feat(M2): Open Food Facts 采集导入 ETL

- adapters/openfoodfacts.py: OFF API 适配器(限速+User-Agent) + JSONL/.gz dump 读取
- etl/transform.py: 纯函数转换(GTIN 校验/净含量解析+归一/营养 per_100g 双能量/过敏原添加剂清洗/关键词分类映射)
- etl/load.py: psycopg upsert(product/food_detail/product_image) + product_source 字段级溯源
- jobs/seed_off.py: CLI(--barcodes API / --dump 文件 / --limit)
- 测试: 13 个离线 transform 单测(fixture) + 可跳过的 DB 集成测试
- 依赖: 增加 psycopg[binary]
- 实跑: 从 OFF API 拉 5 个真实条码入本地 postgres 验证通过
- docs/etl-openfoodfacts.md: 流程/运行/字段映射

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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2026-06-08 07:01:04 +00:00
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"""Load transformed product records into the OpenGoods PostgreSQL database.
Only the ingestion side writes to the database. Every load records OFF as the
source with field-level provenance in `product_source`.
"""
from __future__ import annotations
import json
import os
from typing import Any
import psycopg
from psycopg.types.json import Jsonb
from opengoods.adapters.openfoodfacts import OFF_LICENSE, SOURCE_NAME
OFF_HOMEPAGE = "https://world.openfoodfacts.org"
def default_dsn() -> str:
return os.environ.get(
"OPENGOODS_DATABASE_URL",
"postgres://opengoods:opengoods@localhost:5432/opengoods?sslmode=disable",
)
def _normalize_brand(name: str) -> str:
return " ".join(name.lower().split())
def ensure_source(conn: psycopg.Connection) -> str:
"""Upsert the Open Food Facts source row and return its id."""
row = conn.execute(
"""
INSERT INTO source (name, homepage, license, trust_weight)
VALUES (%s, %s, %s, %s)
ON CONFLICT (name) DO UPDATE SET homepage = EXCLUDED.homepage
RETURNING id
""",
(SOURCE_NAME, OFF_HOMEPAGE, OFF_LICENSE, 0.7),
).fetchone()
return row[0]
def _ensure_brand(conn: psycopg.Connection, name: str | None) -> str | None:
if not name:
return None
row = conn.execute(
"""
INSERT INTO brand (name, normalized_name)
VALUES (%s, %s)
ON CONFLICT (normalized_name) DO UPDATE SET name = brand.name
RETURNING id
""",
(name, _normalize_brand(name)),
).fetchone()
return row[0]
def _category_id(conn: psycopg.Connection, path: str | None) -> tuple[str | None, str | None]:
if not path:
return None, None
row = conn.execute(
"SELECT id, gpc_brick_code FROM category WHERE path = %s::ltree", (path,)
).fetchone()
return (row[0], row[1]) if row else (None, None)
def load_record(conn: psycopg.Connection, rec: dict[str, Any], source_id: str, raw: dict) -> str:
"""Upsert one transformed record; return the product id."""
brand_id = _ensure_brand(conn, rec.get("brand"))
category_id, gpc_brick = _category_id(conn, rec.get("category_path"))
fields = ["name", "brand", "net_content", "category", "country_of_origin"]
if rec.get("gtin"):
prod = conn.execute(
"""
INSERT INTO product (gtin, name, brand_id, category_id, gpc_brick_code,
net_content_value, net_content_unit, net_content_canonical,
country_of_origin, attributes)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)
ON CONFLICT (gtin) WHERE gtin IS NOT NULL DO UPDATE SET
name = EXCLUDED.name,
brand_id = COALESCE(EXCLUDED.brand_id, product.brand_id),
category_id = COALESCE(EXCLUDED.category_id, product.category_id),
gpc_brick_code = COALESCE(EXCLUDED.gpc_brick_code, product.gpc_brick_code),
net_content_value = EXCLUDED.net_content_value,
net_content_unit = EXCLUDED.net_content_unit,
net_content_canonical = EXCLUDED.net_content_canonical,
country_of_origin = EXCLUDED.country_of_origin
RETURNING id
""",
(
rec["gtin"],
rec["name"],
brand_id,
category_id,
gpc_brick,
rec.get("net_content_value"),
rec.get("net_content_unit"),
rec.get("net_content_canonical"),
rec.get("country_of_origin"),
Jsonb({}),
),
).fetchone()
else:
prod = conn.execute(
"""
INSERT INTO product (name, brand_id, category_id, gpc_brick_code,
net_content_value, net_content_unit, net_content_canonical,
country_of_origin, attributes)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s)
RETURNING id
""",
(
rec["name"],
brand_id,
category_id,
gpc_brick,
rec.get("net_content_value"),
rec.get("net_content_unit"),
rec.get("net_content_canonical"),
rec.get("country_of_origin"),
Jsonb({}),
),
).fetchone()
product_id = prod[0]
food = rec.get("food") or {}
conn.execute(
"""
INSERT INTO food_detail (product_id, ingredients_text, allergens, additives,
nutriments, nutrition_basis, serving_size, nutri_score)
VALUES (%s,%s,%s,%s,%s,%s,%s,%s)
ON CONFLICT (product_id) DO UPDATE SET
ingredients_text = EXCLUDED.ingredients_text,
allergens = EXCLUDED.allergens,
additives = EXCLUDED.additives,
nutriments = EXCLUDED.nutriments,
nutrition_basis = EXCLUDED.nutrition_basis,
serving_size = EXCLUDED.serving_size,
nutri_score = EXCLUDED.nutri_score
""",
(
product_id,
food.get("ingredients_text"),
food.get("allergens") or [],
food.get("additives") or [],
Jsonb(food.get("nutriments") or {}),
food.get("nutrition_basis"),
food.get("serving_size"),
food.get("nutri_score"),
),
)
if rec.get("image_url"):
conn.execute(
"""
INSERT INTO product_image (product_id, url, kind, license, source_id)
VALUES (%s,%s,'front',%s,%s)
""",
(product_id, rec["image_url"], "CC-BY-SA", source_id),
)
fields.append("image")
conn.execute(
"""
INSERT INTO product_source (product_id, source_id, url, fields, fetched_at, raw)
VALUES (%s,%s,%s,%s, now(), %s)
""",
(
product_id,
source_id,
f"{OFF_HOMEPAGE}/product/{rec.get('gtin') or ''}",
fields,
Jsonb(_jsonable(raw)),
),
)
return product_id
def _jsonable(raw: dict) -> dict:
"""Drop values that are not JSON-serializable from a raw record."""
try:
json.dumps(raw)
return raw
except (TypeError, ValueError):
return {k: v for k, v in raw.items() if _is_jsonable(v)}
def _is_jsonable(v: object) -> bool:
try:
json.dumps(v)
return True
except (TypeError, ValueError):
return False
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"""Transform raw Open Food Facts records into the OpenGoods internal shape.
Pure functions (no DB, no network) so they are easy to unit-test against
fixtures. The output dict mirrors the columns the loader writes.
"""
from __future__ import annotations
import re
from decimal import Decimal
from opengoods.units import UnitError, normalize
# OFF nutriment key -> our attribute key. Energy handled separately.
_NUTRIMENT_KEYS = {
"proteins_100g": "proteins",
"fat_100g": "fat",
"saturated-fat_100g": "saturated_fat",
"carbohydrates_100g": "carbohydrates",
"sugars_100g": "sugars",
"salt_100g": "salt",
}
# Very small keyword -> category path map (starter; replaced by a proper
# OFF taxonomy -> GPC mapping table later).
_CATEGORY_KEYWORDS: list[tuple[tuple[str, ...], str]] = [
(("water", "eau", "饮用水", "矿泉水"), "food.beverages.water"),
(("soda", "carbonated", "汽水", "碳酸"), "food.beverages.carbonated"),
(("juice", "jus", "果汁"), "food.beverages.juice"),
(("milk", "lait", "牛奶"), "food.dairy.milk"),
(("yogurt", "yoghurt", "yaourt", "酸奶"), "food.dairy.yogurt"),
(("cheese", "fromage", "奶酪", "干酪"), "food.dairy.cheese"),
(("bread", "pain", "面包"), "food.bakery.bread"),
(("biscuit", "cookie", "饼干"), "food.bakery.biscuits"),
(("chips", "crisps", "薯片", "膨化"), "food.snacks.chips"),
(("chocolate", "chocolat", "巧克力"), "food.snacks.chocolate"),
(("rice", "riz", "大米", "稻米"), "food.staple.rice"),
(("noodle", "pasta", "面条", "挂面"), "food.staple.noodles"),
(("oil", "huile", "食用油", "食油"), "food.staple.cooking_oil"),
(("soy sauce", "酱油"), "food.condiments.soy_sauce"),
(("salt", "sel", "食盐"), "food.condiments.salt"),
]
_QTY_RE = re.compile(r"(?P<value>\d+(?:[.,]\d+)?)\s*(?P<unit>[a-zA-Z\u4e00-\u9fff%]+)")
def is_valid_gtin(code: str) -> bool:
"""Validate a GTIN-8/12/13/14 using the standard check digit."""
if not code.isdigit() or len(code) not in (8, 12, 13, 14):
return False
digits = [int(c) for c in code]
check = digits[-1]
body = digits[:-1][::-1]
total = sum(d * (3 if i % 2 == 0 else 1) for i, d in enumerate(body))
return (10 - total % 10) % 10 == check
def parse_quantity(text: str) -> tuple[Decimal, str] | None:
"""Parse a free-text quantity like '500 g' or '1,5 L' -> (value, unit)."""
if not text:
return None
m = _QTY_RE.search(text)
if not m:
return None
value = Decimal(m.group("value").replace(",", "."))
return value, m.group("unit")
def map_category(raw: dict) -> str | None:
"""Best-effort map OFF categories/name to a self-built category path."""
haystack = " ".join(
str(raw.get(k, ""))
for k in ("categories", "categories_tags", "product_name", "product_name_en")
).lower()
for keywords, path in _CATEGORY_KEYWORDS:
if any(kw.lower() in haystack for kw in keywords):
return path
return None
def _clean_tags(tags: list[str] | None, prefix: str = "") -> list[str]:
out: list[str] = []
for t in tags or []:
v = t.split(":", 1)[-1] if ":" in t else t
v = v.strip().replace("-", " ")
if v:
out.append(v)
return out
def transform_nutriments(off_nutriments: dict) -> dict:
"""Build a nutriments dict on a per_100g basis with dual energy units."""
out: dict[str, object] = {}
for off_key, our_key in _NUTRIMENT_KEYS.items():
if off_key in off_nutriments and off_nutriments[off_key] is not None:
out[our_key] = float(off_nutriments[off_key])
kj = off_nutriments.get("energy-kj_100g")
kcal = off_nutriments.get("energy-kcal_100g")
if kj is None and kcal is not None:
kj = float(Decimal(str(kcal)) * Decimal("4.184"))
if kcal is None and kj is not None:
kcal = float(Decimal(str(kj)) / Decimal("4.184"))
if kj is not None:
out["energy_kj"] = round(float(kj), 3)
if kcal is not None:
out["energy_kcal"] = round(float(kcal), 3)
return out
def transform(raw: dict) -> dict | None:
"""Transform one raw OFF product record into an internal product dict.
Returns None if the record lacks a usable name.
"""
name = raw.get("product_name_zh") or raw.get("product_name") or raw.get("product_name_en")
if not name:
return None
code = str(raw.get("code", "")).strip()
gtin = code if code and is_valid_gtin(code) else None
brands = raw.get("brands") or ""
brand = brands.split(",")[0].strip() or None
net_value = net_unit = net_canonical = None
parsed = parse_quantity(raw.get("quantity", ""))
if parsed:
value, unit = parsed
try:
norm = normalize(value, unit)
net_value, net_unit, net_canonical = (
norm.value,
norm.unit,
norm.canonical_value,
)
except UnitError:
net_value, net_unit = value, unit
return {
"gtin": gtin,
"name": str(name).strip(),
"brand": brand,
"category_path": map_category(raw),
"net_content_value": net_value,
"net_content_unit": net_unit,
"net_content_canonical": net_canonical,
"country_of_origin": (raw.get("countries") or "").split(",")[0].strip() or None,
"food": {
"ingredients_text": raw.get("ingredients_text") or None,
"allergens": _clean_tags(raw.get("allergens_tags")),
"additives": _clean_tags(raw.get("additives_tags")),
"nutriments": transform_nutriments(raw.get("nutriments") or {}),
"nutrition_basis": "per_100g",
"serving_size": raw.get("serving_size") or None,
"nutri_score": (raw.get("nutriscore_grade") or "").upper()[:1] or None,
},
"image_url": raw.get("image_front_url") or raw.get("image_url") or None,
}