feat(insta-lab): keyword_extractor with frequency + Claude refinement

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2026-05-16 00:30:38 +09:00
parent c057304981
commit 07c4459085
2 changed files with 148 additions and 0 deletions

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import os
import tempfile
from unittest.mock import patch, MagicMock
import pytest
from app import db as db_module
from app import keyword_extractor
@pytest.fixture
def tmp_db(monkeypatch):
fd, path = tempfile.mkstemp(suffix=".db")
os.close(fd)
monkeypatch.setattr(db_module, "DB_PATH", path)
db_module.init_db()
yield path
# Windows-safe cleanup: close handles + remove sidecars
import gc
gc.collect()
for ext in ("", "-wal", "-shm"):
try:
os.remove(path + ext)
except OSError:
pass
def test_count_nouns_extracts_korean_nouns():
text = "기준금리 인상으로 환율 급등. 기준금리 추가 인상 가능성"
counts = keyword_extractor._count_nouns(text)
assert counts["기준금리"] == 2
assert counts["환율"] == 1
def test_top_candidates_filters_stopwords():
counts = {"기준금리": 5, "있다": 7, "환율": 3, "그리고": 4}
top = keyword_extractor._top_candidates(counts, n=10)
keywords = [k for k, _ in top]
assert "있다" not in keywords
assert "그리고" not in keywords
assert "기준금리" in keywords
def test_extract_for_category_persists(tmp_db):
# seed articles
for i in range(3):
db_module.add_news_article({
"category": "economy",
"title": f"기준금리 인상 {i}",
"link": f"https://example.com/{i}",
"summary": "환율도 영향",
})
# mock LLM refinement
fake_refined = [
{"keyword": "기준금리", "score": 0.92, "reason": "핵심 금융 이슈"},
{"keyword": "환율", "score": 0.71, "reason": "시장 영향"},
]
with patch.object(keyword_extractor, "_refine_with_llm", return_value=fake_refined):
kws = keyword_extractor.extract_for_category("economy", limit=2)
assert len(kws) == 2
assert kws[0]["keyword"] == "기준금리"
persisted = db_module.list_trending_keywords(category="economy")
assert {p["keyword"] for p in persisted} == {"기준금리", "환율"}