182 lines
6.4 KiB
Python
182 lines
6.4 KiB
Python
from __future__ import annotations
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import json
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from dataclasses import asdict, is_dataclass
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from pathlib import Path
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from typing import Any
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from .clients import BlogApiClient, OpenAICompatibleClient, fetch_text as default_fetch_text
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from .config import load_pipeline_config, load_source_configs
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from .env import load_env, resolve_blog_token, resolve_llm_config
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from .models import SourceConfig
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from .pipeline import run_stage0_to_stage8
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from .publish import load_published_urls, update_published_urls
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from .sources.registry import get_source_fetcher
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def _json_default(value: Any):
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if is_dataclass(value):
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return asdict(value)
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raise TypeError(f"Object is not JSON serializable: {type(value).__name__}")
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def _mock_source_configs() -> list[SourceConfig]:
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return [SourceConfig(name="Mock AI HOT", type="mock", role="primary", priority=10)]
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def _mock_fetcher(config: SourceConfig, run_date: str) -> list[dict[str, Any]]:
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return [
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{
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"title_raw": "GPT-5 API 发布",
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"summary_raw": "OpenAI 发布 GPT-5 API，用于本地 mock 测试。",
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"url": "https://example.com/gpt5",
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"source_label": "OpenAI：Blog",
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"section_hint": "模型发布/更新",
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"origin_type": "mock",
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"language_hint": "zh",
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}
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]
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def _mock_semantic_llm(prompt: str) -> str:
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return json.dumps({"duplicate_groups": [], "not_duplicates": [], "uncertain": []}, ensure_ascii=False)
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def _mock_rewrite_llm(prompt: str) -> str:
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payload = json.loads(prompt)
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return json.dumps(
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{
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"rewrites": [
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{
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"id": item["id"],
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"title": item["title_raw"],
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"summary": item["summary_raw"],
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"flags": [],
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}
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for item in payload["items"]
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]
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},
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ensure_ascii=False,
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)
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def _mock_guide_llm(prompt: str) -> str:
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payload = json.loads(prompt)
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item_ids = [item["id"] for item in payload["items"][:3]]
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return json.dumps(
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{
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"intro": "本地 mock 模式已生成 AI 日报，用于验证流水线。",
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"theme": "本地 mock 模式已生成 AI 日报，用于验证流水线。",
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"threads": [
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{
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"title": "本地链路验证",
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"text": "采集、改写、分类、导览、Markdown 和发布报告都已通过 mock 数据串联。",
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"item_ids": item_ids,
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"kind": "thread",
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}
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],
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"conclusion": "本地 mock 结果可用于确认定时任务入口和文件输出是否正常。",
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},
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ensure_ascii=False,
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)
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def run_daily_report(
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*,
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run_date: str,
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mode: str,
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source_mode: str,
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llm_mode: str,
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out_dir: Path,
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base_url: str,
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sources_path: Path | None = None,
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pipeline_path: Path | None = None,
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history_path: Path | None = None,
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fetch_text=None,
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env: dict[str, str] | None = None,
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llm_client_factory=OpenAICompatibleClient,
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blog_client_factory=BlogApiClient,
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) -> dict[str, Any]:
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fetch_text = fetch_text or default_fetch_text
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env = env if env is not None else load_env()
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pipeline_config_path = pipeline_path or Path("config") / "pipeline.json"
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pipeline_config = load_pipeline_config(pipeline_config_path)
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cross_day_config = pipeline_config.get("cross_day_dedup", {}) or {}
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cross_day_enabled = bool(cross_day_config.get("enabled", True))
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cross_day_max_age_days = int(cross_day_config.get("max_age_days", 7))
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configured_history_path = history_path or Path(
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str(cross_day_config.get("history_path") or "~/.hermes/scripts/ai_morning_out/published_urls.json")
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).expanduser()
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published_urls = load_published_urls(configured_history_path) if cross_day_enabled else None
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if source_mode == "mock":
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source_configs = _mock_source_configs()
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fetcher = _mock_fetcher
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elif source_mode == "live":
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if sources_path is None:
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sources_path = Path("config") / "sources.json"
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source_configs = load_source_configs(sources_path)
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def fetcher(config: SourceConfig, current_date: str) -> list[dict[str, Any]]:
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source_fetcher = get_source_fetcher(config.type)
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return source_fetcher(config, current_date, fetch_text)
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else:
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raise ValueError("source_mode must be 'mock' or 'live'")
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if llm_mode == "mock":
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semantic_llm_call = _mock_semantic_llm
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rewrite_llm_call = _mock_rewrite_llm
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guide_llm_call = _mock_guide_llm
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elif llm_mode == "live":
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llm_client = llm_client_factory(**resolve_llm_config(env))
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semantic_llm_call = llm_client.chat
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rewrite_llm_call = llm_client.chat
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guide_llm_call = llm_client.chat
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else:
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raise ValueError("llm_mode must be 'mock' or 'live'")
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blog_client = None
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if mode in ("draft", "publish"):
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token = resolve_blog_token(env)
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if not token:
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raise ValueError("missing_blog_token: set BLOG_SERVICE_TOKEN or EPHRON_SERVICE_TOKEN")
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blog_client = blog_client_factory(base_url=base_url, token=token)
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result = run_stage0_to_stage8(
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source_configs,
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run_date,
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fetcher=fetcher,
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semantic_llm_call=semantic_llm_call,
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rewrite_llm_call=rewrite_llm_call,
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guide_llm_call=guide_llm_call,
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mode=mode,
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base_url=base_url,
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client=blog_client,
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published_urls=published_urls,
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cross_day_dedup_enabled=cross_day_enabled,
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cross_day_dedup_max_age_days=cross_day_max_age_days,
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)
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if cross_day_enabled and result["publish"].mode == "publish" and result["publish"].status == "ok":
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update_published_urls(
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configured_history_path,
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result["items"],
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run_date=run_date,
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max_age_days=cross_day_max_age_days,
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)
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run_dir = out_dir / run_date
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run_dir.mkdir(parents=True, exist_ok=True)
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(run_dir / "blog_markdown.md").write_text(result["markdown"], encoding="utf-8")
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(run_dir / "run_report.json").write_text(
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json.dumps(result["reports"], ensure_ascii=False, indent=2, default=_json_default),
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encoding="utf-8",
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)
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return {
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"run_dir": str(run_dir),
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"markdown": result["markdown"],
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"reports": result["reports"],
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"publish": result["publish"],
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}
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