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README.md
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README.md
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# scrape-giant
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Small grocery-history pipeline for Giant receipts.
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Small grocery-history pipeline for Giant and Costco receipt data.
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The project currently does four things:
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This repo is still a manual, stepwise pipeline. There is no single orchestrator
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script yet. Each stage is run directly, and later stages depend on files
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produced by earlier stages.
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1. scrape Giant in-store order history from an active Firefox session
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2. enrich raw line items into a deterministic `items_enriched.csv`
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3. aggregate retailer-facing observed products and build a manual review queue
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4. create a first-pass canonical product layer plus conservative auto-links
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## What The Project Does
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The work so far is Giant-specific on the ingest side and intentionally simple on
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the shared product-model side.
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The current flow is:
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## Current flow
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1. acquire raw Giant receipt/history data
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2. enrich Giant line items into a shared enriched-item schema
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3. acquire raw Costco receipt data
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4. enrich Costco line items into the same shared enriched-item schema
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5. build observed-product, review, and canonical-product layers
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6. validate that Giant and Costco can flow through the same downstream model
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Run the commands from the repo root with the project venv active, or call them
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directly through `./venv/bin/python`.
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Raw retailer JSON remains the source of truth.
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## Current Scripts
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- `scrape_giant.py`
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Fetch Giant in-store history and order detail payloads from an active Firefox
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session.
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- `scrape_costco.py`
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Fetch Costco receipt summary/detail payloads from an active Firefox session.
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Costco currently prefers `.env` header values first, then falls back to exact
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Firefox local-storage values for session auth.
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- `enrich_giant.py`
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Parse Giant raw order JSON into `giant_output/items_enriched.csv`.
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- `enrich_costco.py`
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Parse Costco raw receipt JSON into `costco_output/items_enriched.csv`.
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- `build_observed_products.py`
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Build retailer-facing observed products from enriched rows.
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- `build_review_queue.py`
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Build a manual review queue for low-confidence or unresolved observed
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products.
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- `build_canonical_layer.py`
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Build shared canonical products and observed-to-canonical links.
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- `validate_cross_retailer_flow.py`
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Write a proof/check output showing that Giant and Costco can meet in the same
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downstream model.
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## Manual Pipeline
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Run these from the repo root with the venv active, or call them through
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`./venv/bin/python`.
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### 1. Acquire Giant raw data
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```bash
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./venv/bin/python scrape_giant.py
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```
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Inputs:
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- active Firefox session for `giantfood.com`
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- `GIANT_USER_ID` and `GIANT_LOYALTY_NUMBER` from `.env`, shell env, or prompt
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Outputs:
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- `giant_output/raw/history.json`
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- `giant_output/raw/<order_id>.json`
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- `giant_output/orders.csv`
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- `giant_output/items.csv`
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### 2. Enrich Giant data
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```bash
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./venv/bin/python scraper.py
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./venv/bin/python enrich_giant.py
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```
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Input:
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- `giant_output/raw/*.json`
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Output:
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- `giant_output/items_enriched.csv`
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### 3. Acquire Costco raw data
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```bash
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./venv/bin/python scrape_costco.py
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```
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Optional useful flags:
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```bash
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./venv/bin/python scrape_costco.py --months-back 36
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./venv/bin/python scrape_costco.py --firefox-profile-dir "C:\\Users\\you\\AppData\\Roaming\\Mozilla\\Firefox\\Profiles\\xxxx.default-release"
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```
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Inputs:
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- active Firefox session for `costco.com`
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- optional `.env` values:
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- `COSTCO_X_AUTHORIZATION`
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- `COSTCO_X_WCS_CLIENTID`
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- `COSTCO_CLIENT_IDENTIFIER`
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- if `COSTCO_X_AUTHORIZATION` is absent, the script falls back to exact Firefox
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local-storage values:
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- `idToken` -> sent as `Bearer <idToken>`
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- `clientID` -> used as `costco-x-wcs-clientId` when env is blank
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Outputs:
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- `costco_output/raw/summary.json`
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- `costco_output/raw/summary_requests.json`
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- `costco_output/raw/<receipt_id>-<timestamp>.json`
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- `costco_output/orders.csv`
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- `costco_output/items.csv`
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### 4. Enrich Costco data
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```bash
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./venv/bin/python enrich_costco.py
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```
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Input:
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- `costco_output/raw/*.json`
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Output:
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- `costco_output/items_enriched.csv`
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### 5. Build shared downstream layers
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```bash
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./venv/bin/python build_observed_products.py
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./venv/bin/python build_review_queue.py
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./venv/bin/python build_canonical_layer.py
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```
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## Inputs
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These scripts consume the enriched item files and generate the downstream
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product-model outputs.
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- Firefox cookies for `giantfood.com`
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- `GIANT_USER_ID` and `GIANT_LOYALTY_NUMBER` in `.env`, shell env, or prompts
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- Giant raw order payloads in `giant_output/raw/`
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Current outputs on disk:
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## Outputs
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- retailer-facing:
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- `giant_output/products_observed.csv`
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- `giant_output/review_queue.csv`
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- `giant_output/products_canonical.csv`
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- `giant_output/product_links.csv`
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- cross-retailer proof/check output:
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- `combined_output/products_observed.csv`
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- `combined_output/products_canonical.csv`
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- `combined_output/product_links.csv`
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- `combined_output/proof_examples.csv`
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Current generated files live under `giant_output/`:
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### 6. Validate cross-retailer flow
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- `orders.csv`: flattened visit/order rows from the Giant history API
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- `items.csv`: flattened raw line items from fetched order detail payloads
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- `items_enriched.csv`: deterministic parsed/enriched line items
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- `products_observed.csv`: retailer-facing observed product groups
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- `review_queue.csv`: products needing manual review
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- `products_canonical.csv`: shared canonical product rows
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- `product_links.csv`: observed-to-canonical links
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```bash
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./venv/bin/python validate_cross_retailer_flow.py
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```
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Raw json remains the source of truth:
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This is a proof/check step, not the main acquisition path.
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- `giant_output/raw/history.json`
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- `giant_output/raw/<order_id>.json`
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## Inputs And Outputs By Directory
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## Scripts
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### `giant_output/`
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- `scraper.py`: fetches Giant history/detail payloads and updates `orders.csv` and `items.csv`
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- `enrich_giant.py`: reads raw Giant order json and writes `items_enriched.csv`
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- `build_observed_products.py`: groups enriched rows into `products_observed.csv`
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- `build_review_queue.py`: generates `review_queue.csv` and preserves review status on reruns
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- `build_canonical_layer.py`: builds `products_canonical.csv` and `product_links.csv`
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Inputs to this layer:
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- Firefox session data for Giant
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- Giant raw JSON payloads
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## Notes on the current model
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Generated files:
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- `raw/history.json`
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- `raw/<order_id>.json`
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- `orders.csv`
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- `items.csv`
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- `items_enriched.csv`
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- `products_observed.csv`
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- `review_queue.csv`
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- `products_canonical.csv`
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- `product_links.csv`
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- Observed products are retailer-specific: Giant, Costco.
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- Canonical products are the first cross-retailer layer.
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- Auto-linking is conservative:
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exact UPC first, then exact normalized name plus exact size/unit context, then
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exact normalized name when there is no size context to conflict.
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- Fee rows are excluded from auto-linking.
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- Unknown values are left blank instead of guessed.
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### `costco_output/`
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Inputs to this layer:
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- Firefox session data for Costco
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- Costco raw GraphQL receipt payloads
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Generated files:
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- `raw/summary.json`
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- `raw/summary_requests.json`
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- `raw/<receipt_id>-<timestamp>.json`
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- `orders.csv`
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- `items.csv`
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- `items_enriched.csv`
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### `combined_output/`
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Generated by cross-retailer proof/build scripts:
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- `products_observed.csv`
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- `products_canonical.csv`
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- `product_links.csv`
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- `proof_examples.csv`
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## Notes
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- The pipeline is intentionally simple and currently manual.
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- Scraping is retailer-specific and fragile; downstream modeling is shared only
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after enrichment.
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- `summary_requests.json` is diagnostic metadata from Costco summary enumeration
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and is not a receipt payload.
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- `enrich_costco.py` skips that file and only parses receipt payloads.
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- The repo may contain archived or sample output files under `archive/`; they
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are not part of the active scrape path.
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## Verification
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Run the test suite with:
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Run the full test suite with:
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```bash
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./venv/bin/python -m unittest discover -s tests
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```
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Useful one-off rebuilds:
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Useful one-off checks:
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```bash
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./venv/bin/python scrape_giant.py --help
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./venv/bin/python scrape_costco.py --help
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./venv/bin/python enrich_giant.py
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./venv/bin/python build_observed_products.py
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./venv/bin/python build_review_queue.py
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./venv/bin/python build_canonical_layer.py
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./venv/bin/python enrich_costco.py
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```
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## Project docs
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## Project Docs
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- `pm/tasks.org`: task log and evidence
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- `pm/data-model.org`: file layout and schema decisions
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## Status
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Completed through `t1.7`:
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- Giant receipt fetch CLI
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- data model and file layout
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- Giant parser/enricher
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- observed products
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- review queue
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- canonical layer scaffold
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- conservative auto-link rules
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Next planned task is `t1.8`: add a Costco raw ingest path.
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- `pm/tasks.org`
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- `pm/data-model.org`
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- `pm/scrape-giant.org`
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