simon
182f788857
- _llm_summarize 内 3 处 _llm_call 调用仍传 model 字符串, 与 _llm_call(config: LLMConfig) 新签名不匹配,生产日报 AI 摘要必崩 - 统一改为传 config 对象;删除不再使用的 model 局部变量 - 新增回归测试:单块/多分块 _llm_summarize → _llm_call 全链路(传 config)
258 lines
10 KiB
Python
258 lines
10 KiB
Python
"""日报结构化组装单元测试（_build_report_data，纯逻辑）。"""
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from __future__ import annotations
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import json
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from datetime import date
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import pytest
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from scheduler.reporter import _build_report_data
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def _fake_event(title: str, importance: int, event_type: str = "其他",
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sentiment: str = "neutral", source_id: str = "cls",
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url: str = "https://x.com/1") -> dict:
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return {
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"title": title,
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"url": url,
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"source_id": source_id,
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"event": {
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"stock_codes": [],
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"company_names": [],
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"industries": [],
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"sentiment": sentiment,
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"importance": importance,
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"event_type": event_type,
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"summary": f"{title}的摘要",
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},
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}
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class TestBuildReportData:
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def test_sections_and_ranks(self) -> None:
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news = {
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"total": 2, "hi_threshold": 4,
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"high": [_fake_event("新闻A", 5), _fake_event("新闻B", 4)],
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"sentiments": {"neutral": 2}, "importances": {5: 1, 4: 1},
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"event_types": {"其他": 2},
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}
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cninfo = {
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"total": 1, "hi_threshold": 2,
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"high": [_fake_event("公告C", 3, event_type="公告")],
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"by_day": {"07月10日": 1}, "announcement": 1, "research": 0, "irm": 0,
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}
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pipeline = {"raw_total": 100, "proc": 90}
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xwlb = {"items": [_fake_event("联播D", 4, event_type="新闻联播", source_id="xwlb")],
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"date": "07月10日"}
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r = _build_report_data(news, cninfo, pipeline, "AI摘要", "20260710", xwlb=xwlb)
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assert r.report_date == date(2026, 7, 10)
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assert r.report_type == "finance"
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assert r.file_name == ""
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assert r.ai_summary == "AI摘要"
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assert [(e.section, e.rank) for e in r.events] == [
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("news", 1), ("news", 2), ("cninfo", 1), ("xwlb", 1),
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]
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assert r.events[0].source == "cls"
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assert r.events[3].source == "xwlb"
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def test_stats_snapshot(self) -> None:
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news = {"total": 1, "hi_threshold": 4, "high": [], "sentiments": {},
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"importances": {}, "event_types": {}}
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cninfo = {"total": 0, "hi_threshold": 0, "high": [], "by_day": {},
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"announcement": 0, "research": 0, "irm": 0}
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r = _build_report_data(news, cninfo, {"raw_total": 100}, "s", "20260710")
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# stats 可 JSON 序列化（入库时 json.dumps）
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json.dumps(r.stats, ensure_ascii=False)
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assert r.stats["pipeline"] == {"raw_total": 100}
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assert r.stats["news"]["total"] == 1
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assert "xwlb" not in r.stats
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def test_title_truncated(self) -> None:
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news = {"total": 1, "hi_threshold": 4,
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"high": [_fake_event("长" * 600, 4)], "sentiments": {},
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"importances": {}, "event_types": {}}
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cninfo = {"total": 0, "hi_threshold": 0, "high": [], "by_day": {},
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"announcement": 0, "research": 0, "irm": 0}
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r = _build_report_data(news, cninfo, {}, "s", "20260710")
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assert len(r.events[0].title) == 512
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def test_empty_events(self) -> None:
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news = {"total": 0, "hi_threshold": 0, "high": [], "sentiments": {},
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"importances": {}, "event_types": {}}
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cninfo = {"total": 0, "hi_threshold": 0, "high": [], "by_day": {},
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"announcement": 0, "research": 0, "irm": 0}
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r = _build_report_data(news, cninfo, {}, None, "20260710")
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assert r.events == []
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assert r.ai_summary is None
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class TestLlmCallRetry:
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"""_llm_call 重试逻辑（纯逻辑，mock client）。"""
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@staticmethod
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def _fake_client(failures: int):
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"""构造 mock client：前 failures 次抛 ConnectionError，之后成功。"""
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from types import SimpleNamespace
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n = {"count": 0}
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class Completions:
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def create(self, **kwargs):
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n["count"] += 1
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if n["count"] <= failures:
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raise ConnectionError("transient")
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return SimpleNamespace(
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choices=[SimpleNamespace(
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message=SimpleNamespace(content="今日要点摘要"),
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finish_reason="stop",
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)]
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)
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return SimpleNamespace(chat=SimpleNamespace(completions=Completions())), n
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@staticmethod
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def _cfg():
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from llm.client import LLMConfig
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return LLMConfig(
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provider="deepseek", model="deepseek-v4-flash",
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api_key="sk-test", base_url="https://api.deepseek.com",
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temperature=0.3,
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)
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def test_success_first_try(self) -> None:
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from scheduler.reporter import _llm_call
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client, n = self._fake_client(0)
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out = _llm_call(client, self._cfg(), "p")
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assert out == "今日要点摘要"
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assert n["count"] == 1
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def test_retry_then_success(self, monkeypatch) -> None:
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import scheduler.reporter as rep
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monkeypatch.setattr(rep, "_LLM_RETRY_TIMES", 3)
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monkeypatch.setattr(rep, "_LLM_RETRY_BACKOFF_SEC", 0.01)
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client, n = self._fake_client(2) # 前 2 次失败，第 3 次成功
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out = rep._llm_call(client, self._cfg(), "p")
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assert out == "今日要点摘要"
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assert n["count"] == 3
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def test_exhausts_retries_raises(self, monkeypatch) -> None:
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import scheduler.reporter as rep
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monkeypatch.setattr(rep, "_LLM_RETRY_TIMES", 2)
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monkeypatch.setattr(rep, "_LLM_RETRY_BACKOFF_SEC", 0.01)
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client, n = self._fake_client(99) # 一直失败
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with pytest.raises(ConnectionError):
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rep._llm_call(client, self._cfg(), "p")
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assert n["count"] == 2 # 重试 2 次后放弃
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def test_llm_summarize_single_chunk_passes_config(self) -> None:
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"""_llm_summarize → _llm_call 全链路:必须传 LLMConfig 而非 model 字符串。
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回归保护:生产日报曾因 _llm_call 收到 str 报
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'str' object has no attribute 'model'。
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"""
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from scheduler.reporter import _llm_summarize
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client, n = self._fake_client(0)
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out = _llm_summarize(client, self._cfg(), ["- 新闻A", "- 新闻B"], "20260812")
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assert out == "今日要点摘要"
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assert n["count"] == 1
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def test_llm_summarize_multi_chunk_passes_config(self) -> None:
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"""多分块场景:每块 + 合并各调用一次 _llm_call,均传 config。"""
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from scheduler.reporter import _llm_summarize
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client, n = self._fake_client(0)
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# 两条长行保证触发分块
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lines = ["- " + "长新闻内容" * 300, "- " + "长新闻内容" * 300]
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out = _llm_summarize(client, self._cfg(), lines, "20260812")
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assert out == "今日要点摘要"
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assert n["count"] == 3 # 2 块 + 1 次合并
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class TestCollectXwlb:
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"""_collect_xwlb 取数逻辑：应查询日报前一日(已播出的联播)，并跳过内容提要。"""
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def test_queries_previous_day_and_skips_toc(self, monkeypatch) -> None:
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import json as _json
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import urllib.request
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captured: dict[str, str] = {}
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def fake_urlopen(req, timeout=15): # noqa: ARG001
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captured["url"] = req.full_url
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class Resp:
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def __enter__(self):
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return self
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def __exit__(self, *args):
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return False
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def read(self):
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return _json.dumps({"data": {"news": [
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{"daily_sub_id": 1, "news_title": "内容提要", "news_days": "2026-08-04", "news_improve": "开场白"},
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{"daily_sub_id": 2, "news_title": "联播要闻A", "news_days": "2026-08-04", "news_improve": "正文A"},
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]}}).encode("utf-8")
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return Resp()
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monkeypatch.setattr(urllib.request, "urlopen", fake_urlopen)
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from scheduler.reporter import _collect_xwlb
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result = _collect_xwlb("20260805")
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# 查询的是前一日(20260804)而非当日
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assert "start_date=20260804" in captured["url"]
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assert "end_date=20260804" in captured["url"]
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# 跳过第 1 条内容提要
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assert len(result["items"]) == 1
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assert result["items"][0]["title"] == "联播要闻A"
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assert result["source_date"] == "20260804"
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assert result["date"] == "08月04日"
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class TestCollectNewsEventsLookback:
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"""_collect_news_events 30 小时回溯逻辑。"""
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def test_filters_30h_and_excludes_cninfo(self, monkeypatch) -> None:
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from datetime import datetime, timedelta
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import scheduler.reporter as rep
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now = datetime.now().astimezone()
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def fake_load(day_str: str) -> list[dict]: # noqa: ARG001
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def ev(title: str, hours_ago: float | None, source: str = "cls",
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importance: int = 5, aware: bool = False) -> dict:
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pt = None
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if hours_ago is not None:
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t = now - timedelta(hours=hours_ago)
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pt = t.isoformat() if not aware else t.astimezone().isoformat()
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return {
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"title": title, "url": "u", "source_id": source,
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"publish_time": pt,
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"event": {"importance": importance, "sentiment": "neutral",
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"event_type": "其他", "summary": "s"},
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}
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return [
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ev("窗口内新闻", 10),
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ev("窗口内新闻带时区", 12, aware=True),
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ev("窗口外旧闻", 40),
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ev("无时间戳", None),
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ev("公告排除", 5, source="cninfo", importance=2),
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]
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monkeypatch.setattr(rep, "_load_events_from_dir", fake_load)
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result = rep._collect_news_events("20260805")
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# 两个日期目录各返回 5 条(共 10): 旧闻×2、公告×2 被滤, 保留 6 条
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assert result["total"] == 6
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titles = {e["title"] for e in result["high"]}
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assert "窗口内新闻" in titles
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assert "窗口内新闻带时区" in titles
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assert "无时间戳" in titles
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assert "窗口外旧闻" not in titles
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assert "公告排除" not in titles
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