simon
c72a5ed13a
- system.yaml 新增 llm_scenes（translation / daily_report，含用途/方法/模型要求说明） - load_llm_config(scene=...) 场景覆盖；日报摘要 temperature 0.3 硬编码 → 配置 - M3 去重：唯一篇记录 source_ids（跨源重复合并，首个来源为 source_id） - source_ids 经翻译透传至 events，日报事件 source 多来源拼接展示（≤3 个） - 已部署 pi5：merge 实证 10 源合并；日报 report_id=224 正常入库
633 lines
24 KiB
Python
633 lines
24 KiB
Python
"""M3 三层去重模块单元测试。"""
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import json
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from pathlib import Path
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import pytest
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from dedup import (
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DEFAULT_HAMMING_THRESHOLD,
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Deduper,
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DedupLayer,
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DedupResult,
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Fingerprint,
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FingerprintStore,
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article_to_fingerprint,
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content_hash,
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hamming,
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normalize_content,
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simhash64,
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)
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from dedup.pipeline import dedup_source
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from extractor.models import ProcessedArticle
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# --------------------------------------------------------------------------- #
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# 辅助工厂函数
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# --------------------------------------------------------------------------- #
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def _make_article(
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*,
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url: str = "https://www.reuters.com/business/1",
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url_hash: str = "abc1234567890000",
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source_id: str = "reuters",
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source_name: str = "Reuters",
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title: str = "Fed Holds Rates Steady as Markets Rally",
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content: str = (
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"The Federal Reserve held interest rates steady on Wednesday, "
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"citing solid economic growth and a strong labor market. "
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"Markets rallied in response, with the S&P 500 gaining 1.2 percent."
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),
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publish_time: str = "2026-06-16T10:00:00",
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word_count: int = 0,
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) -> ProcessedArticle:
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return ProcessedArticle(
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source_id=source_id,
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source_name=source_name,
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url=url,
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url_hash=url_hash,
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title=title,
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content=content,
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publish_time=publish_time,
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word_count=word_count or len(content.split()),
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)
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@pytest.fixture
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def tmp_db(tmp_path: Path) -> Path:
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return tmp_path / "fp.sqlite3"
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# --------------------------------------------------------------------------- #
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# hasher 测试
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# --------------------------------------------------------------------------- #
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class TestNormalizeContent:
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"""normalize_content 函数测试。"""
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def test_strips_punctuation_and_whitespace(self):
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norm = normalize_content("Hello, world!\nThis is text.")
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assert norm == "HelloworldThisistext"
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def test_handles_empty(self):
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assert normalize_content("") == ""
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assert normalize_content(" \n\t ") == ""
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def test_preserves_letters_and_digits(self):
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# $ 是货币符号（Sc 类别），会被保留；% 和 . 是标点（Po）会被移除
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norm = normalize_content("AAPL up 5.2% to $150.00!")
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assert norm == "AAPLup52to$15000"
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def test_handles_chinese_characters(self):
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norm = normalize_content("宁德时代 发布 新一代 麒麟电池！")
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assert norm == "宁德时代发布新一代麒麟电池"
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class TestContentHash:
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"""content_hash 函数测试。"""
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def test_deterministic(self):
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a = "The Fed raised rates today."
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b = "The Fed raised rates today."
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assert content_hash(a) == content_hash(b)
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def test_punctuation_invariant(self):
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a = "Apple reports record earnings."
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b = "Apple, reports... record earnings!!!"
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assert content_hash(a) == content_hash(b)
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def test_differs_for_different_text(self):
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assert content_hash("Fed raises rates") != content_hash("Fed cuts rates")
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class TestSimhash64:
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"""simhash64 函数测试。"""
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def test_identical_text_same_value(self):
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text = "The Federal Reserve held interest rates steady on Wednesday."
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assert simhash64(text) == simhash64(text)
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def test_minor_changes_close_distance(self):
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"""轻量改写，长文本汉明距离应在阈值内。"""
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base = (
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"The Federal Reserve held interest rates steady on Wednesday, "
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"citing solid economic growth and a strong labor market. "
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"Markets rallied in response, with the S&P 500 gaining 1.2 percent. "
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"Analysts expect rates to remain unchanged through the summer."
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) * 2
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rewritten = "WASHINGTON (Reuters) - " + base + " (Reporting by John Smith)"
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d = hamming(simhash64(base), simhash64(rewritten))
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assert d <= DEFAULT_HAMMING_THRESHOLD, (
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f"长文本前后加来源标识汉明距离 {d} 不应超过阈值"
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)
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def test_unrelated_text_far_distance(self):
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"""完全不相关的两段长文本汉明距离应远大于阈值。"""
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a = "The Federal Reserve held interest rates steady on Wednesday." * 5
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b = "Apple announced a new iPhone model with revolutionary features." * 5
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d = hamming(simhash64(a), simhash64(b))
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assert d > DEFAULT_HAMMING_THRESHOLD * 2
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def test_empty_returns_zero(self):
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assert simhash64("") == 0
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assert simhash64(" ") == 0
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class TestHamming:
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"""hamming 距离函数测试。"""
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def test_same_value_zero(self):
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assert hamming(0, 0) == 0
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assert hamming(0xDEADBEEF, 0xDEADBEEF) == 0
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def test_basic(self):
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assert hamming(0xFF, 0x00) == 8
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assert hamming(0xFF00FF00, 0x00FF00FF) == 32
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def test_single_bit(self):
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assert hamming(1, 0) == 1
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assert hamming(1 << 63, 0) == 1
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# --------------------------------------------------------------------------- #
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# FingerprintStore 测试
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# --------------------------------------------------------------------------- #
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class TestFingerprintStore:
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"""FingerprintStore 功能测试。"""
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def test_upsert_and_get(self, tmp_db):
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fp = Fingerprint(
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url_hash="hash1",
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content_hash="ch1",
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simhash=0xDEADBEEFCAFEBABE,
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source_id="reuters",
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url="https://x/1",
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title="Test Title",
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publish_date="2026-06-16",
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)
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with FingerprintStore(tmp_db) as store:
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store.upsert(fp)
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got = store.get_by_url_hash("hash1")
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assert got is not None
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assert got.content_hash == "ch1"
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assert got.simhash == 0xDEADBEEFCAFEBABE
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assert got.publish_date == "2026-06-16"
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def test_upsert_replaces_existing(self, tmp_db):
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base = Fingerprint(
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url_hash="h",
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content_hash="ch1",
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simhash=1,
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source_id="reuters",
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url="u",
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title="t",
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)
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updated = base.model_copy(update={"content_hash": "ch2", "simhash": 999})
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with FingerprintStore(tmp_db) as store:
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store.upsert(base)
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store.upsert(updated)
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got = store.get_by_url_hash("h")
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assert got is not None
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assert got.content_hash == "ch2"
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assert got.simhash == 999
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assert store.count() == 1
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def test_find_by_content_hash(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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store.upsert(Fingerprint(
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url_hash="h1", content_hash="ch", simhash=0,
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source_id="reuters", url="u1", title="t1",
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))
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assert store.find_by_content_hash("ch") is not None
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assert store.find_by_content_hash("nope") is None
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def test_candidates_within_window(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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for d, h in [
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("2026-05-01", "old"),
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("2026-06-15", "near"),
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("2026-07-30", "far"),
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]:
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store.upsert(Fingerprint(
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url_hash=h, content_hash=h, simhash=0,
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source_id="reuters", url=f"u/{h}", title=h, publish_date=d,
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))
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cands = store.candidates_for_simhash("2026-06-16", window_days=7)
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url_hashes = sorted(c.url_hash for c in cands)
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assert url_hashes == ["near"]
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def test_candidates_no_date_returns_all(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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store.upsert(Fingerprint(
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url_hash="h1", content_hash="c1", simhash=0,
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source_id="reuters", url="u", title="t", publish_date=None,
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))
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cands = store.candidates_for_simhash(None, 30)
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assert len(cands) == 1
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def test_simhash_high_bit_hex(self, tmp_db):
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"""64 位 SimHash 高位为 1 时，hex 存取应保持无符号。"""
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high = (1 << 63) | 0x1234
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with FingerprintStore(tmp_db) as store:
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store.upsert(Fingerprint(
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url_hash="h", content_hash="c", simhash=high,
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source_id="reuters", url="u", title="t",
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))
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got = store.get_by_url_hash("h")
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assert got is not None
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assert got.simhash == high
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def test_count_by_source(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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for i, src in enumerate(["reuters", "reuters", "cnbc"]):
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store.upsert(Fingerprint(
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url_hash=f"h{i}", content_hash=f"c{i}", simhash=i,
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source_id=src, url=f"u{i}", title=f"t{i}",
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))
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counts = store.count_by_source()
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assert counts == {"reuters": 2, "cnbc": 1}
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def test_date_range(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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store.upsert(Fingerprint(
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url_hash="h1", content_hash="c1", simhash=0,
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source_id="reuters", url="u1", title="t1",
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publish_date="2026-06-10",
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))
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store.upsert(Fingerprint(
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url_hash="h2", content_hash="c2", simhash=0,
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source_id="cnbc", url="u2", title="t2",
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publish_date="2026-06-20",
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))
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lo, hi = store.date_range()
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assert lo == "2026-06-10"
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assert hi == "2026-06-20"
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def test_delete(self, tmp_db):
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with FingerprintStore(tmp_db) as store:
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store.upsert(Fingerprint(
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url_hash="h1", content_hash="c1", simhash=0,
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source_id="reuters", url="u1", title="t1",
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))
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store.delete("h1")
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assert store.get_by_url_hash("h1") is None
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assert store.count() == 0
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# --------------------------------------------------------------------------- #
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# article_to_fingerprint 测试
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# --------------------------------------------------------------------------- #
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class TestArticleToFingerprint:
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"""article_to_fingerprint 转换测试。"""
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def test_fields(self):
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art = _make_article()
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fp = article_to_fingerprint(art)
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assert fp.url_hash == art.url_hash
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assert fp.simhash == simhash64(art.content)
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assert fp.content_hash == content_hash(art.content)
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assert fp.publish_date == "2026-06-16"
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assert fp.source_id == "reuters"
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def test_handles_empty_publish_time(self):
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art = _make_article(publish_time="")
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fp = article_to_fingerprint(art)
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assert fp.publish_date is None
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# --------------------------------------------------------------------------- #
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# Deduper 三层去重测试
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# --------------------------------------------------------------------------- #
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class TestDeduper:
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"""Deduper 三层去重功能测试。"""
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def test_first_article_is_unique(self, tmp_db):
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art = _make_article()
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with Deduper(db_path=tmp_db) as d:
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result = d.ingest(art)
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assert not result.is_duplicate
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assert result.matched_layer is None
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assert d.stats().total == 1
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def test_layer1_url_hash(self, tmp_db):
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"""同一 url_hash 直接命中 L1。"""
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a1 = _make_article()
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a2 = _make_article() # 同 url_hash 同 url
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with Deduper(db_path=tmp_db) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert result.is_duplicate
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assert result.matched_layer == DedupLayer.URL
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assert d.stats().total == 1, "L1 命中应不写入新指纹"
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def test_layer2_content_hash(self, tmp_db):
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"""url 不同但 content 完全一致 → L2。"""
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a1 = _make_article(url="https://a.com/1", url_hash="hash1aaaaaaaaaaa")
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a2 = _make_article(url="https://b.com/2", url_hash="hash2bbbbbbbbbbb")
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with Deduper(db_path=tmp_db) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert result.is_duplicate
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assert result.matched_layer == DedupLayer.CONTENT
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assert result.matched_url_hash == "hash1aaaaaaaaaaa"
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def test_layer2_punctuation_difference_caught(self, tmp_db):
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"""标点/空白差异不应阻止 L2 命中（normalize_content 应剥离）。"""
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a1 = _make_article(
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url="https://a/1", url_hash="aaaa",
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content="The Fed raised rates today. Markets rallied strongly!"
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)
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a2 = _make_article(
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url="https://b/2", url_hash="bbbb",
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content="The Fed, raised... rates today!!! Markets -- rallied -- strongly."
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)
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assert content_hash(a1.content) == content_hash(a2.content)
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with Deduper(db_path=tmp_db) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert result.matched_layer == DedupLayer.CONTENT
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def test_layer3_simhash_minor_rewrite(self, tmp_db):
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"""长文本 + 转载前后缀，落入 SimHash 层（贴近真实跨源转载场景）。"""
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long_body = (
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"The Federal Reserve held interest rates steady on Wednesday, "
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"citing solid economic growth and a strong labor market. "
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"Markets rallied in response, with the S&P 500 gaining 1.2 percent. "
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"Treasury yields fell as investors welcomed the decision. "
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"Analysts expect the central bank to remain on hold through September."
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) * 2
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rewritten = "By Reuters Staff - " + long_body + " (Additional reporting by Jane Doe)"
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a1 = _make_article(url="https://a/1", url_hash="aaaaa", content=long_body)
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a2 = _make_article(url="https://b/2", url_hash="bbbbb", content=rewritten)
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# 必要前提：content_hash 不同（否则会被 L2 截胡）
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assert content_hash(a1.content) != content_hash(a2.content)
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with Deduper(db_path=tmp_db) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert result.is_duplicate
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assert result.matched_layer == DedupLayer.SIMHASH
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assert result.hamming_distance is not None
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assert result.hamming_distance <= DEFAULT_HAMMING_THRESHOLD
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def test_layer3_unrelated_articles_kept(self, tmp_db):
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"""完全不相关文章不去重。"""
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a1 = _make_article(
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url="https://a/1", url_hash="aaaaa",
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content="The Federal Reserve held interest rates steady on Wednesday." * 5,
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)
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a2 = _make_article(
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url="https://b/2", url_hash="bbbbb",
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content="Apple announced a new iPhone model with revolutionary features." * 5,
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title="Apple Unveils New iPhone",
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)
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with Deduper(db_path=tmp_db) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert not result.is_duplicate
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assert d.stats().total == 2
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def test_layer3_outside_time_window_kept(self, tmp_db):
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"""SimHash 相近，但 publish_date 距离过远（> 30 天）不去重。"""
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body = (
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"The Federal Reserve held interest rates steady on Wednesday, "
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"citing solid economic growth and a strong labor market."
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) * 3
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a1 = _make_article(
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url="https://a/1", url_hash="aaaa1", content=body,
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publish_time="2026-01-01T09:00:00",
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)
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a2 = _make_article(
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url="https://b/2", url_hash="bbbb2", content=body[:50] + body,
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publish_time="2026-06-16T09:00:00",
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)
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assert content_hash(a1.content) != content_hash(a2.content)
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with Deduper(db_path=tmp_db, time_window_days=30) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert not result.is_duplicate, "时间窗口外不应命中 SimHash"
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def test_threshold_zero_only_exact_simhash(self, tmp_db):
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"""阈值 0 → 仅当 SimHash 完全相同才视为重复（且会先被 L2 拦截）。"""
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a1 = _make_article(
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url="https://a/1", url_hash="aaaa1",
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content="The Federal Reserve held rates steady on Wednesday."
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)
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a2 = _make_article(
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url="https://b/2", url_hash="bbbb2",
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content="The Federal Reserve held rates unchanged on Wednesday."
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)
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with Deduper(db_path=tmp_db, simhash_threshold=0) as d:
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d.ingest(a1)
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result = d.ingest(a2)
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assert not result.is_duplicate
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def test_ingest_same_source_mixed(self, tmp_db):
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"""混合重复/不重复文章的同源摄入。"""
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a1 = _make_article(url="https://a/1", url_hash="h1", content="Story A " * 10)
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a2 = _make_article(url="https://a/2", url_hash="h2", content="Story B " * 10)
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a3 = _make_article(url="https://a/3", url_hash="h3", content="Story B " * 10) # 同 a2
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a4 = _make_article(url="https://a/4", url_hash="h4", content="Story C " * 10)
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with Deduper(db_path=tmp_db) as d:
|
|
r1 = d.ingest(a1)
|
|
r2 = d.ingest(a2)
|
|
r3 = d.ingest(a3)
|
|
r4 = d.ingest(a4)
|
|
|
|
assert not r1.is_duplicate
|
|
assert not r2.is_duplicate
|
|
assert r3.is_duplicate # L2 命中
|
|
assert not r4.is_duplicate
|
|
assert d.stats().total == 3
|
|
|
|
|
|
# --------------------------------------------------------------------------- #
|
|
# Deduper - check 不写入
|
|
# --------------------------------------------------------------------------- #
|
|
|
|
|
|
class TestDeduperCheck:
|
|
"""Deduper.check() 只读测试。"""
|
|
|
|
def test_check_does_not_write(self, tmp_db):
|
|
art = _make_article()
|
|
with Deduper(db_path=tmp_db) as d:
|
|
result = d.check(art)
|
|
assert not result.is_duplicate
|
|
assert d.stats().total == 0 # check 不应入库
|
|
|
|
def test_check_detects_duplicate_after_ingest(self, tmp_db):
|
|
a1 = _make_article()
|
|
a2 = _make_article()
|
|
with Deduper(db_path=tmp_db) as d:
|
|
d.ingest(a1)
|
|
result = d.check(a2)
|
|
assert result.is_duplicate
|
|
assert result.matched_layer == DedupLayer.URL
|
|
|
|
|
|
# --------------------------------------------------------------------------- #
|
|
# Deduper - stats
|
|
# --------------------------------------------------------------------------- #
|
|
|
|
|
|
class TestDeduperStats:
|
|
"""统计信息测试。"""
|
|
|
|
def test_stats_aggregates_by_source(self, tmp_db):
|
|
with Deduper(db_path=tmp_db) as d:
|
|
d.ingest(_make_article(
|
|
source_id="reuters", url="https://a/1", url_hash="r000000000000001",
|
|
))
|
|
d.ingest(_make_article(
|
|
source_id="reuters", url="https://a/2", url_hash="r000000000000002",
|
|
content="Another completely different article about markets." * 10,
|
|
))
|
|
d.ingest(_make_article(
|
|
source_id="cnbc", url="https://b/1", url_hash="c000000000000001",
|
|
content="CNBC exclusive report on technology stocks." * 10,
|
|
))
|
|
stats = d.stats()
|
|
assert stats.total == 3
|
|
assert stats.by_source == {"reuters": 2, "cnbc": 1}
|
|
assert stats.earliest is not None
|
|
|
|
|
|
# --------------------------------------------------------------------------- #
|
|
# DedupResult 测试
|
|
# --------------------------------------------------------------------------- #
|
|
|
|
|
|
class TestDedupResult:
|
|
"""DedupResult 模型测试。"""
|
|
|
|
def test_short_summary_unique(self):
|
|
r = DedupResult(url_hash="abc", is_duplicate=False)
|
|
assert r.short_summary() == "[UNIQUE] abc"
|
|
|
|
def test_short_summary_duplicate_url(self):
|
|
r = DedupResult(
|
|
url_hash="abc",
|
|
is_duplicate=True,
|
|
matched_layer=DedupLayer.URL,
|
|
matched_url_hash="xyz",
|
|
)
|
|
assert "[DUP/url]" in r.short_summary()
|
|
|
|
def test_short_summary_duplicate_simhash(self):
|
|
r = DedupResult(
|
|
url_hash="abc",
|
|
is_duplicate=True,
|
|
matched_layer=DedupLayer.SIMHASH,
|
|
matched_url_hash="xyz",
|
|
hamming_distance=2,
|
|
)
|
|
summary = r.short_summary()
|
|
assert "[DUP/simhash]" in summary
|
|
assert "hd=2" in summary
|
|
|
|
|
|
# --------------------------------------------------------------------------- #
|
|
# dedup_source 跨源来源合并（M9.2：最终显示新闻记录多个来源）
|
|
# --------------------------------------------------------------------------- #
|
|
|
|
|
|
class TestMergeSources:
|
|
"""dedup_source 在跨源重复时把来源合并进唯一篇。"""
|
|
|
|
DATE_STR = "20260805"
|
|
|
|
def _write_processed(
|
|
self,
|
|
base: Path,
|
|
source_id: str,
|
|
article: ProcessedArticle,
|
|
) -> None:
|
|
"""写入 data/processed/{source_id}/{date}/{url_hash}.json。"""
|
|
d = base / "data" / "processed" / source_id / self.DATE_STR
|
|
d.mkdir(parents=True, exist_ok=True)
|
|
(d / f"{article.url_hash}.json").write_text(
|
|
article.model_dump_json(indent=2, ensure_ascii=False), encoding="utf-8"
|
|
)
|
|
|
|
def test_cross_source_merge(self, tmp_path, monkeypatch):
|
|
"""同内容两源报道 → 唯一篇 source_ids 记录两个来源，主来源不变。"""
|
|
monkeypatch.chdir(tmp_path) # 隔离 data/ 相对路径与默认指纹库
|
|
|
|
content = ("The Federal Reserve kept interest rates unchanged on Wednesday. "
|
|
"Markets rallied in response.")
|
|
art_a = _make_article(
|
|
url="https://www.reuters.com/business/1", url_hash="aaaa111111111111",
|
|
source_id="reuters", content=content,
|
|
)
|
|
art_b = _make_article(
|
|
url="https://www.cnbc.com/2026/1", url_hash="bbbb222222222222",
|
|
source_id="cnbc", source_name="CNBC", content=content,
|
|
)
|
|
self._write_processed(tmp_path, "reuters", art_a)
|
|
self._write_processed(tmp_path, "cnbc", art_b)
|
|
|
|
with Deduper() as deduper:
|
|
dedup_source("reuters", deduper, self.DATE_STR)
|
|
dedup_source("cnbc", deduper, self.DATE_STR)
|
|
|
|
# 唯一篇 = reuters（先处理），跨源重复后 source_ids 合并
|
|
uniq = tmp_path / "data" / "deduped" / self.DATE_STR / "uniques" / "aaaa111111111111.json"
|
|
assert uniq.exists()
|
|
data = json.loads(uniq.read_text(encoding="utf-8"))
|
|
assert data["source_id"] == "reuters" # 主来源不变
|
|
assert data["source_ids"] == ["reuters", "cnbc"]
|
|
assert (tmp_path / "data" / "deduped" / self.DATE_STR / "uniques"
|
|
/ "bbbb222222222222.json").exists() is False # 重复篇不单独落盘
|
|
|
|
def test_unique_initializes_source_ids(self, tmp_path, monkeypatch):
|
|
"""无重复时唯一篇 source_ids 初始化为 [source_id]。"""
|
|
monkeypatch.chdir(tmp_path)
|
|
art = _make_article(url="https://x.com/1", url_hash="cccc333333333333",
|
|
source_id="ft", source_name="Financial Times")
|
|
self._write_processed(tmp_path, "ft", art)
|
|
|
|
with Deduper() as deduper:
|
|
dedup_source("ft", deduper, self.DATE_STR)
|
|
|
|
uniq = tmp_path / "data" / "deduped" / self.DATE_STR / "uniques" / "cccc333333333333.json"
|
|
data = json.loads(uniq.read_text(encoding="utf-8"))
|
|
assert data["source_ids"] == ["ft"]
|
|
|
|
def test_merge_idempotent(self, tmp_path, monkeypatch):
|
|
"""同一来源重复出现多次合并时去重（不产生重复来源）。"""
|
|
monkeypatch.chdir(tmp_path)
|
|
content = "Identical content across sources for idempotent test."
|
|
art_a = _make_article(
|
|
url="https://www.reuters.com/business/2", url_hash="dddd444444444444",
|
|
source_id="reuters", content=content,
|
|
)
|
|
art_b = _make_article(
|
|
url="https://www.cnbc.com/2026/2", url_hash="eeee555555555555",
|
|
source_id="cnbc", source_name="CNBC", content=content,
|
|
)
|
|
art_c = _make_article(
|
|
url="https://www.marketwatch.com/2", url_hash="ffff666666666666",
|
|
source_id="marketwatch", source_name="MarketWatch", content=content,
|
|
)
|
|
self._write_processed(tmp_path, "reuters", art_a)
|
|
self._write_processed(tmp_path, "cnbc", art_b)
|
|
self._write_processed(tmp_path, "marketwatch", art_c)
|
|
|
|
with Deduper() as deduper:
|
|
dedup_source("reuters", deduper, self.DATE_STR)
|
|
dedup_source("cnbc", deduper, self.DATE_STR)
|
|
dedup_source("marketwatch", deduper, self.DATE_STR)
|
|
|
|
uniq = tmp_path / "data" / "deduped" / self.DATE_STR / "uniques" / "dddd444444444444.json"
|
|
data = json.loads(uniq.read_text(encoding="utf-8"))
|
|
assert data["source_ids"] == ["reuters", "cnbc", "marketwatch"]
|