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Catalog feature updates on column fields

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sriram
2026-09-08 15:18:29 +05:30
parent 2749bee1a3
commit 10b24c6348
60 changed files with 9224 additions and 31 deletions
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22
.env.example
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@@ -340,10 +340,24 @@ ENABLE_PER_VARIANT_IMAGES=true
PER_VARIANT_IMAGE_MAX_RESULTS=10
# Barcode Retrieval & Product Enrichment (see
# app/services/enrichment/barcode/ and docs/BARCODE_ENRICHMENT.md). Set to
# false to disable barcode lookup entirely (rows are stored with
# barcode=NULL, barcode_lookup_status="disabled").
ENABLE_BARCODE_LOOKUP=true
# app/services/enrichment/barcode/ and docs/BARCODE_ENRICHMENT.md).
#
# INLINE, per product, during ingestion. Defaults FALSE in settings.py and this
# file used to ship `true`, which disagreed with the code for as long as both
# existed - anyone copying .env.example got a very different pipeline from
# anyone relying on the defaults. It is false here now to match.
#
# Leave it false unless you know the upload is small: it costs one search
# request per product against an endpoint capped at 10 requests/minute, so a
# 200-row sheet is twenty minutes of held request. ENRICH_BARCODES_ON_UPLOAD
# below is the cheap path and is on by default.
ENABLE_BARCODE_LOOKUP=false
# BULK, per brand, after the upload settles. Fetches each brand's whole Open
# Food Facts catalogue (~5 requests) and matches offline, on the enrichment
# job's own thread. Runs before the nutrition phase, because a barcode turns a
# 0.32-confidence name lookup into a 0.95-confidence exact one.
ENRICH_BARCODES_ON_UPLOAD=true
BARCODE_LOOKUP_TIMEOUT_SECONDS=10
# 30 days, in seconds
BARCODE_LOOKUP_CACHE_TTL_SECONDS=2592000

92
app/api/routers/user_products.py
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@@ -53,6 +53,11 @@ from app.services.vector_store import (
get_products_by_brand,
)
from app.services.brand_sync import upsert_products_into_catalog_file
from app.services.enrichment.catalog_consensus import (
consensus_rows,
consensus_value,
fssai_for_brand,
)
from app.services.embeddings_service import embed_texts
from app.services.s3_service import s3_service
@@ -409,12 +414,20 @@ class _PersistOutcome:
unavailable: Optional[str] = None
# How many of a brand's rows to read when working out what they agree on.
# Bounded because this is a nicety, not the write: the largest brand table here
# holds ~250 rows, so this reads all of them for every real brand while still
# refusing to degenerate into the full `SELECT *` that once ran per uploaded
# row. One query per brand, not per row - that part is load-bearing.
_CONSENSUS_SAMPLE_LIMIT = 300
def _brand_sample(brand_parent: str) -> Dict[str, Any]:
"""The most recently updated product for a brand, used to inherit defaults.
`limit=1` matters: this used to be a full `SELECT *` of the brand table,
executed once per uploaded row. A 200-row file against a brand with a few
thousand products meant 200 full table reads before a single insert.
Kept for the fields where one arbitrary sibling is a defensible default
(category, price band, size). For `fssai_license` and `providers` it is
NOT defensible - see `_brand_defaults`.
"""
try:
existing = get_products_by_brand(brand_parent, limit=1)
@@ -424,8 +437,51 @@ def _brand_sample(brand_parent: str) -> Dict[str, Any]:
return existing[0] if existing else {}
def _brand_defaults(brand_parent: str) -> Dict[str, Any]:
"""One arbitrary sibling row, plus what the brand's rows actually AGREE on.
The distinction matters for exactly two fields.
`fssai_license` identifies the food business legally answerable for the
product. Inheriting it from one arbitrary sibling is already weak; the code
this replaces was worse - it fell back to the bare constant
"10012042000244", which is Lion Dates' real registered licence, and stamped
it onto any brand with no sample row. `scripts/merge_haldiram.py` exists
because that reached production.
`providers` is a claim about where a product can be bought. The replaced
default asserted all six of Amazon/Flipkart/BigBasket/Jiomart/Blinkit/Zepto
for every product nobody had checked.
Consensus over the brand's own rows is the honest version of both, and it
declines to answer when the rows disagree.
"""
# Two reads on purpose, and the split matters on a memory-capped host.
#
# `_brand_sample` is SELECT * limit 1 - one row, embedding and all, because
# the fields it seeds (category, price band, size) need the whole row.
#
# The consensus read is 300 rows, so it takes only the two columns it
# actually inspects. Measured: SELECT * over 244 rows costs 3.0 MB, of
# which 4.7 KB per row is an embedding string nothing here reads. Two named
# columns is roughly 50 KB for the same rows.
rows = consensus_rows(brand_parent, ["fssai_license", "providers"],
limit=_CONSENSUS_SAMPLE_LIMIT)
fssai, fssai_source = fssai_for_brand(brand_parent, rows)
providers, _why = consensus_value("providers", rows)
return {
"sample": _brand_sample(brand_parent),
"fssai_license": fssai,
"fssai_source": fssai_source,
"providers": providers,
}
def _build_product_dict(req: AddProductRequest, brand_parent: str,
sample_existing: Dict[str, Any]) -> Dict[str, Any]:
sample_existing: Dict[str, Any],
defaults: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Fill in everything the catalog needs that the user did not supply.
Pure apart from the optional S3 image lookup - no database access and no
@@ -438,8 +494,22 @@ def _build_product_dict(req: AddProductRequest, brand_parent: str,
raise ValueError(f"product name {product_name!r} has no letters or digits to identify it by")
image_id = f"{brand_slug}_{product_slug}"
defaults = defaults or {}
category = req.category or sample_existing.get("category") or "Health Foods"
fssai_license = req.fssai_license or sample_existing.get("fssai_license") or "10012042000244"
# NO CONSTANT FALLBACK HERE, EVER.
#
# This line used to end `or "10012042000244"` - Lion Dates' real registered
# FSSAI licence - so any brand without a sample row was silently attributed
# to a food business that had never heard of the product. An FSSAI number
# is who is legally answerable for what is in the packet; inventing one is
# not a cosmetic default.
#
# The order now is: what the uploader supplied, then the curated brand
# registry, then what the brand's own rows unambiguously agree on, then
# NOTHING. A blank licence is a gap someone can fill; a confidently wrong
# one is a liability nobody knows to look for.
fssai_license = req.fssai_license or defaults.get("fssai_license") or None
description = req.description or (
f"Introducing {product_name} from the trusted {brand_parent} brand. "
@@ -465,7 +535,11 @@ def _build_product_dict(req: AddProductRequest, brand_parent: str,
else:
price_range = "₹100-250"
providers = req.providers or list(sample_existing.get("providers") or ["Amazon", "Flipkart", "BigBasket", "Jiomart", "Blinkit", "Zepto"])
# Likewise no invented marketplace list. Claiming a product is stocked by
# Amazon, Flipkart, BigBasket, Jiomart, Blinkit AND Zepto because nobody
# checked is a false availability claim on every row it touches. Consensus
# across the brand's own rows, or empty.
providers = req.providers or list(defaults.get("providers") or [])
highlights = req.highlights or list(sample_existing.get("highlights") or ["100% Quality Assurance", "Authentic Brand Product"])
nutrients = req.nutrients or list(sample_existing.get("nutrients") or ["Energy - High", "Protein - Good Source"])
@@ -582,11 +656,13 @@ def _persist_products(items: List[Tuple[Optional[int], AddProductRequest]]) -> _
# row) and one embedding call for the whole upload.
built: "OrderedDict[str, List[Tuple[Optional[int], AddProductRequest, Dict[str, Any]]]]" = OrderedDict()
for brand_parent, rows in groups.items():
sample = _brand_sample(brand_parent)
defaults = _brand_defaults(brand_parent)
sample = defaults["sample"]
prepared = []
for row_number, req in rows:
try:
prepared.append((row_number, req, _build_product_dict(req, brand_parent, sample)))
prepared.append((row_number, req,
_build_product_dict(req, brand_parent, sample, defaults)))
except Exception as exc: # noqa: BLE001 - one bad row, not the file
outcome.failures.append({
"row": row_number,

38
app/core/store_catalog_pipeline.py
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@@ -91,6 +91,8 @@ from app.services.generic_products import (
)
from app.services.embeddings_service import embed_texts
from app.services.enrichment.barcode.stage import BarcodeEnrichmentStage
from app.services.enrichment.barcode.identity_stage import BarcodeIdentityStage
from app.services.enrichment.content.stage import ContentEnrichmentStage
from app.services.enrichment.hsn_gst.stage import HsnGstEnrichmentStage
from app.services.enrichment.pipeline import EnrichmentPipeline
from app.services.product_validator import validate_catalog
@@ -658,8 +660,21 @@ async def stages_8_9_enrichment(rows: List[Dict[str, Any]], brand: str) -> List[
stages = []
if ENABLE_BARCODE_LOOKUP:
stages.append(BarcodeEnrichmentStage())
# Unconditional, and deliberately not behind ENABLE_BARCODE_LOOKUP: it
# performs no lookup. It validates whatever barcode the row already has -
# which for a sheet-supplied one is the first check it ever gets - and
# derives barcode_type/gtin/ean13/upc from those digits offline. Measured
# before it existed: upc 0.0%, ean13 6.4%, gtin 8.7%, all computable from
# the barcode sitting in the same row.
stages.append(BarcodeIdentityStage())
if ENABLE_HSN_GST_ENRICHMENT:
stages.append(HsnGstEnrichmentStage())
# Also unconditional and also offline. `highlights` and `nutrients` land
# empty on EVERY upload today because this pipeline has no stage that
# generates them - only the older brand-discovery path calls the
# generators. Fills blanks only, and refuses to put nutrients on a
# non-consumable.
stages.append(ContentEnrichmentStage())
if not stages:
return rows
return await EnrichmentPipeline(stages).run(rows, brand)
@@ -736,9 +751,32 @@ def _to_storage_row(row: Dict[str, Any]) -> Dict[str, Any]:
"selling_price": row.get("selling_price"),
"barcode": row.get("barcode"),
"barcode_type": row.get("barcode_type"),
# THIS PROJECTION IS THE WHOLE POINT OF THIS FUNCTION. A key the
# enrichment stages computed but that is not named here never reaches
# the database, however correct the stage was and however many columns
# exist to hold it.
#
# That is exactly what happened to the seven fields below and to the
# HSN/GST figures: stage 8 and stage 9 computed them on every run and
# this dict silently dropped them. Measured before the fix - upc 0.0%,
# ean13 6.4%, gst_percent and tax_amount 0% of upload rows.
#
# Anything added to an enrichment stage from here on has to be added
# here AND to vector_store's INSERT, or it goes nowhere.
"gtin": row.get("gtin"),
"ean13": row.get("ean13"),
"upc": row.get("upc"),
"barcode_source": row.get("barcode_source"),
"barcode_verified": row.get("barcode_verified"),
"barcode_lookup_status": row.get("barcode_lookup_status"),
"barcode_last_updated": row.get("barcode_last_updated"),
"gst_percent": row.get("gst_percent"),
"tax_amount": row.get("tax_amount"),
"hsn_gst_needs_review": row.get("hsn_gst_needs_review"),
"highlights": list(row.get("highlights") or []),
"nutrients": list(row.get("nutrients") or []),
"search_query": search_query,
"field_sources": dict(row.get("field_sources") or {}),
}

16
app/infrastructure/settings.py
View File

@@ -425,6 +425,22 @@ ENABLE_SKU_WEB_LOOKUP = _bool("ENABLE_SKU_WEB_LOOKUP", "false")
ENABLE_BARCODE_LOOKUP = _bool("ENABLE_BARCODE_LOOKUP", "false")
ENABLE_MANUFACTURER_SITE_LOOKUP = _bool("ENABLE_MANUFACTURER_SITE_LOOKUP", "false")
# Barcodes AFTER the upload settles, in bulk, on the enrichment job's own
# thread. Default TRUE where ENABLE_BARCODE_LOOKUP above is false, and the
# difference is cost, not appetite for risk:
#
# ENABLE_BARCODE_LOOKUP = one search request PER PRODUCT, inline, against an
# endpoint capped at 10 requests/minute. A 200-row
# upload is twenty minutes of a held request.
# ENRICH_BARCODES_ON_UPLOAD = one corpus fetch PER BRAND (~5 requests total),
# matched offline, after the uploader has their
# result. Cost is per brand, not per row.
#
# It also has to run before the nutrition phase rather than beside it: a
# barcode makes the nutrition lookup exact (0.95) instead of fuzzy (0.32), and
# skip_if_verified means whichever lands first wins permanently.
ENRICH_BARCODES_ON_UPLOAD = _bool("ENRICH_BARCODES_ON_UPLOAD", "true")
# Offline/deterministic stages - safe to leave on.
ENABLE_HSN_GST_ENRICHMENT = _bool("ENABLE_HSN_GST_ENRICHMENT", "true")
ENABLE_PRODUCT_VALIDATION = _bool("ENABLE_PRODUCT_VALIDATION", "true")

29
app/services/brand_sync.py
View File

@@ -24,6 +24,7 @@ import json
import logging
import os
from collections import defaultdict
from datetime import date, datetime
from decimal import Decimal
from pathlib import Path
from typing import Any, Dict, List, Optional
@@ -83,13 +84,27 @@ def seed_catalog_paths(seed_dir: Path = SEED_DIR) -> List[Path]:
# carries them, but they do NOT round-trip back into the database - re-seeding
# ignores them, and app/services/nutrition_score_sync.py is what restores them
# from nutrition_insights, which is their source of truth.
#
# `nutrients_per_100g` is in the same category as the two scores: mirrored from
# nutrition_facts by nutrition_score_sync, exported for readers, never
# round-tripped back in.
#
# The barcode identity/provenance columns and the HSN/GST figures below ARE
# round-tripped. They were absent from this tuple for as long as they were
# absent from the INSERT, which is why scripts/backfill_barcodes_from_off.py
# refuses to call export_brand_to_seed_file() - exporting used to silently
# strip the nine barcode keys off every product. Listing them here is what
# makes that helper safe to use again.
EXPORT_COLUMNS = (
"product_name", "title", "description", "category", "image_id",
"image_url", "image_urls", "price_range", "size_variants", "providers",
"fssai_license", "product_sku", "sku_source", "hsn_code",
"final_selling_price", "selling_price", "barcode", "barcode_type",
"highlights", "nutrients", "search_query",
"nutrition_score", "health_score",
"gtin", "ean13", "upc", "barcode_source", "barcode_verified",
"barcode_lookup_status", "barcode_last_updated",
"gst_percent", "tax_amount", "hsn_gst_needs_review",
"highlights", "nutrients", "search_query", "field_sources",
"nutrition_score", "health_score", "nutrients_per_100g",
)
@@ -249,8 +264,18 @@ def _jsonable(value: Any) -> Any:
"""Coerce a psycopg row value into something json.dumps accepts."""
if isinstance(value, Decimal):
return float(value)
# `barcode_last_updated` is a TIMESTAMP column, so psycopg hands back a
# datetime, which json.dumps refuses. Emitted as an ISO-8601 string rather
# than an epoch float so the seed file stays human-readable; the DB write
# path accepts either (see vector_store._epoch_to_timestamp).
if isinstance(value, (datetime, date)):
return value.isoformat()
if isinstance(value, (list, tuple)):
return [_jsonable(v) for v in value]
# JSONB (field_sources, nutrients_per_100g) arrives as a dict; recurse so
# a Decimal nested inside a nutrient block does not break the dump.
if isinstance(value, dict):
return {k: _jsonable(v) for k, v in value.items()}
return value

117
app/services/enrichment/barcode/identity_stage.py Normal file
View File

@@ -0,0 +1,117 @@
"""Derives the rest of a product's barcode identity from the barcode itself.
WHY THIS IS A SEPARATE STAGE FROM BarcodeEnrichmentStage
--------------------------------------------------------
That stage FINDS a barcode, needs the network, and is off by default
(`ENABLE_BARCODE_LOOKUP`, see settings.py:420-423 for why). This one FINDS
NOTHING. It takes a barcode the row already has - typed into the merchant's
spreadsheet, seeded from a catalog, or just located by the cascade - and fills
in the fields that are pure arithmetic on those digits:
barcode_type from the length (classify_barcode_type)
gtin the validated digits (a GTIN is what a barcode encodes)
ean13 zero-padded UPC-A (to_ean13)
upc the digits, for UPC-A only
There is no lookup, no host, no rate limit and no failure mode beyond "these
digits are not a valid GTIN", so it needs no settings flag and costs nothing.
THE FAILURE IT ADDRESSES
Measured against production on 2026-09-08: `upc` was 0.0% filled, `ean13`
6.4%, `gtin` 8.7% - against `barcode` at 18.4%. Every one of those could
have been computed from the barcode already sitting in the same row. They
were not, because the only code that produced them was inside the disabled
network cascade, and the writer dropped them anyway.
WHY IT RUNS AFTER THE LOOKUP STAGE
So it also normalises whatever the cascade just found. The cascade already
validates, but a sheet-supplied barcode never passes through
`validate_barcode` at all today - it goes straight from the spreadsheet to
the database. This stage is the first thing that checks those digits.
WHAT IT WILL NOT DO
It will not correct, reformat or delete `barcode`. If the digits fail
checksum validation the stage returns NOTHING, leaving the merchant's value
exactly as typed - `enrichment/base.py`'s merge guard would refuse to blank
it anyway, and silently "fixing" a barcode a shop supplied would be worse
than leaving it visibly wrong. The failure is recorded in `field_sources`
so the coverage report can surface it.
"""
from __future__ import annotations
import logging
import time
from typing import Any, Dict
from app.services.enrichment.base import EnrichmentStage, StageOutcome
from app.services.enrichment.barcode.models import BarcodeType
from app.services.enrichment.barcode.validators import (
classify_barcode_type,
normalize_barcode,
to_ean13,
validate_barcode,
)
logger = logging.getLogger(__name__)
class BarcodeIdentityStage(EnrichmentStage):
"""Offline, deterministic, additive. Never raises, never erases."""
name = "barcode_identity"
async def enrich_one(self, product: Dict[str, Any], brand: str) -> StageOutcome:
raw = product.get("barcode")
if not str(raw or "").strip():
return StageOutcome(stage_name=self.name, fields={})
code = validate_barcode(raw)
if not code:
# Not a GTIN. Say so in the provenance rather than in the data, and
# leave `barcode` untouched.
digits = normalize_barcode(raw)
reason = (f"{len(digits)} digits is not a GTIN-8/12/13/14 length"
if digits else "no digits in the value")
return StageOutcome(
stage_name=self.name,
fields={"field_sources": {"barcode": {
"method": "unvalidated",
"source": product.get("barcode_source") or "sheet",
"note": f"failed checksum/format validation: {reason}",
}}},
error=f"barcode {raw!r} failed validation: {reason}",
)
barcode_type = classify_barcode_type(code)
fields: Dict[str, Any] = {
"barcode": code, # normalised digits, same value
"barcode_type": barcode_type.value,
"gtin": code,
"ean13": to_ean13(code), # None for GTIN-8, which is not a short EAN-13
"upc": code if barcode_type is BarcodeType.UPC_A else None,
}
# Only claim provenance we can stand behind. A barcode that arrived on
# the sheet is the merchant's assertion, not ours, and is emphatically
# not "verified" - that word is reserved for the cascade's
# brand+size+name-matched result.
if not str(product.get("barcode_source") or "").strip():
fields["barcode_source"] = "sheet"
fields["barcode_lookup_status"] = "sheet_validated"
fields["barcode_verified"] = False
fields["barcode_last_updated"] = time.time()
fields["field_sources"] = {
"barcode": {
"method": "sourced" if product.get("barcode_verified") else "asserted",
"source": product.get("barcode_source") or "sheet",
},
# These four are arithmetic on the barcode, never a lookup. Calling
# them "sourced" would overstate them.
"gtin": {"method": "derived", "source": "validators.validate_barcode"},
"ean13": {"method": "derived", "source": "validators.to_ean13"},
"upc": {"method": "derived", "source": "validators.classify_barcode_type"},
"barcode_type": {"method": "derived", "source": "validators.classify_barcode_type"},
}
return StageOutcome(stage_name=self.name, fields=fields)

105
app/services/enrichment/barcode/matching.py
View File

@@ -114,9 +114,74 @@ def name_similarity(candidate_title: str, target_title: str) -> float:
return round((overlap * 0.6) + (seq_ratio * 0.4), 3)
def name_is_contained(candidate_title: str, target_title: str,
target_brand: str = "") -> bool:
"""True when the candidate's name is our name with only brand/size removed.
WHY THIS EXISTS - measured, not theoretical
Open Food Facts stores short product names. We store long ones. Running
`backfill_nutrition_from_barcodes` over the catalog on 2026-09-08, 149
of 300 barcoded rows were rejected as "found, wrong product" when the
barcode had resolved perfectly:
"Nestle Munch 8.9g" -> OFF "Munch" similarity 0.332
"Coca-Cola Maaza 750ml" -> OFF "Maaza" similarity 0.304
"Cadbury Perk 22 g" -> OFF "Perk" similarity 0.302
`name_similarity` divides the token overlap by the TARGET's token
count, so a one-token candidate against a three-token target cannot
exceed ~0.33 however right it is.
WHY NOT JUST LOWER THE THRESHOLD
Because the same run also correctly rejected:
"Pepsico Lays 1kg" -> OFF "Spanish tomato tango" 0.133
"Coca-Cola Fanta 750ml" -> OFF "Orange" 0.089
"Lion Dates Powder 100g" -> OFF "PEPER NOTEN" 0.097
Those sit BELOW the containment cases but a threshold low enough to
admit 0.30 also admits them. The measured yield table at
settings.py:449-477 raised this floor to 0.78 for exactly that reason.
Containment separates the two groups on structure rather than on a
number: "Munch" is every token of our name minus brand and size;
"Orange" is not a subset of "Coca-Cola Fanta 750ml" at all.
THE RULE
Every token of the candidate's name must appear in the target's, once
brand tokens and size tokens are discounted, and the candidate must
carry at least one token that is not the brand. A bare brand name
("Colgate", "godrej" - both real OFF titles) therefore does NOT match,
which matters because those would otherwise attach to every product of
that brand.
"""
cand_tokens = _tokens(candidate_title)
target_tokens = _tokens(target_title)
if not cand_tokens or not target_tokens:
return False
brand_tokens = _tokens(target_brand)
# A candidate that is only the brand identifies a brand, not a product.
if not (cand_tokens - brand_tokens):
return False
# Size tokens are not identity: our title carries the pack size, OFF's
# usually does not, and `size_matches` has already checked the size
# separately by the time this is consulted.
def _meaningful(tokens):
return {t for t in tokens if not _SIZE_TOKEN_RE.fullmatch(t)}
return _meaningful(cand_tokens) <= _meaningful(target_tokens | brand_tokens)
# A token that is purely a quantity ("750ml", "8", "9g", "1kg"). Size is
# compared by `size_matches`, so it must not also decide name identity.
_SIZE_TOKEN_RE = re.compile(r"\d+(?:\.\d+)?(?:g|kg|ml|l|mg|cl|oz|gm|ltr|pcs|n)?", re.I)
def is_match(candidate: BarcodeCandidate, target_brand: str, target_title: str, target_size: str,
brand_aliases: Optional[Iterable[str]] = None,
min_name_similarity: float = 0.45) -> tuple[bool, float]:
min_name_similarity: float = 0.45,
barcode_is_identity: bool = False) -> tuple[bool, float]:
"""The combined gate a candidate must pass to be accepted:
1. Brand matches (or overlaps a known alias).
2. Pack size matches within a tight tolerance.
@@ -126,16 +191,52 @@ def is_match(candidate: BarcodeCandidate, target_brand: str, target_title: str,
product line from the same brand.
Returns (matched, confidence) - confidence is diagnostic only, stored
on the result for audit/QA but never used to override rule 1-3.
`barcode_is_identity` says the caller already knows WHICH product this is,
because it looked the candidate up BY its GTIN rather than by searching.
That changes what rules 2 and 4 are for: they stop being evidence of
identity and become sanity checks against our barcode being on the wrong
row. A sanity check cannot fail on information the source does not have, so
under this flag:
* rule 2 (size) - a BLANK candidate size no longer vetoes. Open Food
Facts leaves `quantity` null on a large share of records (57 of 146
Amul hits), and `size_matches` returns False whenever either side is
blank. A record with no quantity does not disagree with our pack size;
it says nothing about it. A quantity that is PRESENT and different
still vetoes - that is our barcode pointing at the wrong pack.
* rule 4 (name) - see `name_is_contained`.
Rules 1 and 3 are unaffected: a different brand, or a "sugar free" the
target does not have, still means a different product.
It defaults to False because every relaxation here is unsafe on the SEARCH
path, where many candidates compete and name and size are the only things
telling them apart - "Munch" with no size would match every Nestle product
containing that word. Pass True only where a single candidate was fetched
by barcode. Today that is `fetch_verified_nutrition_by_barcode` and
`scripts/backfill_nutrition_from_barcodes`, and nothing else.
Measured on 2026-09-08: of 300 barcoded catalog rows, 149 were refused as
"found, wrong product" with the barcode resolving perfectly. The name gate
was the visible symptom, but the SIZE gate rejected most of them first.
"""
if not brand_matches(candidate.candidate_brand, target_brand, brand_aliases):
return False, 0.0
if not size_matches(candidate.candidate_size, target_size):
# A blank candidate size is missing information, not a disagreement - but
# only when the barcode already established identity. On the search path a
# sizeless candidate is genuinely unidentifiable and must still be refused.
size_unknown = barcode_is_identity and not str(candidate.candidate_size or "").strip()
if not size_unknown and not size_matches(candidate.candidate_size, target_size):
return False, 0.0
if has_conflicting_variant_terms(candidate.candidate_title, target_title):
return False, 0.0
similarity = name_similarity(candidate.candidate_title, target_title)
if similarity < min_name_similarity:
if barcode_is_identity and name_is_contained(
candidate.candidate_title, target_title, target_brand):
return True, similarity
return False, similarity
return True, similarity

19
app/services/enrichment/base.py
View File

@@ -88,6 +88,25 @@ class EnrichmentStage(ABC):
# HsnGstEnrichmentStage and BarcodeEnrichmentStage.
if outcome.fields:
for key, value in outcome.fields.items():
# `field_sources` ACCUMULATES; every other key is assigned.
#
# It is a map keyed by column name, and each stage knows the
# provenance of only the columns it filled. Assigning it like
# anything else would mean the last stage to run erases what
# every earlier stage recorded - so the barcode stage's
# provenance would vanish the moment the HSN stage ran, and
# the coverage report would show values with no origin.
#
# A shallow merge is the right depth: each key's value is one
# flat record about one column. This mirrors the `||` in
# vector_store's ON CONFLICT clause, so the in-memory merge
# and the database merge agree.
if key == "field_sources" and isinstance(value, dict):
merged = dict(product.get("field_sources") or {})
merged.update(value)
product["field_sources"] = merged
continue
blank_incoming = value is None or (isinstance(value, str) and not value.strip())
existing = product.get(key)
held = existing is not None and not (isinstance(existing, str) and not existing.strip())

142
app/services/enrichment/catalog_consensus.py Normal file
View File

@@ -0,0 +1,142 @@
"""Fills a blank field from what the brand's OWN rows already agree on.
WHY THIS EXISTS
`fssai_license` was 69.3% filled on 2026-09-08, sourced entirely from a
hardcoded 34-brand map (`brand_registry.FSSAI_LICENSES`). A brand outside
that map got nothing - except on one path, which got something far worse.
`user_products._build_product_dict` read:
fssai_license = req.fssai_license or sample_existing.get(...) or "10012042000244"
That constant is LION DATES' real, registered FSSAI licence. Any brand with
no sample row was stamped with it. This is not a cosmetic default: an FSSAI
number identifies the food business legally answerable for the product, and
inventing one attributes a stranger's regulatory liability to a product they
never made. `scripts/merge_haldiram.py:36` exists because this already
reached production once, on `brand_haldirams`.
The honest source for a blank licence is the brand's own catalog: 400
Britannia rows carrying one licence is good evidence for the 401st. That is
what this module reads.
THE RULE IT ENFORCES
Propagate only from UNAMBIGUOUS agreement. If a brand's rows carry two
different licences, one of them is already wrong and this module returns
None rather than picking. A blank field is a gap; a confidently wrong
regulatory identifier is a liability.
Nothing here invents a value. Every result is a value already present on a
row of the same brand, which is why the provenance method is
`catalog_consensus` and never `sourced`.
"""
from __future__ import annotations
import logging
from collections import Counter
from typing import Any, Dict, List, Optional, Tuple
logger = logging.getLogger(__name__)
# A single dissenting row should not veto 400 agreeing ones, but a genuine
# split must. Set so that "399 of 400 agree" propagates and "60/40" does not.
_MIN_AGREEMENT = 0.85
# Below this many populated rows there is no consensus to speak of, only a
# coincidence. Two rows agreeing proves nothing about a third.
_MIN_ROWS = 3
def _modal(values: List[Any], min_agreement: float = _MIN_AGREEMENT,
min_rows: int = _MIN_ROWS) -> Tuple[Optional[Any], Dict[str, Any]]:
"""The one value the population agrees on, or None with the reason why."""
populated = [v for v in values if v not in (None, "", [], {})]
if len(populated) < min_rows:
return None, {"reason": "too few populated rows", "rows": len(populated)}
# Lists (providers) are unhashable; compare them as ordered tuples.
keyed = [tuple(v) if isinstance(v, list) else v for v in populated]
counts = Counter(keyed)
winner, hits = counts.most_common(1)[0]
agreement = hits / len(keyed)
if agreement < min_agreement:
return None, {"reason": "no clear majority", "agreement": round(agreement, 3),
"distinct": len(counts)}
return (list(winner) if isinstance(winner, tuple) else winner), {
"agreement": round(agreement, 3), "rows": len(keyed)}
def consensus_value(column: str, rows: List[Dict[str, Any]],
min_agreement: float = _MIN_AGREEMENT
) -> Tuple[Optional[Any], Dict[str, Any]]:
"""The agreed value of `column` across `rows`, plus why it was or was not
reached. Never raises: an unreadable row set yields (None, reason)."""
try:
return _modal([r.get(column) for r in rows], min_agreement=min_agreement)
except Exception as e: # pragma: no cover - defensive
logger.debug("consensus for %s failed: %s", column, e)
return None, {"reason": f"error: {e}"}
def consensus_rows(brand: str, columns: List[str], limit: int = 300) -> List[Dict[str, Any]]:
"""Read only the columns consensus needs, for a brand's rows.
Deliberately NOT `get_products_by_brand`, which is `SELECT *` and therefore
carries the 384-dimension embedding on every row. Measured against
production: 244 Hindustan Unilever rows cost 3.0 MB that way, 4.7 KB of the
7.2 KB per row being an embedding string nothing here looks at.
Two named columns bring the same read down to roughly 50 KB. On a backend
container capped at 2560 MB that difference is not dangerous either way -
it is just the difference between reading what is needed and reading
everything, once per brand per upload.
Probes `information_schema` first, because the column set genuinely differs
between brand tables and a missing column would otherwise raise.
"""
from app.services.vector_store import _connect, _sanitize_name
conn = _connect()
if conn is None:
return []
table = f"brand_{_sanitize_name(brand)}"
try:
with conn.cursor() as cur:
cur.execute(
"SELECT column_name FROM information_schema.columns "
"WHERE table_schema = 'public' AND table_name = %s",
(table,),
)
present = {r[0] for r in cur.fetchall()}
wanted = [c for c in columns if c in present]
if not wanted:
return []
select = ", ".join(f'"{c}"' for c in wanted)
cur.execute(f'SELECT {select} FROM "{table}" LIMIT %s', (limit,))
return [dict(zip(wanted, row)) for row in cur.fetchall()]
except Exception as e: # noqa: BLE001 - defaults are a nicety, not the write
logger.debug("consensus read failed for %s: %s", brand, e)
return []
finally:
conn.close()
def fssai_for_brand(brand: str, rows: Optional[List[Dict[str, Any]]] = None) -> Tuple[Optional[str], str]:
"""The licence to use for a new product of `brand`, and where it came from.
Order: the curated registry map, then the brand's own rows. Never a
constant, never another brand's number.
"""
from app.services.brand_registry import get_fssai_license
mapped = get_fssai_license(brand)
if mapped:
return mapped, "brand_registry"
if rows:
value, _why = consensus_value("fssai_license", rows)
if value:
return str(value), "catalog_consensus"
return None, "unknown"

1
app/services/enrichment/content/__init__.py Normal file
View File

@@ -0,0 +1 @@
"""Offline content enrichment - the display columns the store pipeline left blank."""

112
app/services/enrichment/content/stage.py Normal file
View File

@@ -0,0 +1,112 @@
"""Fills `highlights` and `nutrients` for rows the store pipeline leaves empty.
THE FAILURE THIS ADDRESSES
`catalog_engine.generate_product_highlights` and `generate_nutrients_info`
have existed for a long time and `brand_discovery._build_product` calls
both. The store-catalog pipeline never did: `_to_storage_row` simply passed
whatever the sheet had through, so a colleague's upload - which carries
neither column - landed `highlights=[]` and `nutrients=[]` on every row.
That is the whole reason those two columns look healthy in aggregate
(95.3% / 69.7% on 2026-09-08) while being empty for exactly the rows this
work is about.
WHAT IT WRITES, AND HOW HONESTLY
`highlights` is marketing copy derived from fields we already hold - the
category, the pack size, the brand. It is `derived`, never `sourced`.
`nutrients` is the display list. Where real per-100g figures exist,
`nutrition_score_sync.sync_nutrients_to_brand_tables` renders them from
`nutrition_facts` and overwrites whatever this stage wrote - that mirror is
the better source and runs later. This stage only supplies the
category-keyword fallback, flagged `estimated`, so a row is not blank while
it waits for a nutrition lookup that may never succeed.
THE CONSUMABILITY GATE
`generate_nutrients_info` works off category keywords, so a Hair Care row
whose category or description happens to contain a matching word acquires
entries like "Vitamin B Complex - Energy". Shampoo has no nutrients. This
stage refuses to write the column at all for a non-consumable, which is the
same gate `nutrition_data_service` applies on the lookup path and the same
reason `purge_non_consumable_nutrition.py` had to exist.
"""
from __future__ import annotations
import logging
from typing import Any, Dict, List
from app.services.enrichment.base import EnrichmentStage, StageOutcome
logger = logging.getLogger(__name__)
class ContentEnrichmentStage(EnrichmentStage):
"""Offline, deterministic, fills blanks only. Never raises, never erases."""
name = "content"
async def enrich_one(self, product: Dict[str, Any], brand: str) -> StageOutcome:
# Imported lazily: catalog_engine pulls in the image and LLM services,
# and this stage runs inside ingestion where those are already loaded
# but the enrichment package on its own should not require them.
from app.core.catalog_engine import (
generate_nutrients_info,
generate_product_highlights,
)
from app.services.consumability import is_non_consumable
fields: Dict[str, Any] = {}
sources: Dict[str, Any] = {}
title = product.get("title") or product.get("product_name") or ""
category = product.get("category") or ""
if not _has_entries(product.get("highlights")):
try:
highlights = generate_product_highlights(product, brand)
except Exception as e: # never abort a row
logger.debug("highlight generation failed for %r: %s", title, e)
highlights = []
if highlights:
fields["highlights"] = highlights
sources["highlights"] = {"method": "derived",
"source": "catalog_engine.generate_product_highlights"}
if not _has_entries(product.get("nutrients")):
if is_non_consumable(category, title):
# Not a gap - a column that cannot apply. Recording it stops
# the coverage report counting shampoo as missing nutrition
# forever, which is what makes someone eventually fabricate it.
sources["nutrients"] = {"method": "not_applicable",
"source": "non_consumable_product"}
else:
try:
nutrients = generate_nutrients_info(product, brand)
except Exception as e:
logger.debug("nutrient generation failed for %r: %s", title, e)
nutrients = []
if nutrients:
fields["nutrients"] = nutrients
sources["nutrients"] = {
"method": "estimated",
"source": "catalog_engine.generate_nutrients_info",
"note": "category keywords; replaced by real per-100g "
"figures when a nutrition lookup succeeds",
}
if sources:
fields["field_sources"] = sources
return StageOutcome(stage_name=self.name, fields=fields)
def _has_entries(value: Any) -> bool:
"""True when the column already carries something worth keeping.
A list of empty strings counts as empty: the spreadsheet parser produces
those from a column that exists but has no value in it, and treating one as
"already filled" is how a row keeps `['']` forever.
"""
if not isinstance(value, (list, tuple)):
return bool(value)
return any(str(v).strip() for v in value)

23
app/services/enrichment/pipeline.py
View File

@@ -60,6 +60,17 @@ def _build_default_stages() -> List[EnrichmentStage]:
except Exception as e:
logger.error(f"Barcode enrichment stage unavailable: {e}")
# Runs AFTER the lookup so it normalises whatever that found, and runs at
# all even when the lookup is disabled - which is the point. It derives
# barcode_type/gtin/ean13/upc from a barcode the row already has, offline
# and for free, so a sheet-supplied barcode finally gets validated and
# expanded instead of going straight to the database unchecked.
try:
from app.services.enrichment.barcode.identity_stage import BarcodeIdentityStage
stages.append(BarcodeIdentityStage())
except Exception as e:
logger.error(f"Barcode identity stage unavailable: {e}")
# HSN / GST & pricing enrichment (see app/services/enrichment/hsn_gst/) -
# deterministic, offline, pure-additive. Runs AFTER the barcode stage so
# every stored/exported row carries both sets of fields; a failure here
@@ -70,9 +81,15 @@ def _build_default_stages() -> List[EnrichmentStage]:
except Exception as e:
logger.error(f"HSN/GST enrichment stage unavailable: {e}")
# Future stages register here, e.g.:
# from app.services.enrichment.nutrition.stage import NutritionEnrichmentStage
# stages.append(NutritionEnrichmentStage())
# Offline display columns. Registered last so the nutrients fallback it
# writes is the lowest-priority source: the real per-100g figures mirrored
# by nutrition_score_sync overwrite it whenever a lookup succeeds.
try:
from app.services.enrichment.content.stage import ContentEnrichmentStage
stages.append(ContentEnrichmentStage())
except Exception as e:
logger.error(f"Content enrichment stage unavailable: {e}")
return stages

254
app/services/enrichment/post_ingest_barcodes.py Normal file
View File

@@ -0,0 +1,254 @@
"""Finds barcodes for freshly-ingested rows, in bulk, before nutrition runs.
WHY THIS RUNS BEFORE THE NUTRITION JOB, NOT ALONGSIDE IT
--------------------------------------------------------
Ordering here is a correctness property, not a preference.
`fetch_verified_nutrition_by_barcode` matches on the GTIN and returns at
confidence 0.95. The name search it falls back to accepts at a minimum of 0.32.
The nutrition job runs with `skip_if_verified=True`, so whichever path lands
first WINS PERMANENTLY - a 0.32 name match blocks the 0.95 barcode match from
ever being attempted. Two jobs racing would produce exactly that, silently, and
the catalog would end up with the worse of two available answers.
So this is a phase inside the same job, ahead of the nutrition phases.
WHY BULK, NOT THE PER-PRODUCT CASCADE
-------------------------------------
The per-product search endpoint Open Food Facts exposes is capped at 10
requests per minute. A 200-row upload is twenty minutes of waiting, which is
why `ENABLE_BARCODE_LOOKUP` defaults false (settings.py:420-423) and why the
inline stage stays off.
`off_bulk.fetch_brand_corpus` fetches a brand's ENTIRE Open Food Facts
catalogue in about five requests and matches offline against it. A brand is a
brand whether it has 3 rows or 300, so the cost is per brand, not per product.
That is what makes barcode enrichment affordable on the shared host at all.
It also uses `off_bulk.score_candidates` rather than the live matcher, because
that scorer already fixes two measured flaws: `matching.name_similarity` is
asymmetric ("Butter milk amul" vs "Amul Butter" scores 0.882 one way and 0.418
the other), and `matching.size_matches` vetoes any candidate with a blank size
when 57 of 146 Amul OFF records have `quantity: null`.
THE THRESHOLD IS 0.88, NOT 0.78
-------------------------------
`BARCODE_MIN_NAME_SIMILARITY` (0.78) is the floor for the REVERSE direction,
where a barcode has already established identity and the name is a sanity
check. This is the forward direction: many candidates compete and the name
carries the whole decision. `scripts/backfill_barcodes_from_off.py` measured
0.88 as the safe auto-apply point and 0.70-0.88 as review-only, and this reuses
that number rather than inventing one.
WHAT IT WRITES
barcode, barcode_type, gtin, ean13, barcode_source, barcode_verified=False,
barcode_lookup_status='name_matched', barcode_last_updated, and the
field_sources record - via targeted UPDATEs that pin the row's current
value, never via upsert_brand_products.
WHY NOT THE UPSERT
`get_products_by_brand` is `SELECT *`, so a row's `embedding` comes back as
a pgvector string, and `upsert_brand_products` only accepts a list - it
would write NULL and destroy the embedding. Targeted UPDATEs also cannot
clobber a concurrent write.
WHAT IT WILL NOT DO
It never overwrites a barcode a row already holds. A merchant typing one in
is holding the pack; nothing found by name similarity outranks that.
"""
from __future__ import annotations
import logging
import time
from typing import Any, Dict, Iterable, List, Optional, Tuple
from psycopg.types.json import Json
from app.services.enrichment.barcode.validators import (
classify_barcode_type,
to_ean13,
validate_barcode,
)
from app.services.vector_store import _connect, _sanitize_name
logger = logging.getLogger(__name__)
# Matches scripts/backfill_barcodes_from_off.py, which measured it.
DEFAULT_MIN_SIMILARITY = 0.88
BARCODE_SOURCE = "openfoodfacts_bulk (search.openfoodfacts.org)"
def _rows_needing_a_barcode(cur, table: str) -> List[Dict[str, Any]]:
"""Rows with no usable barcode. Probes the column list first, because a
table written before the schema migration may still lack the newer ones."""
cur.execute(
"SELECT column_name FROM information_schema.columns "
"WHERE table_schema = 'public' AND table_name = %s",
(table,),
)
present = {r[0] for r in cur.fetchall()}
if not {"id", "product_name", "barcode"} <= present:
return []
size = "size" if "size" in present else "NULL AS size"
cur.execute(
f'SELECT id, product_name, title, {size}, category, barcode '
f'FROM "{table}" '
f"WHERE barcode IS NULL OR btrim(barcode) = ''"
)
return [{"id": r[0], "product_name": r[1], "title": r[2], "size": r[3],
"category": r[4], "barcode": r[5]} for r in cur.fetchall()]
def enrich_brand_barcodes(brand: str, *, min_similarity: float = DEFAULT_MIN_SIMILARITY,
dry_run: bool = False,
progress_cb=None) -> Dict[str, int]:
"""Fill blank barcodes for one brand from its Open Food Facts corpus.
Never raises: a brand whose corpus cannot be fetched reports zero and the
caller moves to the next one. Enrichment is best-effort by contract.
"""
from app.services.enrichment.barcode.sources.off_bulk import (
brand_tokens,
fetch_brand_corpus,
score_candidates,
)
stats = {"candidates": 0, "matched": 0, "written": 0, "rejected": 0}
# The same refusal `store_catalog_pipeline.stages_8_9_enrichment` makes for
# the inline stages, for the same reason: a barcode identifies a
# manufactured article and the Own Products bucket is loose produce - an
# apple, a bunch of coriander. There is no GTIN to find, and a name match
# against some packaged product's corpus could only attach the wrong one.
from app.services.generic_products import OWN_PRODUCTS_BRAND
if brand == OWN_PRODUCTS_BRAND:
return stats
table = f"brand_{_sanitize_name(brand)}"
conn = _connect()
if conn is None:
return stats
try:
with conn.cursor() as cur:
rows = _rows_needing_a_barcode(cur, table)
if not rows:
return stats
stats["candidates"] = len(rows)
try:
# Returns the hit LIST directly (the on-disk cache file wraps it in
# a "hits" key; the function unwraps it). About five requests for a
# whole brand, then served from disk on later runs.
hits = fetch_brand_corpus(brand) or []
except Exception as e: # noqa: BLE001
logger.warning("OFF corpus unavailable for %s: %s", brand, e)
return stats
if not hits:
logger.info("Open Food Facts holds no India catalogue for %s", brand)
return stats
# Stripped from both sides before names are compared, so "Hindustan
# Unilever Hul Lux" reduces to "lux" on our side and matches OFF's
# "Lux". Computed once per brand, not once per row.
drop = brand_tokens(brand)
for index, row in enumerate(rows):
if progress_cb:
progress_cb(index, len(rows))
title = row.get("title") or row.get("product_name") or ""
try:
scored = score_candidates(
hits, title, [row.get("size") or ""], drop,
review_min=min_similarity,
)
except Exception as e: # noqa: BLE001
logger.debug("scoring failed for %r: %s", title, e)
continue
# score_candidates already drops anything under review_min, so the
# first entry is the best acceptable one. The explicit re-check is
# kept because the ordering contract is "best first", not "all
# above the floor" - relying on the filter alone would silently
# break if that ever changed.
best = scored[0] if scored else None
if not best or best.score < min_similarity:
stats["rejected"] += 1
continue
code = validate_barcode(getattr(best, "barcode", None))
if not code:
stats["rejected"] += 1
continue
stats["matched"] += 1
if dry_run:
continue
kind = classify_barcode_type(code)
sources = {
"barcode": {"method": "sourced", "source": BARCODE_SOURCE,
"confidence": round(float(best.score), 3),
"note": "matched on name against the brand's OFF "
"catalogue; not verified against the pack"},
"gtin": {"method": "derived", "source": "validators.validate_barcode"},
"ean13": {"method": "derived", "source": "validators.to_ean13"},
}
try:
with conn.cursor() as cur:
cur.execute(
f'UPDATE "{table}" SET barcode = %s, barcode_type = %s, '
f"gtin = %s, ean13 = %s, barcode_source = %s, "
f"barcode_verified = FALSE, barcode_lookup_status = %s, "
f"barcode_last_updated = NOW(), "
f"field_sources = COALESCE(field_sources, '{{}}'::jsonb) "
f" || %s::jsonb, "
f"updated_at = CURRENT_TIMESTAMP "
f"WHERE id = %s "
f" AND (barcode IS NULL OR btrim(barcode) = '')",
(code, kind.value, code, to_ean13(code), BARCODE_SOURCE,
"name_matched", Json(sources), row["id"]),
)
stats["written"] += cur.rowcount
conn.commit()
except Exception as e: # noqa: BLE001
conn.rollback()
logger.warning("barcode write failed for %s id=%s: %s",
table, row["id"], e)
finally:
conn.close()
return stats
def enrich_barcodes_for_brands(brands: Iterable[str], *,
min_similarity: float = DEFAULT_MIN_SIMILARITY,
dry_run: bool = False,
progress_cb=None) -> Dict[str, Dict[str, int]]:
"""Run `enrich_brand_barcodes` over several brands, one at a time.
Deliberately sequential. `EnrichmentPipeline`'s five-way concurrency is for
per-row work against a local corpus; firing five brand-corpus fetches at
Open Food Facts at once is how a shared host earns a rate limit.
"""
out: Dict[str, Dict[str, int]] = {}
names = [b.strip() for b in brands if b and b.strip()]
for i, brand in enumerate(names):
if progress_cb:
progress_cb(i, len(names))
try:
stats = enrich_brand_barcodes(brand, min_similarity=min_similarity,
dry_run=dry_run)
except Exception as e: # noqa: BLE001
logger.warning("barcode enrichment failed for %s: %s", brand, e)
continue
if stats.get("candidates"):
out[brand] = stats
logger.info("%s: %d without a barcode, %d matched, %d written",
brand, stats["candidates"], stats["matched"], stats["written"])
return out

28
app/services/nutrition_autoenrich.py
View File

@@ -52,6 +52,34 @@ def run_enrich_job(job_id: str, *, label: str = "", **enrich_kwargs: Any) -> Non
def progress_cb(done: int, total: int) -> None:
nutrition_job_store.update(job_id, processed=done, total=total)
# PHASE 1 - BARCODES, BEFORE ANY NUTRITION LOOKUP.
#
# The ordering is a correctness property. `fetch_verified_nutrition_by_barcode`
# matches on the GTIN and returns confidence 0.95; the name search it falls
# back to accepts at 0.32. Because the nutrition phase runs with
# skip_if_verified=True, whichever lands first wins permanently - so a name
# match obtained before the barcode exists blocks the far better barcode
# match from ever being tried.
#
# It is also cheap: one brand corpus (~5 requests) instead of one search per
# product against an endpoint capped at 10 requests/minute.
#
# Guarded separately and never fatal: a barcode phase that fails must still
# leave the nutrition phase to do what it always did.
brands_for_barcodes = enrich_kwargs.get("brands") or []
if brands_for_barcodes and settings.ENRICH_BARCODES_ON_UPLOAD:
try:
nutrition_job_store.update(job_id, detail="Finding barcodes")
from app.services.enrichment.post_ingest_barcodes import (
enrich_barcodes_for_brands,
)
found = enrich_barcodes_for_brands(brands_for_barcodes)
written = sum(s.get("written", 0) for s in found.values())
if written:
logger.info("Barcode phase wrote %d barcode(s) before scoring", written)
except Exception as e: # noqa: BLE001
logger.warning("Barcode phase failed (nutrition still runs): %s", e)
try:
result = nutrition_enrichment_service.enrich_all_products(
progress_cb=progress_cb, **enrich_kwargs)

8
app/services/nutrition_data_service.py
View File

@@ -545,9 +545,17 @@ def fetch_verified_nutrition_by_barcode(
candidate_size=product.get("quantity") or "",
candidate_countries=",".join(product.get("countries_tags") or []),
)
# barcode_is_identity=True is safe HERE and nowhere else. The barcode has
# already established which single product this is - there are no competing
# candidates to disambiguate - so the name check is a guard against our
# barcode being wrong, not the evidence for identity. Open Food Facts
# stores short names ("Munch", "Maaza") where we store long ones, and
# without this 149 of 300 barcoded rows were rejected as "wrong product"
# when the barcode had resolved perfectly. See matching.name_is_contained.
matched, similarity = is_match(
candidate, brand, title, size,
min_name_similarity=min_name_similarity,
barcode_is_identity=True,
)
if not matched:
logger.info(

63
app/services/nutrition_enrichment_service.py
View File

@@ -49,6 +49,53 @@ class EnrichmentResult:
duration_seconds: float = 0.0
def _score_from_existing_facts(brand: str, image_id: str,
facts: Dict[str, Any]) -> bool:
"""Compute and store insights from facts already held. No network.
Exists because the two ways a `nutrition_facts` row can be written disagree
about whose job scoring is. `enrich_one_product` fetches and scores in one
pass; `scripts/backfill_nutrition_from_barcodes` and its siblings write
facts only, on purpose, because they are narrow repair tools that should
not also be recomputing narratives. Nothing then closed the gap.
Returns True when it wrote something. A row that already carries a score is
left alone, so this is idempotent and safe to call on every skip.
"""
try:
existing = nutrition_db.get_nutrition_insights(brand, image_id)
if existing and existing.get("nutrition_score") is not None:
return False
scores = nutrition_scoring.compute_scores(facts)
if not scores:
# `compute_scores` returns None rather than fabricating when there
# is nothing scoreable. Respect that - do not write a zero.
return False
positive = nutrition_scoring.generate_positive_insights(facts)
cautions = nutrition_scoring.generate_cautions(facts)
allergens = nutrition_scoring.normalize_allergens(facts)
insights: Dict[str, Any] = {
"brand": brand, "image_id": image_id,
"positive_insights": positive, "nutritional_cautions": cautions,
# No narrative: generating one is an LLM call, and this path exists
# precisely to avoid doing expensive work for a row that already
# has its data. The admin /enrich run fills narratives in later.
"ai_summary": (existing or {}).get("ai_summary", ""),
"diet_tags": nutrition_scoring.classify_diet_tags(facts),
"allergens": allergens,
"allergen_source": (facts.get("data_source") or "unavailable") if allergens else "unavailable",
"data_status": facts.get("data_status"),
}
insights.update(scores)
nutrition_db.upsert_nutrition_insights(insights)
return True
except Exception as e: # noqa: BLE001 - scoring is a nicety, not the write
logger.warning("Could not score existing facts for %s/%s: %s", brand, image_id, e)
return False
def enrich_one_product(brand: str, image_id: str, product_name: str, category: str,
skip_if_verified: bool = False, generate_narrative: bool = True) -> str:
"""Runs the full pipeline for a single product. Returns the resulting
@@ -66,6 +113,22 @@ def enrich_one_product(brand: str, image_id: str, product_name: str, category: s
if skip_if_verified:
existing = nutrition_db.get_nutrition_facts(brand, image_id)
if existing and existing.get("data_status") == "verified":
# SKIPPING THE FETCH MUST NOT ALSO SKIP THE SCORING.
#
# This used to `return "verified"` outright, which meant a product
# whose facts arrived from a backfill script could never acquire a
# score. Those scripts write `nutrition_facts` and deliberately do
# not touch `nutrition_insights` - so the row looks verified here,
# gets skipped, and its health score stays NULL forever. Measured
# on 2026-09-08: 157 rows held usable facts with no score, which is
# most of the gap between `nutrients_per_100g` at 65.6% and
# `health_score` at 54.8%.
#
# Scoring is pure computation over facts already in hand - no
# network, no LLM - so doing it here costs one cheap read and
# nothing else. It is skipped when a score already exists, so a
# re-run is a no-op.
_score_from_existing_facts(brand, image_id, existing)
return "verified"
facts = nutrition_data_service.fetch_verified_nutrition(brand, product_name, category or "")

183
app/services/nutrition_score_sync.py
View File

@@ -57,6 +57,8 @@ from decimal import Decimal
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from psycopg.types.json import Json
from app.services.vector_store import (
_connect,
_list_brand_table_suffixes,
@@ -216,6 +218,175 @@ def sync_scores_to_brand_tables(brands: Optional[List[str]] = None,
return changed
# ---------------------------------------------------------------------------
# per-100g nutrients
# ---------------------------------------------------------------------------
# The flat per-100g columns on `nutrition_facts`, with the unit each is stored
# in. Order is the order a label reads, not alphabetical, because it is also
# the order the rendered display list comes out in.
_NUTRIENT_COLUMNS: Tuple[Tuple[str, str, str], ...] = (
("calories_kcal", "Energy", "kcal"),
("protein_g", "Protein", "g"),
("carbohydrates_g", "Carbohydrates", "g"),
("total_sugar_g", "Total sugar", "g"),
("added_sugar_g", "Added sugar", "g"),
("dietary_fiber_g", "Dietary fibre", "g"),
("total_fat_g", "Total fat", "g"),
("saturated_fat_g", "Saturated fat", "g"),
("trans_fat_g", "Trans fat", "g"),
("cholesterol_mg", "Cholesterol", "mg"),
("sodium_mg", "Sodium", "mg"),
("potassium_mg", "Potassium", "mg"),
("calcium_mg", "Calcium", "mg"),
("iron_mg", "Iron", "mg"),
("magnesium_mg", "Magnesium", "mg"),
("zinc_mg", "Zinc", "mg"),
("vitamin_a_mcg", "Vitamin A", "mcg"),
("vitamin_c_mg", "Vitamin C", "mg"),
("vitamin_d_mcg", "Vitamin D", "mcg"),
("vitamin_e_mg", "Vitamin E", "mg"),
("omega_3_g", "Omega-3", "g"),
("omega_6_g", "Omega-6", "g"),
)
# How many rendered lines the display column carries. The old keyword
# generator capped at 8 and the UI is laid out for roughly that.
_DISPLAY_LIMIT = 8
def render_nutrient_lines(facts: Dict[str, Any], limit: int = _DISPLAY_LIMIT) -> List[str]:
"""The `nutrients TEXT[]` display list, built from real measured numbers.
WHY THIS REPLACES WHAT WAS THERE
`catalog_engine.generate_nutrients_info` produced this column from
category keywords - "Energy - High", "Protein - Good Source" - with no
connection to `nutrition_facts` at all. A product could show
"Vitamin B Complex - Energy" because its category name matched a word.
Where real per-100g figures exist they are strictly better, and they
are what the merchant console is actually being asked for.
Values are emitted per 100 g because that is the basis `nutrition_facts`
stores and the basis Indian FSSAI labelling uses. A missing nutrient is
omitted rather than rendered as zero - "Trans fat 0 g" is a claim, and an
absent measurement is not evidence of absence.
"""
lines: List[str] = []
for column, label, unit in _NUTRIENT_COLUMNS:
value = facts.get(column)
if value is None:
continue
try:
number = float(value)
except (TypeError, ValueError):
continue
# Trim a trailing .0 so "Protein 12 g" does not read as "12.0".
text = f"{number:g}"
lines.append(f"{label} {text} {unit} per 100 g")
if len(lines) >= limit:
break
return lines
def sync_nutrients_to_brand_tables(brands: Optional[List[str]] = None,
*, dry_run: bool = False) -> Dict[str, int]:
"""Mirror `nutrition_facts` per-100g figures onto the brand tables.
Writes two columns, for two different readers:
* `nutrients_per_100g` (JSONB) - the numbers, for anything computing on
them. This is a denormalised copy so a single product read needs no
join, exactly as `nutrition_score`/`health_score` already are.
* `nutrients` (TEXT[]) - the same numbers rendered for display, replacing
the category-keyword strings.
`nutrition_facts` remains the source of truth; nothing here writes back to
it. Like the score mirror this is a MIRROR, not an accumulator: a row whose
facts have gone (the non-consumable purge removes them) has its copy
cleared rather than left stale.
A row whose facts exist but are `unavailable` is left alone rather than
blanked - `data_status='unavailable'` means "we looked and found nothing",
which is not a reason to destroy a display list the catalog already had.
"""
conn = _connect()
if conn is None:
logger.warning("nutrient sync: no database connection")
return {}
columns = ", ".join(c for c, _, _ in _NUTRIENT_COLUMNS)
changed: Dict[str, int] = {}
try:
with conn.cursor() as cur:
targets = _target_suffixes(cur, brands)
for suffix, display in targets:
table = f"brand_{suffix}"
if not dry_run:
ensure_brand_schema(display)
try:
with conn.cursor() as cur:
cur.execute(
f"SELECT image_id, {columns} FROM nutrition_facts "
f"WHERE lower(brand) = lower(%s) AND data_status <> 'unavailable'",
(display,),
)
rows = cur.fetchall()
if not rows:
continue
names = [c for c, _, _ in _NUTRIENT_COLUMNS]
updates = 0
for row in rows:
image_id = row[0]
facts = {n: v for n, v in zip(names, row[1:]) if v is not None}
if not facts:
continue
block = {n: float(v) for n, v in facts.items()}
lines = render_nutrient_lines(facts)
if dry_run:
cur.execute(
f'SELECT 1 FROM "{table}" WHERE image_id = %s '
f"AND nutrients_per_100g IS DISTINCT FROM %s::jsonb",
(image_id, Json(block)),
)
updates += 1 if cur.fetchone() else 0
continue
cur.execute(
f'UPDATE "{table}" SET nutrients_per_100g = %s::jsonb, '
f" nutrients = %s, "
f" field_sources = COALESCE(field_sources, '{{}}'::jsonb) || %s::jsonb, "
f" updated_at = CURRENT_TIMESTAMP "
f" WHERE image_id = %s "
f" AND nutrients_per_100g IS DISTINCT FROM %s::jsonb",
(Json(block), lines,
Json({"nutrients": {"method": "sourced",
"source": "nutrition_facts"},
"nutrients_per_100g": {"method": "sourced",
"source": "nutrition_facts"}}),
image_id, Json(block)),
)
updates += cur.rowcount
if not dry_run:
conn.commit()
except Exception as e: # noqa: BLE001
conn.rollback()
logger.warning("nutrient sync failed for %s: %s", table, e)
continue
if updates:
changed[display] = updates
logger.info("%s %d row(s) in %s",
"would update" if dry_run else "updated", updates, table)
finally:
conn.close()
return changed
# ---------------------------------------------------------------------------
# seed catalog JSON
# ---------------------------------------------------------------------------
@@ -356,3 +527,15 @@ def sync_after_write(brands: Optional[List[str]] = None) -> None:
except Exception as e: # noqa: BLE001
logger.warning("score mirror failed (scores are still in "
"nutrition_insights): %s", e)
# Separately guarded: a failure mirroring the numbers must not lose the
# scores that were just mirrored successfully, and vice versa. Both are
# copies of data that is safe in nutrition_facts / nutrition_insights.
try:
changed = sync_nutrients_to_brand_tables(brands)
if changed:
logger.info("mirrored nutrients onto %d brand table(s): %s",
len(changed), ", ".join(sorted(changed)))
except Exception as e: # noqa: BLE001
logger.warning("nutrient mirror failed (figures are still in "
"nutrition_facts): %s", e)

219
app/services/vector_store.py
View File

@@ -6,7 +6,10 @@ import logging
import os
import re
import time
from datetime import datetime
import psycopg
from psycopg.types.json import Json
from app.infrastructure.settings import (
USE_PGVECTOR, DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD,
@@ -56,6 +59,56 @@ def _sanitize_name(name: str) -> str:
return name.strip('_')
def _epoch_to_timestamp(value: Any) -> Optional[datetime]:
"""Convert BarcodeResult.barcode_last_updated to what the column holds.
`BarcodeResult` defaults this field to `time.time()` - a float epoch - but
the seven brand tables that already carry `barcode_last_updated` were
migrated out-of-band as TIMESTAMP. Handing psycopg a bare float for a
timestamp column raises, so the conversion happens here rather than being
pushed onto every caller. A datetime is passed through untouched, and
anything unparseable degrades to None.
"""
if value is None:
return None
if isinstance(value, datetime):
return value
# A seed catalog round-trips this as ISO-8601 (brand_sync._jsonable), while
# BarcodeResult and the older JSON files carry a float epoch. Both have to
# load, or re-seeding a brand would drop the timestamp it just exported.
if isinstance(value, str):
text = value.strip()
if not text:
return None
try:
return datetime.fromisoformat(text.replace("Z", "+00:00"))
except ValueError:
pass
try:
return datetime.fromtimestamp(float(value))
except (ValueError, TypeError, OSError, OverflowError):
return None
def _to_numeric_or_none(value: Any) -> Optional[float]:
"""Coerce an enrichment stage's numeric output for a NUMERIC column.
Returns None for anything unparseable rather than raising, because a
malformed tax figure must degrade to "no tax figure stored" and never
abort a whole batch's write. Strips a leading currency symbol and commas,
which is how these arrive when a sheet supplied them as text.
"""
if value is None or isinstance(value, bool):
return None
try:
if isinstance(value, str):
cleaned = re.sub(r"[^\d.\-]", "", value)
return float(cleaned) if cleaned not in ("", "-", ".", "-.") else None
return float(value)
except (ValueError, TypeError):
return None
DDL_CREATE_EXTENSION = "CREATE EXTENSION IF NOT EXISTS vector;"
def get_brand_table_ddl(brand: str) -> str:
"""Generate DDL for brand-specific table - simplified with only essential fields"""
@@ -88,12 +141,52 @@ def get_brand_table_ddl(brand: str) -> str:
selling_price NUMERIC,
barcode TEXT,
barcode_type TEXT,
-- The rest of what BarcodeResult.as_product_fields() produces. Until
-- these existed the barcode stage returned nine fields and the INSERT
-- named two, so seven were computed and then dropped on the floor -
-- including the provenance needed to tell a verified GTIN from a
-- name-matched guess.
--
-- TYPES ARE ADOPTED, NOT CHOSEN. Seven brand tables already carry
-- these columns, added out-of-band before any code created them.
-- ADD COLUMN IF NOT EXISTS does not reconcile a type difference, so
-- picking a "better" type here would leave 7 tables disagreeing with
-- 49 forever. Verified against information_schema on 2026-09-08:
-- barcode_last_updated is TIMESTAMP (not the float epoch
-- BarcodeResult carries - see _epoch_to_timestamp), and the tax
-- columns are REAL (not NUMERIC).
gtin TEXT,
ean13 TEXT,
upc TEXT,
barcode_source TEXT,
barcode_verified BOOLEAN,
barcode_lookup_status TEXT,
barcode_last_updated TIMESTAMP,
-- Computed by the HSN/GST stage, likewise discarded before this.
gst_percent REAL,
tax_amount REAL,
hsn_gst_needs_review BOOLEAN,
-- Essential fields
highlights TEXT[],
nutrients TEXT[],
-- Real per-100g figures mirrored from nutrition_facts by
-- nutrition_score_sync. `nutrients` above stays the human-readable
-- marketing list; these are the numbers.
nutrients_per_100g JSONB,
search_query TEXT,
-- Per-field provenance, keyed by column name; each value records how
-- that column's value was arrived at. One JSONB map rather than ~60
-- scalar columns, because every scalar column would have to be named
-- explicitly in the INSERT below and in brand_sync.EXPORT_COLUMNS or
-- it gets silently dropped - the exact failure the barcode columns
-- above are here to fix.
-- `method` is one of: sourced | estimated | derived | not_applicable.
field_sources JSONB DEFAULT '{{}}'::jsonb,
-- Nutrition scores, mirrored from nutrition_insights by
-- nutrition_score_sync. Deliberately NOT written by
-- upsert_brand_products - see the comment on its INSERT.
@@ -160,9 +253,30 @@ def _ensure_columns(cur, table_name: str) -> None:
"selling_price": "NUMERIC",
"barcode": "TEXT",
"barcode_type": "TEXT",
# The other seven fields BarcodeResult.as_product_fields() returns.
# Adding them here is what puts them on all 56 existing brand tables -
# before this only 7 tables had them, added out-of-band, and even those
# never received a value because the INSERT did not name them.
# Types adopted from the 7 tables that already have them, NOT chosen -
# see get_brand_table_ddl. TIMESTAMP and REAL are what is on disk.
"gtin": "TEXT",
"ean13": "TEXT",
"upc": "TEXT",
"barcode_source": "TEXT",
"barcode_verified": "BOOLEAN",
"barcode_lookup_status": "TEXT",
"barcode_last_updated": "TIMESTAMP",
# Computed by the HSN/GST stage and discarded before this.
"gst_percent": "REAL",
"tax_amount": "REAL",
"hsn_gst_needs_review": "BOOLEAN",
"highlights": "TEXT[]",
"nutrients": "TEXT[]",
"nutrients_per_100g": "JSONB",
"search_query": "TEXT",
# Per-field provenance map - see get_brand_table_ddl for why this is
# one JSONB column and not sixty scalar ones.
"field_sources": "JSONB",
# Mirrored from nutrition_insights by nutrition_score_sync, never by
# the INSERT below. This dict is the only migration mechanism there
# is (no Alembic), so listing them here is what creates them on every
@@ -193,7 +307,11 @@ def _ensure_columns(cur, table_name: str) -> None:
# nutrition_score_sync, not legacy debris from an older DDL, and this sweep
# must never claim them. (Both are nullable, so it would be a no-op today -
# the entry is here so it stays a no-op if that ever changes.)
inserted_cols = {"id", "product_name", "title", "description", "category", "image_id", "image_url", "image_urls", "price_range", "size_variants", "providers", "fssai_license", "product_sku", "sku_source", "hsn_code", "final_selling_price", "selling_price", "barcode", "barcode_type", "highlights", "nutrients", "search_query", "nutrition_score", "health_score", "embedding", "created_at", "updated_at"}
#
# `nutrients_per_100g` joins them: it is mirrored from nutrition_facts by
# the same sync, not written by the INSERT, and is likewise a current
# column rather than legacy debris.
inserted_cols = {"id", "product_name", "title", "description", "category", "image_id", "image_url", "image_urls", "price_range", "size_variants", "providers", "fssai_license", "product_sku", "sku_source", "hsn_code", "final_selling_price", "selling_price", "barcode", "barcode_type", "gtin", "ean13", "upc", "barcode_source", "barcode_verified", "barcode_lookup_status", "barcode_last_updated", "gst_percent", "tax_amount", "hsn_gst_needs_review", "highlights", "nutrients", "nutrients_per_100g", "field_sources", "search_query", "nutrition_score", "health_score", "embedding", "created_at", "updated_at"}
for col, is_nullable, col_def in col_info:
if col not in inserted_cols and is_nullable == 'NO' and col_def is None:
cur.execute(f"ALTER TABLE {table_name} ALTER COLUMN {col} DROP NOT NULL")
@@ -346,6 +464,30 @@ def upsert_brand_products(brand: str, products: List[Dict[str, Any]], cleanup: b
barcode = str(p.get("barcode") or p.get("Barcode") or "").strip() or None
barcode_type = str(p.get("barcode_type") or p.get("Barcode_Type") or "").strip() or None
# The remaining barcode fields. A product dict that never went through
# the barcode stage simply has none of these, so they arrive as None
# and the COALESCE in DO UPDATE SET below keeps whatever is already
# stored rather than blanking it.
gtin = str(p.get("gtin") or "").strip() or None
ean13 = str(p.get("ean13") or "").strip() or None
upc = str(p.get("upc") or "").strip() or None
barcode_source = str(p.get("barcode_source") or "").strip() or None
barcode_lookup_status = str(p.get("barcode_lookup_status") or "").strip() or None
barcode_verified = p.get("barcode_verified")
barcode_verified = bool(barcode_verified) if barcode_verified is not None else None
barcode_last_updated = _epoch_to_timestamp(p.get("barcode_last_updated"))
# HSN/GST stage output beyond hsn_code itself.
gst_percent = _to_numeric_or_none(p.get("gst_percent"))
tax_amount = _to_numeric_or_none(p.get("tax_amount"))
hsn_gst_needs_review = p.get("hsn_gst_needs_review")
hsn_gst_needs_review = bool(hsn_gst_needs_review) if hsn_gst_needs_review is not None else None
# Per-field provenance. Must be a JSON object; anything else is
# dropped rather than stored as a shape readers cannot index into.
field_sources = p.get("field_sources")
field_sources = Json(field_sources) if isinstance(field_sources, dict) and field_sources else None
# Essential fields
highlights = p.get("highlights", [])
if not isinstance(highlights, list):
@@ -387,9 +529,20 @@ def upsert_brand_products(brand: str, products: List[Dict[str, Any]], cleanup: b
selling_price,
barcode,
barcode_type,
gtin,
ean13,
upc,
barcode_source,
barcode_verified,
barcode_lookup_status,
barcode_last_updated,
gst_percent,
tax_amount,
hsn_gst_needs_review,
highlights, # TEXT[] - psycopg will handle conversion
nutrients, # TEXT[] - psycopg will handle conversion
search_query,
field_sources,
embedding_str
))
@@ -430,8 +583,11 @@ def upsert_brand_products(brand: str, products: List[Dict[str, Any]], cleanup: b
f"""
INSERT INTO {table_name}
(product_name, title, description, category, image_id, image_url, image_urls, price_range, size_variants, providers,
fssai_license, product_sku, sku_source, hsn_code, final_selling_price, selling_price, barcode, barcode_type, highlights, nutrients, search_query, embedding)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
fssai_license, product_sku, sku_source, hsn_code, final_selling_price, selling_price, barcode, barcode_type,
gtin, ean13, upc, barcode_source, barcode_verified, barcode_lookup_status, barcode_last_updated,
gst_percent, tax_amount, hsn_gst_needs_review,
highlights, nutrients, search_query, field_sources, embedding)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (image_id) DO UPDATE SET
product_name = EXCLUDED.product_name,
title = EXCLUDED.title,
@@ -448,12 +604,63 @@ def upsert_brand_products(brand: str, products: List[Dict[str, Any]], cleanup: b
hsn_code = EXCLUDED.hsn_code,
final_selling_price = EXCLUDED.final_selling_price,
selling_price = EXCLUDED.selling_price,
barcode = EXCLUDED.barcode,
barcode_type = EXCLUDED.barcode_type,
-- barcode/barcode_type are COALESCEd with the seven
-- columns below rather than assigned like their legacy
-- neighbours, because they are the same identity group.
-- Proven against brand_zzsmoketest on 2026-09-08: a bare
-- re-seed (a product dict with no barcode keys, which is
-- what the seed loader and user_products build) set
-- barcode to NULL while COALESCE kept gtin and ean13 -
-- leaving a row claiming a GTIN with no barcode. That
-- half-erased state is worse than either whole one.
--
-- It also aligns this statement with the invariant the
-- rest of the barcode code already enforces: stage.py:38
-- skips a row that has a barcode, and base.py:76-100
-- refuses to blank a held value. The upsert was the one
-- place that still could.
barcode = COALESCE(EXCLUDED.barcode, {table_name}.barcode),
barcode_type = COALESCE(EXCLUDED.barcode_type, {table_name}.barcode_type),
highlights = EXCLUDED.highlights,
nutrients = EXCLUDED.nutrients,
search_query = EXCLUDED.search_query,
embedding = EXCLUDED.embedding,
-- COALESCE, NOT PLAIN EXCLUDED, FOR EVERY COLUMN BELOW.
--
-- These are enrichment outputs. Most writers that reach
-- this statement never carry them: the seed loader,
-- user_products._build_product_dict and brand_sync's
-- re-seed all build a product dict from a spreadsheet,
-- so their EXCLUDED values are NULL. A plain assignment
-- would therefore wipe a hard-won barcode and its
-- provenance on the next re-seed of the brand - the same
-- failure the score-column comment above describes, which
-- is why those two columns are omitted entirely.
-- (Naming them here, even inside a comment, trips the
-- substring guard in tests/test_brand_table_scores.py.
-- That guard is crude on purpose; leave it that way.)
--
-- COALESCE keeps the stored value when nothing new
-- arrives, while still letting a real incoming value
-- correct a stored one. That matches the enrichment
-- invariant in enrichment/base.py: a stage may fill a gap
-- or correct a value, it may not erase one.
gtin = COALESCE(EXCLUDED.gtin, {table_name}.gtin),
ean13 = COALESCE(EXCLUDED.ean13, {table_name}.ean13),
upc = COALESCE(EXCLUDED.upc, {table_name}.upc),
barcode_source = COALESCE(EXCLUDED.barcode_source, {table_name}.barcode_source),
barcode_verified = COALESCE(EXCLUDED.barcode_verified, {table_name}.barcode_verified),
barcode_lookup_status = COALESCE(EXCLUDED.barcode_lookup_status, {table_name}.barcode_lookup_status),
barcode_last_updated = COALESCE(EXCLUDED.barcode_last_updated, {table_name}.barcode_last_updated),
gst_percent = COALESCE(EXCLUDED.gst_percent, {table_name}.gst_percent),
tax_amount = COALESCE(EXCLUDED.tax_amount, {table_name}.tax_amount),
hsn_gst_needs_review = COALESCE(EXCLUDED.hsn_gst_needs_review, {table_name}.hsn_gst_needs_review),
-- Merged, not replaced: a run that learns the provenance
-- of one field must not drop what is known about the
-- others. `||` is a shallow merge, which is the right
-- depth here - each key's value is one flat record.
field_sources = COALESCE({table_name}.field_sources, '{{}}'::jsonb)
|| COALESCE(EXCLUDED.field_sources, '{{}}'::jsonb),
updated_at = CURRENT_TIMESTAMP
""",
rows,

7
data/cache/off_brand_corpus/24_mantra.json vendored Normal file
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@@ -0,0 +1,7 @@
{
"brand": "24 mantra",
"country": "india",
"fetched_at": 1788852019.7252567,
"fetched_at_human": "2026-09-08 12:50:19",
"hits": []
}

354
data/cache/off_brand_corpus/balaji.json vendored Normal file
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@@ -0,0 +1,354 @@
{
"brand": "balaji",
"country": "india",
"fetched_at": 1788852012.4404967,
"fetched_at_human": "2026-09-08 12:50:12",
"hits": [
{
"code": "8906010503987",
"brands": [
"Balaji"
],
"quantity": "100000",
"countries_tags": [
"en:india"
],
"product_name": "Soft Imli",
"product_name_en": "Soft Imli"
},
{
"code": "8906010500016",
"brands": [
"balaji"
],
"quantity": "40",
"countries_tags": [
"en:india"
],
"product_name": "POTATO WAFERS",
"product_name_en": "POTATO WAFERS"
},
{
"code": "8906010501228",
"brands": [
"Balaji Wafers"
],
"countries_tags": [
"en:india"
],
"product_name": "Scoopitos Masala Flavour",
"product_name_en": "Scoopitos Masala Flavour"
},
{
"code": "8906010502782",
"brands": [
"Balaji Wafer"
],
"quantity": "60g",
"countries_tags": [
"en:india"
],
"product_name": "Stack Up - Simply Salted",
"product_name_en": "Stack Up - Simply Salted"
},
{
"code": "8906010500269",
"brands": [
"Balaji"
],
"quantity": "22 g",
"countries_tags": [
"en:india"
],
"product_name": "Poprings Masala flavour",
"product_name_en": "Poprings Masala flavour"
},
{
"code": "8906010502171",
"brands": [
"Balaji"
],
"quantity": "300g",
"countries_tags": [
"en:india"
],
"product_name": "Gathiya",
"product_name_en": "Gathiya"
},
{
"code": "8906010503611",
"brands": [
"Balaji"
],
"quantity": "135g",
"countries_tags": [
"en:india"
],
"product_name": "CHRUNCHEX",
"product_name_en": "CHRUNCHEX"
},
{
"code": "8906010500559",
"brands": [
"Balaji"
],
"quantity": "25 g",
"countries_tags": [
"en:india"
],
"product_name": "Mung Dal",
"product_name_en": "Mung Dal"
},
{
"code": "8906010501419",
"brands": [
"balaji"
],
"quantity": "30g",
"countries_tags": [
"en:india"
],
"product_name": "poprings",
"product_name_en": "poprings"
},
{
"code": "8906010505431",
"brands": [
"Balaji"
],
"countries_tags": [
"en:india"
],
"product_name": "Pop Rings",
"product_name_en": "Pop Rings"
},
{
"code": "8906010500139",
"brands": [
"Balaji"
],
"quantity": "35 g",
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers",
"product_name_en": "Balaji Wafers"
},
{
"code": "8906010501952",
"brands": [
"Balaji",
"Balaji Wafers",
"Rumbles"
],
"quantity": "40 g",
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers Rumbles Potato Chips Pudina Twist flavour",
"product_name_en": "Balaji Wafers Rumbles Potato Chips Pudina Twist flavour"
},
{
"code": "8906010500900",
"brands": [
"Balaji"
],
"quantity": "22 g",
"countries_tags": [
"en:india"
],
"product_name": "wheels",
"product_name_en": "wheels"
},
{
"code": "8906010502591",
"brands": [
"Balaji Wafers"
],
"quantity": "72g",
"countries_tags": [
"en:india"
],
"product_name": "Crunchem",
"product_name_en": "Crunchem"
},
{
"code": "8906010501822",
"brands": [
"Balaji"
],
"quantity": "gippi",
"countries_tags": [
"en:india"
],
"product_name": "GIPPI",
"product_name_en": "GIPPI"
},
{
"code": "8906010503581",
"brands": [
"Balaji"
],
"quantity": "420g",
"countries_tags": [
"en:india"
],
"product_name": "GIPPI masala noodles",
"product_name_en": "GIPPI masala noodles"
},
{
"code": "8906010500047",
"brands": [
"Balaji"
],
"quantity": "15g",
"countries_tags": [
"en:india"
],
"product_name": "masala masti",
"product_name_en": "masala masti"
},
{
"code": "8906010500535",
"brands": [
"Balaji"
],
"quantity": "35 g",
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers Chaat Chaska",
"product_name_en": "Balaji Wafers Chaat Chaska"
},
{
"code": "8906010500023",
"brands": [
"Balaji"
],
"countries_tags": [
"en:india"
],
"product_name": "POTATO WAFERS",
"product_name_en": "POTATO WAFERS"
},
{
"code": "8906010500221",
"brands": [
"Balaji"
],
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers Mung Dal",
"product_name_en": "Balaji Wafers Mung Dal"
},
{
"code": "0710859306108",
"brands": [
"Vittal Balaji"
],
"quantity": "500g",
"countries_tags": [
"en:india"
],
"product_name": "CASHEWS",
"product_name_en": "CASHEWS"
},
{
"code": "8906010500368",
"brands": [
"Balaji Namkeen"
],
"quantity": "30 g",
"countries_tags": [
"en:india"
],
"product_name": "Tikha Mitha Mix",
"product_name_en": "Tikha Mitha Mix"
},
{
"code": "8906010500276",
"brands": [
"Balaji Wafers"
],
"quantity": "25g",
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers Shing Bhujia",
"product_name_en": "Balaji Wafers Shing Bhujia"
},
{
"code": "8906010500764",
"brands": [
"Balaji"
],
"quantity": "25g",
"countries_tags": [
"en:india"
],
"product_name": "balaji wafers chataka pataka",
"product_name_en": "balaji wafers chataka pataka"
},
{
"code": "8906010500290",
"brands": [
"Balaji"
],
"quantity": "210g",
"countries_tags": [
"en:india"
],
"product_name": "Shing Bhujia",
"product_name_en": "Shing Bhujia"
},
{
"code": "8906010502508",
"brands": [
"Balaji"
],
"quantity": "35g",
"countries_tags": [
"en:india"
],
"product_name": "Crunchem Peri Peri Flavour",
"product_name_en": "Crunchem Peri Peri Flavour"
},
{
"code": "8906010503529",
"brands": [
"Balaji"
],
"quantity": "150",
"countries_tags": [
"en:ghana",
"en:india"
],
"product_name": "CHRUNCHEM Masala Masti",
"product_name_en": "CHRUNCHEM Masala Masti"
},
{
"code": "26246246",
"brands": [
"balaji"
],
"quantity": "25g",
"countries_tags": [
"en:india"
],
"product_name": "aloo sev",
"product_name_en": "aloo sev"
},
{
"code": "8906010500092",
"brands": [
"Balaji Wafers"
],
"countries_tags": [
"en:india"
],
"product_name": "Balaji Wafers Crunchex",
"product_name_en": "Balaji Wafers Crunchex"
}
]
}

7
data/cache/off_brand_corpus/brooke_bond.json vendored Normal file
View File

@@ -0,0 +1,7 @@
{
"brand": "brooke bond",
"country": "india",
"fetched_at": 1788851994.807076,
"fetched_at_human": "2026-09-08 12:49:54",
"hits": []
}

343
data/cache/off_brand_corpus/catch.json vendored Normal file
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@@ -0,0 +1,343 @@
{
"brand": "catch",
"country": "india",
"fetched_at": 1788852025.2826416,
"fetched_at_human": "2026-09-08 12:50:25",
"hits": [
{
"code": "8901192204011",
"brands": [
"Catch"
],
"quantity": "50gm",
"countries_tags": [
"en:india"
],
"product_name": "CHATPATA CHAT MASALA catch",
"product_name_en": "CHATPATA CHAT MASALA catch"
},
{
"code": "8901192217110",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "JALJEERA",
"product_name_en": "JALJEERA"
},
{
"code": "8901192101013",
"brands": [
"Catch"
],
"quantity": "50g",
"countries_tags": [
"en:india"
],
"product_name": "BLACK PEPPER POWDER Catch",
"product_name_en": "BLACK PEPPER POWDER Catch"
},
{
"code": "8901192208019",
"brands": [
"Catch"
],
"quantity": "50gm",
"countries_tags": [
"en:india"
],
"product_name": "KITCHEN KING catch",
"product_name_en": "KITCHEN KING catch"
},
{
"code": "8901192214010",
"brands": [
"Catch"
],
"quantity": "50 gm",
"countries_tags": [
"en:india"
],
"product_name": "CHICKEN MASALA Catch",
"product_name_en": "CHICKEN MASALA Catch"
},
{
"code": "8901192000101",
"brands": [
"catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "catch sprinklers Table Salt",
"product_name_en": "catch sprinklers Table Salt"
},
{
"code": "8901192205100",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "catch chat masala",
"product_name_en": "catch chat masala"
},
{
"code": "8901192226150",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "Hing Asafoetida",
"product_name_en": "Hing Asafoetida"
},
{
"code": "8901192227003",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "Dahi Masala",
"product_name_en": "Dahi Masala"
},
{
"code": "8901192002105",
"brands": [
"Catch"
],
"quantity": "100 Grams",
"countries_tags": [
"en:india"
],
"product_name": "Catch Pink Rock Salt(Sendha Namak)",
"product_name_en": "Catch Pink Rock Salt(Sendha Namak)"
},
{
"code": "8901192204110",
"brands": [
"Catch"
],
"countries_tags": [
"en:india"
],
"product_name": "Catch Chatpata Chat Masala",
"product_name_en": "Catch Chatpata Chat Masala"
},
{
"code": "8901192205261",
"brands": [
"Catch"
],
"quantity": "200 g",
"countries_tags": [
"en:india"
],
"product_name": "Ginger Garlic - Culinary Paste",
"product_name_en": "Ginger Garlic - Culinary Paste"
},
{
"code": "8901192215017",
"brands": [
"Catch"
],
"quantity": "50gm",
"countries_tags": [
"en:india"
],
"product_name": "Sabzi masala",
"product_name_en": "Sabzi masala"
},
{
"code": "8901192216014",
"brands": [
"Catch"
],
"quantity": "50 gm",
"countries_tags": [
"en:india"
],
"product_name": "Garam masala",
"product_name_en": "Garam masala"
},
{
"code": "8901192226051",
"brands": [
"Catch"
],
"quantity": "50g",
"countries_tags": [
"en:india"
],
"product_name": "Hing",
"product_name_en": "Hing"
},
{
"code": "8901192103116",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "Kashmiri mirch power",
"product_name_en": "Kashmiri mirch power"
},
{
"code": "8901192105110",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "Amchur power",
"product_name_en": "Amchur power"
},
{
"code": "8901192223111",
"brands": [
"Catch"
],
"quantity": "100g",
"countries_tags": [
"en:india"
],
"product_name": "Rajma masala",
"product_name_en": "Rajma masala"
},
{
"code": "8901192213112",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "MEAT MASALA Catch",
"product_name_en": "MEAT MASALA Catch"
},
{
"code": "8901192219220",
"brands": [
"Catch"
],
"quantity": "200g",
"countries_tags": [
"en:india"
],
"product_name": "Catch Super Garam Masala",
"product_name_en": "Catch Super Garam Masala"
},
{
"code": "8901192107626",
"brands": [
"Catch"
],
"quantity": "500gm",
"countries_tags": [
"en:india"
],
"product_name": "Haldi powder",
"product_name_en": "Haldi powder"
},
{
"code": "8901192105011",
"brands": [
"Catch"
],
"quantity": "50gm",
"countries_tags": [
"en:india"
],
"product_name": "AMCHUR POWDER Catch",
"product_name_en": "AMCHUR POWDER Catch"
},
{
"code": "8901192221018",
"brands": [
"Catch"
],
"quantity": "50gm",
"countries_tags": [
"en:india"
],
"product_name": "CHHOLE MASALA Catch",
"product_name_en": "CHHOLE MASALA Catch"
},
{
"code": "8901192214119",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "CHICKEN MASALA Catch",
"product_name_en": "CHICKEN MASALA Catch"
},
{
"code": "8901192202116",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "SAMBHAR MASALA Catch",
"product_name_en": "SAMBHAR MASALA Catch"
},
{
"code": "8901192208118",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "KITCHEN KING Catch",
"product_name_en": "KITCHEN KING Catch"
},
{
"code": "8901192216113",
"brands": [
"Catch"
],
"quantity": "100gm",
"countries_tags": [
"en:india"
],
"product_name": "Garam masala",
"product_name_en": "Garam masala"
},
{
"code": "8901192114006",
"brands": [
"Catch"
],
"quantity": "50 g",
"countries_tags": [
"en:india"
],
"product_name": "Sprinkler Black Pepper",
"product_name_en": "Sprinkler Black Pepper"
}
]
}

7
data/cache/off_brand_corpus/clinic_all_clear.json vendored Normal file
View File

@@ -0,0 +1,7 @@
{
"brand": "clinic all clear",
"country": "india",
"fetched_at": 1788852017.1407056,
"fetched_at_human": "2026-09-08 12:50:17",
"hits": []
}

7
data/cache/off_brand_corpus/colin.json vendored Normal file
View File

@@ -0,0 +1,7 @@
{
"brand": "colin",
"country": "india",
"fetched_at": 1788852016.0197957,
"fetched_at_human": "2026-09-08 12:50:16",
"hits": []
}

56
data/cache/off_brand_corpus/complan.json vendored Normal file
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@@ -0,0 +1,56 @@
{
"brand": "complan",
"country": "india",
"fetched_at": 1788852010.1317456,
"fetched_at_human": "2026-09-08 12:50:10",
"hits": [
{
"code": "8901542002298",
"brands": [
"Complan"
],
"quantity": "18g",
"countries_tags": [
"en:india"
],
"product_name": "Complan",
"product_name_en": "Complan"
},
{
"code": "8901542001796",
"brands": [
"Complan"
],
"quantity": "500 g",
"countries_tags": [
"en:india"
],
"product_name": "Explore Knowledge world",
"product_name_en": "Explore Knowledge world"
},
{
"code": "8901542003158",
"brands": [
"Complan"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "Complan Rich Chocolate flavor",
"product_name_en": "Complan Rich Chocolate flavor"
},
{
"code": "8901542000959",
"brands": [
"Complan"
],
"quantity": "500",
"countries_tags": [
"en:india"
],
"product_name": "Complan royale chocolate flavour",
"product_name_en": "Complan royale chocolate flavour"
}
]
}

102
data/cache/off_brand_corpus/daawat.json vendored Normal file
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@@ -0,0 +1,102 @@
{
"brand": "daawat",
"country": "india",
"fetched_at": 1788852002.8657465,
"fetched_at_human": "2026-09-08 12:50:02",
"hits": [
{
"code": "8901537079595",
"brands": [
"Daawat"
],
"countries_tags": [
"en:india"
],
"product_name": "White basmati rice",
"product_name_en": "White basmati rice"
},
{
"code": "8901537077485",
"brands": [
"Daawat"
],
"countries_tags": [
"en:india"
],
"product_name": "Daawat Cuppa Rice Rajma Chawal",
"product_name_en": "Daawat Cuppa Rice Rajma Chawal"
},
{
"code": "8901537074415",
"brands": [
"Daawat"
],
"quantity": "5 kg",
"countries_tags": [
"en:india"
],
"product_name": "Sona Masoori Rice",
"product_name_en": "Sona Masoori Rice"
},
{
"code": "2000000150588",
"brands": [
"Daawat"
],
"quantity": "5 kg",
"countries_tags": [
"en:india"
],
"product_name": "Sona Masoori Rice",
"product_name_en": "Sona Masoori Rice"
},
{
"code": "4901135025127",
"brands": [
"Daawat"
],
"quantity": "1 kg",
"countries_tags": [
"en:india"
],
"product_name": "Dawat super basmati rice 1 kg",
"product_name_en": "Dawat super basmati rice 1 kg"
},
{
"code": "8901537074231",
"brands": [
"Daawat"
],
"quantity": "1 kg",
"countries_tags": [
"en:india"
],
"product_name": "Daawat pulav basmati rice 1kg",
"product_name_en": "Daawat pulav basmati rice 1kg"
},
{
"code": "8901537007116",
"brands": [
"Daawat"
],
"quantity": "5 kg",
"countries_tags": [
"en:india"
],
"product_name": "Daawat Rozana Basmati Rice",
"product_name_en": "Daawat Rozana Basmati Rice"
},
{
"code": "8901537078314",
"brands": [
"Daawat"
],
"quantity": "334 g",
"countries_tags": [
"en:india"
],
"product_name": "Biryani Kit Lucknowi",
"product_name_en": "Biryani Kit Lucknowi"
}
]
}

43
data/cache/off_brand_corpus/dhara.json vendored Normal file
View File

@@ -0,0 +1,43 @@
{
"brand": "dhara",
"country": "india",
"fetched_at": 1788852026.4211528,
"fetched_at_human": "2026-09-08 12:50:26",
"hits": [
{
"code": "8906004620751",
"brands": [
"Dhara Life"
],
"quantity": "1l",
"countries_tags": [
"en:india"
],
"product_name": "Refined Ricebran Oil",
"product_name_en": "Refined Ricebran Oil"
},
{
"code": "8906004620256",
"brands": [
"Dhara"
],
"countries_tags": [
"en:india"
],
"product_name": "Kachi Ghani Mustard Oil",
"product_name_en": "Kachi Ghani Mustard Oil"
},
{
"code": "8906004620485",
"brands": [
"Dhara"
],
"quantity": "5l",
"countries_tags": [
"en:india"
],
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}

296
data/cache/off_brand_corpus/fortune.json vendored Normal file
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@@ -0,0 +1,296 @@
{
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7
data/cache/off_brand_corpus/india_gate.json vendored Normal file
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@@ -0,0 +1,7 @@
{
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466
data/cache/off_brand_corpus/itc.json vendored Normal file
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@@ -0,0 +1,466 @@
{
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241
data/cache/off_brand_corpus/kissan.json vendored Normal file
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@@ -0,0 +1,241 @@
{
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45
data/cache/off_brand_corpus/kohinoor.json vendored Normal file
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{
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46
data/cache/off_brand_corpus/marico.json vendored Normal file
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{
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7
data/cache/off_brand_corpus/mother_dairy.json vendored Normal file
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@@ -0,0 +1,7 @@
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289
data/cache/off_brand_corpus/nandini.json vendored Normal file
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7
data/cache/off_brand_corpus/nature_fresh.json vendored Normal file
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@@ -0,0 +1,7 @@
{
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152
data/cache/off_brand_corpus/nivea.json vendored Normal file
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@@ -0,0 +1,152 @@
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7
data/cache/off_brand_corpus/paper_boat.json vendored Normal file
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@@ -0,0 +1,7 @@
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1724
data/cache/off_brand_corpus/parle.json vendored Normal file
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482
data/cache/off_brand_corpus/patanjali.json vendored Normal file
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@@ -0,0 +1,482 @@
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{
"code": "8904109449291",
"brands": [
"Patanjali"
],
"countries_tags": [
"en:india"
],
"product_name": "Patanjali Drishti Eye Drop",
"product_name_en": "Patanjali Drishti Eye Drop"
},
{
"code": "8904422700826",
"brands": [
"Patanjali"
],
"quantity": "250 g",
"countries_tags": [
"en:india"
],
"product_name": "Honey",
"product_name_en": "Honey"
},
{
"code": "8904422704992",
"brands": [
"Patanjali"
],
"quantity": "200 g",
"countries_tags": [
"en:india"
],
"product_name": "Ragi Digestive Cookies",
"product_name_en": "Ragi Digestive Cookies"
},
{
"code": "8904109400773",
"brands": [
"Patanjali"
],
"countries_tags": [
"en:india"
],
"product_name": "Patanjali Soundarya Shower Gel",
"product_name_en": "Patanjali Soundarya Shower Gel"
},
{
"code": "8904109400667",
"brands": [
"Patanjali"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "Soundarya sabun",
"product_name_en": "Soundarya sabun"
},
{
"code": "8904422702417",
"brands": [
"Patanjali"
],
"quantity": "250g",
"countries_tags": [
"en:india"
],
"product_name": "Amla Chatpata Candy",
"product_name_en": "Amla Chatpata Candy"
},
{
"code": "709358",
"brands": [
"Patanjali"
],
"quantity": "1 kg",
"countries_tags": [
"en:germany",
"en:india"
],
"product_name": "Patanjali Chyawanprash",
"product_name_en": "Patanjali Chyawanprash"
},
{
"code": "8904109410093",
"brands": [
"Patanjali"
],
"quantity": "84 g",
"countries_tags": [
"en:india"
],
"product_name": "Patanjali cream feast ( milk vanilla )",
"product_name_en": "Patanjali cream feast ( milk vanilla )"
},
{
"code": "8904109401268",
"brands": [
"Patanjali"
],
"quantity": "80g",
"countries_tags": [
"en:india"
],
"product_name": "Toothbrush",
"product_name_en": "Toothbrush"
},
{
"code": "8904422701151",
"brands": [
"Patanjali"
],
"quantity": "1kg",
"countries_tags": [
"en:india"
],
"product_name": "Patanjali Rock Salt",
"product_name_en": "Patanjali Rock Salt"
}
]
}

69
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{
"brand": "priyagold",
"country": "india",
"fetched_at": 1788852013.6838248,
"fetched_at_human": "2026-09-08 12:50:13",
"hits": [
{
"code": "8906029790033",
"brands": [
"Priyagold"
],
"quantity": "24 g",
"countries_tags": [
"en:india"
],
"product_name": "Snakker",
"product_name_en": "Snakker"
},
{
"code": "8901652140897",
"brands": [
"Priyagold"
],
"quantity": "40g",
"countries_tags": [
"en:india"
],
"product_name": "Marie lite biscuit",
"product_name_en": "Marie lite biscuit"
},
{
"code": "8901652140620",
"brands": [
"PRIYAGOLD"
],
"quantity": "40g",
"countries_tags": [
"en:india"
],
"product_name": "Puff",
"product_name_en": "Puff"
},
{
"code": "8901652142136",
"brands": [
"priyagold"
],
"quantity": "35 g",
"countries_tags": [
"en:australia",
"en:india"
],
"product_name": "Butter Delite",
"product_name_en": "Butter Delite"
},
{
"code": "8901652140583",
"brands": [
"Priyagold"
],
"quantity": "40g",
"countries_tags": [
"en:india"
],
"product_name": "Puff choco vanilla flavoured sandwich biscuits",
"product_name_en": "Puff choco vanilla flavoured sandwich biscuits"
}
]
}

20
data/cache/off_brand_corpus/rajdhani.json vendored Normal file
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@@ -0,0 +1,20 @@
{
"brand": "rajdhani",
"country": "india",
"fetched_at": 1788852000.6414518,
"fetched_at_human": "2026-09-08 12:50:00",
"hits": [
{
"code": "8906002348169",
"brands": [
"Rajdhani"
],
"quantity": "500g",
"countries_tags": [
"en:india"
],
"product_name": "Poha Mota",
"product_name_en": "Poha Mota"
}
]
}

7
data/cache/off_brand_corpus/reckitt_benckiser.json vendored Normal file
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@@ -0,0 +1,7 @@
{
"brand": "reckitt benckiser",
"country": "india",
"fetched_at": 1788851995.867008,
"fetched_at_human": "2026-09-08 12:49:55",
"hits": []
}

44
data/cache/off_brand_corpus/society.json vendored Normal file
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@@ -0,0 +1,44 @@
{
"brand": "society",
"country": "india",
"fetched_at": 1788852008.9179409,
"fetched_at_human": "2026-09-08 12:50:08",
"hits": [
{
"code": "8901095001465",
"brands": [
"Society Daily"
],
"quantity": "14 g",
"countries_tags": [
"en:india"
],
"product_name": "Masala Flavour Instant Tea Premix",
"product_name_en": "Masala Flavour Instant Tea Premix"
},
{
"code": "8901095900089",
"brands": [
"society Indian leaf tea masala"
],
"quantity": "250 grams",
"countries_tags": [
"en:india"
],
"product_name": "tea masala",
"product_name_en": "tea masala"
},
{
"code": "8901095000482",
"brands": [
"society"
],
"quantity": "40g",
"countries_tags": [
"en:india"
],
"product_name": "society Sweet spicy mango pickle",
"product_name_en": "society Sweet spicy mango pickle"
}
]
}

7
data/cache/off_brand_corpus/too_yumm.json vendored Normal file
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@@ -0,0 +1,7 @@
{
"brand": "too yumm",
"country": "india",
"fetched_at": 1788852014.7936301,
"fetched_at_human": "2026-09-08 12:50:14",
"hits": []
}

399
data/cache/off_brand_corpus/unibic.json vendored Normal file
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@@ -0,0 +1,399 @@
{
"brand": "unibic",
"country": "india",
"fetched_at": 1788852005.1305947,
"fetched_at_human": "2026-09-08 12:50:05",
"hits": [
{
"code": "8906009070902",
"brands": [
"Unibic"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "Unibic Honey Oatmeal Cookies",
"product_name_en": "Unibic Honey Oatmeal Cookies"
},
{
"code": "8906009075600",
"brands": [
"UNIBIC"
],
"countries_tags": [
"en:india"
],
"product_name": "Swaadesi shahi kaju katli",
"product_name_en": "Swaadesi shahi kaju katli"
},
{
"code": "8906009079288",
"brands": [
"Unibic"
],
"quantity": "200g",
"countries_tags": [
"en:india"
],
"product_name": "Kesar Cashew Badam Cookies",
"product_name_en": "Kesar Cashew Badam Cookies"
},
{
"code": "8906009075068",
"brands": [
"Unibic"
],
"quantity": "495 g",
"countries_tags": [
"en:india"
],
"product_name": "Atta Marie Thinz",
"product_name_en": "Atta Marie Thinz"
},
{
"code": "9900409072640",
"brands": [
"UNIBIC"
],
"quantity": "300g",
"countries_tags": [
"en:india"
],
"product_name": "UNIBIC Snappers Potato Crackers",
"product_name_en": "UNIBIC Snappers Potato Crackers"
},
{
"code": "8906009077420",
"brands": [
"UNIBIC"
],
"countries_tags": [
"en:india"
],
"product_name": "Pineapple cookies (sugar free)",
"product_name_en": "Pineapple cookies (sugar free)"
},
{
"code": "8906009077802",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Shortbread"
},
{
"code": "8906009072906",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "UNIBIC Cashew Cookies",
"product_name_en": "UNIBIC Cashew Cookies"
},
{
"code": "8906009072678",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Choco nut Cookies",
"product_name_en": "Unibic Choco nut Cookies"
},
{
"code": "8906009071183",
"brands": [
"Unibic"
],
"quantity": "20g",
"countries_tags": [
"en:india"
],
"product_name": "Chocokiss",
"product_name_en": "Chocokiss"
},
{
"code": "8906009075204",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Dates & Carrot cake 140g",
"product_name_en": "Unibic Dates & Carrot cake 140g"
},
{
"code": "8906009072012",
"brands": [
"Unibic"
],
"quantity": "75",
"countries_tags": [
"en:india"
],
"product_name": "Pista badam",
"product_name_en": "Pista badam"
},
{
"code": "8906009077277",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Butter cookies",
"product_name_en": "Butter Cookies Sugar Free75g"
},
{
"code": "19066554",
"brands": [
"UNIBIC"
],
"quantity": "150 g",
"countries_tags": [
"en:india"
],
"product_name": "Oats Digestive",
"product_name_en": "Oats Digestive"
},
{
"code": "8906009072784",
"brands": [
"Unibic"
],
"quantity": "250g",
"countries_tags": [
"en:india"
],
"product_name": "Biscott",
"product_name_en": "Biscott"
},
{
"code": "8906009078014",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Butter cookies",
"product_name_en": "Butter cookies"
},
{
"code": "8906009077017",
"brands": [
"unibic"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "fruit & nut cookies",
"product_name_en": "fruit & nut cookies"
},
{
"code": "8906009079844",
"brands": [
"UNIBIC"
],
"quantity": "30g",
"countries_tags": [
"en:india"
],
"product_name": "UNIBIC Cashew Badam Cookies",
"product_name_en": "UNIBIC Cashew Badam Cookies"
},
{
"code": "8906009078021",
"brands": [
"UNIBIC"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "UNIBIC Cashew Cookies",
"product_name_en": "UNIBIC Cashew Cookies"
},
{
"code": "8906009073958",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Coconut Cookies",
"product_name_en": "Unibic Coconut Cookies"
},
{
"code": "8906009077543",
"brands": [
"Unibic"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "Sugar Free Multigrain Cookies",
"product_name_en": "Sugar Free Multigrain Cookies"
},
{
"code": "8906009078762",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic oats 50",
"product_name_en": "Unibic oats 50"
},
{
"code": "4906409077291",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Sugar Free Buscits",
"product_name_en": "Unibic Sugar Free Buscits"
},
{
"code": "8906009073729",
"brands": [
"Unibic"
],
"quantity": "300 g",
"countries_tags": [
"en:india"
],
"product_name": "Big & Bold Fruit Blast",
"product_name_en": "Big & Bold Fruit Blast"
},
{
"code": "8906009072449",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Snappers Cream n'Onion",
"product_name_en": "Snappers Cream n'Onion"
},
{
"code": "8906009078670",
"brands": [
"Unibic"
],
"quantity": "150 g",
"countries_tags": [
"en:india"
],
"product_name": "Unibic Frunit and Nut cookies",
"product_name_en": "Unibic Frunit and Nut cookies"
},
{
"code": "8906009077314",
"brands": [
"Unibic"
],
"quantity": "150 g",
"countries_tags": [
"en:india"
],
"product_name": "Honey Oatmeal Cookies"
},
{
"code": "8906009074788",
"brands": [
"Unibic"
],
"quantity": "58 g",
"countries_tags": [
"en:india"
],
"product_name": "Oats Marie Thinz",
"product_name_en": "Oats Marie Thinz"
},
{
"code": "8906009079363",
"brands": [
"Unibic"
],
"quantity": "37.5g",
"countries_tags": [
"en:india"
],
"product_name": "Fruit & nut cookis",
"product_name_en": "Fruit & nut cookis"
},
{
"code": "7906048075181",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Fruit N Nut 140g",
"product_name_en": "Unibic Fruit N Nut 140g"
},
{
"code": "8906009075167",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Unibic Royal Vanilla cake 140g",
"product_name_en": "Unibic Royal Vanilla cake 140g"
},
{
"code": "8906009073163",
"brands": [
"swaadesi",
"unibic"
],
"quantity": "180g",
"countries_tags": [
"en:india"
],
"product_name": "swaadesi ghee besan laddoo",
"product_name_en": "swaadesi ghee besan laddoo"
},
{
"code": "8906009077581",
"brands": [
"Unibic"
],
"quantity": "75g",
"countries_tags": [
"en:india"
],
"product_name": "Choco Kiss Cookies",
"product_name_en": "Choco Kiss Cookies"
},
{
"code": "8906009077291",
"brands": [
"Unibic"
],
"countries_tags": [
"en:india"
],
"product_name": "Oatmeal cookies Sugar free"
}
]
}

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# Barcode enrichment
`app/services/enrichment/barcode/` — how a catalog row acquires a barcode, what
the barcode is then allowed to claim, and which knob to turn.
Referenced from `.env.example` and `app/services/enrichment/pipeline.py`, both
of which pointed at this file for a long time before it existed.
---
## The four ways a row gets a barcode
| # | Path | When | Cost | Confidence |
|---|---|---|---|---|
| 1 | **The sheet** | The merchant typed it | free | highest — they are holding the pack |
| 2 | **Identity derivation** | Always, inline | free, offline | derives `gtin`/`ean13`/`upc`/`barcode_type` from a barcode already present |
| 3 | **Bulk brand corpus** | After upload, per brand | ~5 requests **per brand** | name-matched at ≥ 0.88 |
| 4 | **Per-product cascade** | Only if enabled | 1 request **per product**, 10/min cap | brand + size + name matched |
Paths 1 and 2 are always on. Path 3 is on by default. Path 4 is off by default.
### Why path 4 is off and path 3 is on
They differ in cost, not in appetite for risk.
The Open Food Facts per-product search endpoint is capped at **10 requests per
minute**. A 200-row upload on path 4 is twenty minutes of a held HTTP request,
on a shared 8 GB host — which is what `settings.py:420-423` is about.
Path 3 fetches a brand's *entire* India catalogue in about five requests, caches
it to `data/cache/off_brand_corpus/<slug>.json`, and matches offline. A brand
costs the same whether it has 3 rows or 300.
```
ENABLE_BARCODE_LOOKUP=false # path 4: per product, inline
ENRICH_BARCODES_ON_UPLOAD=true # path 3: per brand, after the upload settles
```
> `.env.example` shipped `ENABLE_BARCODE_LOOKUP=true` while `settings.py`
> defaulted it `false`, for as long as both existed. Anyone copying the example
> got a materially different pipeline from anyone relying on defaults. Fixed;
> if you see the two disagree again, `settings.py` is authoritative.
---
## Two thresholds, and why they are different numbers
| Constant | Value | Direction | Used by |
|---|---|---|---|
| `BARCODE_MIN_NAME_SIMILARITY` | 0.78 | reverse — barcode known, name is a sanity check | `fetch_verified_nutrition_by_barcode` |
| `OFF bulk review_min` | 0.88 | forward — name carries the whole decision | `post_ingest_barcodes`, `backfill_barcodes_from_off` |
`settings.py:449-477` records the measurement behind 0.78: at 0.45 the cascade
accepted 15 candidates of which 8 were wrong; at 0.78 it accepted 2 and none
were wrong. **Do not lower it to fix a coverage complaint.**
### The containment rule
Open Food Facts stores short names. We store long ones. Measured over the
catalog on 2026-09-08, 149 of 300 barcoded rows were refused as "found, wrong
product" when the barcode had resolved perfectly:
```
ours "Nestle Munch 8.9g" OFF "Munch" similarity 0.332
ours "Coca-Cola Maaza 750ml" OFF "Maaza" similarity 0.304
```
`name_similarity` divides overlap by *our* token count, so a one-token candidate
cannot exceed ~0.33 however right it is. But the same run correctly refused:
```
ours "Pepsico Lays 1kg" OFF "Spanish tomato tango" 0.133
ours "Coca-Cola Fanta 750ml" OFF "Orange" 0.089
```
A threshold low enough to admit the first group admits the second. So
`matching.name_is_contained` separates them **structurally**: every candidate
token must be one of ours once brand and size tokens are discounted, and a bare
brand name ("Colgate", "godrej" — both real OFF titles) never matches.
### `barcode_is_identity` — and the gate that actually blocked most of them
The name gate was the *visible* symptom. When the fix was measured it moved only
3 rows to 8, and the reason is that `is_match` applies its rules in order and the
**size** gate rejects first:
```
Nestle Munch 38.5 g <- OFF "Munch" blocked by SIZE (off quantity = None)
Coca-Cola Maaza 750ml <- OFF "Maaza" blocked by SIZE (off quantity = None)
Cadbury Perk 22 g <- OFF "Perk" blocked by SIZE (off quantity = None)
```
`size_matches` returns False whenever *either* side is blank, and Open Food
Facts leaves `quantity` null on a large share of records — 57 of 146 Amul hits.
`off_bulk` had already documented this and worked around it by treating size as
a ranking bonus rather than a veto.
So the flag is `barcode_is_identity`, not `allow_containment`, because it
describes the precondition rather than one of its consequences: **the caller
already knows which product this is, because it fetched by GTIN.** Under it,
name and size stop being evidence of identity and become sanity checks against
our barcode being on the wrong row — and a sanity check cannot fail on
information the source does not have:
| Rule | Normally | Under `barcode_is_identity` |
|---|---|---|
| 1. brand | must match | unchanged |
| 2. size | blank ⇒ reject | **blank ⇒ no information, allowed.** Present-and-different still rejects |
| 3. variant terms | must not conflict | unchanged |
| 4. name similarity | ≥ floor | floor, **or** containment |
**Defaults off.** On the search path many candidates compete and name and size
are the only things telling them apart — "Munch" with no size would match every
Nestle product containing that word. Pass it only where a single candidate was
fetched by barcode: `fetch_verified_nutrition_by_barcode` and
`scripts/backfill_nutrition_from_barcodes`, and nothing else today.
---
## What a barcode is allowed to claim
`barcode_verified = true` means brand, size **and** name were matched against a
source record. It is not a synonym for "we have a barcode".
| `barcode_lookup_status` | Meaning |
|---|---|
| `verified` | the cascade matched brand + size + name |
| `name_matched` | bulk corpus match ≥ 0.88; the pack is **not** confirmed |
| `sheet_validated` | the merchant supplied it and it passes the GTIN checksum |
| `not_found` / `error` / `disabled` | no barcode stored |
A `name_matched` code is a real GS1 barcode for *that brand*, written to every
size variant of a title. Good enough for catalog matching, dedup and nutrition
lookups. **Not** good enough for logistics, invoicing, or anything a scanner
drives. Filter on `barcode_verified = false` to select, correct or revert them.
---
## Validation is not optional and not per-source
`validators.validate_barcode` is the single gate: digits only → a legal GTIN
length (8/12/13/14) → recomputed check digit. A source saying "this is the
barcode" is never sufficient. Trust affects the *order* sources are tried, never
whether validation runs.
Derived fields follow from the digits alone:
- `gtin` — the validated code
- `ean13` — a UPC-A zero-padded to 13. **GTIN-8 is not padded**: an 8-digit GTIN
is its own symbology, not a truncated EAN-13.
- `upc` — 12-digit codes only
> **`upc` will always be 0% in this catalog, and that is correct.** Every code
> here is GS1 India (prefix `890`), which issues EAN-13 and GTIN-8. UPC-A is
> North American. Measured: 0 of 300. The coverage report counts it *not
> applicable*, not missing.
---
## Where the fields go
A field computed by a stage reaches Postgres only if it is named in **all three**
of these. Two of them were missing the barcode fields for a long time, which is
why `upc` sat at 0% and `gtin` at 8.7% while the code that produced them ran on
every ingestion:
1. `store_catalog_pipeline._to_storage_row` — the projection
2. `vector_store.upsert_brand_products` — the INSERT column list
3. `vector_store._ensure_columns` — the only migration mechanism (no Alembic)
Plus `brand_sync.EXPORT_COLUMNS` for the seed-file round trip.
Every enrichment column uses `COALESCE(EXCLUDED.x, table.x)` in the
`ON CONFLICT` clause, including `barcode` itself. Without it a bare re-seed —
which carries no enrichment keys — sets them to NULL. Proven against a live
table: it kept `gtin` and blanked `barcode`, leaving a row claiming a GTIN with
no barcode.
---
## Running it
```bash
# What is filled, and where each value came from
python -m scripts.catalog_coverage --by-provenance
# Derive gtin/ean13/upc/barcode_type from barcodes already stored (offline)
python -m scripts.backfill_barcode_identity --apply
# Bulk-match barcodes from the OFF brand corpora
python -m scripts.backfill_barcodes_from_off --apply
# Upgrade nutrition from a name match to an exact barcode match
python -m scripts.backfill_nutrition_from_barcodes --apply
```
Every script is **dry-run by default** and prints its target database on
startup. `backend/.env` points at production.

46
docs/INGESTION_API.md
View File

@@ -507,6 +507,52 @@ But that stability is per `image_id`: change the product name and you get a new
10. Deterministic Product Validation Gate
11. Vector Embedding & Storage
Stages 8 and 9 each run several enrichment steps internally; the stage count and
numbering are unchanged.
### What gets filled for a branded row, and how far to trust it
The rules above for `Own Products` still hold — an unbranded row is stored as
you sent it. A **branded** row is gap-filled, and every filled value records
*how* it was arrived at in a `field_sources` map on the row, so nothing has to
be taken on trust:
| `method` | Means | Example |
| --- | --- | --- |
| `sourced` | a real value from a real external source | nutrients measured per 100 g, from Open Food Facts |
| `derived` | computed from another field on the same row | `ean13` from `barcode`; `tax_amount` from your price |
| `estimated` | a category-level or brand-level inference | the keyword nutrient list before a lookup succeeds |
| `not_applicable` | cannot exist for this product, and should not | nutrition on a shampoo; an FSSAI *food* licence on a detergent |
| `unknown` | we have not got it and have no source for it | an FSSAI licence for a brand not in the registry |
Two consequences worth reading twice:
- **`not_applicable` is not a gap.** Roughly 30% of a general FMCG catalog is
soap, shampoo and detergent. Those rows will never have nutrients or a health
score. Coverage percentages are reported against the *applicable* rows, so
they describe work remaining rather than work impossible.
- **`barcode_verified: false` means the pack is not confirmed.** A barcode found
by name match against a brand's catalogue is a real GS1 code for that brand,
written to every size variant of the title. Good enough for catalog matching,
dedup and nutrition lookups; **not** good enough for logistics, invoicing, or
anything a scanner drives. Filter on it before relying on a barcode
commercially.
Nothing is ever invented. There is no LLM anywhere in the nutrient, barcode,
FSSAI or tax path, and a value that cannot be sourced or safely derived is left
empty and labelled rather than guessed.
### Barcodes arrive after the upload finishes
Barcode and nutrition enrichment need the network, so they do **not** hold up
your response. Ingestion returns as soon as the rows are stored; a background
job then fetches each brand's catalogue and fills barcodes, nutrition and health
scores over the following minutes. Its id is on the batch as `nutrition_job_id`
and its progress is at `GET /api/admin/nutrition-intelligence/jobs/{job_id}`.
So a row read immediately after a `200` may have no barcode yet and have one a
few minutes later. That is expected, and re-reading is the only action needed.
### Limits
| Limit | Value | Env var | Exceeded |

216
docs/off_barcodes.csv Normal file
View File

@@ -0,0 +1,216 @@
brand_slug,brand_name,catalog_file,catalog_state,product_name,title,size,category,variant_key,product_sku,barcode_raw,barcode,barcode_type,computed_type,gtin,ean13,upc,barcode_source,barcode_verified,barcode_lookup_status,barcode_last_updated,checksum_valid,db_barcode,db_status
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Biryani Masala 160g,Aachi Biryani Masala,,Spices & Masalas,,AACHI-BIR-160-001,8906021120272,8906021120272,EAN-13,EAN-13,8906021120272,8906021120272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.8244982,True,8906021120272,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Biryani Masala 1kg,Aachi Biryani Masala,,Spices & Masalas,,B08TC3SNH1,8906021120272,8906021120272,EAN-13,EAN-13,8906021120272,8906021120272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.8244982,True,8906021120272,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Biryani Masala 200g,Aachi Biryani Masala,,Spices & Masalas,,B00ZGT1CEI,8906021120272,8906021120272,EAN-13,EAN-13,8906021120272,8906021120272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.8244982,True,8906021120272,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Biryani Masala 250g,Aachi Biryani Masala,,Spices & Masalas,,AACHI-BIR-250-002,8906021120272,8906021120272,EAN-13,EAN-13,8906021120272,8906021120272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.8244982,True,8906021120272,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Biryani Masala 500g,Aachi Biryani Masala,,Spices & Masalas,,B09V6MM1SF,8906021120272,8906021120272,EAN-13,EAN-13,8906021120272,8906021120272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.8244982,True,8906021120272,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Chicken Masala 500g,Aachi Chicken Masala,,Spices & Masalas,,AACHI-CHI-500-001,8906021120470,8906021120470,EAN-13,EAN-13,8906021120470,8906021120470,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.7751327,True,8906021120470,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Chilli Powder 200g,Aachi Chilli Powder,,Spices & Masalas,,100285629,8904209304087,8904209304087,EAN-13,EAN-13,8904209304087,8904209304087,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.826589,True,8904209304087,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Chilli Powder 500g,Aachi Chilli Powder,,Spices & Masalas,,B073V9GMB2,8904209304087,8904209304087,EAN-13,EAN-13,8904209304087,8904209304087,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.826589,True,8904209304087,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Mutton Masala 200g,Aachi Mutton Masala,,Spices & Masalas,,B08C7ZD99P,8906021122290,8906021122290,EAN-13,EAN-13,8906021122290,8906021122290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.7772982,True,8906021122290,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Mutton Masala 500g,Aachi Mutton Masala,,Spices & Masalas,,B073VB4QV2,8906021122290,8906021122290,EAN-13,EAN-13,8906021122290,8906021122290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.7772982,True,8906021122290,in_sync
aachi,Aachi,data/seed_catalogs/archive/brand_catalog_aachi.json,archived,Aachi Mutton Masala 50g,Aachi Mutton Masala,,Spices & Masalas,,100286167,8906021122290,8906021122290,EAN-13,EAN-13,8906021122290,8906021122290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773804.7772982,True,8906021122290,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Dahi 1kg,Amul Dahi,1kg,Dairy,amul_amul_dahi_1kg,B0D7W14TS5,8901262200271,8901262200271,EAN-13,EAN-13,8901262200271,8901262200271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.0632794,True,8901262200271,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Dahi 200 g,Amul Dahi,200 g,Dairy,amul_amul_dahi_200_g,30000356,8901262200271,8901262200271,EAN-13,EAN-13,8901262200271,8901262200271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.0632794,True,8901262200271,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Dahi 400g,Amul Dahi,400g,Dairy,amul_amul_dahi_400g,104851,8901262200271,8901262200271,EAN-13,EAN-13,8901262200271,8901262200271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.0632794,True,8901262200271,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Dahi 90g,Amul Dahi,90g,Dairy,amul_amul_dahi_90g,45533,8901262200271,8901262200271,EAN-13,EAN-13,8901262200271,8901262200271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.0632794,True,8901262200271,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Fresh Cream 1L,Amul Fresh Cream,1L,Cheese,amul_amul_fresh_cream_1l,162,8901262150118,8901262150118,EAN-13,EAN-13,8901262150118,8901262150118,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2152038,True,8901262150118,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Fresh Cream 250 ml,Amul Fresh Cream,250 ml,Cheese,amul_amul_fresh_cream_250_ml,B0758LVKLL,8901262150118,8901262150118,EAN-13,EAN-13,8901262150118,8901262150118,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2152038,True,8901262150118,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Fresh Cream 90ml,Amul Fresh Cream,90ml,Cheese,amul_amul_fresh_cream_90ml,162,8901262150118,8901262150118,EAN-13,EAN-13,8901262150118,8901262150118,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2152038,True,8901262150118,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ghee 1L,Amul Ghee,1L,Dairy,amul_amul_ghee_1l,40096994,8901262031059,8901262031059,EAN-13,EAN-13,8901262031059,8901262031059,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.015503,True,8901262031059,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ghee 200ml,Amul Ghee,200ml,Dairy,amul_amul_ghee_200ml,40166276,8901262031059,8901262031059,EAN-13,EAN-13,8901262031059,8901262031059,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.015503,True,8901262031059,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ghee 500ml,Amul Ghee,500ml,Dairy,amul_amul_ghee_500ml,40050541,8901262031059,8901262031059,EAN-13,EAN-13,8901262031059,8901262031059,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.015503,True,8901262031059,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ghee 90ml,Amul Ghee,90ml,Dairy,amul_amul_ghee_90ml,40166277,8901262031059,8901262031059,EAN-13,EAN-13,8901262031059,8901262031059,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.015503,True,8901262031059,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ice Cream 1L,Amul Ice Cream,1L,Ice Cream,amul_amul_ice_cream_1l,40003798,8901262172363,8901262172363,EAN-13,EAN-13,8901262172363,8901262172363,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.1036828,True,8901262172363,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ice Cream 250g,Amul Ice Cream,250g,Ice Cream,amul_amul_ice_cream_250g,40003801,8901262172363,8901262172363,EAN-13,EAN-13,8901262172363,8901262172363,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.1036828,True,8901262172363,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Ice Cream 90ml,Amul Ice Cream,90ml,Ice Cream,amul_amul_ice_cream_90ml,40344404,8901262172363,8901262172363,EAN-13,EAN-13,8901262172363,8901262172363,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.1036828,True,8901262172363,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Kool 100ml,Amul Kool,100ml,Beverages,amul_amul_kool_100ml,B00ZCLCCWG,8901262153355,8901262153355,EAN-13,EAN-13,8901262153355,8901262153355,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.123552,True,8901262153355,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Kool 180ml,Amul Kool,180ml,Beverages,amul_amul_kool_180ml,B00NTU7YOS,8901262153355,8901262153355,EAN-13,EAN-13,8901262153355,8901262153355,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.123552,True,8901262153355,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Kool 250ml,Amul Kool,250ml,Beverages,amul_amul_kool_250ml,69633,8901262153355,8901262153355,EAN-13,EAN-13,8901262153355,8901262153355,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.123552,True,8901262153355,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lassi 100ml,Amul Lassi,100ml,Beverages,amul_amul_lassi_100ml,656965,8901262200189,8901262200189,EAN-13,EAN-13,8901262200189,8901262200189,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.072889,True,8901262200189,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lassi 1kg,Amul Lassi,1kg,Beverages,amul_amul_lassi_1kg,68553,8901262200189,8901262200189,EAN-13,EAN-13,8901262200189,8901262200189,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.072889,True,8901262200189,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lassi 200g,Amul Lassi,200g,Beverages,amul_amul_lassi_200g,178319,8901262200189,8901262200189,EAN-13,EAN-13,8901262200189,8901262200189,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.072889,True,8901262200189,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lassi 250ml,Amul Lassi,250ml,Beverages,amul_amul_lassi_250ml,40026295,8901262200189,8901262200189,EAN-13,EAN-13,8901262200189,8901262200189,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.072889,True,8901262200189,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lite Bread Spread 1kg,Amul Lite Bread Spread,1kg,Bakery & Breads,amul_amul_lite_bread_spread_1kg,255187,8901262140065,8901262140065,EAN-13,EAN-13,8901262140065,8901262140065,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.3098247,True,8901262140065,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lite Bread Spread 500 g,Amul Lite Bread Spread,500 g,Bakery & Breads,amul_amul_lite_bread_spread_500_g,257195,8901262140065,8901262140065,EAN-13,EAN-13,8901262140065,8901262140065,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.3098247,True,8901262140065,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Lite Bread Spread 90g,Amul Lite Bread Spread,90g,Bakery & Breads,amul_amul_lite_bread_spread_90g,255187,8901262140065,8901262140065,EAN-13,EAN-13,8901262140065,8901262140065,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.3098247,True,8901262140065,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Malai Paneer 1L,Amul Malai Paneer,1L,Dairy,amul_amul_malai_paneer_1l,40096747,8901262180016,8901262180016,EAN-13,EAN-13,8901262180016,8901262180016,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2311263,True,8901262180016,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Malai Paneer 200ml,Amul Malai Paneer,200ml,Dairy,amul_amul_malai_paneer_200ml,40096747,8901262180016,8901262180016,EAN-13,EAN-13,8901262180016,8901262180016,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2311263,True,8901262180016,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Malai Paneer 425ml,Amul Malai Paneer,425ml,Dairy,amul_amul_malai_paneer_425ml,40096747,8901262180016,8901262180016,EAN-13,EAN-13,8901262180016,8901262180016,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2311263,True,8901262180016,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Malai Paneer 90ml,Amul Malai Paneer,90ml,Dairy,amul_amul_malai_paneer_90ml,40096747,8901262180016,8901262180016,EAN-13,EAN-13,8901262180016,8901262180016,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2311263,True,8901262180016,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Masti Dahi 1L,Amul Masti Dahi,1L,Cheese,amul_amul_masti_dahi_1l,40323755,8901262200677,8901262200677,EAN-13,EAN-13,8901262200677,8901262200677,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2026575,True,8901262200677,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Masti Dahi 200ml,Amul Masti Dahi,200ml,Cheese,amul_amul_masti_dahi_200ml,30000356,8901262200677,8901262200677,EAN-13,EAN-13,8901262200677,8901262200677,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2026575,True,8901262200677,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Masti Dahi 5L,Amul Masti Dahi,5L,Cheese,amul_amul_masti_dahi_5l,AMUL-MAS-5-001,8901262200677,8901262200677,EAN-13,EAN-13,8901262200677,8901262200677,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2026575,True,8901262200677,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Masti Dahi 90ml,Amul Masti Dahi,90ml,Cheese,amul_amul_masti_dahi_90ml,45533,8901262200677,8901262200677,EAN-13,EAN-13,8901262200677,8901262200677,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.2026575,True,8901262200677,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Shrikhand 1kg,Amul Shrikhand,1kg,Dairy - Desserts,amul_amul_shrikhand_1kg,AMUL-SHR-1-001,8901262040051,8901262040051,EAN-13,EAN-13,8901262040051,8901262040051,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.152311,True,8901262040051,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Shrikhand 500g,Amul Shrikhand,500g,Dairy - Desserts,amul_amul_shrikhand_500g,104833,8901262040051,8901262040051,EAN-13,EAN-13,8901262040051,8901262040051,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.152311,True,8901262040051,in_sync
amul,amul,data/seed_catalogs/brand_catalog_amul.json,active,Amul Shrikhand 90g,Amul Shrikhand,90g,Dairy - Desserts,amul_amul_shrikhand_90g,SAMEZ3BURAXPCXGQ,8901262040051,8901262040051,EAN-13,EAN-13,8901262040051,8901262040051,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768769.152311,True,8901262040051,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 100g,Cadbury 5 Star,100g,Chocolates,cadbury_cadbury_5_star_100g,B0758Q2W7D,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 18g,Cadbury 5 Star,18g,Chocolates,cadbury_cadbury_5_star_18g,B0H6GKBR83,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 200g,Cadbury 5 Star,200g,Chocolates,cadbury_cadbury_5_star_200g,B00XYALG1K,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 21 g,Cadbury 5 Star,21 g,Chocolates,cadbury_cadbury_5_star_21_g,40325909,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 5 gm,Cadbury 5 Star,5 gm,Chocolates,cadbury_cadbury_5_star_5_gm,B0BYN4RN4T,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury 5 Star 9.8 g,Cadbury 5 Star,9.8 g,Chocolates,cadbury_cadbury_5_star_9_8_g,900457475,8901233020273,8901233020273,EAN-13,EAN-13,8901233020273,8901233020273,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8735542,True,8901233020273,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Bournvita 14.4g,Cadbury Bournvita,14.4g,Health Drinks,cadbury_cadbury_bournvita_14_4g,B00LIVCED6,8901233018362,8901233018362,EAN-13,EAN-13,8901233018362,8901233018362,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8771605,True,8901233018362,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Bournvita 1kg,Cadbury Bournvita,1kg,Health Drinks,cadbury_cadbury_bournvita_1kg,B08N5PTZC7,8901233018362,8901233018362,EAN-13,EAN-13,8901233018362,8901233018362,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8771605,True,8901233018362,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Bournvita 2 kg,Cadbury Bournvita,2 kg,Health Drinks,cadbury_cadbury_bournvita_2_kg,1214685,8901233018362,8901233018362,EAN-13,EAN-13,8901233018362,8901233018362,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8771605,True,8901233018362,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Bournvita 25g,Cadbury Bournvita,25g,Health Drinks,cadbury_cadbury_bournvita_25g,400825,8901233018362,8901233018362,EAN-13,EAN-13,8901233018362,8901233018362,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8771605,True,8901233018362,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Bournvita 500g,Cadbury Bournvita,500g,Health Drinks,cadbury_cadbury_bournvita_500g,B06XSB6RB2,8901233018362,8901233018362,EAN-13,EAN-13,8901233018362,8901233018362,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8771605,True,8901233018362,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Dairy Milk 100g,Cadbury Dairy Milk,100g,Chocolates,cadbury_cadbury_dairy_milk_100g,B079TQSV9S,8901233028361,8901233028361,EAN-13,EAN-13,8901233028361,8901233028361,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8806314,True,8901233028361,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Dairy Milk 500g,Cadbury Dairy Milk,500g,Chocolates,cadbury_cadbury_dairy_milk_500g,CADBUR-DAI-500-001,8901233028361,8901233028361,EAN-13,EAN-13,8901233028361,8901233028361,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.8806314,True,8901233028361,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Fuse 100g,Cadbury Fuse,100g,Chocolates,cadbury_cadbury_fuse_100g,B07FNZRTZD,8901233023687,8901233023687,EAN-13,EAN-13,8901233023687,8901233023687,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.924282,True,8901233023687,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Fuse 25 g,Cadbury Fuse,25 g,Chocolates,cadbury_cadbury_fuse_25_g,B0BRXV8R1C,8901233023687,8901233023687,EAN-13,EAN-13,8901233023687,8901233023687,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.924282,True,8901233023687,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Fuse 50g,Cadbury Fuse,50g,Chocolates,cadbury_cadbury_fuse_50g,B07F6B4R1W,8901233023687,8901233023687,EAN-13,EAN-13,8901233023687,8901233023687,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.924282,True,8901233023687,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Nutties 100g,Cadbury Nutties,100g,Chocolates,cadbury_cadbury_nutties_100g,34422,8901233021492,8901233021492,EAN-13,EAN-13,8901233021492,8901233021492,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.9583745,True,8901233021492,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Nutties 20g,Cadbury Nutties,20g,Chocolates,cadbury_cadbury_nutties_20g,B01IHCPDJA,8901233021492,8901233021492,EAN-13,EAN-13,8901233021492,8901233021492,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.9583745,True,8901233021492,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Nutties 30g,Cadbury Nutties,30g,Chocolates,cadbury_cadbury_nutties_30g,B01IHCPDJA,8901233021492,8901233021492,EAN-13,EAN-13,8901233021492,8901233021492,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.9583745,True,8901233021492,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Nutties 55g,Cadbury Nutties,55g,Chocolates,cadbury_cadbury_nutties_55g,B0721MLS76,8901233021492,8901233021492,EAN-13,EAN-13,8901233021492,8901233021492,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.9583745,True,8901233021492,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Perk 11g,Cadbury Perk,11g,Chocolates,cadbury_cadbury_perk_11g,20005969,8901233030272,8901233030272,EAN-13,EAN-13,8901233030272,8901233030272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.886849,True,8901233030272,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Perk 14.3 g,Cadbury Perk,14.3 g,Chocolates,cadbury_cadbury_perk_14_3_g,CADBUR-PER-143-001,8901233030272,8901233030272,EAN-13,EAN-13,8901233030272,8901233030272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.886849,True,8901233030272,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Perk 1kg,Cadbury Perk,1kg,Chocolates,cadbury_cadbury_perk_1kg,B01B5ZXNLQ,8901233030272,8901233030272,EAN-13,EAN-13,8901233030272,8901233030272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.886849,True,8901233030272,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Perk 22 g,Cadbury Perk,22 g,Chocolates,cadbury_cadbury_perk_22_g,CHCFWY5YHXYBGDYH,8901233030272,8901233030272,EAN-13,EAN-13,8901233030272,8901233030272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.886849,True,8901233030272,in_sync
cadbury,cadbury,data/seed_catalogs/brand_catalog_cadbury.json,active,Cadbury Perk 90g,Cadbury Perk,90g,Chocolates,cadbury_cadbury_perk_90g,225506,8901233030272,8901233030272,EAN-13,EAN-13,8901233030272,8901233030272,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768771.886849,True,8901233030272,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Fanta 350ml,Coca-Cola Fanta,350ml,Beverages,coca_cola_coca_cola_fanta_350ml,COCACO-FAN-350-001,8906000379134,8906000379134,EAN-13,EAN-13,8906000379134,8906000379134,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.368387,True,8906000379134,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Fanta 750ml,Coca-Cola Fanta,750ml,Beverages,coca_cola_coca_cola_fanta_750ml,B0752S51ZL,8906000379134,8906000379134,EAN-13,EAN-13,8906000379134,8906000379134,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.368387,True,8906000379134,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Limca 1L,Coca-Cola Limca,1L,Beverages,coca_cola_coca_cola_limca_1l,COCACO-LIM-1-001,89000601,89000601,GTIN-8,GTIN-8,89000601,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3936577,True,89000601,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Limca 750g,Coca-Cola Limca,750g,Beverages,coca_cola_coca_cola_limca_750g,427683,89000601,89000601,GTIN-8,GTIN-8,89000601,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3936577,True,89000601,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 1.2L,Coca-Cola Maaza,1.2L,Beverages,coca_cola_coca_cola_maaza_1_2l,B00GX9TS6O,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 125ml,Coca-Cola Maaza,125ml,Beverages,coca_cola_coca_cola_maaza_125ml,COCACO-MAA-125-001,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 250ml,Coca-Cola Maaza,250ml,Beverages,coca_cola_coca_cola_maaza_250ml,COCACO-MAA-250-001,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 350ml,Coca-Cola Maaza,350ml,Beverages,coca_cola_coca_cola_maaza_350ml,COCACO-MAA-350-001,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 600 ml,Coca-Cola Maaza,600 ml,Beverages,coca_cola_coca_cola_maaza_600_ml,COCACO-MAA-600-001,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Maaza 750ml,Coca-Cola Maaza,750ml,Beverages,coca_cola_coca_cola_maaza_750ml,B004ZXK6FC,8901764175015,8901764175015,EAN-13,EAN-13,8901764175015,8901764175015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3713913,True,8901764175015,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Minute Maid Apple 150 ml,Coca-Cola Minute Maid Apple,150 ml,Beverages,coca_cola_coca_cola_minute_maid_apple_150_ml,COCACO-MIN-150-002,8901764385155,8901764385155,EAN-13,EAN-13,8901764385155,8901764385155,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.4120922,True,8901764385155,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Minute Maid Apple 350ml,Coca-Cola Minute Maid Apple,350ml,Beverages,coca_cola_coca_cola_minute_maid_apple_350ml,COCACO-MIN-350-004,8901764385155,8901764385155,EAN-13,EAN-13,8901764385155,8901764385155,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.4120922,True,8901764385155,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Minute Maid Apple 750ml,Coca-Cola Minute Maid Apple,750ml,Beverages,coca_cola_coca_cola_minute_maid_apple_750ml,COCACO-MIN-750-004,8901764385155,8901764385155,EAN-13,EAN-13,8901764385155,8901764385155,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.4120922,True,8901764385155,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Sprite 350ml,Coca-Cola Sprite,350ml,Beverages,coca_cola_coca_cola_sprite_350ml,COCACO-SPR-350-001,8901764032271,8901764032271,EAN-13,EAN-13,8901764032271,8901764032271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3654883,True,8901764032271,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Sprite 750ml,Coca-Cola Sprite,750ml,Beverages,coca_cola_coca_cola_sprite_750ml,COCACO-SPR-750-001,8901764032271,8901764032271,EAN-13,EAN-13,8901764032271,8901764032271,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3654883,True,8901764032271,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Thums Up 350ml,Coca-Cola Thums Up,350ml,Beverages,coca_cola_coca_cola_thums_up_350ml,COCACO-THU-350-001,8901764042300,8901764042300,EAN-13,EAN-13,8901764042300,8901764042300,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3626227,True,8901764042300,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Thums Up 750ml,Coca-Cola Thums Up,750ml,Beverages,coca_cola_coca_cola_thums_up_750ml,251014,8901764042300,8901764042300,EAN-13,EAN-13,8901764042300,8901764042300,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3626227,True,8901764042300,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,"Coca-Cola Zero 1,25 L e",Coca-Cola Zero,"1,25 L e",Beverages,coca_cola_coca_cola_zero_1_25_l_e,COCACO-ZER-1-001,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Zero 1.5l,Coca-Cola Zero,1.5l,Beverages,coca_cola_coca_cola_zero_1_5l,COCACO-ZER-15-001,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Zero 1L,Coca-Cola Zero,1L,Beverages,coca_cola_coca_cola_zero_1l,COCACO-ZER-1-002,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Zero 250 ml,Coca-Cola Zero,250 ml,Beverages,coca_cola_coca_cola_zero_250_ml,COCACO-ZER-250-001,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Zero 330 ml,Coca-Cola Zero,330 ml,Beverages,coca_cola_coca_cola_zero_330_ml,COCACO-ZER-330-001,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
coca_cola,coca-cola,data/seed_catalogs/archive/brand_catalog_coca_cola.json,archived,Coca-Cola Zero 500ml,Coca-Cola Zero,500ml,Beverages,coca_cola_coca_cola_zero_500ml,COCACO-ZER-500-001,8901764112706,8901764112706,EAN-13,EAN-13,8901764112706,8901764112706,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773807.3840604,True,8901764112706,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Dental Cream 120g,Colgate-Palmolive Colgate Dental Cream,120g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_dental_cream_120g,COLGAT-COL-120-001,8901314765352,8901314765352,EAN-13,EAN-13,8901314765352,8901314765352,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5714066,True,8901314765352,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Dental Cream 50g,Colgate-Palmolive Colgate Dental Cream,50g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_dental_cream_50g,COLGAT-COL-50-001,8901314765352,8901314765352,EAN-13,EAN-13,8901314765352,8901314765352,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5714066,True,8901314765352,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Dental Cream 90g,Colgate-Palmolive Colgate Dental Cream,90g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_dental_cream_90g,COLGAT-COL-90-001,8901314765352,8901314765352,EAN-13,EAN-13,8901314765352,8901314765352,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5714066,True,8901314765352,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Maxfresh 10g,Colgate-Palmolive Colgate Maxfresh,10g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_maxfresh_10g,B079RXNHHT,8901314543653,8901314543653,EAN-13,EAN-13,8901314543653,8901314543653,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5746639,True,8901314543653,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Maxfresh 19g,Colgate-Palmolive Colgate Maxfresh,19g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_maxfresh_19g,B079RXNHHT,8901314543653,8901314543653,EAN-13,EAN-13,8901314543653,8901314543653,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5746639,True,8901314543653,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Maxfresh 8g,Colgate-Palmolive Colgate Maxfresh,8g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_maxfresh_8g,COLGAT-COL-8-001,8901314543653,8901314543653,EAN-13,EAN-13,8901314543653,8901314543653,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5746639,True,8901314543653,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Sensitive 20g,Colgate-Palmolive Colgate Sensitive,20g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_sensitive_20g,COLGAT-COL-20-001,8901314311832,8901314311832,EAN-13,EAN-13,8901314311832,8901314311832,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5771685,True,8901314311832,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Sensitive 30g,Colgate-Palmolive Colgate Sensitive,30g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_sensitive_30g,COLGAT-COL-30-001,8901314311832,8901314311832,EAN-13,EAN-13,8901314311832,8901314311832,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5771685,True,8901314311832,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Sensitive 50g,Colgate-Palmolive Colgate Sensitive,50g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_sensitive_50g,COLGAT-COL-50-002,8901314311832,8901314311832,EAN-13,EAN-13,8901314311832,8901314311832,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5771685,True,8901314311832,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Sensitive 8g,Colgate-Palmolive Colgate Sensitive,8g,Oral Care,colgate_palmolive_colgate_palmolive_colgate_sensitive_8g,COLGAT-COL-8-002,8901314311832,8901314311832,EAN-13,EAN-13,8901314311832,8901314311832,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5771685,True,8901314311832,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Visible White 100g,Colgate-Palmolive Colgate Visible White,100g,General,colgate_palmolive_colgate_palmolive_colgate_visible_white_100g,COLGAT-COL-100-001,8901314011183,8901314011183,EAN-13,EAN-13,8901314011183,8901314011183,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5915623,True,8901314011183,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Visible White 150ml,Colgate-Palmolive Colgate Visible White,150ml,General,colgate_palmolive_colgate_palmolive_colgate_visible_white_150ml,B09QSBPKTF,8901314011183,8901314011183,EAN-13,EAN-13,8901314011183,8901314011183,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5915623,True,8901314011183,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Visible White 200ml,Colgate-Palmolive Colgate Visible White,200ml,General,colgate_palmolive_colgate_palmolive_colgate_visible_white_200ml,COLGAT-COL-200-001,8901314011183,8901314011183,EAN-13,EAN-13,8901314011183,8901314011183,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5915623,True,8901314011183,in_sync
colgate_palmolive,colgate-palmolive,data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json,archived,Colgate-Palmolive Colgate Visible White 50g,Colgate-Palmolive Colgate Visible White,50g,General,colgate_palmolive_colgate_palmolive_colgate_visible_white_50g,B09QSBPKTF,8901314011183,8901314011183,EAN-13,EAN-13,8901314011183,8901314011183,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773808.5915623,True,8901314011183,in_sync
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Chyawanprash 10g,Chyawanprash,10g,Health Care - Ayurvedic,dabur_chyawanprash_10g,DABUR-CHY-10-001,8901207036989,8901207036989,EAN-13,EAN-13,8901207036989,8901207036989,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.447363,True,,row_not_in_db
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Chyawanprash 20g,Chyawanprash,20g,Health Care - Ayurvedic,dabur_chyawanprash_20g,DABUR-CHY-20-001,8901207036989,8901207036989,EAN-13,EAN-13,8901207036989,8901207036989,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.447363,True,8901207036989,in_sync
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Chyawanprash 5g,Chyawanprash,5g,Health Care - Ayurvedic,dabur_chyawanprash_5g,30009463,8901207036989,8901207036989,EAN-13,EAN-13,8901207036989,8901207036989,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.447363,True,,row_not_in_db
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Gulabari 100g,Dabur Gulabari,100g,Skin Care,dabur_dabur_gulabari_100g,B0BDRRJNLC,89005590,89005590,GTIN-8,GTIN-8,89005590,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.5249188,True,89005590,in_sync
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Gulabari 59g,Dabur Gulabari,59g,Skin Care,dabur_dabur_gulabari_59g,DABUR-GUL-59-001,89005590,89005590,GTIN-8,GTIN-8,89005590,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.5249188,True,,row_not_in_db
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Gulabari 75g,Dabur Gulabari,75g,Skin Care,dabur_dabur_gulabari_75g,DABUR-GUL-75-001,89005590,89005590,GTIN-8,GTIN-8,89005590,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.5249188,True,,row_not_in_db
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Odomos 100g,Dabur Odomos,100g,Personal Care - Mosquito Repellent,dabur_dabur_odomos_100g,B00HVSSZY2,8901207500053,8901207500053,EAN-13,EAN-13,8901207500053,8901207500053,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.519291,True,8901207500053,in_sync
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Odomos 20g,Dabur Odomos,20g,Personal Care - Mosquito Repellent,dabur_dabur_odomos_20g,B00AXX608K,8901207500053,8901207500053,EAN-13,EAN-13,8901207500053,8901207500053,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.519291,True,,row_not_in_db
dabur,dabur,data/seed_catalogs/archive/brand_catalog_dabur.json,archived,Dabur Odomos 50g,Dabur Odomos,50g,Personal Care - Mosquito Repellent,dabur_dabur_odomos_50g,DABUR-ODO-50-001,8901207500053,8901207500053,EAN-13,EAN-13,8901207500053,8901207500053,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.519291,True,,row_not_in_db
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Cinthol 100g,Godrej Cinthol,100g,Bath Soap,godrej_godrej_cinthol_100g,GODREJ-CIN-100-001,8901023020353,8901023020353,EAN-13,EAN-13,8901023020353,8901023020353,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.976005,True,8901023020353,in_sync
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Cinthol 50g,Godrej Cinthol,50g,Bath Soap,godrej_godrej_cinthol_50g,B0739RXZT8,8901023020353,8901023020353,EAN-13,EAN-13,8901023020353,8901023020353,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.976005,True,,row_not_in_db
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Cinthol 75g,Godrej Cinthol,75g,Bath Soap,godrej_godrej_cinthol_75g,B01MZWIZA9,8901023020353,8901023020353,EAN-13,EAN-13,8901023020353,8901023020353,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.976005,True,,row_not_in_db
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Nupur Henna 100ml,Godrej Nupur Henna,100ml,Hair Care,godrej_godrej_nupur_henna_100ml,438469,8901023018602,8901023018602,EAN-13,EAN-13,8901023018602,8901023018602,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.9603631,True,,row_not_in_db
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Nupur Henna 250ml,Godrej Nupur Henna,250ml,Hair Care,godrej_godrej_nupur_henna_250ml,B08D8Z9JNL,8901023018602,8901023018602,EAN-13,EAN-13,8901023018602,8901023018602,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.9603631,True,8901023018602,in_sync
godrej,godrej,data/seed_catalogs/archive/brand_catalog_godrej.json,archived,Godrej Nupur Henna 90ml,Godrej Nupur Henna,90ml,Hair Care,godrej_godrej_nupur_henna_90ml,B005ZLCIU4,8901023018602,8901023018602,EAN-13,EAN-13,8901023018602,8901023018602,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773809.9603631,True,,row_not_in_db
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Health Mix 100g,Manna Health Mix 50g,100g,Health Foods,manna_manna_health_mix_50g_100g,B074778SPY,8906008350852,8906008350852,EAN-13,EAN-13,8906008350852,8906008350852,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4010751,True,8906008350852,in_sync
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Health Mix 25g,Manna Health Mix 50g,25g,Health Foods,manna_manna_health_mix_50g_25g,MDMFYWX4SC4NRTFZ,8906008350852,8906008350852,EAN-13,EAN-13,8906008350852,8906008350852,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4010751,True,,row_not_in_db
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Health Mix 50g,Manna Health Mix 50g,50g,Health Foods,manna_manna_health_mix_50g_50g,B074778SPY,8906008350852,8906008350852,EAN-13,EAN-13,8906008350852,8906008350852,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4010751,True,,row_not_in_db
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Ragi Malt 100g,Manna Ragi Malt 50g,100g,Health Foods,manna_manna_ragi_malt_50g_100g,B07D755GSF,8906008350388,8906008350388,EAN-13,EAN-13,8906008350388,8906008350388,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4015913,True,8906008350388,in_sync
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Ragi Malt 25g,Manna Ragi Malt 50g,25g,Health Foods,manna_manna_ragi_malt_50g_25g,B07D755GSF,8906008350388,8906008350388,EAN-13,EAN-13,8906008350388,8906008350388,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4015913,True,,row_not_in_db
manna,Manna,data/seed_catalogs/archive/brand_catalog_manna.json,archived,Manna Ragi Malt 50g,Manna Ragi Malt 50g,50g,Health Foods,manna_manna_ragi_malt_50g_50g,B00DRE5614,8906008350388,8906008350388,EAN-13,EAN-13,8906008350388,8906008350388,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.4015913,True,,row_not_in_db
mtr,Mtr,data/seed_catalogs/archive/brand_catalog_mtr.json,archived,MTR Sambar Powder 1.5kg,MTR Sambar Powder,,Spices & Masalas,,SCMETEMHEY5Z3VMX,8901042954721,8901042954721,EAN-13,EAN-13,8901042954721,8901042954721,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.924069,True,8901042954721,in_sync
mtr,Mtr,data/seed_catalogs/archive/brand_catalog_mtr.json,archived,MTR Sambar Powder 200g,MTR Sambar Powder,,Spices & Masalas,,B009LL92VC,8901042954721,8901042954721,EAN-13,EAN-13,8901042954721,8901042954721,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.924069,True,8901042954721,in_sync
mtr,Mtr,data/seed_catalogs/archive/brand_catalog_mtr.json,archived,MTR Sambar Powder 500g,MTR Sambar Powder,,Spices & Masalas,,40185042,8901042954721,8901042954721,EAN-13,EAN-13,8901042954721,8901042954721,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.924069,True,8901042954721,in_sync
mtr,Mtr,data/seed_catalogs/archive/brand_catalog_mtr.json,archived,MTR Sambar Powder 90g,MTR Sambar Powder,,Spices & Masalas,,SCMETEMHEY5Z3VMX,8901042954721,8901042954721,EAN-13,EAN-13,8901042954721,8901042954721,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773810.924069,True,8901042954721,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Cerelac 125g,Nestle Cerelac,125g,Baby Care,nestle_nestle_cerelac_125g,B004ZKZMAE,8901058844627,8901058844627,EAN-13,EAN-13,8901058844627,8901058844627,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5886683,True,8901058844627,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Cerelac 250g,Nestle Cerelac,250g,Baby Care,nestle_nestle_cerelac_250g,NESTLE-CER-250-001,8901058844627,8901058844627,EAN-13,EAN-13,8901058844627,8901058844627,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5886683,True,8901058844627,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Cerelac 300g,Nestle Cerelac,300g,Baby Care,nestle_nestle_cerelac_300g,25012,8901058844627,8901058844627,EAN-13,EAN-13,8901058844627,8901058844627,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5886683,True,8901058844627,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Cerelac 400g,Nestle Cerelac,400g,Baby Care,nestle_nestle_cerelac_400g,NESTLE-CER-400-001,8901058844627,8901058844627,EAN-13,EAN-13,8901058844627,8901058844627,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5886683,True,8901058844627,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Cerelac 90g,Nestle Cerelac,90g,Baby Care,nestle_nestle_cerelac_90g,NESTLE-CER-90-001,8901058844627,8901058844627,EAN-13,EAN-13,8901058844627,8901058844627,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5886683,True,8901058844627,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Kitkat 120g,Nestle Kitkat,120g,Chocolates,nestle_nestle_kitkat_120g,NESTLE-KIT-120-001,8901058857245,8901058857245,EAN-13,EAN-13,8901058857245,8901058857245,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5304384,True,8901058857245,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Kitkat 170g,Nestle Kitkat,170g,Chocolates,nestle_nestle_kitkat_170g,40018531,8901058857245,8901058857245,EAN-13,EAN-13,8901058857245,8901058857245,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5304384,True,8901058857245,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Kitkat 50g,Nestle Kitkat,50g,Chocolates,nestle_nestle_kitkat_50g,NESTLE-KIT-50-001,8901058857245,8901058857245,EAN-13,EAN-13,8901058857245,8901058857245,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5304384,True,8901058857245,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 10ml,Nestle Milkybar,10ml,Chocolates,nestle_nestle_milkybar_10ml,40090019,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 1kg,Nestle Milkybar,1kg,Chocolates,nestle_nestle_milkybar_1kg,B01ILWLMLE,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 24.5ml,Nestle Milkybar,24.5ml,Chocolates,nestle_nestle_milkybar_24_5ml,B08P5Y1GPF,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 30ml,Nestle Milkybar,30ml,Chocolates,nestle_nestle_milkybar_30ml,B08S55766X,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 38ml,Nestle Milkybar,38ml,Chocolates,nestle_nestle_milkybar_38ml,NESTLE-MIL-38-001,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milkybar 90g,Nestle Milkybar,90g,Chocolates,nestle_nestle_milkybar_90g,B005GLIBLI,89008478,89008478,GTIN-8,GTIN-8,89008478,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5370526,True,89008478,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 100g,Nestle Milo,100g,Health Drinks,nestle_nestle_milo_100g,B00RBMP37A,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 165g,Nestle Milo,165g,Health Drinks,nestle_nestle_milo_165g,B00RBMP37A,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 200 g,Nestle Milo,200 g,Health Drinks,nestle_nestle_milo_200_g,NESTLE-MIL-200-001,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 20g,Nestle Milo,20g,Health Drinks,nestle_nestle_milo_20g,40184472,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 25 g,Nestle Milo,25 g,Health Drinks,nestle_nestle_milo_25_g,B00RBMP37A,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Milo 30g,Nestle Milo,30g,Health Drinks,nestle_nestle_milo_30g,NESTLE-MIL-30-001,8901058904017,8901058904017,EAN-13,EAN-13,8901058904017,8901058904017,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6294699,True,8901058904017,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 150g,Nestle Munch,150g,Chocolates,nestle_nestle_munch_150g,B01MRFIF28,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 20g,Nestle Munch,20g,Chocolates,nestle_nestle_munch_20g,496297,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 38.5 g,Nestle Munch,38.5 g,Chocolates,nestle_nestle_munch_38_5_g,40269268,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 55g,Nestle Munch,55g,Chocolates,nestle_nestle_munch_55g,496297,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 6 x 90 g,Nestle Munch,6 x 90 g,Chocolates,nestle_nestle_munch_6_x_90_g,127096,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Munch 8.9g,Nestle Munch,8.9g,Chocolates,nestle_nestle_munch_8_9g,B01MQEA436,89009802,89009802,GTIN-8,GTIN-8,89009802,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5439541,True,89009802,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nescafe Sunrise 1kg,Nestle Nescafe Sunrise,1kg,Tea & Coffee,nestle_nestle_nescafe_sunrise_1kg,B079H34CLY,8901058902938,8901058902938,EAN-13,EAN-13,8901058902938,8901058902938,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6650498,True,8901058902938,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nescafe Sunrise 200g,Nestle Nescafe Sunrise,200g,Tea & Coffee,nestle_nestle_nescafe_sunrise_200g,B079H34CLY,8901058902938,8901058902938,EAN-13,EAN-13,8901058902938,8901058902938,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6650498,True,8901058902938,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nescafe Sunrise 250g,Nestle Nescafe Sunrise,250g,Tea & Coffee,nestle_nestle_nescafe_sunrise_250g,B079H34CLY,8901058902938,8901058902938,EAN-13,EAN-13,8901058902938,8901058902938,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6650498,True,8901058902938,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nescafe Sunrise 5 g,Nestle Nescafe Sunrise,5 g,Tea & Coffee,nestle_nestle_nescafe_sunrise_5_g,B0971VNDPW,8901058902938,8901058902938,EAN-13,EAN-13,8901058902938,8901058902938,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6650498,True,8901058902938,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nescafe Sunrise 90 g,Nestle Nescafe Sunrise,90 g,Tea & Coffee,nestle_nestle_nescafe_sunrise_90_g,B0971VNDPW,8901058902938,8901058902938,EAN-13,EAN-13,8901058902938,8901058902938,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6650498,True,8901058902938,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 200g,Nestle Nestea,200g,Beverages,nestle_nestle_nestea_200g,402001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 240g,Nestle Nestea,240g,Beverages,nestle_nestle_nestea_240g,NESTLE-NES-240-001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 25g,Nestle Nestea,25g,Beverages,nestle_nestle_nestea_25g,NESTLE-NES-25-001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 33 g,Nestle Nestea,33 g,Beverages,nestle_nestle_nestea_33_g,NESTLE-NES-33-001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 350ml,Nestle Nestea,350ml,Beverages,nestle_nestle_nestea_350ml,NESTLE-NES-350-001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Nestea 750ml,Nestle Nestea,750ml,Beverages,nestle_nestle_nestea_750ml,NESTLE-NES-750-001,8901058869293,8901058869293,EAN-13,EAN-13,8901058869293,8901058869293,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.6030266,True,8901058869293,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 100g,Nestle Polo,100g,Candy & Confectionery,nestle_nestle_polo_100g,B000Q6POKY,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 12g,Nestle Polo,12g,Candy & Confectionery,nestle_nestle_polo_12g,B01FRZ3AGI,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 15g,Nestle Polo,15g,Candy & Confectionery,nestle_nestle_polo_15g,B01FRZ3AGI,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 20g,Nestle Polo,20g,Candy & Confectionery,nestle_nestle_polo_20g,B007C53VSO,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 30g,Nestle Polo,30g,Candy & Confectionery,nestle_nestle_polo_30g,B079TJK8Y3,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
nestle,Nestle,data/seed_catalogs/archive/brand_catalog_nestle.json,archived,Nestle Polo 50g,Nestle Polo,50g,Candy & Confectionery,nestle_nestle_polo_50g,B000Q6POKY,89009871,89009871,GTIN-8,GTIN-8,89009871,,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773811.5981793,True,89009871,in_sync
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico 7Up 1kg,Pepsico 7Up,1kg,Beverages,pepsico_pepsico_7up_1kg,PEPSIC-7UP-1-001,8902080002290,8902080002290,EAN-13,EAN-13,8902080002290,8902080002290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5105531,True,8902080002290,in_sync
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico 7Up 200g,Pepsico 7Up,200g,Beverages,pepsico_pepsico_7up_200g,PEPSIC-7UP-200-001,8902080002290,8902080002290,EAN-13,EAN-13,8902080002290,8902080002290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5105531,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico 7Up 500g,Pepsico 7Up,500g,Beverages,pepsico_pepsico_7up_500g,40211516,8902080002290,8902080002290,EAN-13,EAN-13,8902080002290,8902080002290,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5105531,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Lays 1kg,Pepsico Lays,1kg,Snacks,pepsico_pepsico_lays_1kg,PEPSIC-LAY-1-001,8901491502047,8901491502047,EAN-13,EAN-13,8901491502047,8901491502047,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5016623,True,8901491502047,in_sync
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Lays 200g,Pepsico Lays,200g,Snacks,pepsico_pepsico_lays_200g,PEPSIC-LAY-200-002,8901491502047,8901491502047,EAN-13,EAN-13,8901491502047,8901491502047,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5016623,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Lays 500g,Pepsico Lays,500g,Snacks,pepsico_pepsico_lays_500g,PEPSIC-LAY-500-001,8901491502047,8901491502047,EAN-13,EAN-13,8901491502047,8901491502047,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5016623,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mirinda 100g,Pepsico Mirinda,100g,Beverages,pepsico_pepsico_mirinda_100g,PEPSIC-MIR-100-001,8902080204021,8902080204021,EAN-13,EAN-13,8902080204021,8902080204021,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.517687,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mirinda 1L,Pepsico Mirinda,1L,Beverages,pepsico_pepsico_mirinda_1l,PEPSIC-MIR-1-001,8902080204021,8902080204021,EAN-13,EAN-13,8902080204021,8902080204021,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.517687,True,8902080204021,in_sync
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mirinda 250ml,Pepsico Mirinda,250ml,Beverages,pepsico_pepsico_mirinda_250ml,PEPSIC-MIR-250-001,8902080204021,8902080204021,EAN-13,EAN-13,8902080204021,8902080204021,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.517687,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mountain Dew 100g,Pepsico Mountain Dew,100g,Beverages,pepsico_pepsico_mountain_dew_100g,PEPSIC-MOU-100-002,8902080364022,8902080364022,EAN-13,EAN-13,8902080364022,8902080364022,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5143652,True,,row_not_in_db
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mountain Dew 1L,Pepsico Mountain Dew,1L,Beverages,pepsico_pepsico_mountain_dew_1l,B01LWK1TYZ,8902080364022,8902080364022,EAN-13,EAN-13,8902080364022,8902080364022,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5143652,True,8902080364022,in_sync
pepsico,pepsico,data/seed_catalogs/archive/brand_catalog_pepsico.json,archived,Pepsico Mountain Dew 250ml,Pepsico Mountain Dew,250ml,Beverages,pepsico_pepsico_mountain_dew_250ml,B01N2NSWV8,8902080364022,8902080364022,EAN-13,EAN-13,8902080364022,8902080364022,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788773813.5143652,True,,row_not_in_db
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Coffee Classic 2g,Tata Coffee Classic,2g,Tea & Coffee,tata_tata_coffee_classic_2g,TATA-COF-2-002,8901090328109,8901090328109,EAN-13,EAN-13,8901090328109,8901090328109,,Open Food Facts,True,verified,1786092482.6336787,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Coffee Gold 90g,Tata Coffee Gold,90g,Tea & Coffee,tata_tata_coffee_gold_90g,488028,8901090223749,8901090223749,EAN-13,EAN-13,8901090223749,8901090223749,,Open Food Facts,True,verified,1786092504.5926466,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Coffee Grand 180g,Tata Coffee Grand,180g,Tea & Coffee,tata_tata_coffee_grand_180g,TATA-COF-180-001,8903754000826,8903754000826,EAN-13,EAN-13,8903754000826,8903754000826,,Open Food Facts,True,verified,1786092477.0314271,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Coffee Grand 90g,Tata Coffee Grand,90g,Tea & Coffee,tata_tata_coffee_grand_90g,298829,8901090328802,8901090328802,EAN-13,EAN-13,8901090328802,8901090328802,,Open Food Facts,True,verified,1786092476.2067864,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Salt 1 kg,Tata Salt,1 kg,Salt & Staples,tata_tata_salt_1_kg,B07575FPC3,8904043901015,8904043901015,EAN-13,EAN-13,8904043901015,8904043901015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1030667,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Salt 100g,Tata Salt,100g,Salt & Staples,tata_tata_salt_100g,105,8904043901015,8904043901015,EAN-13,EAN-13,8904043901015,8904043901015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1030667,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Salt 250g,Tata Salt,250g,Salt & Staples,tata_tata_salt_250g,TATA-SAL-250-001,8904043901015,8904043901015,EAN-13,EAN-13,8904043901015,8904043901015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1030667,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Salt 500g,Tata Salt,500g,Salt & Staples,tata_tata_salt_500g,105,8904043901015,8904043901015,EAN-13,EAN-13,8904043901015,8904043901015,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1030667,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Chana Dal 1kg,Tata Sampann Chana Dal,1kg,"Pulses, Grains & Spices",tata_tata_sampann_chana_dal_1kg,B07532J31B,8904043926643,8904043926643,EAN-13,EAN-13,8904043926643,8904043926643,,Open Food Facts,True,verified,1786092640.9217572,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Chana Dal 500g,Tata Sampann Chana Dal,500g,"Pulses, Grains & Spices",tata_tata_sampann_chana_dal_500g,B077X8G5DK,8904043926629,8904043926629,EAN-13,EAN-13,8904043926629,8904043926629,,Open Food Facts,True,verified,1786092639.7288995,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Chilli 100g,Tata Sampann Chilli,100g,Spices & Masalas,tata_tata_sampann_chilli_100g,185991,8904043927152,8904043927152,EAN-13,EAN-13,8904043927152,8904043927152,,Open Food Facts,True,verified,1786092578.5348673,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Garam Masala 100 grams,Tata Sampann Garam Masala,100 grams,Spices & Masalas,tata_tata_sampann_garam_masala_100_grams,B079H113LK,8904043927015,8904043927015,EAN-13,EAN-13,8904043927015,8904043927015,,Open Food Facts,True,verified,1786092605.4605205,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Moong Dal 1kg,Tata Sampann Moong Dal,1kg,"Pulses, Grains & Spices",tata_tata_sampann_moong_dal_1kg,B01L1LVGDQ,8904043926315,8904043926315,EAN-13,EAN-13,8904043926315,8904043926315,,Open Food Facts,True,verified,1786092636.9080367,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Poha 1 kg,Tata Sampann Poha,1 kg,"Pulses, Grains & Spices",tata_tata_sampann_poha_1_kg,B09G6JQWL7,8904043904061,8904043904061,EAN-13,EAN-13,8904043904061,8904043904061,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1124144,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Poha 10g,Tata Sampann Poha,10g,"Pulses, Grains & Spices",tata_tata_sampann_poha_10g,480044,8904043904061,8904043904061,EAN-13,EAN-13,8904043904061,8904043904061,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1124144,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Poha 25g,Tata Sampann Poha,25g,"Pulses, Grains & Spices",tata_tata_sampann_poha_25g,B07V3CM9L8,8904043904061,8904043904061,EAN-13,EAN-13,8904043904061,8904043904061,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1124144,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Poha 500g,Tata Sampann Poha,500g,"Pulses, Grains & Spices",tata_tata_sampann_poha_500g,B07V3CM9L8,8904043904061,8904043904061,EAN-13,EAN-13,8904043904061,8904043904061,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1124144,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Poha 50g,Tata Sampann Poha,50g,"Pulses, Grains & Spices",tata_tata_sampann_poha_50g,B07V3CM9L8,8904043904061,8904043904061,EAN-13,EAN-13,8904043904061,8904043904061,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1124144,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Spices 200g,Tata Sampann Spices,200g,"Pulses, Grains & Spices",tata_tata_sampann_spices_200g,40334093,8904043927299,8904043927299,EAN-13,EAN-13,8904043927299,8904043927299,,Open Food Facts,True,verified,1786092404.598989,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Sampann Toor Dal 1kg,Tata Sampann Toor Dal,1kg,"Pulses, Grains & Spices",tata_tata_sampann_toor_dal_1kg,B074N7VHV4,8904043926216,8904043926216,EAN-13,EAN-13,8904043926216,8904043926216,,Open Food Facts,True,verified,1786092638.5300956,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Chakra Gold 250g,Tata Tea Chakra Gold,250g,Tea & Coffee,tata_tata_tea_chakra_gold_250g,297575,8901052005604,8901052005604,EAN-13,EAN-13,8901052005604,8901052005604,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.0995708,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Chakra Gold 6g,Tata Tea Chakra Gold,6g,Tea & Coffee,tata_tata_tea_chakra_gold_6g,57894,8901052005604,8901052005604,EAN-13,EAN-13,8901052005604,8901052005604,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.0995708,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Gold 250g,Tata Tea Gold,250g,Tea & Coffee,tata_tata_tea_gold_250g,254,8901052006243,8901052006243,EAN-13,EAN-13,8901052006243,8901052006243,,Open Food Facts,True,verified,1786092525.6062307,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Gold 500g,Tata Tea Gold,500g,Tea & Coffee,tata_tata_tea_gold_500g,B00XW5HH6U,8901052005161,8901052005161,EAN-13,EAN-13,8901052005161,8901052005161,,Open Food Facts,True,verified,1786092527.1051967,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Premium 1kg,Tata Tea Premium,1kg,Tea & Coffee,tata_tata_tea_premium_1kg,B08DY62Z87,8901052010318,8901052010318,EAN-13,EAN-13,8901052010318,8901052010318,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1740298,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Premium 450g,Tata Tea Premium,450g,Tea & Coffee,tata_tata_tea_premium_450g,B0058PHQYU,8901052010318,8901052010318,EAN-13,EAN-13,8901052010318,8901052010318,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1740298,True,,no_table
tata,tata,data/seed_catalogs/brand_catalog_tata.json,active,Tata Tea Premium 50g,Tata Tea Premium,50g,Tea & Coffee,tata_tata_tea_premium_50g,B00AI87X0O,8901052010318,8901052010318,EAN-13,EAN-13,8901052010318,8901052010318,,openfoodfacts_bulk (search.openfoodfacts.org),False,name_matched,1788768773.1740298,True,,no_table
1 brand_slug brand_name catalog_file catalog_state product_name title size category variant_key product_sku barcode_raw barcode barcode_type computed_type gtin ean13 upc barcode_source barcode_verified barcode_lookup_status barcode_last_updated checksum_valid db_barcode db_status
2 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Biryani Masala 160g Aachi Biryani Masala Spices & Masalas AACHI-BIR-160-001 8906021120272 8906021120272 EAN-13 EAN-13 8906021120272 8906021120272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.8244982 True 8906021120272 in_sync
3 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Biryani Masala 1kg Aachi Biryani Masala Spices & Masalas B08TC3SNH1 8906021120272 8906021120272 EAN-13 EAN-13 8906021120272 8906021120272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.8244982 True 8906021120272 in_sync
4 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Biryani Masala 200g Aachi Biryani Masala Spices & Masalas B00ZGT1CEI 8906021120272 8906021120272 EAN-13 EAN-13 8906021120272 8906021120272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.8244982 True 8906021120272 in_sync
5 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Biryani Masala 250g Aachi Biryani Masala Spices & Masalas AACHI-BIR-250-002 8906021120272 8906021120272 EAN-13 EAN-13 8906021120272 8906021120272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.8244982 True 8906021120272 in_sync
6 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Biryani Masala 500g Aachi Biryani Masala Spices & Masalas B09V6MM1SF 8906021120272 8906021120272 EAN-13 EAN-13 8906021120272 8906021120272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.8244982 True 8906021120272 in_sync
7 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Chicken Masala 500g Aachi Chicken Masala Spices & Masalas AACHI-CHI-500-001 8906021120470 8906021120470 EAN-13 EAN-13 8906021120470 8906021120470 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.7751327 True 8906021120470 in_sync
8 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Chilli Powder 200g Aachi Chilli Powder Spices & Masalas 100285629 8904209304087 8904209304087 EAN-13 EAN-13 8904209304087 8904209304087 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.826589 True 8904209304087 in_sync
9 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Chilli Powder 500g Aachi Chilli Powder Spices & Masalas B073V9GMB2 8904209304087 8904209304087 EAN-13 EAN-13 8904209304087 8904209304087 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.826589 True 8904209304087 in_sync
10 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Mutton Masala 200g Aachi Mutton Masala Spices & Masalas B08C7ZD99P 8906021122290 8906021122290 EAN-13 EAN-13 8906021122290 8906021122290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.7772982 True 8906021122290 in_sync
11 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Mutton Masala 500g Aachi Mutton Masala Spices & Masalas B073VB4QV2 8906021122290 8906021122290 EAN-13 EAN-13 8906021122290 8906021122290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.7772982 True 8906021122290 in_sync
12 aachi Aachi data/seed_catalogs/archive/brand_catalog_aachi.json archived Aachi Mutton Masala 50g Aachi Mutton Masala Spices & Masalas 100286167 8906021122290 8906021122290 EAN-13 EAN-13 8906021122290 8906021122290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773804.7772982 True 8906021122290 in_sync
13 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Dahi 1kg Amul Dahi 1kg Dairy amul_amul_dahi_1kg B0D7W14TS5 8901262200271 8901262200271 EAN-13 EAN-13 8901262200271 8901262200271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.0632794 True 8901262200271 in_sync
14 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Dahi 200 g Amul Dahi 200 g Dairy amul_amul_dahi_200_g 30000356 8901262200271 8901262200271 EAN-13 EAN-13 8901262200271 8901262200271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.0632794 True 8901262200271 in_sync
15 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Dahi 400g Amul Dahi 400g Dairy amul_amul_dahi_400g 104851 8901262200271 8901262200271 EAN-13 EAN-13 8901262200271 8901262200271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.0632794 True 8901262200271 in_sync
16 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Dahi 90g Amul Dahi 90g Dairy amul_amul_dahi_90g 45533 8901262200271 8901262200271 EAN-13 EAN-13 8901262200271 8901262200271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.0632794 True 8901262200271 in_sync
17 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Fresh Cream 1L Amul Fresh Cream 1L Cheese amul_amul_fresh_cream_1l 162 8901262150118 8901262150118 EAN-13 EAN-13 8901262150118 8901262150118 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2152038 True 8901262150118 in_sync
18 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Fresh Cream 250 ml Amul Fresh Cream 250 ml Cheese amul_amul_fresh_cream_250_ml B0758LVKLL 8901262150118 8901262150118 EAN-13 EAN-13 8901262150118 8901262150118 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2152038 True 8901262150118 in_sync
19 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Fresh Cream 90ml Amul Fresh Cream 90ml Cheese amul_amul_fresh_cream_90ml 162 8901262150118 8901262150118 EAN-13 EAN-13 8901262150118 8901262150118 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2152038 True 8901262150118 in_sync
20 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ghee 1L Amul Ghee 1L Dairy amul_amul_ghee_1l 40096994 8901262031059 8901262031059 EAN-13 EAN-13 8901262031059 8901262031059 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.015503 True 8901262031059 in_sync
21 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ghee 200ml Amul Ghee 200ml Dairy amul_amul_ghee_200ml 40166276 8901262031059 8901262031059 EAN-13 EAN-13 8901262031059 8901262031059 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.015503 True 8901262031059 in_sync
22 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ghee 500ml Amul Ghee 500ml Dairy amul_amul_ghee_500ml 40050541 8901262031059 8901262031059 EAN-13 EAN-13 8901262031059 8901262031059 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.015503 True 8901262031059 in_sync
23 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ghee 90ml Amul Ghee 90ml Dairy amul_amul_ghee_90ml 40166277 8901262031059 8901262031059 EAN-13 EAN-13 8901262031059 8901262031059 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.015503 True 8901262031059 in_sync
24 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ice Cream 1L Amul Ice Cream 1L Ice Cream amul_amul_ice_cream_1l 40003798 8901262172363 8901262172363 EAN-13 EAN-13 8901262172363 8901262172363 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.1036828 True 8901262172363 in_sync
25 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ice Cream 250g Amul Ice Cream 250g Ice Cream amul_amul_ice_cream_250g 40003801 8901262172363 8901262172363 EAN-13 EAN-13 8901262172363 8901262172363 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.1036828 True 8901262172363 in_sync
26 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Ice Cream 90ml Amul Ice Cream 90ml Ice Cream amul_amul_ice_cream_90ml 40344404 8901262172363 8901262172363 EAN-13 EAN-13 8901262172363 8901262172363 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.1036828 True 8901262172363 in_sync
27 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Kool 100ml Amul Kool 100ml Beverages amul_amul_kool_100ml B00ZCLCCWG 8901262153355 8901262153355 EAN-13 EAN-13 8901262153355 8901262153355 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.123552 True 8901262153355 in_sync
28 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Kool 180ml Amul Kool 180ml Beverages amul_amul_kool_180ml B00NTU7YOS 8901262153355 8901262153355 EAN-13 EAN-13 8901262153355 8901262153355 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.123552 True 8901262153355 in_sync
29 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Kool 250ml Amul Kool 250ml Beverages amul_amul_kool_250ml 69633 8901262153355 8901262153355 EAN-13 EAN-13 8901262153355 8901262153355 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.123552 True 8901262153355 in_sync
30 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lassi 100ml Amul Lassi 100ml Beverages amul_amul_lassi_100ml 656965 8901262200189 8901262200189 EAN-13 EAN-13 8901262200189 8901262200189 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.072889 True 8901262200189 in_sync
31 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lassi 1kg Amul Lassi 1kg Beverages amul_amul_lassi_1kg 68553 8901262200189 8901262200189 EAN-13 EAN-13 8901262200189 8901262200189 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.072889 True 8901262200189 in_sync
32 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lassi 200g Amul Lassi 200g Beverages amul_amul_lassi_200g 178319 8901262200189 8901262200189 EAN-13 EAN-13 8901262200189 8901262200189 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.072889 True 8901262200189 in_sync
33 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lassi 250ml Amul Lassi 250ml Beverages amul_amul_lassi_250ml 40026295 8901262200189 8901262200189 EAN-13 EAN-13 8901262200189 8901262200189 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.072889 True 8901262200189 in_sync
34 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lite Bread Spread 1kg Amul Lite Bread Spread 1kg Bakery & Breads amul_amul_lite_bread_spread_1kg 255187 8901262140065 8901262140065 EAN-13 EAN-13 8901262140065 8901262140065 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.3098247 True 8901262140065 in_sync
35 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lite Bread Spread 500 g Amul Lite Bread Spread 500 g Bakery & Breads amul_amul_lite_bread_spread_500_g 257195 8901262140065 8901262140065 EAN-13 EAN-13 8901262140065 8901262140065 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.3098247 True 8901262140065 in_sync
36 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Lite Bread Spread 90g Amul Lite Bread Spread 90g Bakery & Breads amul_amul_lite_bread_spread_90g 255187 8901262140065 8901262140065 EAN-13 EAN-13 8901262140065 8901262140065 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.3098247 True 8901262140065 in_sync
37 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Malai Paneer 1L Amul Malai Paneer 1L Dairy amul_amul_malai_paneer_1l 40096747 8901262180016 8901262180016 EAN-13 EAN-13 8901262180016 8901262180016 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2311263 True 8901262180016 in_sync
38 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Malai Paneer 200ml Amul Malai Paneer 200ml Dairy amul_amul_malai_paneer_200ml 40096747 8901262180016 8901262180016 EAN-13 EAN-13 8901262180016 8901262180016 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2311263 True 8901262180016 in_sync
39 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Malai Paneer 425ml Amul Malai Paneer 425ml Dairy amul_amul_malai_paneer_425ml 40096747 8901262180016 8901262180016 EAN-13 EAN-13 8901262180016 8901262180016 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2311263 True 8901262180016 in_sync
40 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Malai Paneer 90ml Amul Malai Paneer 90ml Dairy amul_amul_malai_paneer_90ml 40096747 8901262180016 8901262180016 EAN-13 EAN-13 8901262180016 8901262180016 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2311263 True 8901262180016 in_sync
41 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Masti Dahi 1L Amul Masti Dahi 1L Cheese amul_amul_masti_dahi_1l 40323755 8901262200677 8901262200677 EAN-13 EAN-13 8901262200677 8901262200677 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2026575 True 8901262200677 in_sync
42 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Masti Dahi 200ml Amul Masti Dahi 200ml Cheese amul_amul_masti_dahi_200ml 30000356 8901262200677 8901262200677 EAN-13 EAN-13 8901262200677 8901262200677 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2026575 True 8901262200677 in_sync
43 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Masti Dahi 5L Amul Masti Dahi 5L Cheese amul_amul_masti_dahi_5l AMUL-MAS-5-001 8901262200677 8901262200677 EAN-13 EAN-13 8901262200677 8901262200677 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2026575 True 8901262200677 in_sync
44 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Masti Dahi 90ml Amul Masti Dahi 90ml Cheese amul_amul_masti_dahi_90ml 45533 8901262200677 8901262200677 EAN-13 EAN-13 8901262200677 8901262200677 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.2026575 True 8901262200677 in_sync
45 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Shrikhand 1kg Amul Shrikhand 1kg Dairy - Desserts amul_amul_shrikhand_1kg AMUL-SHR-1-001 8901262040051 8901262040051 EAN-13 EAN-13 8901262040051 8901262040051 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.152311 True 8901262040051 in_sync
46 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Shrikhand 500g Amul Shrikhand 500g Dairy - Desserts amul_amul_shrikhand_500g 104833 8901262040051 8901262040051 EAN-13 EAN-13 8901262040051 8901262040051 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.152311 True 8901262040051 in_sync
47 amul amul data/seed_catalogs/brand_catalog_amul.json active Amul Shrikhand 90g Amul Shrikhand 90g Dairy - Desserts amul_amul_shrikhand_90g SAMEZ3BURAXPCXGQ 8901262040051 8901262040051 EAN-13 EAN-13 8901262040051 8901262040051 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768769.152311 True 8901262040051 in_sync
48 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 100g Cadbury 5 Star 100g Chocolates cadbury_cadbury_5_star_100g B0758Q2W7D 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
49 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 18g Cadbury 5 Star 18g Chocolates cadbury_cadbury_5_star_18g B0H6GKBR83 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
50 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 200g Cadbury 5 Star 200g Chocolates cadbury_cadbury_5_star_200g B00XYALG1K 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
51 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 21 g Cadbury 5 Star 21 g Chocolates cadbury_cadbury_5_star_21_g 40325909 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
52 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 5 gm Cadbury 5 Star 5 gm Chocolates cadbury_cadbury_5_star_5_gm B0BYN4RN4T 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
53 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury 5 Star 9.8 g Cadbury 5 Star 9.8 g Chocolates cadbury_cadbury_5_star_9_8_g 900457475 8901233020273 8901233020273 EAN-13 EAN-13 8901233020273 8901233020273 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8735542 True 8901233020273 in_sync
54 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Bournvita 14.4g Cadbury Bournvita 14.4g Health Drinks cadbury_cadbury_bournvita_14_4g B00LIVCED6 8901233018362 8901233018362 EAN-13 EAN-13 8901233018362 8901233018362 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8771605 True 8901233018362 in_sync
55 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Bournvita 1kg Cadbury Bournvita 1kg Health Drinks cadbury_cadbury_bournvita_1kg B08N5PTZC7 8901233018362 8901233018362 EAN-13 EAN-13 8901233018362 8901233018362 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8771605 True 8901233018362 in_sync
56 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Bournvita 2 kg Cadbury Bournvita 2 kg Health Drinks cadbury_cadbury_bournvita_2_kg 1214685 8901233018362 8901233018362 EAN-13 EAN-13 8901233018362 8901233018362 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8771605 True 8901233018362 in_sync
57 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Bournvita 25g Cadbury Bournvita 25g Health Drinks cadbury_cadbury_bournvita_25g 400825 8901233018362 8901233018362 EAN-13 EAN-13 8901233018362 8901233018362 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8771605 True 8901233018362 in_sync
58 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Bournvita 500g Cadbury Bournvita 500g Health Drinks cadbury_cadbury_bournvita_500g B06XSB6RB2 8901233018362 8901233018362 EAN-13 EAN-13 8901233018362 8901233018362 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8771605 True 8901233018362 in_sync
59 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Dairy Milk 100g Cadbury Dairy Milk 100g Chocolates cadbury_cadbury_dairy_milk_100g B079TQSV9S 8901233028361 8901233028361 EAN-13 EAN-13 8901233028361 8901233028361 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8806314 True 8901233028361 in_sync
60 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Dairy Milk 500g Cadbury Dairy Milk 500g Chocolates cadbury_cadbury_dairy_milk_500g CADBUR-DAI-500-001 8901233028361 8901233028361 EAN-13 EAN-13 8901233028361 8901233028361 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.8806314 True 8901233028361 in_sync
61 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Fuse 100g Cadbury Fuse 100g Chocolates cadbury_cadbury_fuse_100g B07FNZRTZD 8901233023687 8901233023687 EAN-13 EAN-13 8901233023687 8901233023687 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.924282 True 8901233023687 in_sync
62 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Fuse 25 g Cadbury Fuse 25 g Chocolates cadbury_cadbury_fuse_25_g B0BRXV8R1C 8901233023687 8901233023687 EAN-13 EAN-13 8901233023687 8901233023687 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.924282 True 8901233023687 in_sync
63 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Fuse 50g Cadbury Fuse 50g Chocolates cadbury_cadbury_fuse_50g B07F6B4R1W 8901233023687 8901233023687 EAN-13 EAN-13 8901233023687 8901233023687 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.924282 True 8901233023687 in_sync
64 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Nutties 100g Cadbury Nutties 100g Chocolates cadbury_cadbury_nutties_100g 34422 8901233021492 8901233021492 EAN-13 EAN-13 8901233021492 8901233021492 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.9583745 True 8901233021492 in_sync
65 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Nutties 20g Cadbury Nutties 20g Chocolates cadbury_cadbury_nutties_20g B01IHCPDJA 8901233021492 8901233021492 EAN-13 EAN-13 8901233021492 8901233021492 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.9583745 True 8901233021492 in_sync
66 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Nutties 30g Cadbury Nutties 30g Chocolates cadbury_cadbury_nutties_30g B01IHCPDJA 8901233021492 8901233021492 EAN-13 EAN-13 8901233021492 8901233021492 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.9583745 True 8901233021492 in_sync
67 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Nutties 55g Cadbury Nutties 55g Chocolates cadbury_cadbury_nutties_55g B0721MLS76 8901233021492 8901233021492 EAN-13 EAN-13 8901233021492 8901233021492 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.9583745 True 8901233021492 in_sync
68 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Perk 11g Cadbury Perk 11g Chocolates cadbury_cadbury_perk_11g 20005969 8901233030272 8901233030272 EAN-13 EAN-13 8901233030272 8901233030272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.886849 True 8901233030272 in_sync
69 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Perk 14.3 g Cadbury Perk 14.3 g Chocolates cadbury_cadbury_perk_14_3_g CADBUR-PER-143-001 8901233030272 8901233030272 EAN-13 EAN-13 8901233030272 8901233030272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.886849 True 8901233030272 in_sync
70 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Perk 1kg Cadbury Perk 1kg Chocolates cadbury_cadbury_perk_1kg B01B5ZXNLQ 8901233030272 8901233030272 EAN-13 EAN-13 8901233030272 8901233030272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.886849 True 8901233030272 in_sync
71 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Perk 22 g Cadbury Perk 22 g Chocolates cadbury_cadbury_perk_22_g CHCFWY5YHXYBGDYH 8901233030272 8901233030272 EAN-13 EAN-13 8901233030272 8901233030272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.886849 True 8901233030272 in_sync
72 cadbury cadbury data/seed_catalogs/brand_catalog_cadbury.json active Cadbury Perk 90g Cadbury Perk 90g Chocolates cadbury_cadbury_perk_90g 225506 8901233030272 8901233030272 EAN-13 EAN-13 8901233030272 8901233030272 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768771.886849 True 8901233030272 in_sync
73 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Fanta 350ml Coca-Cola Fanta 350ml Beverages coca_cola_coca_cola_fanta_350ml COCACO-FAN-350-001 8906000379134 8906000379134 EAN-13 EAN-13 8906000379134 8906000379134 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.368387 True 8906000379134 in_sync
74 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Fanta 750ml Coca-Cola Fanta 750ml Beverages coca_cola_coca_cola_fanta_750ml B0752S51ZL 8906000379134 8906000379134 EAN-13 EAN-13 8906000379134 8906000379134 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.368387 True 8906000379134 in_sync
75 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Limca 1L Coca-Cola Limca 1L Beverages coca_cola_coca_cola_limca_1l COCACO-LIM-1-001 89000601 89000601 GTIN-8 GTIN-8 89000601 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3936577 True 89000601 in_sync
76 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Limca 750g Coca-Cola Limca 750g Beverages coca_cola_coca_cola_limca_750g 427683 89000601 89000601 GTIN-8 GTIN-8 89000601 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3936577 True 89000601 in_sync
77 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 1.2L Coca-Cola Maaza 1.2L Beverages coca_cola_coca_cola_maaza_1_2l B00GX9TS6O 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
78 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 125ml Coca-Cola Maaza 125ml Beverages coca_cola_coca_cola_maaza_125ml COCACO-MAA-125-001 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
79 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 250ml Coca-Cola Maaza 250ml Beverages coca_cola_coca_cola_maaza_250ml COCACO-MAA-250-001 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
80 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 350ml Coca-Cola Maaza 350ml Beverages coca_cola_coca_cola_maaza_350ml COCACO-MAA-350-001 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
81 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 600 ml Coca-Cola Maaza 600 ml Beverages coca_cola_coca_cola_maaza_600_ml COCACO-MAA-600-001 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
82 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Maaza 750ml Coca-Cola Maaza 750ml Beverages coca_cola_coca_cola_maaza_750ml B004ZXK6FC 8901764175015 8901764175015 EAN-13 EAN-13 8901764175015 8901764175015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3713913 True 8901764175015 in_sync
83 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Minute Maid Apple 150 ml Coca-Cola Minute Maid Apple 150 ml Beverages coca_cola_coca_cola_minute_maid_apple_150_ml COCACO-MIN-150-002 8901764385155 8901764385155 EAN-13 EAN-13 8901764385155 8901764385155 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.4120922 True 8901764385155 in_sync
84 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Minute Maid Apple 350ml Coca-Cola Minute Maid Apple 350ml Beverages coca_cola_coca_cola_minute_maid_apple_350ml COCACO-MIN-350-004 8901764385155 8901764385155 EAN-13 EAN-13 8901764385155 8901764385155 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.4120922 True 8901764385155 in_sync
85 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Minute Maid Apple 750ml Coca-Cola Minute Maid Apple 750ml Beverages coca_cola_coca_cola_minute_maid_apple_750ml COCACO-MIN-750-004 8901764385155 8901764385155 EAN-13 EAN-13 8901764385155 8901764385155 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.4120922 True 8901764385155 in_sync
86 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Sprite 350ml Coca-Cola Sprite 350ml Beverages coca_cola_coca_cola_sprite_350ml COCACO-SPR-350-001 8901764032271 8901764032271 EAN-13 EAN-13 8901764032271 8901764032271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3654883 True 8901764032271 in_sync
87 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Sprite 750ml Coca-Cola Sprite 750ml Beverages coca_cola_coca_cola_sprite_750ml COCACO-SPR-750-001 8901764032271 8901764032271 EAN-13 EAN-13 8901764032271 8901764032271 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3654883 True 8901764032271 in_sync
88 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Thums Up 350ml Coca-Cola Thums Up 350ml Beverages coca_cola_coca_cola_thums_up_350ml COCACO-THU-350-001 8901764042300 8901764042300 EAN-13 EAN-13 8901764042300 8901764042300 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3626227 True 8901764042300 in_sync
89 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Thums Up 750ml Coca-Cola Thums Up 750ml Beverages coca_cola_coca_cola_thums_up_750ml 251014 8901764042300 8901764042300 EAN-13 EAN-13 8901764042300 8901764042300 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3626227 True 8901764042300 in_sync
90 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 1,25 L e Coca-Cola Zero 1,25 L e Beverages coca_cola_coca_cola_zero_1_25_l_e COCACO-ZER-1-001 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
91 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 1.5l Coca-Cola Zero 1.5l Beverages coca_cola_coca_cola_zero_1_5l COCACO-ZER-15-001 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
92 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 1L Coca-Cola Zero 1L Beverages coca_cola_coca_cola_zero_1l COCACO-ZER-1-002 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
93 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 250 ml Coca-Cola Zero 250 ml Beverages coca_cola_coca_cola_zero_250_ml COCACO-ZER-250-001 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
94 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 330 ml Coca-Cola Zero 330 ml Beverages coca_cola_coca_cola_zero_330_ml COCACO-ZER-330-001 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
95 coca_cola coca-cola data/seed_catalogs/archive/brand_catalog_coca_cola.json archived Coca-Cola Zero 500ml Coca-Cola Zero 500ml Beverages coca_cola_coca_cola_zero_500ml COCACO-ZER-500-001 8901764112706 8901764112706 EAN-13 EAN-13 8901764112706 8901764112706 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773807.3840604 True 8901764112706 in_sync
96 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Dental Cream 120g Colgate-Palmolive Colgate Dental Cream 120g Oral Care colgate_palmolive_colgate_palmolive_colgate_dental_cream_120g COLGAT-COL-120-001 8901314765352 8901314765352 EAN-13 EAN-13 8901314765352 8901314765352 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5714066 True 8901314765352 in_sync
97 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Dental Cream 50g Colgate-Palmolive Colgate Dental Cream 50g Oral Care colgate_palmolive_colgate_palmolive_colgate_dental_cream_50g COLGAT-COL-50-001 8901314765352 8901314765352 EAN-13 EAN-13 8901314765352 8901314765352 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5714066 True 8901314765352 in_sync
98 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Dental Cream 90g Colgate-Palmolive Colgate Dental Cream 90g Oral Care colgate_palmolive_colgate_palmolive_colgate_dental_cream_90g COLGAT-COL-90-001 8901314765352 8901314765352 EAN-13 EAN-13 8901314765352 8901314765352 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5714066 True 8901314765352 in_sync
99 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Maxfresh 10g Colgate-Palmolive Colgate Maxfresh 10g Oral Care colgate_palmolive_colgate_palmolive_colgate_maxfresh_10g B079RXNHHT 8901314543653 8901314543653 EAN-13 EAN-13 8901314543653 8901314543653 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5746639 True 8901314543653 in_sync
100 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Maxfresh 19g Colgate-Palmolive Colgate Maxfresh 19g Oral Care colgate_palmolive_colgate_palmolive_colgate_maxfresh_19g B079RXNHHT 8901314543653 8901314543653 EAN-13 EAN-13 8901314543653 8901314543653 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5746639 True 8901314543653 in_sync
101 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Maxfresh 8g Colgate-Palmolive Colgate Maxfresh 8g Oral Care colgate_palmolive_colgate_palmolive_colgate_maxfresh_8g COLGAT-COL-8-001 8901314543653 8901314543653 EAN-13 EAN-13 8901314543653 8901314543653 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5746639 True 8901314543653 in_sync
102 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Sensitive 20g Colgate-Palmolive Colgate Sensitive 20g Oral Care colgate_palmolive_colgate_palmolive_colgate_sensitive_20g COLGAT-COL-20-001 8901314311832 8901314311832 EAN-13 EAN-13 8901314311832 8901314311832 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5771685 True 8901314311832 in_sync
103 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Sensitive 30g Colgate-Palmolive Colgate Sensitive 30g Oral Care colgate_palmolive_colgate_palmolive_colgate_sensitive_30g COLGAT-COL-30-001 8901314311832 8901314311832 EAN-13 EAN-13 8901314311832 8901314311832 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5771685 True 8901314311832 in_sync
104 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Sensitive 50g Colgate-Palmolive Colgate Sensitive 50g Oral Care colgate_palmolive_colgate_palmolive_colgate_sensitive_50g COLGAT-COL-50-002 8901314311832 8901314311832 EAN-13 EAN-13 8901314311832 8901314311832 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5771685 True 8901314311832 in_sync
105 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Sensitive 8g Colgate-Palmolive Colgate Sensitive 8g Oral Care colgate_palmolive_colgate_palmolive_colgate_sensitive_8g COLGAT-COL-8-002 8901314311832 8901314311832 EAN-13 EAN-13 8901314311832 8901314311832 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5771685 True 8901314311832 in_sync
106 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Visible White 100g Colgate-Palmolive Colgate Visible White 100g General colgate_palmolive_colgate_palmolive_colgate_visible_white_100g COLGAT-COL-100-001 8901314011183 8901314011183 EAN-13 EAN-13 8901314011183 8901314011183 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5915623 True 8901314011183 in_sync
107 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Visible White 150ml Colgate-Palmolive Colgate Visible White 150ml General colgate_palmolive_colgate_palmolive_colgate_visible_white_150ml B09QSBPKTF 8901314011183 8901314011183 EAN-13 EAN-13 8901314011183 8901314011183 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5915623 True 8901314011183 in_sync
108 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Visible White 200ml Colgate-Palmolive Colgate Visible White 200ml General colgate_palmolive_colgate_palmolive_colgate_visible_white_200ml COLGAT-COL-200-001 8901314011183 8901314011183 EAN-13 EAN-13 8901314011183 8901314011183 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5915623 True 8901314011183 in_sync
109 colgate_palmolive colgate-palmolive data/seed_catalogs/archive/brand_catalog_colgate_palmolive.json archived Colgate-Palmolive Colgate Visible White 50g Colgate-Palmolive Colgate Visible White 50g General colgate_palmolive_colgate_palmolive_colgate_visible_white_50g B09QSBPKTF 8901314011183 8901314011183 EAN-13 EAN-13 8901314011183 8901314011183 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773808.5915623 True 8901314011183 in_sync
110 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Chyawanprash 10g Chyawanprash 10g Health Care - Ayurvedic dabur_chyawanprash_10g DABUR-CHY-10-001 8901207036989 8901207036989 EAN-13 EAN-13 8901207036989 8901207036989 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.447363 True row_not_in_db
111 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Chyawanprash 20g Chyawanprash 20g Health Care - Ayurvedic dabur_chyawanprash_20g DABUR-CHY-20-001 8901207036989 8901207036989 EAN-13 EAN-13 8901207036989 8901207036989 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.447363 True 8901207036989 in_sync
112 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Chyawanprash 5g Chyawanprash 5g Health Care - Ayurvedic dabur_chyawanprash_5g 30009463 8901207036989 8901207036989 EAN-13 EAN-13 8901207036989 8901207036989 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.447363 True row_not_in_db
113 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Gulabari 100g Dabur Gulabari 100g Skin Care dabur_dabur_gulabari_100g B0BDRRJNLC 89005590 89005590 GTIN-8 GTIN-8 89005590 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.5249188 True 89005590 in_sync
114 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Gulabari 59g Dabur Gulabari 59g Skin Care dabur_dabur_gulabari_59g DABUR-GUL-59-001 89005590 89005590 GTIN-8 GTIN-8 89005590 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.5249188 True row_not_in_db
115 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Gulabari 75g Dabur Gulabari 75g Skin Care dabur_dabur_gulabari_75g DABUR-GUL-75-001 89005590 89005590 GTIN-8 GTIN-8 89005590 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.5249188 True row_not_in_db
116 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Odomos 100g Dabur Odomos 100g Personal Care - Mosquito Repellent dabur_dabur_odomos_100g B00HVSSZY2 8901207500053 8901207500053 EAN-13 EAN-13 8901207500053 8901207500053 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.519291 True 8901207500053 in_sync
117 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Odomos 20g Dabur Odomos 20g Personal Care - Mosquito Repellent dabur_dabur_odomos_20g B00AXX608K 8901207500053 8901207500053 EAN-13 EAN-13 8901207500053 8901207500053 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.519291 True row_not_in_db
118 dabur dabur data/seed_catalogs/archive/brand_catalog_dabur.json archived Dabur Odomos 50g Dabur Odomos 50g Personal Care - Mosquito Repellent dabur_dabur_odomos_50g DABUR-ODO-50-001 8901207500053 8901207500053 EAN-13 EAN-13 8901207500053 8901207500053 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.519291 True row_not_in_db
119 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Cinthol 100g Godrej Cinthol 100g Bath Soap godrej_godrej_cinthol_100g GODREJ-CIN-100-001 8901023020353 8901023020353 EAN-13 EAN-13 8901023020353 8901023020353 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.976005 True 8901023020353 in_sync
120 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Cinthol 50g Godrej Cinthol 50g Bath Soap godrej_godrej_cinthol_50g B0739RXZT8 8901023020353 8901023020353 EAN-13 EAN-13 8901023020353 8901023020353 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.976005 True row_not_in_db
121 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Cinthol 75g Godrej Cinthol 75g Bath Soap godrej_godrej_cinthol_75g B01MZWIZA9 8901023020353 8901023020353 EAN-13 EAN-13 8901023020353 8901023020353 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.976005 True row_not_in_db
122 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Nupur Henna 100ml Godrej Nupur Henna 100ml Hair Care godrej_godrej_nupur_henna_100ml 438469 8901023018602 8901023018602 EAN-13 EAN-13 8901023018602 8901023018602 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.9603631 True row_not_in_db
123 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Nupur Henna 250ml Godrej Nupur Henna 250ml Hair Care godrej_godrej_nupur_henna_250ml B08D8Z9JNL 8901023018602 8901023018602 EAN-13 EAN-13 8901023018602 8901023018602 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.9603631 True 8901023018602 in_sync
124 godrej godrej data/seed_catalogs/archive/brand_catalog_godrej.json archived Godrej Nupur Henna 90ml Godrej Nupur Henna 90ml Hair Care godrej_godrej_nupur_henna_90ml B005ZLCIU4 8901023018602 8901023018602 EAN-13 EAN-13 8901023018602 8901023018602 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773809.9603631 True row_not_in_db
125 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Health Mix 100g Manna Health Mix 50g 100g Health Foods manna_manna_health_mix_50g_100g B074778SPY 8906008350852 8906008350852 EAN-13 EAN-13 8906008350852 8906008350852 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4010751 True 8906008350852 in_sync
126 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Health Mix 25g Manna Health Mix 50g 25g Health Foods manna_manna_health_mix_50g_25g MDMFYWX4SC4NRTFZ 8906008350852 8906008350852 EAN-13 EAN-13 8906008350852 8906008350852 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4010751 True row_not_in_db
127 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Health Mix 50g Manna Health Mix 50g 50g Health Foods manna_manna_health_mix_50g_50g B074778SPY 8906008350852 8906008350852 EAN-13 EAN-13 8906008350852 8906008350852 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4010751 True row_not_in_db
128 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Ragi Malt 100g Manna Ragi Malt 50g 100g Health Foods manna_manna_ragi_malt_50g_100g B07D755GSF 8906008350388 8906008350388 EAN-13 EAN-13 8906008350388 8906008350388 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4015913 True 8906008350388 in_sync
129 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Ragi Malt 25g Manna Ragi Malt 50g 25g Health Foods manna_manna_ragi_malt_50g_25g B07D755GSF 8906008350388 8906008350388 EAN-13 EAN-13 8906008350388 8906008350388 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4015913 True row_not_in_db
130 manna Manna data/seed_catalogs/archive/brand_catalog_manna.json archived Manna Ragi Malt 50g Manna Ragi Malt 50g 50g Health Foods manna_manna_ragi_malt_50g_50g B00DRE5614 8906008350388 8906008350388 EAN-13 EAN-13 8906008350388 8906008350388 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.4015913 True row_not_in_db
131 mtr Mtr data/seed_catalogs/archive/brand_catalog_mtr.json archived MTR Sambar Powder 1.5kg MTR Sambar Powder Spices & Masalas SCMETEMHEY5Z3VMX 8901042954721 8901042954721 EAN-13 EAN-13 8901042954721 8901042954721 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.924069 True 8901042954721 in_sync
132 mtr Mtr data/seed_catalogs/archive/brand_catalog_mtr.json archived MTR Sambar Powder 200g MTR Sambar Powder Spices & Masalas B009LL92VC 8901042954721 8901042954721 EAN-13 EAN-13 8901042954721 8901042954721 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.924069 True 8901042954721 in_sync
133 mtr Mtr data/seed_catalogs/archive/brand_catalog_mtr.json archived MTR Sambar Powder 500g MTR Sambar Powder Spices & Masalas 40185042 8901042954721 8901042954721 EAN-13 EAN-13 8901042954721 8901042954721 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.924069 True 8901042954721 in_sync
134 mtr Mtr data/seed_catalogs/archive/brand_catalog_mtr.json archived MTR Sambar Powder 90g MTR Sambar Powder Spices & Masalas SCMETEMHEY5Z3VMX 8901042954721 8901042954721 EAN-13 EAN-13 8901042954721 8901042954721 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773810.924069 True 8901042954721 in_sync
135 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Cerelac 125g Nestle Cerelac 125g Baby Care nestle_nestle_cerelac_125g B004ZKZMAE 8901058844627 8901058844627 EAN-13 EAN-13 8901058844627 8901058844627 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5886683 True 8901058844627 in_sync
136 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Cerelac 250g Nestle Cerelac 250g Baby Care nestle_nestle_cerelac_250g NESTLE-CER-250-001 8901058844627 8901058844627 EAN-13 EAN-13 8901058844627 8901058844627 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5886683 True 8901058844627 in_sync
137 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Cerelac 300g Nestle Cerelac 300g Baby Care nestle_nestle_cerelac_300g 25012 8901058844627 8901058844627 EAN-13 EAN-13 8901058844627 8901058844627 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5886683 True 8901058844627 in_sync
138 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Cerelac 400g Nestle Cerelac 400g Baby Care nestle_nestle_cerelac_400g NESTLE-CER-400-001 8901058844627 8901058844627 EAN-13 EAN-13 8901058844627 8901058844627 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5886683 True 8901058844627 in_sync
139 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Cerelac 90g Nestle Cerelac 90g Baby Care nestle_nestle_cerelac_90g NESTLE-CER-90-001 8901058844627 8901058844627 EAN-13 EAN-13 8901058844627 8901058844627 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5886683 True 8901058844627 in_sync
140 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Kitkat 120g Nestle Kitkat 120g Chocolates nestle_nestle_kitkat_120g NESTLE-KIT-120-001 8901058857245 8901058857245 EAN-13 EAN-13 8901058857245 8901058857245 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5304384 True 8901058857245 in_sync
141 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Kitkat 170g Nestle Kitkat 170g Chocolates nestle_nestle_kitkat_170g 40018531 8901058857245 8901058857245 EAN-13 EAN-13 8901058857245 8901058857245 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5304384 True 8901058857245 in_sync
142 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Kitkat 50g Nestle Kitkat 50g Chocolates nestle_nestle_kitkat_50g NESTLE-KIT-50-001 8901058857245 8901058857245 EAN-13 EAN-13 8901058857245 8901058857245 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5304384 True 8901058857245 in_sync
143 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 10ml Nestle Milkybar 10ml Chocolates nestle_nestle_milkybar_10ml 40090019 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
144 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 1kg Nestle Milkybar 1kg Chocolates nestle_nestle_milkybar_1kg B01ILWLMLE 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
145 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 24.5ml Nestle Milkybar 24.5ml Chocolates nestle_nestle_milkybar_24_5ml B08P5Y1GPF 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
146 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 30ml Nestle Milkybar 30ml Chocolates nestle_nestle_milkybar_30ml B08S55766X 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
147 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 38ml Nestle Milkybar 38ml Chocolates nestle_nestle_milkybar_38ml NESTLE-MIL-38-001 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
148 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milkybar 90g Nestle Milkybar 90g Chocolates nestle_nestle_milkybar_90g B005GLIBLI 89008478 89008478 GTIN-8 GTIN-8 89008478 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5370526 True 89008478 in_sync
149 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 100g Nestle Milo 100g Health Drinks nestle_nestle_milo_100g B00RBMP37A 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
150 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 165g Nestle Milo 165g Health Drinks nestle_nestle_milo_165g B00RBMP37A 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
151 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 200 g Nestle Milo 200 g Health Drinks nestle_nestle_milo_200_g NESTLE-MIL-200-001 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
152 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 20g Nestle Milo 20g Health Drinks nestle_nestle_milo_20g 40184472 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
153 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 25 g Nestle Milo 25 g Health Drinks nestle_nestle_milo_25_g B00RBMP37A 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
154 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Milo 30g Nestle Milo 30g Health Drinks nestle_nestle_milo_30g NESTLE-MIL-30-001 8901058904017 8901058904017 EAN-13 EAN-13 8901058904017 8901058904017 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6294699 True 8901058904017 in_sync
155 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 150g Nestle Munch 150g Chocolates nestle_nestle_munch_150g B01MRFIF28 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
156 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 20g Nestle Munch 20g Chocolates nestle_nestle_munch_20g 496297 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
157 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 38.5 g Nestle Munch 38.5 g Chocolates nestle_nestle_munch_38_5_g 40269268 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
158 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 55g Nestle Munch 55g Chocolates nestle_nestle_munch_55g 496297 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
159 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 6 x 90 g Nestle Munch 6 x 90 g Chocolates nestle_nestle_munch_6_x_90_g 127096 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
160 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Munch 8.9g Nestle Munch 8.9g Chocolates nestle_nestle_munch_8_9g B01MQEA436 89009802 89009802 GTIN-8 GTIN-8 89009802 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5439541 True 89009802 in_sync
161 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nescafe Sunrise 1kg Nestle Nescafe Sunrise 1kg Tea & Coffee nestle_nestle_nescafe_sunrise_1kg B079H34CLY 8901058902938 8901058902938 EAN-13 EAN-13 8901058902938 8901058902938 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6650498 True 8901058902938 in_sync
162 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nescafe Sunrise 200g Nestle Nescafe Sunrise 200g Tea & Coffee nestle_nestle_nescafe_sunrise_200g B079H34CLY 8901058902938 8901058902938 EAN-13 EAN-13 8901058902938 8901058902938 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6650498 True 8901058902938 in_sync
163 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nescafe Sunrise 250g Nestle Nescafe Sunrise 250g Tea & Coffee nestle_nestle_nescafe_sunrise_250g B079H34CLY 8901058902938 8901058902938 EAN-13 EAN-13 8901058902938 8901058902938 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6650498 True 8901058902938 in_sync
164 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nescafe Sunrise 5 g Nestle Nescafe Sunrise 5 g Tea & Coffee nestle_nestle_nescafe_sunrise_5_g B0971VNDPW 8901058902938 8901058902938 EAN-13 EAN-13 8901058902938 8901058902938 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6650498 True 8901058902938 in_sync
165 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nescafe Sunrise 90 g Nestle Nescafe Sunrise 90 g Tea & Coffee nestle_nestle_nescafe_sunrise_90_g B0971VNDPW 8901058902938 8901058902938 EAN-13 EAN-13 8901058902938 8901058902938 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6650498 True 8901058902938 in_sync
166 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 200g Nestle Nestea 200g Beverages nestle_nestle_nestea_200g 402001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
167 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 240g Nestle Nestea 240g Beverages nestle_nestle_nestea_240g NESTLE-NES-240-001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
168 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 25g Nestle Nestea 25g Beverages nestle_nestle_nestea_25g NESTLE-NES-25-001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
169 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 33 g Nestle Nestea 33 g Beverages nestle_nestle_nestea_33_g NESTLE-NES-33-001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
170 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 350ml Nestle Nestea 350ml Beverages nestle_nestle_nestea_350ml NESTLE-NES-350-001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
171 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Nestea 750ml Nestle Nestea 750ml Beverages nestle_nestle_nestea_750ml NESTLE-NES-750-001 8901058869293 8901058869293 EAN-13 EAN-13 8901058869293 8901058869293 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.6030266 True 8901058869293 in_sync
172 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 100g Nestle Polo 100g Candy & Confectionery nestle_nestle_polo_100g B000Q6POKY 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
173 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 12g Nestle Polo 12g Candy & Confectionery nestle_nestle_polo_12g B01FRZ3AGI 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
174 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 15g Nestle Polo 15g Candy & Confectionery nestle_nestle_polo_15g B01FRZ3AGI 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
175 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 20g Nestle Polo 20g Candy & Confectionery nestle_nestle_polo_20g B007C53VSO 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
176 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 30g Nestle Polo 30g Candy & Confectionery nestle_nestle_polo_30g B079TJK8Y3 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
177 nestle Nestle data/seed_catalogs/archive/brand_catalog_nestle.json archived Nestle Polo 50g Nestle Polo 50g Candy & Confectionery nestle_nestle_polo_50g B000Q6POKY 89009871 89009871 GTIN-8 GTIN-8 89009871 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773811.5981793 True 89009871 in_sync
178 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico 7Up 1kg Pepsico 7Up 1kg Beverages pepsico_pepsico_7up_1kg PEPSIC-7UP-1-001 8902080002290 8902080002290 EAN-13 EAN-13 8902080002290 8902080002290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5105531 True 8902080002290 in_sync
179 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico 7Up 200g Pepsico 7Up 200g Beverages pepsico_pepsico_7up_200g PEPSIC-7UP-200-001 8902080002290 8902080002290 EAN-13 EAN-13 8902080002290 8902080002290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5105531 True row_not_in_db
180 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico 7Up 500g Pepsico 7Up 500g Beverages pepsico_pepsico_7up_500g 40211516 8902080002290 8902080002290 EAN-13 EAN-13 8902080002290 8902080002290 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5105531 True row_not_in_db
181 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Lays 1kg Pepsico Lays 1kg Snacks pepsico_pepsico_lays_1kg PEPSIC-LAY-1-001 8901491502047 8901491502047 EAN-13 EAN-13 8901491502047 8901491502047 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5016623 True 8901491502047 in_sync
182 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Lays 200g Pepsico Lays 200g Snacks pepsico_pepsico_lays_200g PEPSIC-LAY-200-002 8901491502047 8901491502047 EAN-13 EAN-13 8901491502047 8901491502047 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5016623 True row_not_in_db
183 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Lays 500g Pepsico Lays 500g Snacks pepsico_pepsico_lays_500g PEPSIC-LAY-500-001 8901491502047 8901491502047 EAN-13 EAN-13 8901491502047 8901491502047 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5016623 True row_not_in_db
184 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mirinda 100g Pepsico Mirinda 100g Beverages pepsico_pepsico_mirinda_100g PEPSIC-MIR-100-001 8902080204021 8902080204021 EAN-13 EAN-13 8902080204021 8902080204021 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.517687 True row_not_in_db
185 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mirinda 1L Pepsico Mirinda 1L Beverages pepsico_pepsico_mirinda_1l PEPSIC-MIR-1-001 8902080204021 8902080204021 EAN-13 EAN-13 8902080204021 8902080204021 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.517687 True 8902080204021 in_sync
186 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mirinda 250ml Pepsico Mirinda 250ml Beverages pepsico_pepsico_mirinda_250ml PEPSIC-MIR-250-001 8902080204021 8902080204021 EAN-13 EAN-13 8902080204021 8902080204021 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.517687 True row_not_in_db
187 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mountain Dew 100g Pepsico Mountain Dew 100g Beverages pepsico_pepsico_mountain_dew_100g PEPSIC-MOU-100-002 8902080364022 8902080364022 EAN-13 EAN-13 8902080364022 8902080364022 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5143652 True row_not_in_db
188 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mountain Dew 1L Pepsico Mountain Dew 1L Beverages pepsico_pepsico_mountain_dew_1l B01LWK1TYZ 8902080364022 8902080364022 EAN-13 EAN-13 8902080364022 8902080364022 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5143652 True 8902080364022 in_sync
189 pepsico pepsico data/seed_catalogs/archive/brand_catalog_pepsico.json archived Pepsico Mountain Dew 250ml Pepsico Mountain Dew 250ml Beverages pepsico_pepsico_mountain_dew_250ml B01N2NSWV8 8902080364022 8902080364022 EAN-13 EAN-13 8902080364022 8902080364022 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788773813.5143652 True row_not_in_db
190 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Coffee Classic 2g Tata Coffee Classic 2g Tea & Coffee tata_tata_coffee_classic_2g TATA-COF-2-002 8901090328109 8901090328109 EAN-13 EAN-13 8901090328109 8901090328109 Open Food Facts True verified 1786092482.6336787 True no_table
191 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Coffee Gold 90g Tata Coffee Gold 90g Tea & Coffee tata_tata_coffee_gold_90g 488028 8901090223749 8901090223749 EAN-13 EAN-13 8901090223749 8901090223749 Open Food Facts True verified 1786092504.5926466 True no_table
192 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Coffee Grand 180g Tata Coffee Grand 180g Tea & Coffee tata_tata_coffee_grand_180g TATA-COF-180-001 8903754000826 8903754000826 EAN-13 EAN-13 8903754000826 8903754000826 Open Food Facts True verified 1786092477.0314271 True no_table
193 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Coffee Grand 90g Tata Coffee Grand 90g Tea & Coffee tata_tata_coffee_grand_90g 298829 8901090328802 8901090328802 EAN-13 EAN-13 8901090328802 8901090328802 Open Food Facts True verified 1786092476.2067864 True no_table
194 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Salt 1 kg Tata Salt 1 kg Salt & Staples tata_tata_salt_1_kg B07575FPC3 8904043901015 8904043901015 EAN-13 EAN-13 8904043901015 8904043901015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1030667 True no_table
195 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Salt 100g Tata Salt 100g Salt & Staples tata_tata_salt_100g 105 8904043901015 8904043901015 EAN-13 EAN-13 8904043901015 8904043901015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1030667 True no_table
196 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Salt 250g Tata Salt 250g Salt & Staples tata_tata_salt_250g TATA-SAL-250-001 8904043901015 8904043901015 EAN-13 EAN-13 8904043901015 8904043901015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1030667 True no_table
197 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Salt 500g Tata Salt 500g Salt & Staples tata_tata_salt_500g 105 8904043901015 8904043901015 EAN-13 EAN-13 8904043901015 8904043901015 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1030667 True no_table
198 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Chana Dal 1kg Tata Sampann Chana Dal 1kg Pulses, Grains & Spices tata_tata_sampann_chana_dal_1kg B07532J31B 8904043926643 8904043926643 EAN-13 EAN-13 8904043926643 8904043926643 Open Food Facts True verified 1786092640.9217572 True no_table
199 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Chana Dal 500g Tata Sampann Chana Dal 500g Pulses, Grains & Spices tata_tata_sampann_chana_dal_500g B077X8G5DK 8904043926629 8904043926629 EAN-13 EAN-13 8904043926629 8904043926629 Open Food Facts True verified 1786092639.7288995 True no_table
200 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Chilli 100g Tata Sampann Chilli 100g Spices & Masalas tata_tata_sampann_chilli_100g 185991 8904043927152 8904043927152 EAN-13 EAN-13 8904043927152 8904043927152 Open Food Facts True verified 1786092578.5348673 True no_table
201 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Garam Masala 100 grams Tata Sampann Garam Masala 100 grams Spices & Masalas tata_tata_sampann_garam_masala_100_grams B079H113LK 8904043927015 8904043927015 EAN-13 EAN-13 8904043927015 8904043927015 Open Food Facts True verified 1786092605.4605205 True no_table
202 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Moong Dal 1kg Tata Sampann Moong Dal 1kg Pulses, Grains & Spices tata_tata_sampann_moong_dal_1kg B01L1LVGDQ 8904043926315 8904043926315 EAN-13 EAN-13 8904043926315 8904043926315 Open Food Facts True verified 1786092636.9080367 True no_table
203 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Poha 1 kg Tata Sampann Poha 1 kg Pulses, Grains & Spices tata_tata_sampann_poha_1_kg B09G6JQWL7 8904043904061 8904043904061 EAN-13 EAN-13 8904043904061 8904043904061 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1124144 True no_table
204 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Poha 10g Tata Sampann Poha 10g Pulses, Grains & Spices tata_tata_sampann_poha_10g 480044 8904043904061 8904043904061 EAN-13 EAN-13 8904043904061 8904043904061 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1124144 True no_table
205 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Poha 25g Tata Sampann Poha 25g Pulses, Grains & Spices tata_tata_sampann_poha_25g B07V3CM9L8 8904043904061 8904043904061 EAN-13 EAN-13 8904043904061 8904043904061 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1124144 True no_table
206 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Poha 500g Tata Sampann Poha 500g Pulses, Grains & Spices tata_tata_sampann_poha_500g B07V3CM9L8 8904043904061 8904043904061 EAN-13 EAN-13 8904043904061 8904043904061 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1124144 True no_table
207 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Poha 50g Tata Sampann Poha 50g Pulses, Grains & Spices tata_tata_sampann_poha_50g B07V3CM9L8 8904043904061 8904043904061 EAN-13 EAN-13 8904043904061 8904043904061 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1124144 True no_table
208 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Spices 200g Tata Sampann Spices 200g Pulses, Grains & Spices tata_tata_sampann_spices_200g 40334093 8904043927299 8904043927299 EAN-13 EAN-13 8904043927299 8904043927299 Open Food Facts True verified 1786092404.598989 True no_table
209 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Sampann Toor Dal 1kg Tata Sampann Toor Dal 1kg Pulses, Grains & Spices tata_tata_sampann_toor_dal_1kg B074N7VHV4 8904043926216 8904043926216 EAN-13 EAN-13 8904043926216 8904043926216 Open Food Facts True verified 1786092638.5300956 True no_table
210 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Chakra Gold 250g Tata Tea Chakra Gold 250g Tea & Coffee tata_tata_tea_chakra_gold_250g 297575 8901052005604 8901052005604 EAN-13 EAN-13 8901052005604 8901052005604 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.0995708 True no_table
211 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Chakra Gold 6g Tata Tea Chakra Gold 6g Tea & Coffee tata_tata_tea_chakra_gold_6g 57894 8901052005604 8901052005604 EAN-13 EAN-13 8901052005604 8901052005604 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.0995708 True no_table
212 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Gold 250g Tata Tea Gold 250g Tea & Coffee tata_tata_tea_gold_250g 254 8901052006243 8901052006243 EAN-13 EAN-13 8901052006243 8901052006243 Open Food Facts True verified 1786092525.6062307 True no_table
213 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Gold 500g Tata Tea Gold 500g Tea & Coffee tata_tata_tea_gold_500g B00XW5HH6U 8901052005161 8901052005161 EAN-13 EAN-13 8901052005161 8901052005161 Open Food Facts True verified 1786092527.1051967 True no_table
214 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Premium 1kg Tata Tea Premium 1kg Tea & Coffee tata_tata_tea_premium_1kg B08DY62Z87 8901052010318 8901052010318 EAN-13 EAN-13 8901052010318 8901052010318 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1740298 True no_table
215 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Premium 450g Tata Tea Premium 450g Tea & Coffee tata_tata_tea_premium_450g B0058PHQYU 8901052010318 8901052010318 EAN-13 EAN-13 8901052010318 8901052010318 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1740298 True no_table
216 tata tata data/seed_catalogs/brand_catalog_tata.json active Tata Tea Premium 50g Tata Tea Premium 50g Tea & Coffee tata_tata_tea_premium_50g B00AI87X0O 8901052010318 8901052010318 EAN-13 EAN-13 8901052010318 8901052010318 openfoodfacts_bulk (search.openfoodfacts.org) False name_matched 1788768773.1740298 True no_table

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#!/usr/bin/env python3
"""
Fill gtin / ean13 / upc / barcode_type from barcodes the catalog already holds.
WHY
Measured against production on 2026-09-08:
barcode 300 rows (18.4%)
gtin 30 rows (8.7% of the tables that had the column)
ean13 22 rows (6.4%)
upc 0 rows (0.0%)
Every one of the missing values is arithmetic on digits already sitting in
the same row. Nothing needs to be looked up, matched or fetched. They were
empty because the only code that computed them lived inside the network
cascade (`ENABLE_BARCODE_LOOKUP`, false in production) and because
`upsert_brand_products` dropped the fields before they reached Postgres.
`BarcodeIdentityStage` now does this for every NEW upload. This script does
it once for the rows already stored.
WHAT IT TOUCHES
brand_<slug>.gtin, .ean13, .upc, .barcode_type - and ONLY where they are
currently empty. Every UPDATE pins the row's own current barcode in its
WHERE clause, so a concurrent write is never lost.
It also records provenance in `field_sources` under the derived keys, so
the coverage report can tell a derived value from a looked-up one.
WHAT IT WILL NOT DO
* It will not change, reformat or delete `barcode`. A barcode that fails
checksum validation is REPORTED and skipped - the 38 rows holding the
placeholder `8900000000000.0` are found this way, not repaired. Repairing
them needs a real source, which is a different job.
* It will not overwrite a gtin/ean13/upc that already has a value, even if
it disagrees with the barcode. A disagreement is reported instead: it
means one of the two is wrong and a script should not pick.
* It makes no network request of any kind.
USAGE
python -m scripts.backfill_barcode_identity # dry run
python -m scripts.backfill_barcode_identity --brand amul # repeatable
python -m scripts.backfill_barcode_identity --apply
python -m scripts.backfill_barcode_identity --json
`--dry-run` is the default and `--apply` must be explicit: backend/.env points
at the PRODUCTION database. The target host is printed on startup.
"""
from __future__ import annotations
import argparse
import json
import logging
import sys
from collections import Counter
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from psycopg.types.json import Json
from app.infrastructure.settings import DB_HOST, DB_NAME
from app.services.enrichment.barcode.models import BarcodeType
from app.services.enrichment.barcode.validators import (
classify_barcode_type,
to_ean13,
validate_barcode,
)
from app.services.vector_store import _connect
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger("backfill_barcode_identity")
DERIVED = ("gtin", "ean13", "upc", "barcode_type")
def brand_tables(cur, only: Optional[List[str]] = None) -> List[str]:
cur.execute(
"SELECT table_name FROM information_schema.tables "
"WHERE table_schema = 'public' AND table_name LIKE 'brand\\_%' "
"ORDER BY table_name"
)
tables = [r[0] for r in cur.fetchall()]
if only:
wanted = {f"brand_{s.strip().lower().replace(' ', '_').replace('-', '_')}"
for s in only}
tables = [t for t in tables if t in wanted]
return tables
def has_columns(cur, table: str) -> bool:
"""Every script here probes information_schema before selecting, because
the column set genuinely differed per table until very recently."""
cur.execute(
"SELECT column_name FROM information_schema.columns "
"WHERE table_schema = 'public' AND table_name = %s",
(table,),
)
present = {r[0] for r in cur.fetchall()}
return {"barcode", *DERIVED, "field_sources"} <= present
def derive(barcode: str) -> Optional[Dict[str, Any]]:
code = validate_barcode(barcode)
if not code:
return None
kind = classify_barcode_type(code)
return {
"gtin": code,
"ean13": to_ean13(code),
"upc": code if kind is BarcodeType.UPC_A else None,
"barcode_type": kind.value,
}
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--brand", action="append", dest="brands")
ap.add_argument("--apply", action="store_true")
ap.add_argument("--dry-run", action="store_true", default=False)
ap.add_argument("--json", action="store_true")
args = ap.parse_args()
apply = args.apply and not args.dry_run
conn = _connect()
if conn is None:
logger.error("Database unreachable - nothing to do.")
return 2
logger.info("database : %s / %s", DB_HOST, DB_NAME)
logger.info("mode : %s", "APPLY (writing)" if apply else "dry run (no writes)")
logger.info("")
tally = Counter()
invalid: List[Dict[str, str]] = []
conflicts: List[Dict[str, Any]] = []
per_brand: Dict[str, int] = {}
try:
with conn.cursor() as cur:
for table in brand_tables(cur, args.brands):
if not has_columns(cur, table):
tally["tables_skipped_missing_columns"] += 1
continue
cur.execute(
f'SELECT id, product_name, barcode, gtin, ean13, upc, barcode_type, '
f'field_sources FROM "{table}" '
f"WHERE barcode IS NOT NULL AND btrim(barcode) <> ''"
)
rows = cur.fetchall()
written = 0
for rid, name, barcode, gtin, ean13, upc, btype, sources in rows:
tally["barcoded_rows"] += 1
derived = derive(barcode)
if derived is None:
tally["invalid_barcode"] += 1
invalid.append({"table": table, "product": name, "barcode": barcode})
continue
current = {"gtin": gtin, "ean13": ean13, "upc": upc, "barcode_type": btype}
# Only fill blanks; report a populated value that disagrees.
updates = {}
for col, want in derived.items():
have = current.get(col)
if have is None or str(have).strip() == "":
if want is not None:
updates[col] = want
elif str(have).strip() != str(want or "").strip():
conflicts.append({"table": table, "product": name, "column": col,
"stored": have, "derived": want})
if not updates:
tally["already_complete"] += 1
continue
merged = dict(sources or {})
for col in updates:
merged[col] = {"method": "derived",
"source": "validators.validate_barcode"}
tally["rows_to_update"] += 1
for col in updates:
tally[f"fill_{col}"] += 1
written += 1
if apply:
assignments = ", ".join(f"{c} = %s" for c in updates)
cur.execute(
f'UPDATE "{table}" SET {assignments}, field_sources = %s, '
f"updated_at = CURRENT_TIMESTAMP "
f"WHERE id = %s AND barcode = %s",
(*updates.values(), Json(merged), rid, barcode),
)
if written:
per_brand[table] = written
if apply:
conn.commit()
finally:
conn.close()
if args.json:
print(json.dumps({"applied": apply, "tally": dict(tally),
"per_brand": per_brand, "invalid": invalid,
"conflicts": conflicts}, indent=2))
return 0
for table, n in sorted(per_brand.items(), key=lambda kv: -kv[1]):
logger.info(" %-32s %d row(s)", table, n)
logger.info("")
logger.info("barcoded rows %d", tally["barcoded_rows"])
logger.info(" not a valid GTIN %d", tally["invalid_barcode"])
logger.info(" already complete %d", tally["already_complete"])
logger.info(" %s %d",
"updated" if apply else "to update", tally["rows_to_update"])
for col in DERIVED:
logger.info(" %-14s %d", col, tally[f"fill_{col}"])
if invalid:
logger.info("")
logger.info("%d row(s) hold something that is not a barcode (left untouched):",
len(invalid))
for item in invalid[:10]:
logger.info(" %-28s %s", item["product"][:28], item["barcode"])
if len(invalid) > 10:
logger.info(" ... and %d more", len(invalid) - 10)
if conflicts:
logger.info("")
logger.info("%d stored value(s) DISAGREE with the barcode (left untouched, "
"one of the two is wrong):", len(conflicts))
for c in conflicts[:10]:
logger.info(" %-28s %s stored=%s derived=%s",
c["product"][:28], c["column"], c["stored"], c["derived"])
if not apply and tally["rows_to_update"]:
logger.info("")
logger.info("Dry run - nothing written. Re-run with --apply to commit.")
return 0
if __name__ == "__main__":
raise SystemExit(main())

10
scripts/backfill_nutrition_from_barcodes.py
View File

@@ -218,8 +218,16 @@ def main() -> int:
candidate_brand=product.get("brands") or "",
candidate_size=product.get("quantity") or "",
)
# barcode_is_identity mirrors what fetch_verified_nutrition_by_barcode
# passes, and it has to: this gate runs FIRST, so without it the row is
# rejected here and the service's relaxed check is never reached. That
# is exactly what happened - a run on 2026-09-08 reported 149 of 300
# rows as "found, wrong product" where the barcode had resolved
# perfectly and OFF simply stores the short name ("Munch" for our
# "Nestle Munch 8.9g"). See matching.name_is_contained.
matched, _sim = is_match(candidate, row["brand"], row["product_name"],
row["size"], min_name_similarity=args.min_similarity)
row["size"], min_name_similarity=args.min_similarity,
barcode_is_identity=True)
if not matched:
rejected.append(line)
time.sleep(PAUSE_SECONDS)

212
scripts/backfill_offline_fields.py Normal file
View File

@@ -0,0 +1,212 @@
#!/usr/bin/env python3
"""
Fill the fields that need no network: FSSAI licences, and not-applicable marks.
WHY
Two separate gaps, both closable from data already on this machine.
1. FSSAI licences on brands that HAVE one.
Measured 2026-09-08: 264 rows have no `fssai_license`. Of those, 185
belong to brands with a curated licence in
`brand_registry.FSSAI_LICENSES` - Amul 10, HUL 82, MTR 39, Dabur 17,
CavinKare 17, Nestle 16, Kaleesuwari 4. The map knows the answer; the
rows were written before stage 1 filled it, or by a path that skipped it.
Consensus over a brand's own rows was the other candidate mechanism and
it fills ZERO of these - every brand with blanks either already has a
mapping or has no populated row to learn from. It is left in place for
future uploads into an established brand, but it is not what closes this.
2. Marking what cannot apply.
Roughly 30% of the catalog is shampoo, soap, detergent and toothpaste.
Those rows will never have nutrients, a health score or an FSSAI FOOD
licence, and a coverage report that counts them as "missing" shows a
permanent red number - which is exactly the pressure that eventually
gets it "fixed" by inventing values. This writes an explicit
`not_applicable` into `field_sources` so the report can exclude them
honestly.
WHAT IT TOUCHES
* brand_<slug>.fssai_license - ONLY where blank, and ONLY from
`FSSAI_LICENSES`. Never from another brand, never a constant.
* brand_<slug>.field_sources - the provenance record for both operations.
WHAT IT WILL NOT DO
* It will not invent an FSSAI number. A brand absent from the curated map
gets nothing. `10012042000244` is Lion Dates' real licence and the reason
this rule is written down - see tests/test_no_fabricated_identifiers.py.
* It will not overwrite an existing licence, even one that disagrees with
the map. A disagreement is reported instead.
* It makes no network request.
USAGE
python -m scripts.backfill_offline_fields # dry run
python -m scripts.backfill_offline_fields --apply
python -m scripts.backfill_offline_fields --json
`--dry-run` is the default; backend/.env points at PRODUCTION.
"""
from __future__ import annotations
import argparse
import json
import logging
import sys
from collections import Counter
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from psycopg.types.json import Json
from app.infrastructure.settings import DB_HOST, DB_NAME
from app.services.brand_registry import get_fssai_license
from app.services.consumability import is_non_consumable
from app.services.vector_store import _connect
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger("backfill_offline_fields")
# Columns a non-consumable row can never have a value for.
NON_FOOD_NA = ("nutrients", "nutrients_per_100g", "nutrition_score",
"health_score", "fssai_license")
def brand_tables(cur, only: Optional[List[str]] = None) -> List[str]:
cur.execute(
"SELECT table_name FROM information_schema.tables "
"WHERE table_schema = 'public' AND table_name LIKE 'brand\\_%' "
"AND table_name <> 'brand_zzsmoketest' ORDER BY table_name"
)
tables = [r[0] for r in cur.fetchall()]
if only:
wanted = {f"brand_{s.strip().lower().replace(' ', '_').replace('-', '_')}"
for s in only}
tables = [t for t in tables if t in wanted]
return tables
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--brand", action="append", dest="brands")
ap.add_argument("--apply", action="store_true")
ap.add_argument("--dry-run", action="store_true", default=False)
ap.add_argument("--json", action="store_true")
args = ap.parse_args()
apply = args.apply and not args.dry_run
conn = _connect()
if conn is None:
logger.error("Database unreachable.")
return 2
logger.info("database : %s / %s", DB_HOST, DB_NAME)
logger.info("mode : %s", "APPLY (writing)" if apply else "dry run (no writes)")
logger.info("")
tally = Counter()
per_brand: Dict[str, Dict[str, int]] = {}
disagreements: List[Dict[str, str]] = []
try:
with conn.cursor() as cur:
for table in brand_tables(cur, args.brands):
display = table[len("brand_"):].replace("_", " ")
mapped = get_fssai_license(display)
cur.execute(
f'SELECT id, product_name, category, fssai_license, field_sources '
f'FROM "{table}"'
)
rows = cur.fetchall()
counts = Counter()
for rid, name, category, licence, sources in rows:
merged = dict(sources or {})
updates: Dict[str, Any] = {}
non_food = is_non_consumable(category or "", name or "")
if non_food:
for column in NON_FOOD_NA:
if merged.get(column, {}).get("method") != "not_applicable":
merged[column] = {"method": "not_applicable",
"source": "non_consumable_product"}
counts["marked_not_applicable"] += 1
elif not (licence or "").strip():
if mapped:
updates["fssai_license"] = mapped
merged["fssai_license"] = {"method": "sourced",
"source": "brand_registry"}
counts["fssai_filled"] += 1
else:
merged["fssai_license"] = {"method": "unknown",
"source": "no_registry_entry"}
counts["fssai_unknown"] += 1
elif mapped and licence.strip() != mapped:
disagreements.append({"table": table, "product": name,
"stored": licence, "registry": mapped})
counts["fssai_disagrees"] += 1
if merged == (sources or {}) and not updates:
continue
if apply:
if updates:
cur.execute(
f'UPDATE "{table}" SET fssai_license = %s, '
f"field_sources = %s, updated_at = CURRENT_TIMESTAMP "
f"WHERE id = %s AND (fssai_license IS NULL "
f" OR btrim(fssai_license) = '')",
(updates["fssai_license"], Json(merged), rid),
)
else:
cur.execute(
f'UPDATE "{table}" SET field_sources = %s, '
f"updated_at = CURRENT_TIMESTAMP WHERE id = %s",
(Json(merged), rid),
)
if counts:
per_brand[table] = dict(counts)
tally.update(counts)
if apply:
conn.commit()
finally:
conn.close()
if args.json:
print(json.dumps({"applied": apply, "tally": dict(tally),
"per_brand": per_brand,
"disagreements": disagreements}, indent=2))
return 0
for table, counts in sorted(per_brand.items(),
key=lambda kv: -sum(kv[1].values())):
parts = ", ".join(f"{k.replace('_', ' ')} {v}" for k, v in sorted(counts.items()))
logger.info(" %-30s %s", table, parts)
logger.info("")
logger.info("fssai filled from the brand registry %d", tally["fssai_filled"])
logger.info("fssai left blank, no registry entry %d", tally["fssai_unknown"])
logger.info("rows marked not-applicable (non-food) %d", tally["marked_not_applicable"])
if disagreements:
logger.info("")
logger.info("%d row(s) hold a licence that DISAGREES with the registry "
"(left untouched):", len(disagreements))
for d in disagreements[:10]:
logger.info(" %-28s stored=%s registry=%s",
d["product"][:28], d["stored"], d["registry"])
if not apply and sum(tally.values()):
logger.info("")
logger.info("Dry run - nothing written. Re-run with --apply to commit.")
return 0
if __name__ == "__main__":
raise SystemExit(main())

279
scripts/catalog_coverage.py Normal file
View File

@@ -0,0 +1,279 @@
#!/usr/bin/env python3
"""
How completely is the catalog filled, and where did each value come from?
WHY
"Fill every column" is not actually the goal, and a report that treats it
as one produces a permanent, unfixable red number that somebody eventually
"fixes" by inventing data. Three of these columns can never be filled for
large parts of the catalog, and that is correct:
* `upc` - every barcode here is GS1 India (prefix 890), which issues
EAN-13 and GTIN-8. UPC-A is a North American symbology. Measured: 0 of
300 barcodes are UPC-A, and none ever will be.
* `nutrients` / `health_score` - roughly 30% of rows are shampoo, soap,
detergent and toothpaste. Soap has no protein content.
* `fssai_license` - an FSSAI licence covers a FOOD business. P&G,
Colgate-Palmolive and Reckitt Benckiser should not carry one.
So this report counts four states, not two:
sourced a real value from a real source
derived computed from another field we hold (gtin from barcode)
estimated a category-level or consensus guess, flagged as such
not applicable cannot exist for this row, and should not
MISSING we have not got it yet - the only number worth chasing
Coverage percentages are taken against the APPLICABLE denominator, so the
numbers describe work remaining rather than work impossible.
WHAT IT TOUCHES
Nothing. Every statement is a SELECT. There is no --apply because there is
nothing to apply.
USAGE
python -m scripts.catalog_coverage
python -m scripts.catalog_coverage --brand amul --brand cadbury
python -m scripts.catalog_coverage --column barcode --column nutrients
python -m scripts.catalog_coverage --json > coverage.json
python -m scripts.catalog_coverage --by-provenance
Run it before and after any enrichment change: the diff of two --json runs is
the evidence that the change did what it claimed.
"""
from __future__ import annotations
import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional, Set
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from app.infrastructure.settings import DB_HOST, DB_NAME
from app.services.vector_store import _connect
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger("catalog_coverage")
# Columns worth reporting on, in the order a reader wants them.
TRACKED = [
"product_name", "title", "description", "category", "image_url",
"price_range", "size_variants", "providers", "highlights",
"fssai_license", "product_sku", "hsn_code", "gst_percent", "tax_amount",
"selling_price", "final_selling_price",
"barcode", "barcode_type", "gtin", "ean13", "upc",
"nutrients", "nutrients_per_100g", "nutrition_score", "health_score",
]
# Columns that simply cannot apply to some rows, and the rule for which.
#
# food_only - meaningless for a non-consumable product
# india_only - UPC-A does not occur in a GS1 India catalog
# barcoded - derived from a barcode, so absent when the barcode is
NOT_APPLICABLE_RULES = {
"nutrients": "food_only",
"nutrients_per_100g": "food_only",
"nutrition_score": "food_only",
"health_score": "food_only",
"fssai_license": "food_only",
"upc": "india_only",
"gtin": "barcoded",
"ean13": "barcoded",
"barcode_type": "barcoded",
}
# A value that is present but means "nothing here".
EMPTY_LITERALS = ("", "[]", "{}", "null", "0", "Uncategorized")
def brand_tables(cur, only: Optional[List[str]] = None) -> List[str]:
cur.execute(
"SELECT table_name FROM information_schema.tables "
"WHERE table_schema = 'public' AND table_name LIKE 'brand\\_%' "
"AND table_name <> 'brand_zzsmoketest' ORDER BY table_name"
)
tables = [r[0] for r in cur.fetchall()]
if only:
wanted = {f"brand_{s.strip().lower().replace(' ', '_').replace('-', '_')}"
for s in only}
tables = [t for t in tables if t in wanted]
return tables
def columns_of(cur, table: str) -> Set[str]:
"""Probed per table rather than assumed. The column set genuinely differed
per table until the schema migration, and a script that assumes otherwise
dies on the first old table it meets."""
cur.execute(
"SELECT column_name FROM information_schema.columns "
"WHERE table_schema = 'public' AND table_name = %s",
(table,),
)
return {r[0] for r in cur.fetchall()}
def _is_non_food(category: str) -> bool:
from app.services.consumability import is_non_consumable
try:
return bool(is_non_consumable(category or "", ""))
except Exception:
return False
def scan(cur, table: str, wanted: List[str]) -> Dict[str, Dict[str, int]]:
present = columns_of(cur, table)
cols = [c for c in wanted if c in present]
if not cols:
return {}
select = ", ".join(f'"{c}"' for c in cols)
extra = ', "category"' if "category" in present else ""
barcode_idx = cols.index("barcode") if "barcode" in cols else None
cur.execute(f'SELECT {select}{extra} FROM "{table}"')
rows = cur.fetchall()
stats: Dict[str, Dict[str, int]] = {
c: {"rows": 0, "filled": 0, "not_applicable": 0} for c in cols
}
for row in rows:
category = row[len(cols)] if extra else ""
non_food = _is_non_food(category)
has_barcode = bool(barcode_idx is not None and row[barcode_idx])
for i, col in enumerate(cols):
s = stats[col]
s["rows"] += 1
rule = NOT_APPLICABLE_RULES.get(col)
if ((rule == "food_only" and non_food)
or (rule == "india_only")
or (rule == "barcoded" and not has_barcode)):
s["not_applicable"] += 1
continue
value = row[i]
if value is None:
continue
if isinstance(value, (list, tuple, dict)) and not value:
continue
if isinstance(value, str) and value.strip() in EMPTY_LITERALS:
continue
s["filled"] += 1
return stats
def provenance(cur, table: str) -> Dict[str, Dict[str, int]]:
"""How each filled value was arrived at, read from `field_sources`."""
if "field_sources" not in columns_of(cur, table):
return {}
cur.execute(f'SELECT field_sources FROM "{table}" '
f"WHERE field_sources IS NOT NULL AND field_sources <> '{{}}'::jsonb")
out: Dict[str, Dict[str, int]] = {}
for (blob,) in cur.fetchall():
for column, record in (blob or {}).items():
method = (record or {}).get("method", "unspecified")
out.setdefault(column, {}).setdefault(method, 0)
out[column][method] += 1
return out
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--brand", action="append", dest="brands")
ap.add_argument("--column", action="append", dest="columns")
ap.add_argument("--json", action="store_true")
ap.add_argument("--by-provenance", action="store_true",
help="also break filled values down by how they were obtained")
args = ap.parse_args()
wanted = args.columns or TRACKED
conn = _connect()
if conn is None:
logger.error("Database unreachable.")
return 2
totals: Dict[str, Dict[str, int]] = {c: {"rows": 0, "filled": 0, "not_applicable": 0}
for c in wanted}
per_brand: Dict[str, Any] = {}
prov_totals: Dict[str, Dict[str, int]] = {}
try:
with conn.cursor() as cur:
tables = brand_tables(cur, args.brands)
for table in tables:
stats = scan(cur, table, wanted)
if not stats:
continue
per_brand[table] = stats
for col, s in stats.items():
for k in ("rows", "filled", "not_applicable"):
totals[col][k] += s[k]
if args.by_provenance:
for col, methods in provenance(cur, table).items():
for method, n in methods.items():
prov_totals.setdefault(col, {}).setdefault(method, 0)
prov_totals[col][method] += n
finally:
conn.close()
def applicable(s):
return s["rows"] - s["not_applicable"]
if args.json:
print(json.dumps({
"database": f"{DB_HOST}/{DB_NAME}",
"totals": {c: {**s, "applicable": applicable(s),
"pct": round(100 * s["filled"] / applicable(s), 1)
if applicable(s) else None}
for c, s in totals.items() if s["rows"]},
"provenance": prov_totals,
"per_brand": per_brand,
}, indent=2))
return 0
row_count = max((s["rows"] for s in totals.values()), default=0)
logger.info("database : %s / %s", DB_HOST, DB_NAME)
logger.info("%d product rows across %d brand tables", row_count, len(per_brand))
logger.info("")
logger.info("%-22s %8s %8s %7s %s", "column", "filled", "of", "pct", "not applicable")
logger.info("%s", "-" * 72)
for col in wanted:
s = totals.get(col)
if not s or not s["rows"]:
continue
app_n = applicable(s)
pct = f'{100 * s["filled"] / app_n:5.1f}%' if app_n else " -"
na = f'{s["not_applicable"]:d}' if s["not_applicable"] else ""
flag = ""
if app_n and s["filled"] < app_n:
flag = f' <- {app_n - s["filled"]} missing'
logger.info("%-22s %8d %8d %7s %-6s%s", col, s["filled"], app_n, pct, na, flag)
if args.by_provenance and prov_totals:
logger.info("")
logger.info("provenance of filled values (from field_sources)")
logger.info("%s", "-" * 72)
for col in sorted(prov_totals):
methods = ", ".join(f"{m} {n}" for m, n in
sorted(prov_totals[col].items(), key=lambda kv: -kv[1]))
logger.info("%-22s %s", col, methods)
elif args.by_provenance:
logger.info("")
logger.info("No field_sources recorded yet - run an enrichment pass first.")
return 0
if __name__ == "__main__":
raise SystemExit(main())

257
scripts/migrate_brand_schema.py Normal file
View File

@@ -0,0 +1,257 @@
#!/usr/bin/env python3
"""
Bring every brand table up to the current schema.
WHY
`vector_store._ensure_columns` is the only migration mechanism in this
repo - there is no Alembic and no migrations directory - and it runs only
as a side effect of `ensure_brand_schema`, i.e. on the next WRITE to a
table. A brand nobody uploads to therefore never gets a new column.
Measured on 2026-09-08, before the enrichment columns landed: seven of
fifty-six brand tables carried `gtin` / `ean13` / `upc` /
`barcode_source` / `barcode_verified` / `barcode_lookup_status` /
`barcode_last_updated`, all added out-of-band by hand. The other
forty-nine did not, and no code path would ever have added them. This
script closes that gap deliberately instead of waiting for a write that
may never come.
WHAT IT TOUCHES
* `ALTER TABLE brand_<slug> ADD COLUMN IF NOT EXISTS ...` for every column
in `_ensure_columns`' `col_defs` that the table does not already have.
* The retroactive UNIQUE index on `image_id`, and the DROP NOT NULL sweep
over legacy columns - both are part of `_ensure_columns` and cannot be
run separately.
Nothing else. No row is read, updated or deleted by this script.
WHAT IT WILL NOT DO
* It will not change the type of a column that already exists.
`ADD COLUMN IF NOT EXISTS` skips a column that is present, whatever its
type. This is deliberate: the seven hand-migrated tables define the
types the rest must match, which is why `col_defs` says TIMESTAMP for
`barcode_last_updated` and REAL for the tax figures rather than the
types those values look like they want. Type drift is REPORTED here,
never silently "fixed".
* It will not create a brand table that does not exist.
* It will not touch `nutrition_facts` or any non-brand table.
WHY IT IS SAFE ON A LIVE DATABASE
`ADD COLUMN` with no DEFAULT and no NOT NULL is a catalogue-only change in
PostgreSQL 11+: no table rewrite, no full-table lock, no time proportional
to row count. On 1 630 rows across 56 tables this is milliseconds. The one
exception is `field_sources`, which does carry a DEFAULT - and since
PostgreSQL 11 a non-volatile default is also metadata-only.
USAGE
python -m scripts.migrate_brand_schema # dry run, all brands
python -m scripts.migrate_brand_schema --brand amul # repeatable
python -m scripts.migrate_brand_schema --apply
python -m scripts.migrate_brand_schema --json
`--dry-run` is the default and `--apply` must be explicit: backend/.env points
at the PRODUCTION database, so an accidental run must not be able to write.
The target host is printed on startup.
"""
from __future__ import annotations
import argparse
import json
import logging
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from app.infrastructure.settings import DB_HOST, DB_NAME
from app.services.vector_store import _connect, _ensure_columns
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger("migrate_brand_schema")
def brand_tables(cur, only: Optional[List[str]] = None) -> List[str]:
"""Every brand table actually present, in name order."""
cur.execute(
"SELECT table_name FROM information_schema.tables "
"WHERE table_schema = 'public' AND table_name LIKE 'brand\\_%' "
"ORDER BY table_name"
)
tables = [r[0] for r in cur.fetchall()]
if only:
wanted = {f"brand_{s.strip().lower().replace(' ', '_').replace('-', '_')}"
for s in only}
tables = [t for t in tables if t in wanted]
return tables
def existing_columns(cur, table: str) -> Dict[str, str]:
cur.execute(
"SELECT column_name, data_type FROM information_schema.columns "
"WHERE table_schema = 'public' AND table_name = %s",
(table,),
)
return {r[0]: r[1] for r in cur.fetchall()}
# What `information_schema.data_type` reports for each col_defs type, so a
# type-drift check does not raise false alarms on spelling differences.
_TYPE_ALIASES = {
"TEXT": {"text"},
"TEXT[]": {"ARRAY"},
"NUMERIC": {"numeric"},
"REAL": {"real"},
"BOOLEAN": {"boolean"},
"TIMESTAMP": {"timestamp without time zone"},
"JSONB": {"jsonb"},
"DOUBLE PRECISION": {"double precision"},
"vector(384)": {"USER-DEFINED"},
}
class RecordingCursor:
"""Wraps a real cursor so a dry run can see the statements without
executing them. Reads are passed through - the whole point is to compute
the diff against what is really on the table."""
def __init__(self, inner):
self._inner = inner
self.statements: List[str] = []
def execute(self, sql, params=None):
text = " ".join(str(sql).split())
upper = text.upper()
if upper.startswith("SELECT"):
return self._inner.execute(sql, params)
self.statements.append(text)
return None
def fetchall(self):
return self._inner.fetchall()
def fetchone(self):
return self._inner.fetchone()
def main() -> int:
ap = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
ap.add_argument("--brand", action="append", dest="brands",
help="brand table suffix; repeatable. Default: every brand table.")
ap.add_argument("--apply", action="store_true", help="actually run the ALTERs")
ap.add_argument("--dry-run", action="store_true", default=False,
help="report only (the default)")
ap.add_argument("--json", action="store_true", help="machine-readable output")
args = ap.parse_args()
apply = args.apply and not args.dry_run
conn = _connect()
if conn is None:
logger.error("Database unreachable - nothing to do.")
return 2
logger.info("database : %s / %s", DB_HOST, DB_NAME)
logger.info("mode : %s", "APPLY (writing)" if apply else "dry run (no writes)")
logger.info("")
declared_types = _declared_types()
if not declared_types:
logger.error("Could not read col_defs out of _ensure_columns - refusing to "
"guess at the schema. Has that function been restructured?")
return 2
report: List[Dict[str, Any]] = []
total_missing = 0
total_drift = 0
try:
with conn.cursor() as cur:
tables = brand_tables(cur, args.brands)
if not tables:
logger.error("No brand tables matched.")
return 1
for table in tables:
before = existing_columns(cur, table)
recorder = RecordingCursor(cur)
_ensure_columns(recorder, table)
adds = [s for s in recorder.statements if "ADD COLUMN" in s]
# Type drift: a column that exists but whose type is not what
# col_defs would have created. Reported, never altered.
drift = []
for col, declared in declared_types.items():
actual = before.get(col)
if actual is None:
continue
allowed = _TYPE_ALIASES.get(declared.upper(), set())
if allowed and actual not in allowed:
drift.append({"column": col, "declared": declared, "actual": actual})
entry = {
"table": table,
"missing_columns": [s.split("ADD COLUMN IF NOT EXISTS ")[1] for s in adds],
"type_drift": drift,
}
report.append(entry)
total_missing += len(adds)
total_drift += len(drift)
if apply and adds:
_ensure_columns(cur, table)
if apply:
conn.commit()
finally:
conn.close()
if args.json:
print(json.dumps({"applied": apply, "tables": report}, indent=2))
return 0
width = max(len(e["table"]) for e in report)
changed = [e for e in report if e["missing_columns"] or e["type_drift"]]
for entry in sorted(changed, key=lambda e: -len(e["missing_columns"])):
logger.info("%-*s %d column(s) missing", width, entry["table"],
len(entry["missing_columns"]))
for col in entry["missing_columns"]:
logger.info("%-*s + %s", width, "", col)
for d in entry["type_drift"]:
logger.info("%-*s ! %s is %s, col_defs declares %s (NOT changed)",
width, "", d["column"], d["actual"], d["declared"])
logger.info("")
logger.info("%d table(s) scanned, %d already current",
len(report), len(report) - len(changed))
logger.info("%d column(s) %s, %d type mismatch(es) reported",
total_missing, "added" if apply else "would be added", total_drift)
if not apply and total_missing:
logger.info("")
logger.info("Re-run with --apply to write these changes.")
return 0
def _declared_types() -> Dict[str, str]:
"""The col_defs dict, read back out of the function that owns it.
Parsed from source rather than duplicated here, so this script cannot
drift from the single migration mechanism it exists to drive.
"""
import ast
import inspect
import textwrap
tree = ast.parse(textwrap.dedent(inspect.getsource(_ensure_columns)))
for node in ast.walk(tree):
if isinstance(node, ast.Assign) and getattr(node.targets[0], "id", "") == "col_defs":
return {ast.literal_eval(k): ast.literal_eval(v)
for k, v in zip(node.value.keys, node.value.values)}
return {}
if __name__ == "__main__":
raise SystemExit(main())

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"""The offline stage that expands a barcode into the rest of its identity.
THE FAILURE THIS FILE EXISTS FOR
--------------------------------
Measured against the production database on 2026-09-08:
barcode 18.4% filled
gtin 8.7%
ean13 6.4%
upc 0.0%
Every one of those three could have been computed from the barcode already
sitting in the same row - they are arithmetic on the digits, not a lookup. They
were empty because the only code that produced them lived inside
`BarcodeEnrichmentStage`, which is off by default (`ENABLE_BARCODE_LOOKUP`,
false in production), and because the writer dropped the fields anyway.
`BarcodeIdentityStage` closes that. It performs NO lookup, so it needs no
settings flag and costs nothing, and it runs on every ingestion.
The three properties that matter, each pinned below:
1. It expands a valid barcode into barcode_type / gtin / ean13 / upc.
2. It NEVER touches a barcode that fails validation. A sheet-supplied barcode
is the merchant's assertion; silently "correcting" or deleting one would be
worse than leaving it visibly wrong. The failure goes into `field_sources`,
not into the data.
3. It never claims a value is "verified". That word is reserved for the
cascade's brand+size+name-matched result, and a barcode typed into a
spreadsheet has passed no such check.
No network and no database: the stage has neither.
"""
from __future__ import annotations
import asyncio
import pytest
from app.services.enrichment.barcode.identity_stage import BarcodeIdentityStage
def run(product, brand="Cadbury"):
"""Apply the stage the way EnrichmentPipeline does, returning the row."""
stage = BarcodeIdentityStage()
return asyncio.run(stage.apply(dict(product), brand))
# ---------------------------------------------------------------------------
# 1. Expansion
# ---------------------------------------------------------------------------
def test_an_ean13_expands_into_gtin_and_ean13():
"""8901233018362 is Cadbury Bournvita's real barcode - the one that had to
be repaired in production by hand on 2026-09-08."""
row = run({"product_name": "Cadbury Bournvita 500g", "barcode": "8901233018362"})
assert row["barcode"] == "8901233018362"
assert row["barcode_type"] == "EAN-13"
assert row["gtin"] == "8901233018362"
assert row["ean13"] == "8901233018362"
assert row["upc"] is None # a 13-digit code is not a UPC-A
def test_a_upc_a_expands_into_both_upc_and_a_padded_ean13():
"""UPC-A is numerically a GTIN-13 with a leading zero, so both fields are
real for the same pack - the zero-padded form is what an EAN-13 scanner
reports."""
row = run({"product_name": "Imported Bar 50g", "barcode": "036000291452"})
assert row["barcode_type"] == "UPC-A"
assert row["upc"] == "036000291452"
assert row["ean13"] == "0036000291452"
assert row["gtin"] == "036000291452"
def test_a_gtin8_is_not_padded_into_an_ean13():
"""An 8-digit GTIN is its own symbology, not a truncated EAN-13. Padding it
would invent a code that identifies nothing. 89009802 is the real GTIN-8
Open Food Facts holds for Nestle Munch."""
row = run({"product_name": "Nestle Munch 8.9g", "barcode": "89009802"})
assert row["barcode_type"] == "GTIN-8"
assert row["gtin"] == "89009802"
assert row["ean13"] is None
assert row["upc"] is None
def test_separators_are_stripped_but_the_value_is_not_otherwise_changed():
row = run({"product_name": "Amul Butter 100g", "barcode": " 8901262-010016 "})
assert row["barcode"] == "8901262010016"
assert row["gtin"] == "8901262010016"
# ---------------------------------------------------------------------------
# 2. It never damages what the merchant supplied
# ---------------------------------------------------------------------------
def test_an_invalid_barcode_is_left_exactly_as_typed():
"""The placeholder `8900000000000.0` sat in 38 production rows. It is not a
barcode, but it is also not this stage's to delete - a value visibly wrong
is findable, a value silently blanked is not."""
row = run({"product_name": "Cadbury 5 Star 24g", "barcode": "8900000000000.0"})
assert row["barcode"] == "8900000000000.0"
assert row.get("gtin") is None
assert row.get("ean13") is None
assert row.get("barcode_type") is None
def test_a_failed_checksum_is_recorded_in_provenance_not_in_the_data():
"""13 digits of the right length but the wrong check digit."""
row = run({"product_name": "Probe", "barcode": "8901233018363"})
assert row["barcode"] == "8901233018363"
assert row["field_sources"]["barcode"]["method"] == "unvalidated"
assert "failed checksum" in row["field_sources"]["barcode"]["note"]
def test_a_row_with_no_barcode_is_untouched():
row = run({"product_name": "Amul Butter 100g", "category": "Dairy"})
assert "gtin" not in row
assert "field_sources" not in row
def test_it_never_invents_a_barcode():
"""The stage has no source and no network. If the row has no barcode, it
cannot acquire one here - that is BarcodeEnrichmentStage's job."""
row = run({"product_name": "Unknown Product 1kg", "barcode": ""})
assert not row.get("barcode")
assert not row.get("gtin")
# ---------------------------------------------------------------------------
# 3. It does not overstate what it knows
# ---------------------------------------------------------------------------
def test_a_sheet_barcode_is_never_marked_verified():
row = run({"product_name": "Probe 100g", "barcode": "8901233018362"})
assert row["barcode_verified"] is False
assert row["barcode_lookup_status"] == "sheet_validated"
assert row["barcode_source"] == "sheet"
def test_the_derived_fields_are_flagged_derived_not_sourced():
"""gtin/ean13/upc are arithmetic on the barcode. Recording them as
`sourced` would claim a lookup confirmed them, which is the exact
overstatement the provenance map exists to prevent."""
row = run({"product_name": "Probe 100g", "barcode": "8901233018362"})
for field in ("gtin", "ean13", "upc", "barcode_type"):
assert row["field_sources"][field]["method"] == "derived", field
def test_an_existing_source_is_not_overwritten_by_sheet():
"""When the cascade found the barcode, its provenance is the real one and
must survive this stage running afterwards."""
row = run({
"product_name": "Probe 100g",
"barcode": "8901233018362",
"barcode_source": "Open Food Facts",
"barcode_verified": True,
"barcode_lookup_status": "verified",
})
assert row["barcode_source"] == "Open Food Facts"
assert row["barcode_verified"] is True
assert row["field_sources"]["barcode"]["method"] == "sourced"
# ---------------------------------------------------------------------------
# 4. Provenance accumulates across stages
# ---------------------------------------------------------------------------
def test_field_sources_from_an_earlier_stage_is_merged_not_replaced():
"""`base.apply()` assigns every key except this one. Assigning it would
mean the last stage to run erases what every earlier stage recorded, so a
value would end up in the database with no origin."""
row = run({
"product_name": "Probe 100g",
"barcode": "8901233018362",
"field_sources": {"hsn_code": {"method": "estimated", "source": "category"}},
})
assert row["field_sources"]["hsn_code"]["method"] == "estimated"
assert row["field_sources"]["gtin"]["method"] == "derived"
def test_the_stage_never_raises_on_a_malformed_row():
"""The EnrichmentStage contract: a stage bug degrades to "no fields added",
never an aborted catalog row."""
for barcode in (None, "", "abc", 12345, [], {"nested": 1}, 8901233018362):
row = run({"product_name": "Probe", "barcode": barcode})
assert isinstance(row, dict)

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"""Accepting an Open Food Facts record whose name is shorter than ours.
THE FAILURE THIS FILE EXISTS FOR
--------------------------------
Running `scripts/backfill_nutrition_from_barcodes` over the production catalog
on 2026-09-08 reported, of 300 barcoded rows:
not a valid GTIN 37
not in Open Food Facts 100
matched but empty 11
found, WRONG PRODUCT 149 <-- this file
would write 3
The 149 were not wrong products. The barcode resolved perfectly; Open Food
Facts simply stores a short name where we store a long one:
ours "Nestle Munch 8.9g" OFF "Munch" similarity 0.332
ours "Coca-Cola Maaza 750ml" OFF "Maaza" similarity 0.304
ours "Cadbury Perk 22 g" OFF "Perk" similarity 0.302
`name_similarity` divides token overlap by the TARGET's token count, so a
one-token candidate against a three-token target cannot exceed about 0.33 no
matter how correct it is.
WHY THE FIX IS NOT A LOWER THRESHOLD
The same run correctly rejected these, which sit BELOW the containment cases
but not far enough below to be separable by a number:
ours "Pepsico Lays 1kg" OFF "Spanish tomato tango" 0.133
ours "Coca-Cola Fanta 750ml" OFF "Orange" 0.089
and `settings.py:449-477` records the measurement that raised this floor to
0.78 in the first place (at 0.45: 15 accepted / 8 wrong; at 0.78: 2 / 0).
Lowering it re-admits exactly what it was raised to exclude.
Containment separates the groups structurally instead. It also has to reject
two cases a naive substring check would wave through, both of which are real
Open Food Facts titles: the bare brand name ("Colgate", "godrej"), which would
otherwise attach to every product of that brand, and a same-brand sibling
("Dairy Milk Silk" against our "Cadbury Dairy Milk").
The relaxation is OFF by default and enabled on exactly one call site -
`fetch_verified_nutrition_by_barcode` - because there the barcode has already
established identity and there are no competing candidates. On the search path,
where many candidates compete and the name is the only discriminator, "Munch"
would match every Nestle product containing that word.
"""
from __future__ import annotations
import pytest
from app.services.enrichment.barcode.matching import is_match, name_is_contained
from app.services.enrichment.barcode.models import BarcodeCandidate
# (our stored title, what OFF calls it, brand) - all measured on 2026-09-08
CONTAINED = [
("Nestle Munch 8.9g", "Munch", "Nestle"),
("Coca-Cola Maaza 750ml", "Maaza", "Coca-Cola"),
("Cadbury Perk 22 g", "Perk", "Cadbury"),
("Nestle Milo 25 g", "MILO", "Nestle"),
("Cadbury Fuse 25 g", "FUSE", "Cadbury"),
("Coca-Cola Limca 750g", "limca", "Coca-Cola"),
("Nestle Milkybar 25g", "Milkybar", "Nestle"),
]
NOT_CONTAINED = [
("Pepsico Lays 1kg", "Spanish tomato tango", "Pepsico"),
("Lion Dates Powder 100g", "PEPER NOTEN", "Lion Dates"),
("Coca-Cola Fanta 750ml", "Orange", "Coca-Cola"),
# Bare brand names. Both are real OFF product_name values.
("Colgate Total Toothpaste 150g", "Colgate", "Colgate"),
("Godrej No1 Soap 100g", "godrej", "Godrej"),
# Same brand, different product - the case the name gate exists for.
("Amul Butter 100g", "Amul Cheese", "Amul"),
("Cadbury Dairy Milk 50g", "Dairy Milk Silk", "Cadbury"),
]
@pytest.mark.parametrize("ours,theirs,brand", CONTAINED)
def test_a_short_off_name_is_recognised_as_ours(ours, theirs, brand):
assert name_is_contained(theirs, ours, brand) is True
@pytest.mark.parametrize("ours,theirs,brand", NOT_CONTAINED)
def test_a_different_product_is_still_refused(ours, theirs, brand):
assert name_is_contained(theirs, ours, brand) is False
def test_a_bare_brand_name_never_matches():
""""Colgate" as a product name identifies a brand, not a product. Accepting
it would attach one arbitrary pack's nutrition to every Colgate row."""
assert name_is_contained("Colgate", "Colgate MaxFresh Toothpaste 150g", "Colgate") is False
def test_size_tokens_do_not_decide_identity():
"""`size_matches` has already compared the pack size by the time this is
consulted, so a size token in our title must not make the names differ."""
assert name_is_contained("Munch", "Nestle Munch 8.9g", "Nestle") is True
assert name_is_contained("Munch", "Nestle Munch 38.5 g", "Nestle") is True
def test_an_extra_token_in_the_candidate_breaks_containment():
"""Containment is one-directional on purpose: every candidate token must be
ours. "Dairy Milk Silk" carries "silk", which our "Cadbury Dairy Milk" does
not, so it is a different product."""
assert name_is_contained("Dairy Milk Silk", "Cadbury Dairy Milk 50g", "Cadbury") is False
def test_empty_names_are_refused_rather_than_treated_as_contained():
"""The empty set is a subset of everything - the one case where the maths
says yes and the answer is obviously no."""
assert name_is_contained("", "Nestle Munch 8.9g", "Nestle") is False
assert name_is_contained("Munch", "", "Nestle") is False
# ---------------------------------------------------------------------------
# The gate as a whole
# ---------------------------------------------------------------------------
def _candidate(title, brand, size=""):
return BarcodeCandidate(barcode="8901058857245", source_name="Open Food Facts",
candidate_title=title, candidate_brand=brand,
candidate_size=size)
def test_containment_is_off_by_default():
"""The search path must not get this relaxation: there, many candidates
compete and "Munch" would match every Nestle product containing it."""
matched, _ = is_match(_candidate("Munch", "Nestle", "8.9g"),
"Nestle", "Nestle Munch 8.9g", "8.9g",
min_name_similarity=0.78)
assert matched is False
def test_containment_accepts_when_explicitly_enabled():
matched, similarity = is_match(_candidate("Munch", "Nestle", "8.9g"),
"Nestle", "Nestle Munch 8.9g", "8.9g",
min_name_similarity=0.78,
barcode_is_identity=True)
assert matched is True
# The reported confidence is still the honest similarity, not 1.0 - it is
# stored on the row for audit and must not be inflated by the relaxation.
assert similarity < 0.5
def test_containment_does_not_bypass_the_brand_gate():
"""Rules 1-3 still apply. A containment name match with the wrong brand is
still a wrong product."""
matched, _ = is_match(_candidate("Munch", "Britannia", "8.9g"),
"Nestle", "Nestle Munch 8.9g", "8.9g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is False
def test_a_conflicting_size_is_still_refused():
"""A quantity that is PRESENT and different means our barcode is on the
wrong row. That is exactly what the sanity check is for."""
matched, _ = is_match(_candidate("Munch", "Nestle", "500g"),
"Nestle", "Nestle Munch 8.9g", "8.9g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is False
# ---------------------------------------------------------------------------
# The gate that actually blocked most of the 149
# ---------------------------------------------------------------------------
def test_a_blank_candidate_size_no_longer_vetoes_under_a_barcode():
"""The real blocker, found only after measuring the containment fix.
`size_matches` returns False whenever EITHER side is blank, and Open Food
Facts leaves `quantity` null on a large share of records - 57 of 146 Amul
hits. Because `is_match` applies its rules in order, that rejected these
rows before the name rule was ever consulted, so fixing the name gate alone
moved the measured result from 3 rows to 8 rather than to ~149.
A record with no quantity does not disagree with our pack size. It says
nothing about it, and the barcode has already established identity.
"""
matched, _ = is_match(_candidate("Munch", "Nestle", ""),
"Nestle", "Nestle Munch 38.5 g", "38.5 g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is True
def test_a_blank_candidate_size_still_vetoes_on_the_search_path():
"""Without a barcode a sizeless candidate is genuinely unidentifiable: it
could be any pack of that product, and a GTIN belongs to exactly one."""
matched, _ = is_match(_candidate("Nestle Munch", "Nestle", ""),
"Nestle", "Nestle Munch 38.5 g", "38.5 g",
min_name_similarity=0.45)
assert matched is False
def test_the_size_relaxation_does_not_also_relax_the_brand_gate():
matched, _ = is_match(_candidate("Munch", "Britannia", ""),
"Nestle", "Nestle Munch 38.5 g", "38.5 g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is False
def test_the_size_relaxation_does_not_also_relax_variant_conflicts():
matched, _ = is_match(_candidate("Munch sugar free", "Nestle", ""),
"Nestle", "Nestle Munch 38.5 g", "38.5 g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is False
def test_containment_does_not_bypass_the_variant_conflict_gate():
""""sugar free" on the candidate but not on ours is a different product
however well the rest of the name contains."""
matched, _ = is_match(_candidate("Munch sugar free", "Nestle", "8.9g"),
"Nestle", "Nestle Munch 8.9g", "8.9g",
min_name_similarity=0.78, barcode_is_identity=True)
assert matched is False
def test_a_normal_high_similarity_match_is_unaffected():
"""The relaxation is only consulted when the similarity floor fails, so it
cannot change any decision the existing gate already made."""
matched, similarity = is_match(_candidate("Nestle Munch", "Nestle", "8.9g"),
"Nestle", "Nestle Munch", "8.9g",
min_name_similarity=0.78)
assert matched is True
assert similarity >= 0.78

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"""The enrichment columns on the brand tables, and the write path that fills them.
THE FAILURE THIS FILE EXISTS FOR
--------------------------------
The barcode stage returns nine fields - `BarcodeResult.as_product_fields()` -
and `upsert_brand_products` named two of them. The other seven were computed on
every run and then dropped on the floor by the writer. Measured against the
production database on 2026-09-08, before this landed:
upc 0.0% (column existed on 7 of 56 tables, never written)
ean13 6.4% (added out-of-band, never written by any code)
gtin 8.7%
barcode 18.4%
The same was true of the HSN/GST stage: it computes `gst_percent`, `tax_amount`
and `hsn_gst_needs_review`, and `_to_storage_row` projected none of them.
Three distinct things have to hold for a value to survive, and each of them
broke independently at some point, so each gets a test here:
1. The column has to EXIST. `_ensure_columns` is the only migration mechanism
in the repo - there is no Alembic and no migrations directory - so a column
missing from its `col_defs` dict never appears on the 56 brand tables that
already exist.
2. The INSERT has to NAME it. Adding the column is not enough; that is exactly
how seven tables ended up carrying `gtin` and `ean13` columns that no code
ever wrote a value into.
3. A later re-seed must not BLANK it. `ON CONFLICT DO UPDATE SET x =
EXCLUDED.x` overwrites with whatever arrived, and the seed loader,
`user_products._build_product_dict` and `brand_sync`'s re-seed all build a
product dict from a spreadsheet with no enrichment keys in it - so their
EXCLUDED values are NULL. This is the same trap `test_brand_table_scores`
documents for the score columns, which is why those are omitted from the
statement entirely. These columns cannot be omitted (the pipeline is what
writes them), so they use COALESCE instead.
`barcode` and `barcode_type` are COALESCEd alongside the seven, though they are
older columns. Proven against a live table: a bare re-seed set `barcode` to
NULL while COALESCE kept `gtin` and `ean13`, leaving a row that claimed a GTIN
with no barcode. A half-erased identity is worse than either whole state, and
the rest of the barcode package already promises never to erase one -
`stage.py` skips a row that has a barcode and `enrichment/base.py` refuses to
blank a held value. The upsert was the one place that still could.
No database is involved: the cursor is a recorder, so the assertions are about
the exact SQL sent.
"""
from __future__ import annotations
import re
from app.services import brand_sync, vector_store
# The columns this change added, with the type each MUST be created as.
#
# THE TYPES ARE ADOPTED, NOT CHOSEN. Seven brand tables already carried these
# columns before any code created them, and `ADD COLUMN IF NOT EXISTS` does not
# reconcile a type difference - it silently leaves the old table alone. Picking
# a "better" type here (NUMERIC for the tax figures, DOUBLE PRECISION for the
# epoch) would leave 7 tables permanently disagreeing with 49. These are what
# `information_schema` reported for those 7 tables on 2026-09-08.
PIPELINE_OWNED = {
"gtin": "TEXT",
"ean13": "TEXT",
"upc": "TEXT",
"barcode_source": "TEXT",
"barcode_verified": "BOOLEAN",
"barcode_lookup_status": "TEXT",
"barcode_last_updated": "TIMESTAMP",
"gst_percent": "REAL",
"tax_amount": "REAL",
"hsn_gst_needs_review": "BOOLEAN",
"field_sources": "JSONB",
}
# Written only by nutrition_score_sync, never by the INSERT - the same
# category as nutrition_score / health_score.
MIRROR_OWNED = {"nutrients_per_100g": "JSONB"}
class MigrationCursor:
"""A brand table that exists but has none of the current columns."""
def __init__(self):
self.statements = []
def execute(self, sql, params=None):
self.statements.append(" ".join(str(sql).split()))
def fetchall(self):
return [] # no existing columns -> every column is missing
def _insert_statement() -> str:
"""The INSERT ... ON CONFLICT text, whitespace-normalised."""
source = vector_store.upsert_brand_products.__doc__ or ""
# The statement is built inline, so read it off the module source rather
# than reaching into a closure.
import inspect
body = inspect.getsource(vector_store.upsert_brand_products)
match = re.search(r"INSERT INTO \{table_name\}.*?updated_at = CURRENT_TIMESTAMP",
body, re.S)
assert match, "could not locate the INSERT statement in upsert_brand_products"
return " ".join(match.group(0).split())
# ---------------------------------------------------------------------------
# 1. The columns exist
# ---------------------------------------------------------------------------
def test_the_migration_adds_every_enrichment_column():
cur = MigrationCursor()
vector_store._ensure_columns(cur, "brand_cadbury")
for col, col_type in {**PIPELINE_OWNED, **MIRROR_OWNED}.items():
assert (f"ALTER TABLE brand_cadbury ADD COLUMN IF NOT EXISTS "
f"{col} {col_type}") in cur.statements, col
def test_the_types_match_the_tables_that_already_had_these_columns():
"""A wrong type here is invisible until a write fails on one of the seven
pre-existing tables, because ADD COLUMN IF NOT EXISTS skips them silently.
`barcode_last_updated` is the one most likely to be "corrected" by a future
reader: `BarcodeResult` carries a float epoch, so DOUBLE PRECISION looks
right. The column on disk is TIMESTAMP, and vector_store._epoch_to_timestamp
is what bridges the two.
"""
assert vector_store._ensure_columns.__doc__ is not None
import inspect
body = inspect.getsource(vector_store._ensure_columns)
assert '"barcode_last_updated": "TIMESTAMP"' in body
assert '"gst_percent": "REAL"' in body
assert '"tax_amount": "REAL"' in body
def test_the_create_table_ddl_carries_them_too():
"""`col_defs` migrates existing tables; the DDL is what a brand table
created from scratch gets. A column in one but not the other means a new
brand's table differs from every other brand's."""
ddl = vector_store.get_brand_table_ddl("Cadbury")
for col in {**PIPELINE_OWNED, **MIRROR_OWNED}:
assert re.search(rf"^\s*{col}\s", ddl, re.M), col
# ---------------------------------------------------------------------------
# 2. The INSERT names them - and does not name the mirror-owned ones
# ---------------------------------------------------------------------------
def test_the_insert_names_every_pipeline_owned_column():
statement = _insert_statement()
column_list = statement.split("VALUES")[0]
for col in PIPELINE_OWNED:
assert re.search(rf"[(,] ?{col}[,)]", column_list), col
def test_the_insert_does_not_name_the_mirror_owned_columns():
"""Same rule as nutrition_score / health_score: a column this statement
never names is a column it cannot damage."""
statement = _insert_statement()
for col in MIRROR_OWNED:
assert col not in statement, col
def test_the_placeholder_count_matches_the_column_count():
"""An arity mismatch here is a runtime error on every single write, so it
is worth catching at import time rather than on the next upload."""
statement = _insert_statement()
match = re.search(r"INSERT INTO \{table_name\} \((.*?)\) VALUES \((.*?)\)", statement)
columns = [c.strip() for c in match.group(1).split(",") if c.strip()]
assert len(columns) == match.group(2).count("%s")
# ---------------------------------------------------------------------------
# 3. A re-seed cannot blank them
# ---------------------------------------------------------------------------
def test_every_enrichment_column_is_coalesced_on_conflict():
"""This is the guard that makes "a re-seed wipes the enrichment"
structurally impossible rather than merely unlikely."""
statement = _insert_statement()
do_update = statement.split("DO UPDATE SET", 1)[1]
for col in PIPELINE_OWNED:
if col == "field_sources":
continue # merged, asserted separately below
assert (f"{col} = COALESCE(EXCLUDED.{col}, {{table_name}}.{col})"
in do_update), col
def test_the_barcode_pair_is_coalesced_with_its_identity_group():
"""`barcode` and `barcode_type` predate this change but belong to the same
identity group as gtin/ean13/upc. Leaving them on plain EXCLUDED produced a
row with a GTIN and no barcode after a bare re-seed."""
do_update = _insert_statement().split("DO UPDATE SET", 1)[1]
for col in ("barcode", "barcode_type"):
assert (f"{col} = COALESCE(EXCLUDED.{col}, {{table_name}}.{col})"
in do_update), col
def test_field_sources_is_merged_rather_than_replaced():
"""A run that learns the provenance of one field must not drop what is
already known about the others, so this one is `||`, not COALESCE."""
do_update = _insert_statement().split("DO UPDATE SET", 1)[1]
# Read off the module source, so the f-string's escaped braces are still
# doubled here - `'{{}}'` is what renders as the SQL literal `'{}'`.
assert "field_sources = COALESCE({table_name}.field_sources, '{{}}'::jsonb) " \
"|| COALESCE(EXCLUDED.field_sources, '{{}}'::jsonb)" in do_update
# ---------------------------------------------------------------------------
# 4. The seed export carries them
# ---------------------------------------------------------------------------
def test_the_seed_export_carries_every_new_column():
"""`export_brand_to_seed_file` rebuilds a product from EXPORT_COLUMNS and
replaces the whole dict. A column missing from that tuple is stripped out
of the catalog file on export - which is precisely why
scripts/backfill_barcodes_from_off.py refuses to call that helper today."""
for col in {**PIPELINE_OWNED, **MIRROR_OWNED}:
assert col in brand_sync.EXPORT_COLUMNS, col
def test_the_export_coerces_values_json_dumps_would_refuse():
"""`barcode_last_updated` comes back from psycopg as a datetime and
`field_sources` as a dict. json.dumps refuses the first outright and chokes
on a Decimal nested in the second."""
from datetime import datetime
from decimal import Decimal
assert brand_sync._jsonable(datetime(2026, 9, 8, 10, 51, 42)) == "2026-09-08T10:51:42"
assert brand_sync._jsonable({"barcode": {"confidence": Decimal("0.91")}}) == {
"barcode": {"confidence": 0.91}
}
# ---------------------------------------------------------------------------
# 5. The epoch/timestamp bridge
# ---------------------------------------------------------------------------
def test_the_timestamp_bridge_accepts_every_shape_that_reaches_it():
"""Three writers feed this column and they disagree about the type:
BarcodeResult emits a float epoch, a re-seeded catalog file carries the
ISO-8601 string brand_sync exported, and a DB read hands back a datetime.
All three have to load or a round-trip drops the value it just wrote."""
from datetime import datetime
assert vector_store._epoch_to_timestamp(1788773811.0) == datetime.fromtimestamp(1788773811.0)
assert vector_store._epoch_to_timestamp("2026-09-08T10:51:42") == datetime(2026, 9, 8, 10, 51, 42)
assert vector_store._epoch_to_timestamp(datetime(2026, 1, 1)) == datetime(2026, 1, 1)
assert vector_store._epoch_to_timestamp("not a date") is None
assert vector_store._epoch_to_timestamp("") is None
assert vector_store._epoch_to_timestamp(None) is None
def test_the_numeric_coercion_refuses_rather_than_raises():
"""A malformed tax figure must degrade to "no figure stored" and never
abort a whole batch's write."""
assert vector_store._to_numeric_or_none("18%") == 18.0
assert vector_store._to_numeric_or_none("₹1,250.50") == 1250.50
assert vector_store._to_numeric_or_none(12.5) == 12.5
assert vector_store._to_numeric_or_none("not a number") is None
assert vector_store._to_numeric_or_none(None) is None
# bool is an int subclass; True must not become 1.0 in a NUMERIC column
assert vector_store._to_numeric_or_none(True) is None

159
tests/test_content_enrichment_stage.py Normal file
View File

@@ -0,0 +1,159 @@
"""The stage that fills `highlights` and `nutrients` on an uploaded row.
THE FAILURE THIS FILE EXISTS FOR
--------------------------------
`catalog_engine.generate_product_highlights` and `generate_nutrients_info` have
existed for a long time, and `brand_discovery._build_product` calls both. The
store-catalog pipeline never did - `_to_storage_row` passed through whatever
the sheet carried, and a colleague's sheet carries neither column. So every
single uploaded row landed `highlights=[]` and `nutrients=[]`.
That is why those columns look healthy in aggregate (95.3% / 69.7% measured on
2026-09-08) while being empty for exactly the rows this work is about: the
percentages come from the older brand-discovery path.
The second thing this file pins is the consumability gate.
`generate_nutrients_info` matches category keywords, so without a gate a Hair
Care row can acquire "Vitamin B Complex - Energy". Shampoo has no nutrients.
`scripts/purge_non_consumable_nutrition.py` exists because this already
happened once at the `nutrition_facts` level; the display column needs the same
refusal, and it must record `not_applicable` rather than leave a silent blank -
a permanent unexplained gap is what eventually gets "fixed" by fabricating.
No network, no database, no LLM: the generators are keyword functions over the
row dict.
"""
from __future__ import annotations
import asyncio
import pytest
from app.services.enrichment.content.stage import ContentEnrichmentStage, _has_entries
def run(product, brand="Cadbury"):
return asyncio.run(ContentEnrichmentStage().apply(dict(product), brand))
FOOD_ROW = {
"product_name": "Cadbury Dairy Milk 50g",
"title": "Cadbury Dairy Milk",
"category": "Chocolates",
"size": "50g",
"description": "Smooth milk chocolate bar.",
}
SHAMPOO_ROW = {
"product_name": "Dove Daily Shine Shampoo 340ml",
"title": "Dove Daily Shine Shampoo",
"category": "Hair Care",
"size": "340ml",
"description": "Nourishing shampoo for daily use.",
}
# ---------------------------------------------------------------------------
# It fills what the pipeline used to leave empty
# ---------------------------------------------------------------------------
def test_an_uploaded_food_row_gets_highlights():
row = run(FOOD_ROW)
assert row["highlights"], "every upload landed highlights=[] before this stage"
assert all(isinstance(h, str) and h.strip() for h in row["highlights"])
def test_an_uploaded_food_row_gets_nutrients():
row = run(FOOD_ROW)
assert row["nutrients"]
def test_highlights_are_flagged_derived_not_sourced():
"""They are marketing copy computed from fields we already hold. Calling
them sourced would claim something confirmed them."""
row = run(FOOD_ROW)
assert row["field_sources"]["highlights"]["method"] == "derived"
def test_the_keyword_nutrients_are_flagged_estimated():
"""They are category guesses standing in until a real lookup succeeds, and
the mirror from nutrition_facts overwrites them when one does."""
row = run(FOOD_ROW)
assert row["field_sources"]["nutrients"]["method"] == "estimated"
# ---------------------------------------------------------------------------
# The consumability gate
# ---------------------------------------------------------------------------
def test_a_shampoo_gets_no_nutrients():
"""The real defect: keyword matching gave personal-care rows entries like
"Vitamin B Complex - Energy"."""
row = run(SHAMPOO_ROW, brand="Dove")
assert not row.get("nutrients")
def test_a_shampoo_records_not_applicable_rather_than_a_silent_blank():
"""A gap nobody can explain is the one somebody eventually fills with
invented data. The coverage report reads this to exclude the row from the
nutrition denominator instead of reporting it missing forever."""
row = run(SHAMPOO_ROW, brand="Dove")
assert row["field_sources"]["nutrients"]["method"] == "not_applicable"
def test_a_shampoo_still_gets_highlights():
"""Non-consumable rules out nutrition, not description. A shampoo has
perfectly good highlights."""
row = run(SHAMPOO_ROW, brand="Dove")
assert row["highlights"]
# ---------------------------------------------------------------------------
# It fills blanks only
# ---------------------------------------------------------------------------
def test_values_the_sheet_supplied_are_never_overwritten():
"""The pipeline's first rule: the store's own data is authoritative."""
row = run({**FOOD_ROW,
"highlights": ["Fairtrade cocoa"],
"nutrients": ["Protein 7.3 g per 100 g"]})
assert row["highlights"] == ["Fairtrade cocoa"]
assert row["nutrients"] == ["Protein 7.3 g per 100 g"]
def test_a_column_of_empty_strings_counts_as_blank():
"""The spreadsheet parser produces [''] from a column that exists with no
value in it. Treating that as "already filled" keeps the row [''] forever."""
assert _has_entries([""]) is False
assert _has_entries(["", " "]) is False
assert _has_entries(["Real"]) is True
assert _has_entries([]) is False
row = run({**FOOD_ROW, "highlights": [""]})
assert row["highlights"] != [""]
# ---------------------------------------------------------------------------
# The stage contract
# ---------------------------------------------------------------------------
def test_provenance_from_an_earlier_stage_survives():
row = run({**FOOD_ROW,
"field_sources": {"gtin": {"method": "derived"}}})
assert row["field_sources"]["gtin"]["method"] == "derived"
assert row["field_sources"]["highlights"]["method"] == "derived"
def test_the_stage_never_raises_on_a_malformed_row():
for product in ({}, {"product_name": None}, {"category": 123},
{"title": "", "category": None, "highlights": "not a list"}):
assert isinstance(run(product), dict)

177
tests/test_no_fabricated_identifiers.py Normal file
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@@ -0,0 +1,177 @@
"""Defaults that invent a regulatory identifier or a commercial claim.
THE FAILURE THIS FILE EXISTS FOR
--------------------------------
`user_products._build_product_dict` filled two blanks with constants:
fssai_license = req.fssai_license or sample.get("fssai_license") or "10012042000244"
providers = req.providers or list(sample.get("providers") or
["Amazon", "Flipkart", "BigBasket", "Jiomart", "Blinkit", "Zepto"])
The first constant is not a placeholder. `10012042000244` is Lion Dates' real,
registered FSSAI licence - it is still in `brand_registry.FSSAI_LICENSES` under
that brand. Every product uploaded for a brand with no existing row was stamped
with it, which attributes legal responsibility for that food to a business that
never made it. It reached production at least once:
`scripts/merge_haldiram.py` exists specifically to strip it back off
`brand_haldirams`.
The second asserts a product is stocked by six named marketplaces on the basis
of nothing at all.
Both are now sourced from the brand's own rows, or left empty. The tests below
pin three things:
1. The literal constants are gone from the module.
2. An unknown brand gets NO licence rather than someone else's.
3. Consensus refuses to answer when a brand's own rows disagree - because at
that point one of them is already wrong and a tie-break would just be
picking which product to mislabel.
No database: `consensus_value` and `fssai_for_brand` take the rows as an
argument, which is what makes them testable at all.
"""
from __future__ import annotations
import ast
import inspect
from app.api.routers import user_products
from app.services.enrichment.catalog_consensus import consensus_value, fssai_for_brand
LION_DATES_LICENCE = "10012042000244"
# ---------------------------------------------------------------------------
# 1. The constants are gone
# ---------------------------------------------------------------------------
def _executable_string_literals(module) -> list:
"""Every string constant the module can actually evaluate.
Docstrings and comments are excluded deliberately: the fix's own comment
has to be free to name the constant it removed, or the explanation of why
the bug mattered cannot be written down next to the code that had it.
"""
tree = ast.parse(inspect.getsource(module))
docstrings = set()
for node in ast.walk(tree):
if isinstance(node, (ast.Module, ast.ClassDef, ast.FunctionDef, ast.AsyncFunctionDef)):
body = getattr(node, "body", [])
if (body and isinstance(body[0], ast.Expr)
and isinstance(body[0].value, ast.Constant)
and isinstance(body[0].value.value, str)):
docstrings.add(id(body[0].value))
return [n.value for n in ast.walk(tree)
if isinstance(n, ast.Constant) and isinstance(n.value, str)
and id(n) not in docstrings]
def test_lion_dates_licence_is_not_a_fallback_anywhere_in_the_upload_path():
literals = _executable_string_literals(user_products)
assert LION_DATES_LICENCE not in literals, (
"A real registered FSSAI licence must never appear as a default. "
"It belongs to Lion Dates and to no other brand."
)
def test_the_six_marketplace_default_is_gone():
literals = _executable_string_literals(user_products)
for marketplace in ("Blinkit", "BigBasket", "Jiomart", "Zepto"):
assert marketplace not in literals, (
f"{marketplace} appears as an evaluable literal. Listing "
f"marketplaces nobody verified is a false availability claim."
)
# ---------------------------------------------------------------------------
# 2. An unknown brand gets nothing
# ---------------------------------------------------------------------------
def test_an_unknown_brand_with_no_rows_gets_no_licence():
value, source = fssai_for_brand("Entirely Unknown Brand", rows=[])
assert value is None
assert source == "unknown"
def test_a_registry_brand_still_gets_its_mapped_licence():
"""The curated map remains the first and best source."""
value, source = fssai_for_brand("Lion Dates", rows=[])
assert value == LION_DATES_LICENCE
assert source == "brand_registry"
def test_an_unmapped_brand_inherits_from_its_own_agreeing_rows():
"""400 Britannia rows carrying one licence is good evidence for the 401st -
unlike a constant, this value genuinely belongs to the brand."""
rows = [{"fssai_license": "11223344556677"} for _ in range(20)]
value, source = fssai_for_brand("Some Unmapped Brand", rows=rows)
assert value == "11223344556677"
assert source == "catalog_consensus"
# ---------------------------------------------------------------------------
# 3. Consensus declines rather than guesses
# ---------------------------------------------------------------------------
def test_conflicting_licences_propagate_nothing():
"""Two different licences on one brand means one is already wrong. Picking
the more common one would just spread whichever error is ahead."""
rows = ([{"fssai_license": "11111111111111"}] * 10 +
[{"fssai_license": "22222222222222"}] * 8)
value, source = fssai_for_brand("Conflicted Brand", rows=rows)
assert value is None
assert source == "unknown"
def test_a_single_dissenting_row_does_not_veto_a_clear_majority():
"""One bad row among many should not block the other 19 from being useful."""
rows = [{"fssai_license": "11111111111111"}] * 19 + [{"fssai_license": "99999999999999"}]
value, _ = consensus_value("fssai_license", rows)
assert value == "11111111111111"
def test_too_few_rows_is_not_a_consensus():
"""Two rows agreeing proves nothing about a third."""
value, why = consensus_value("fssai_license", [{"fssai_license": "1"}, {"fssai_license": "1"}])
assert value is None
assert why["reason"] == "too few populated rows"
def test_blank_values_are_not_counted_as_agreement():
"""A column that is empty on every row must not come back as a consensus of
empties - that would read as "the brand agrees there is no licence"."""
value, _ = consensus_value("fssai_license", [{"fssai_license": None}] * 20)
assert value is None
def test_list_valued_columns_reach_consensus_too():
"""`providers` is a TEXT[]; lists are unhashable, so the modal calculation
has to key them as tuples or it raises."""
rows = [{"providers": ["Amazon", "Flipkart"]}] * 10
value, _ = consensus_value("providers", rows)
assert value == ["Amazon", "Flipkart"]
def test_consensus_never_raises_on_unusable_rows():
value, why = consensus_value("providers", [{"providers": {"unhashable": ["dict"]}}] * 5)
assert value is None
assert "reason" in why

31
tests/test_nutrition_by_barcode.py
View File

@@ -184,15 +184,36 @@ def test_a_matching_record_with_no_usable_values_is_unavailable(off_returns):
assert facts["data_status"] == "unavailable"
def test_the_floor_can_be_relaxed_per_call(off_returns):
"""The threshold is a judgement, not a constant, so it is a parameter.
def test_a_shorter_off_name_no_longer_needs_the_floor_relaxed(off_returns):
""""Aachi Biryani Masala" against our "Aachi Biryani Masala 50 g" is plainly
the same product and scores 0.716 - below the conservative 0.78 default.
"Aachi Biryani Masala" against our "Aachi Biryani Masala 50 g" is plainly
the same product and scores 0.716 - below the conservative default. The
backfill script exposes this as --min-similarity for exactly this reason.
It used to need `min_name_similarity=0.70` to get through. It no longer
does: this path passes `barcode_is_identity=True`, and the candidate's name
is ours minus the pack size, so the containment rule accepts it while the
floor stays where the measured yield table put it. See
tests/test_barcode_name_containment.py - 149 of 300 barcoded rows were
being refused this way.
"""
off_returns["body"] = {"status": 1, "product": _product(
"Aachi Biryani Masala", "Aachi", "50 g")}
facts = nds.fetch_verified_nutrition_by_barcode(
"8906021120272", "Aachi", "Aachi Biryani Masala 50 g", "50 g")
assert facts["data_status"] == "verified"
def test_the_floor_can_still_be_relaxed_per_call(off_returns):
"""The threshold is a judgement, not a constant, so it is a parameter, and
the backfill script exposes it as --min-similarity.
"Aachi Biryani Masala Mix" carries a token ours does not, so containment
does NOT rescue it - it is exactly the shape the floor exists to judge. It
scores 0.703: refused at the 0.78 default, accepted at 0.70.
"""
off_returns["body"] = {"status": 1, "product": _product(
"Aachi Biryani Masala Mix", "Aachi", "50 g")}
args = ("8906021120272", "Aachi", "Aachi Biryani Masala 50 g", "50 g")
assert nds.fetch_verified_nutrition_by_barcode(*args)["data_status"] == "unavailable"