
- normalized quantity basis
notes
- this is the first cross-retailer abstraction layer
- do not require llm assistance for v1
evidence
- commit:
- tests:
- date:
[ ] t1.7: implement auto-link rules for easy matches (2-3 commits)
acceptance criteria
- auto-link can match observed products to canonical products using deterministic rules
-
rules include at least:
- exact upc
- exact normalized name
- exact size/unit match where available
- low-confidence cases remain unlinked for review
notes
- keep the rules conservative
- false positives are worse than unresolved items
evidence
- commit:
- tests:
- date:
[ ] t1.8: support costco raw ingest path (2-5 commits)
acceptance criteria
- add a costco-specific raw ingest/export path
- output costco line items into the same shared raw/enriched schema family
-
confirm at least one product class can exist as:
- giant observed product
- costco observed product
- one shared canonical product
notes
- this is the proof that the architecture generalizes
- don’t chase perfection before the second retailer lands
evidence
- commit:
- tests:
- date:
[ ] t1.9: compute normalized comparison metrics (2-3 commits)
acceptance criteria
-
derive normalized comparison fields where possible:
- price per lb
- price per oz
- price per each
- price per count
- metrics are attached at canonical or linked-observed level as appropriate
- emit obvious nulls when basis is unknown rather than inventing values
notes
- this is where “gala apples 5 lb bag vs other gala apples” becomes possible
- units discipline matters a lot here
evidence
- commit:
- tests:
- date:
[ ] t1.10: add optional llm-assisted suggestion workflow for unresolved products (2-4 commits)
acceptance criteria
- llm suggestions are generated only for unresolved observed products
- llm outputs are stored as suggestions, not auto-applied truth
- reviewer can approve/edit/reject suggestions
- approved decisions are persisted into canonical/link files
notes
- bounded assistant, not autonomous goblin
- image urls may become useful here
evidence
- commit:
- tests:
- date: