3218485270
f493bde8a9
- 缓存热点批量流程（有采集缓存不触发 Google） - 简报不足直接从采集缓存生成（轻量补齐） - 三图合成（模特/印花/底图）+ 底图压缩 <2MB - 热点去重→风格去重自动切换 + 不适合类目 review 兜底 - 透明背景（background=transparent）+ 提示词清洗（敏感词/背景描述） - 任务前 basemap 校验 + 模板国家校验 + 模特任务级分配
56 lines
2.5 KiB
Python
56 lines
2.5 KiB
Python
"""节点 1/6：抓取（fetch）。
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按 config.sources 启用各可插拔数据源，汇总统一格式行。
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单源失败不影响其它源（内部逐个 try），整体再套 with_fallback 兜底。
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"""
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from typing import Any, Dict, List
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from graph.sources import get_source
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from graph.validate import validate_rows, with_fallback
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@with_fallback("fetch")
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def fetch_node(state: Dict[str, Any]) -> Dict[str, Any]:
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country = state["country"]
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config = state["config"]
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cc = state["country_config"]
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enabled = config.get("sources") or ["google_trends"]
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rows: List[Dict[str, Any]] = []
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errors = list(state.get("errors") or [])
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# 采集缓存优先：采集（fetch_keywords）成功后写入 output/<国>/collected_keywords.json，
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# 这里直接用（跳过 Google 重抓），避免重复撞限流；无缓存才走数据源抓取
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use_collected = (config.get("fetch") or {}).get("use_collected", True)
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if use_collected:
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try:
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import json as _json
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from pathlib import Path as _Path
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p = _Path(state.get("cache_dir") or state.get("output_dir", "")) / "collected_keywords.json"
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if p.exists():
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data = _json.loads(p.read_text(encoding="utf-8"))
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cached_rows = data.get("keywords") or []
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if cached_rows:
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rows = [dict(r) for r in cached_rows] # 已过滤去重的关键词
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print(f"[fetch] 使用采集缓存 {len(rows)} 条（{country}，跳过 Google 抓取）")
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stats = dict(state.get("stats") or {})
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stats["fetch"] = {"raw_rows": len(rows), "sources": ["collected_cache"], "errors": 0}
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return {"raw_rows": rows, "errors": errors, "stats": stats}
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except Exception as e: # noqa: BLE001
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print(f"[fetch] 读取采集缓存失败（回退数据源）: {e}")
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for name in enabled:
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try:
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src = get_source(name)
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rows.extend(src.fetch(country, cc, config))
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except Exception as e: # noqa: BLE001
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errors.append({
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"node": "fetch", "type": type(e).__name__,
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"message": f"source[{name}]: {e}", "trace": "",
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})
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print(f"[fetch] 数据源 {name} 失败（跳过）: {e}")
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rows = validate_rows(rows, "fetch")
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stats = dict(state.get("stats") or {})
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stats["fetch"] = {"raw_rows": len(rows), "sources": enabled, "errors": len(errors)}
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return {"raw_rows": rows, "errors": errors, "stats": stats}
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