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/indexer/enrich-c...
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def _mk_products(n: int) -> List[Dict[str, str]]:
return [{"id": str(i), "title": f"title-{i}"} for i in range(n)]
def test_analyze_products_caps_batch_size_to_20(monkeypatch):
monkeypatch.setattr(process_products, "API_KEY", "fake-key")
seen_batch_sizes: List[int] = []
def _fake_process_batch(batch_data: List[Dict[str, str]], batch_num: int, target_lang: str = "zh"):
seen_batch_sizes.append(len(batch_data))
return [
{
"id": item["id"],
"lang": target_lang,
"title_input": item["title"],
"title": "",
"category_path": "",
"tags": "",
"target_audience": "",
"usage_scene": "",
"season": "",
"key_attributes": "",
"material": "",
"features": "",
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"anchor_text": "",
}
for item in batch_data
]
monkeypatch.setattr(process_products, "process_batch", _fake_process_batch)
monkeypatch.setattr(process_products, "_set_cached_anchor_result", lambda *args, **kwargs: None)
out = process_products.analyze_products(
products=_mk_products(45),
target_lang="zh",
batch_size=200,
tenant_id="162",
)
assert len(out) == 45
assert seen_batch_sizes == [20, 20, 5]
def test_analyze_products_uses_min_batch_size_1(monkeypatch):
monkeypatch.setattr(process_products, "API_KEY", "fake-key")
seen_batch_sizes: List[int] = []
def _fake_process_batch(batch_data: List[Dict[str, str]], batch_num: int, target_lang: str = "zh"):
seen_batch_sizes.append(len(batch_data))
return [
{
"id": item["id"],
"lang": target_lang,
"title_input": item["title"],
"title": "",
"category_path": "",
"tags": "",
"target_audience": "",
"usage_scene": "",
"season": "",
"key_attributes": "",
"material": "",
"features": "",
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"anchor_text": "",
}
for item in batch_data
]
monkeypatch.setattr(process_products, "process_batch", _fake_process_batch)
monkeypatch.setattr(process_products, "_set_cached_anchor_result", lambda *args, **kwargs: None)
out = process_products.analyze_products(
products=_mk_products(3),
target_lang="zh",
batch_size=0,
tenant_id="162",
)
assert len(out) == 3
assert seen_batch_sizes == [1, 1, 1]
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