{ "sha": "e51a50d5bdcd06ce27749c2380af6121bd1ad71f", "message": "以每日新聞與 LLM 產生搜尋提示 (#75)\n\n每天臺北時間 00:01 依已發布新聞產生六組中英文搜尋提示，並提供文章綁定驗證與確定性備援。", "author": { "login": "nnneeeooo-nnneeeooo", "name": "", "email": "" }, "committer": { "login": "web-flow", "name": "", "email": "" }, "created_at": "2026-08-09T22:55:37Z", "files": [ { "filename": ".github/workflows/hourly.yml", "previous_filename": "", "status": "modified", "additions": 4, "deletions": 1, "changes": 5 }, { "filename": "pipeline/CONTRACTS.md", "previous_filename": "", "status": "modified", "additions": 9, "deletions": 0, "changes": 9 }, { "filename": "pipeline/build.py", "previous_filename": "", "status": "modified", "additions": 23, "deletions": 1, "changes": 24 }, { "filename": "pipeline/search_prompts.py", "previous_filename": "", "status": "added", "additions": 308, "deletions": 0, "changes": 308 }, { "filename": "tests/test_search.py", "previous_filename": "", "status": "modified", "additions": 3, "deletions": 16, "changes": 19 }, { "filename": "tests/test_search_prompts.py", "previous_filename": "", "status": "added", "additions": 116, "deletions": 0, "changes": 116 } ], "diff": "@@ -2,7 +2,7 @@ name: hourly-update\\n \\n on:\\n schedule:\\n- - cron: \"0 * * * *\" # every hour on the hour (UTC)\\n+ - cron: \"1 * * * *\" # hourly; 16:01 UTC is 00:01 Asia/Taipei\\n workflow_dispatch:\\n push:\\n branches: [main]\\n@@ -65,6 +65,9 @@ jobs:\\n python pipeline/dedupe.py\\n python pipeline/write.py\\n python pipeline/images.py\\n+ # The saved Taipei date makes this one LLM-assisted refresh per day.\\n+ # Missing/invalid model output falls back to published article titles.\\n+ python pipeline/search_prompts.py --daily-if-due\\n # a briefing run may have committed newer briefing JSONs since this\\n # job checked out; deploys race (last wins), so refresh them before\\n # building or this deploy can put a stale briefing page back live\\n@@ -317,6 +317,15 @@ Planespotters.net (same airframe, by registration) or Wikimedia Commons\\n caches per-article match/none results. build.normalize_image() is the\\n only reader; templates always show kind + credit + license + backlink.\\n \\n+## data/search-prompts.json (written by search_prompts.py, read by build.py)\\n+\\n+One daily set of six bilingual search-box suggestions. `targetDateTpe` prevents\\n+more than one generation per Taipei calendar day; `generationMode` is `llm`\\n+only after every suggestion passes article-ID, length, safety, uniqueness and\\n+title-anchor validation. `sourceArticleIds` records the published articles used.\\n+When no provider is available or model output is invalid, deterministic prompts\\n+are built from those article titles instead. No model reasoning is stored.\\n+\\n ## data/usage.json (written by usage.py, read by build.py)\\n \\n Cumulative LLM API spend: per-model-label {calls, inputTokens,\\n@@ -20,6 +20,7 @@\\n import time\\n import unicodedata\\n from datetime import timedelta, timezone\\n+from functools import lru_cache\\n from pathlib import Path\\n from urllib.parse import urlsplit\\n \\n@@ -1001,6 +1002,24 @@ def collect_articles():\\n return sorted(by_id.values(), key=lambda a: a[\"dt\"], reverse=True)\\n \\n \\n+@lru_cache(maxsize=2)\\n+def header_search_placeholders(lang: str) -> tuple[str, ...]:\\n+ \"\"\"Load the daily LLM output, with the built-in prompts as fallback.\"\"\"\\n+ fallback = tuple(L[lang][\"headerSearchPlaceholders\"])\\n+ payload = load_json(DATA_DIR / \"search-prompts.json\", {})\\n+ prompts = ((payload.get(\"prompts\") or {}).get(lang)\\n+ if isinstance(payload, dict) else None)\\n+ if not isinstance(prompts, list) or not 2 <= len(prompts) <= 8:\\n+ return fallback\\n+ cleaned = []\\n+ for value in prompts:\\n+ text = re.sub(r\"\\s+\", \" \", str(value or \"\")).strip()\\n+ if not text or len(text) > 96 or re.search(r\"[\\x00-\\x1f<>]\", text):\\n+ return fallback\\n+ cleaned.append(text)\\n+ return tuple(cleaned) if len(set(cleaned)) == len(cleaned) else fallback\\n+\\n+\\n def notification_summary(value: str, lang: str) -> str:\\n \"\"\"Make legacy article summaries glanceable without changing source data.\"\"\"\\n text = re.sub(r\"\\s+\", \" \", str(value or \"\")).strip()\\n@@ -2323,7 +2342,10 @@ def render_manual_workbench(env, build) -> int:\\n # ── rendering ────────────────────────────────────────────────────────────────\\n \\n def base_ctx(lang, page, sub, *, title, description, ticker, build, hreflang=True):\\n- t = L[lang]\\n+ t = dict(L[lang])\\n+ prompts = list(header_search_placeholders(lang))\\n+ t[\"headerSearchPlaceholders\"] = prompts\\n+ t[\"headerSearchPlaceholder\"] = prompts[0]\\n nav_defs = [(\"home\", \"\"), (\"briefings\", \"briefings/\"),\\n (\"radar\", \"radar/\"),\\n (\"incidents\", \"incidents/\"),\\n@@ -0,0 +1,308 @@\\n+\"\"\"Generate daily, article-grounded search-box prompts.\\n+\\n+The hourly workflow calls this once per run. A saved Taipei calendar date\\n+keeps the LLM budget to one successful generation per day; the first run after\\n+midnight refreshes the file. Invalid or unavailable model output always falls\\n+back to deterministic prompts made from published article titles.\\n+\"\"\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+import json\\n+import os\\n+import re\\n+import sys\\n+from datetime import datetime, timedelta, timezone\\n+from pathlib import Path\\n+\\n+sys.path.insert(0, str(Path(__file__).resolve().parent))\\n+from common import DATA_DIR, iso_minute, load_json, now_utc, parse_iso, save_json # noqa: E402\\n+\\n+TPE = timezone(timedelta(hours=8), \"UTC+8\")\\n+OUTPUT_NAME = \"search-prompts.json\"\\n+PROMPT_COUNT = 6\\n+MAX_CANDIDATES = 12\\n+MAX_ZH_CHARS = 26\\n+MAX_EN_CHARS = 58\\n+\\n+PROMPT_SCHEMA = {\\n+ \"type\": \"object\",\\n+ \"properties\": {\\n+ \"prompts\": {\\n+ \"type\": \"array\",\\n+ \"items\": {\\n+ \"type\": \"object\",\\n+ \"properties\": {\\n+ \"articleId\": {\"type\": \"string\"},\\n+ \"zh\": {\"type\": \"string\"},\\n+ \"en\": {\"type\": \"string\"},\\n+ },\\n+ \"required\": [\"articleId\", \"zh\", \"en\"],\\n+ \"additionalProperties\": False,\\n+ },\\n+ },\\n+ },\\n+ \"required\": [\"prompts\"],\\n+ \"additionalProperties\": False,\\n+}\\n+\\n+SYSTEM_PROMPT = \"\"\"You create six short search-box suggestions for SKYTICAL.\\n+Use ONLY the supplied published articles. Pick six distinct article IDs and\\n+return JSON matching the schema. Each suggestion must clearly reuse a named\\n+airline, flight number, airport, city, aircraft type, registration or other\\n+searchable entity from that article's title. Do not add facts, dates, numbers,\\n+causes or conclusions. Traditional Chinese must use Taiwan wording and be at\\n+most 26 characters; English must be at most 58 characters. Do not use URLs,\\n+HTML, markdown, relative-time words such as today/yesterday, or generic prompts.\\n+The suggestions should feel friendly and natural, not like headlines.\"\"\"\\n+\\n+_SPACE_RE = re.compile(r\"\\s+\")\\n+_ASCII_TOKEN_RE = re.compile(r\"[A-Za-z0-9][A-Za-z0-9-]{1,}\")\\n+_CJK_RE = re.compile(r\"[\\u3400-\\u9fff]+\")\\n+_UNSAFE_RE = re.compile(\\n+ r\"https?://|<|>|reasoning_content|\\b(?:today|yesterday)\\b|今天|昨天\",\\n+ re.IGNORECASE,\\n+)\\n+\\n+\\n+def _text(value) -> str:\\n+ return _SPACE_RE.sub(\" \", str(value or \"\")).strip()\\n+\\n+\\n+def _article_time(raw: dict) -> datetime | None:\\n+ try:\\n+ return parse_iso(str(raw.get(\"publishedUtc\") or \"\"))\\n+ except (TypeError, ValueError):\\n+ return None\\n+\\n+\\n+def collect_candidates(articles_dir: Path, now: datetime) -> tuple[list[dict], str]:\\n+ \"\"\"Prefer today's Taipei stories, then month-to-date, then latest.\"\"\"\\n+ rows = []\\n+ for path in sorted(articles_dir.glob(\"*.json\")) if articles_dir.is_dir() else []:\\n+ payload = load_json(path, {})\\n+ article_rows = payload.get(\"articles\") if isinstance(payload, dict) else []\\n+ for raw in article_rows if isinstance(article_rows, list) else []:\\n+ if not isinstance(raw, dict) or raw.get(\"archived\") is True:\\n+ continue\\n+ article_id = _text(raw.get(\"id\"))\\n+ published = _article_time(raw)\\n+ if not article_id or published is None or published > now + timedelta(hours=6):\\n+ continue\\n+ zh = raw.get(\"zh\") if isinstance(raw.get(\"zh\"), dict) else {}\\n+ en = raw.get(\"en\") if isinstance(raw.get(\"en\"), dict) else {}\\n+ zh_title = _text(zh.get(\"title\") or en.get(\"title\"))\\n+ en_title = _text(en.get(\"title\") or zh.get(\"title\"))\\n+ if not zh_title or not en_title:\\n+ continue\\n+ rows.append({\\n+ \"id\": article_id,\\n+ \"publishedUtc\": iso_minute(published),\\n+ \"publishedTpeDate\": published.astimezone(TPE).strftime(\"%Y-%m-%d\"),\\n+ \"zhTitle\": zh_title,\\n+ \"zhSummary\": _text(zh.get(\"summary\"))[:240],\\n+ \"enTitle\": en_title,\\n+ \"enSummary\": _text(en.get(\"summary\"))[:320],\\n+ \"_dt\": published,\\n+ })\\n+\\n+ by_id = {}\\n+ for row in sorted(rows, key=lambda item: item[\"_dt\"], reverse=True):\\n+ by_id.setdefault(row[\"id\"], row)\\n+ ordered = list(by_id.values())\\n+ today = now.astimezone(TPE).date()\\n+ month_key = (today.year, today.month)\\n+ today_rows = [row for row in ordered if row[\"_dt\"].astimezone(TPE).date() == today]\\n+ month_rows = [row for row in ordered\\n+ if (row[\"_dt\"].astimezone(TPE).year,\\n+ row[\"_dt\"].astimezone(TPE).month) == month_key]\\n+\\n+ selected = []\\n+ for pool in (today_rows, month_rows, ordered):\\n+ for row in pool:\\n+ if row[\"id\"] not in {item[\"id\"] for item in selected}:\\n+ selected.append(row)\\n+ if len(selected) >= MAX_CANDIDATES:\\n+ break\\n+ if len(selected) >= MAX_CANDIDATES:\\n+ break\\n+\\n+ if len(today_rows) >= PROMPT_COUNT:\\n+ window = \"today\"\\n+ elif month_rows:\\n+ window = \"month_to_date\"\\n+ else:\\n+ window = \"latest\"\\n+ for row in selected:\\n+ row.pop(\"_dt\", None)\\n+ return selected, window\\n+\\n+\\n+def _shares_anchor(prompt: str, title: str) -> bool:\\n+ folded_prompt = prompt.casefold()\\n+ tokens = {\\n+ token.casefold() for token in _ASCII_TOKEN_RE.findall(title)\\n+ if len(token) >= 3\\n+ }\\n+ if any(token in folded_prompt for token in tokens):\\n+ return True\\n+ for run in _CJK_RE.findall(title):\\n+ if any(run[index:index + 2] in prompt\\n+ for index in range(max(0, len(run) - 1))):\\n+ return True\\n+ return False\\n+\\n+\\n+def validate_llm_prompts(draft, candidates: list[dict]) -> tuple[dict, list[str]] | None:\\n+ rows = draft.get(\"prompts\") if isinstance(draft, dict) else None\\n+ if not isinstance(rows, list) or len(rows) != PROMPT_COUNT:\\n+ return None\\n+ source_by_id = {row[\"id\"]: row for row in candidates}\\n+ seen_ids, source_ids, zh_prompts, en_prompts = set(), [], [], []\\n+ for row in rows:\\n+ if not isinstance(row, dict):\\n+ return None\\n+ article_id = _text(row.get(\"articleId\"))\\n+ zh = _text(row.get(\"zh\"))\\n+ en = _text(row.get(\"en\"))\\n+ source = source_by_id.get(article_id)\\n+ if source is None or article_id in seen_ids:\\n+ return None\\n+ if not (4 <= len(zh) <= MAX_ZH_CHARS and 8 <= len(en) <= MAX_EN_CHARS):\\n+ return None\\n+ if _UNSAFE_RE.search(zh) or _UNSAFE_RE.search(en):\\n+ return None\\n+ if not _shares_anchor(zh, source[\"zhTitle\"]):\\n+ return None\\n+ if not _shares_anchor(en, source[\"enTitle\"]):\\n+ return None\\n+ seen_ids.add(article_id)\\n+ source_ids.append(article_id)\\n+ zh_prompts.append(zh)\\n+ en_prompts.append(en)\\n+ if len(set(zh_prompts)) != PROMPT_COUNT or len(set(en_prompts)) != PROMPT_COUNT:\\n+ return None\\n+ return {\"zh\": zh_prompts, \"en\": en_prompts}, source_ids\\n+\\n+\\n+def _truncate(text: str, limit: int) -> str:\\n+ return text if len(text) <= limit else text[:limit - 1].rstrip() + \"…\"\\n+\\n+\\n+def deterministic_prompts(candidates: list[dict]) -> tuple[dict, list[str]]:\\n+ zh, en, source_ids = [], [], []\\n+ for row in candidates:\\n+ zh_prompt = _truncate(f\"搜尋：{row['zhTitle']}\", MAX_ZH_CHARS)\\n+ en_prompt = _truncate(f\"Explore: {row['enTitle']}\", MAX_EN_CHARS)\\n+ if zh_prompt in zh or en_prompt in en:\\n+ continue\\n+ zh.append(zh_prompt)\\n+ en.append(en_prompt)\\n+ source_ids.append(row[\"id\"])\\n+ if len(zh) >= PROMPT_COUNT:\\n+ break\\n+ generic_zh = [\\n+ \"搜尋班號，例如 CI100\", \"搜尋航空公司\", \"搜尋機場或城市\",\\n+ \"搜尋機型，例如 A350\", \"探索本月航空新聞\", \"看看最新航空動態\",\\n+ ]\\n+ generic_en = [\\n+ \"Search a flight number, e.g. CI100\", \"Search an airline\",\\n+ \"Search an airport or city\", \"Search an aircraft type, e.g. A350\",\\n+ \"Explore this month's aviation news\", \"See the latest aviation news\",\\n+ ]\\n+ for zh_prompt, en_prompt in zip(generic_zh, generic_en):\\n+ if len(zh) >= PROMPT_COUNT:\\n+ break\\n+ if zh_prompt in zh or en_prompt in en:\\n+ continue\\n+ zh.append(zh_prompt)\\n+ en.append(en_prompt)\\n+ return {\"zh\": zh, \"en\": en}, source_ids\\n+\\n+\\n+def _provider_limit() -> int:\\n+ try:\\n+ return max(1, min(int(os.environ.get(\"SEARCH_PROMPTS_LLM_ATTEMPTS\", \"3\")), 5))\\n+ except ValueError:\\n+ return 3\\n+\\n+\\n+def update_daily_prompts(*, data_dir: Path = DATA_DIR, now: datetime | None = None,\\n+ force: bool = False, providers=None,\\n+ record_usage: bool = True) -> bool:\\n+ now = now or now_utc()\\n+ output_path = data_dir / OUTPUT_NAME\\n+ target_date = now.astimezone(TPE).strftime(\"%Y-%m-%d\")\\n+ existing = load_json(output_path, {})\\n+ if not force and isinstance(existing, dict) \\\\n+ and existing.get(\"targetDateTpe\") == target_date:\\n+ print(f\"search-prompts: {target_date} already generated\")\\n+ return False\\n+\\n+ candidates, window = collect_candidates(data_dir / \"articles\", now)\\n+ prompts, source_ids = deterministic_prompts(candidates)\\n+ generation_mode, provider_label = \"deterministic\", None\\n+ attempted = []\\n+\\n+ if len(candidates) >= PROMPT_COUNT:\\n+ if providers is None:\\n+ try:\\n+ from providers import build_providers\\n+ providers = build_providers()\\n+ except Exception as exc:\\n+ print(f\"search-prompts: providers unavailable ({type(exc).__name__})\")\\n+ providers = []\\n+ digest = json.dumps(candidates, ensure_ascii=False, separators=(\",\", \":\"))\\n+ for provider in list(providers or [])[:_provider_limit()]:\\n+ attempted.append(provider)\\n+ try:\\n+ draft = provider.draft(SYSTEM_PROMPT, digest, PROMPT_SCHEMA)\\n+ validated = validate_llm_prompts(draft, candidates)\\n+ except Exception as exc:\\n+ print(f\"search-prompts: {getattr(provider, 'label', '?')} failed \"\\n+ f\"({type(exc).__name__})\")\\n+ continue\\n+ if validated is None:\\n+ print(f\"search-prompts: {getattr(provider, 'label', '?')} \"\\n+ \"returned invalid prompts\")\\n+ continue\\n+ prompts, source_ids = validated\\n+ generation_mode = \"llm\"\\n+ provider_label = str(getattr(provider, \"label\", \"unknown\"))[:160]\\n+ break\\n+\\n+ if record_usage and attempted:\\n+ try:\\n+ import usage\\n+ usage.record_providers(attempted)\\n+ except Exception as exc:\\n+ print(f\"search-prompts: usage update failed ({type(exc).__name__})\")\\n+\\n+ payload = {\\n+ \"version\": 1,\\n+ \"generatedUtc\": iso_minute(now),\\n+ \"targetDateTpe\": target_date,\\n+ \"sourceWindow\": window,\\n+ \"generationMode\": generation_mode,\\n+ \"provider\": provider_label,\\n+ \"sourceArticleIds\": source_ids,\\n+ \"prompts\": prompts,\\n+ }\\n+ save_json(output_path, payload)\\n+ print(f\"search-prompts: wrote {PROMPT_COUNT} prompts for {target_date} \"\\n+ f\"({generation_mode}, {window})\")\\n+ return True\\n+\\n+\\n+def main() -> int:\\n+ parser = argparse.ArgumentParser()\\n+ group = parser.add_mutually_exclusive_group()\\n+ group.add_argument(\"--daily-if-due\", action=\"store_true\")\\n+ group.add_argument(\"--force\", action=\"store_true\")\\n+ args = parser.parse_args()\\n+ update_daily_prompts(force=args.force)\\n+ return 0\\n+\\n+\\n+if __name__ == \"__main__\":\\n+ raise SystemExit(main())\\n@@ -89,22 +89,9 @@ def main() -> None:\\n r\"data-search-placeholders='([^']+)'\", zh).group(1))\\n en_placeholders = json.loads(re.search(\\n r\"data-search-placeholders='([^']+)'\", en).group(1))\\n- assert zh_placeholders == [\\n- \"搜尋班號，例如 CI100\",\\n- \"搜尋航空公司\",\\n- \"搜尋機場或城市\",\\n- \"搜尋機型，例如 A350\",\\n- \"隨意搜尋想看的 SKYTICAL 內容\",\\n- \"今天想看什麼航空新聞？\",\\n- ]\\n- assert en_placeholders == [\\n- \"Search a flight number, e.g. CI100\",\\n- \"Search an airline\",\\n- \"Search an airport or city\",\\n- \"Search an aircraft type, e.g. A350\",\\n- \"Explore anything on SKYTICAL\",\\n- \"What aviation news are you looking for?\",\\n- ]\\n+ assert zh_placeholders == list(build.header_search_placeholders(\"zh\"))\\n+ assert en_placeholders == list(build.header_search_placeholders(\"en\"))\\n+ assert len(zh_placeholders) == len(en_placeholders) == 6\\n assert zh.count('id=\"news-search-form\"') == 1\\n assert 'class=\"header-search-form\"' in home\\n assert 'class=\"header-search-form\"' in article\\n@@ -0,0 +1,116 @@\\n+\"\"\"Offline checks for daily article-grounded search suggestions.\"\"\"\\n+from __future__ import annotations\\n+\\n+import json\\n+import sys\\n+import tempfile\\n+from datetime import datetime, timezone\\n+from pathlib import Path\\n+\\n+ROOT = Path(__file__).resolve().parent.parent\\n+sys.path.insert(0, str(ROOT / \"pipeline\"))\\n+\\n+import search_prompts # noqa: E402\\n+\\n+\\n+class FakeProvider:\\n+ label = \"fake:test-model\"\\n+ http_calls = 1\\n+ usage = {\"inputTokens\": 100, \"outputTokens\": 50, \"usageEvents\": 1}\\n+\\n+ def __init__(self):\\n+ self.calls = 0\\n+\\n+ def draft(self, system_prompt, user_prompt, schema):\\n+ self.calls += 1\\n+ assert \"ONLY the supplied published articles\" in system_prompt\\n+ assert schema == search_prompts.PROMPT_SCHEMA\\n+ candidates = json.loads(user_prompt)\\n+ return {\"prompts\": [\\n+ {\\n+ \"articleId\": row[\"id\"],\\n+ \"zh\": f\"關注：{row['zhTitle']}\",\\n+ \"en\": f\"Explore {row['enTitle']}\",\\n+ }\\n+ for row in candidates[:6]\\n+ ]}\\n+\\n+\\n+def article(index: int) -> dict:\\n+ return {\\n+ \"id\": f\"a-20260810-000{index}-test-{index}\",\\n+ \"publishedUtc\": f\"2026-08-10T00:0{index}:00Z\",\\n+ \"zh\": {\\n+ \"title\": f\"航空{index} A32{index} 抵達桃園\",\\n+ \"summary\": f\"航空{index}的 A32{index} 航機抵達桃園機場。\",\\n+ },\\n+ \"en\": {\\n+ \"title\": f\"Airline {index} A32{index} arrives at Taoyuan\",\\n+ \"summary\": f\"Airline {index}'s A32{index} arrived at Taoyuan.\",\\n+ },\\n+ }\\n+\\n+\\n+def main() -> None:\\n+ now = datetime(2026, 8, 10, 6, 30, tzinfo=timezone.utc)\\n+ with tempfile.TemporaryDirectory() as tmp:\\n+ data_dir = Path(tmp)\\n+ articles_dir = data_dir / \"articles\"\\n+ articles_dir.mkdir()\\n+ (articles_dir / \"batch.json\").write_text(\\n+ json.dumps({\"articles\": [article(i) for i in range(8)]},\\n+ ensure_ascii=False),\\n+ encoding=\"utf-8\",\\n+ )\\n+\\n+ candidates, window = search_prompts.collect_candidates(articles_dir, now)\\n+ assert len(candidates) == 8\\n+ assert window == \"today\"\\n+\\n+ provider = FakeProvider()\\n+ changed = search_prompts.update_daily_prompts(\\n+ data_dir=data_dir, now=now, providers=[provider],\\n+ record_usage=False)\\n+ assert changed is True and provider.calls == 1\\n+ payload = json.loads(\\n+ (data_dir / \"search-prompts.json\").read_text(encoding=\"utf-8\"))\\n+ assert payload[\"targetDateTpe\"] == \"2026-08-10\"\\n+ assert payload[\"generationMode\"] == \"llm\"\\n+ assert payload[\"provider\"] == \"fake:test-model\"\\n+ assert len(payload[\"prompts\"][\"zh\"]) == 6\\n+ assert len(payload[\"sourceArticleIds\"]) == 6\\n+\\n+ second_provider = FakeProvider()\\n+ assert search_prompts.update_daily_prompts(\\n+ data_dir=data_dir, now=now, providers=[second_provider],\\n+ record_usage=False) is False\\n+ assert second_provider.calls == 0\\n+\\n+ fallback_dir = data_dir / \"fallback\"\\n+ fallback_articles = fallback_dir / \"articles\"\\n+ fallback_articles.mkdir(parents=True)\\n+ (fallback_articles / \"batch.json\").write_text(\\n+ json.dumps({\"articles\": [article(i) for i in range(8)]},\\n+ ensure_ascii=False),\\n+ encoding=\"utf-8\",\\n+ )\\n+ invalid = FakeProvider()\\n+ invalid.draft = lambda *_args: {\"prompts\": []}\\n+ search_prompts.update_daily_prompts(\\n+ data_dir=fallback_dir, now=now, force=True, providers=[invalid],\\n+ record_usage=False)\\n+ fallback = json.loads(\\n+ (fallback_dir / \"search-prompts.json\").read_text(encoding=\"utf-8\"))\\n+ assert fallback[\"generationMode\"] == \"deterministic\"\\n+ assert fallback[\"provider\"] is None\\n+ assert fallback[\"prompts\"][\"zh\"][0].startswith(\"搜尋：航空7 A327\")\\n+\\n+ workflow = (ROOT / \".github\" / \"workflows\" / \"hourly.yml\").read_text(\\n+ encoding=\"utf-8\")\\n+ assert 'cron: \"1 * * * *\"' in workflow\\n+ assert \"python pipeline/search_prompts.py --daily-if-due\" in workflow\\n+ print(\"test_search_prompts: OK\")\\n+\\n+\\n+if __name__ == \"__main__\":\\n+ main()" }