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
112 lines
4.9 KiB
Python
112 lines
4.9 KiB
Python
"""动态策略：以 yaml 静态种子为基础，叠加动态种子（统一池 + 加权随机 + 用完全用）。
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种子词机制（v49 起）：
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1. 全部类型放一起（统一池）：静态 style + 静态 related + 月份主题 + 节日 + LLM 动态
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——合并去重（跨类型同词只保留一个，权重累加 = 多来源更受重视）；
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2. 节日种子词提供权重：节日权重 3.0 > 月份主题 2.0 > 静态/动态 1.0，随机抽取时加权；
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3. 每次随机取：从池中按权重随机抽取（不重复），limit 内数量；
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4. 用完全用：池中种子数 ≤ 需要数时全部使用（不再随机限量/截断）；
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5. 每个国家独立配置：configs/countries/<country>.yaml 的 style.seeds / related.seed_keywords。
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limit 由 seed_node 从 context 注入（max_style_seeds / max_related_seeds，0 或缺失=不限）。
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LLM 后端生成失败时自动回退到静态+节日主题，保证不中断。
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"""
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import random
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from typing import Any, Dict, List
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from .base import SeedStrategy
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def _weighted_sample(pool: List[Dict[str, Any]], k: int) -> List[Dict[str, Any]]:
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"""按权重随机不重复取 k 个；池数量 ≤ k（或用完）时全部返回（不随机限量）。"""
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if k <= 0 or len(pool) <= k:
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return list(pool)
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out: List[Dict[str, Any]] = []
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rest = list(pool)
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for _ in range(k):
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weights = [max(float(it["weight"]), 0.0) for it in rest]
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if sum(weights) <= 0:
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out.extend(rest)
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break
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idx = random.choices(range(len(rest)), weights=weights)[0]
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out.append(rest.pop(idx))
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return out
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class DynamicStrategy(SeedStrategy):
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name = "dynamic"
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def resolve(
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self,
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country: str,
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cc: Dict[str, Any],
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context: Dict[str, Any],
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llm_backend: Any = None,
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) -> Dict[str, Any]:
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base_style = list((cc.get("style", {}) or {}).get("seeds", []) or [])
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base_related = list((cc.get("related", {}) or {}).get("seed_keywords", []) or [])
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month_style = list(context.get("month_themes", []) or [])
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holidays = list(context.get("upcoming_holidays", []) or [])
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holiday_style = [f"{h.lower()} aesthetic" for h in holidays]
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# 节日主题同时扩充 related 源（提高节日权重 + 增加 related 扩展）
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holiday_related = [f"{h.lower()} tee" if not h.lower().endswith("day") else f"{h.lower()} gift"
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for h in holidays]
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# LLM 动态种子（失败回退，不影响静态/节日）
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dyn_style: List[str] = []
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dyn_related: List[str] = []
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if llm_backend is not None and hasattr(llm_backend, "generate_seeds"):
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try:
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res = llm_backend.generate_seeds(context) or {}
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dyn_style = list(res.get("style_seeds", []) or [])
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dyn_related = list(res.get("related_seeds", []) or [])
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except Exception as e: # noqa: BLE001
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print(f"[seed] LLM 生成种子失败，仅用静态+节日主题: {e}")
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# 1) 统一池：全部类型合并，跨类型去重（同词权重累加 = 多来源更受重视）
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pool: Dict[str, Dict[str, Any]] = {}
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def add(items: List[str], weight: float, src: str) -> None:
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for it in items:
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it = (it or "").strip()
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if not it:
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continue
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key = it.lower()
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if key in pool:
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pool[key]["weight"] += weight
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pool[key]["sources"].append(src)
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else:
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pool[key] = {"word": it, "weight": weight, "sources": [src]}
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add(base_style, 1.0, "static")
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add(base_related, 1.0, "static")
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add(month_style, 2.0, "month")
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add(holiday_style, 3.0, "holiday")
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add(holiday_related, 3.0, "holiday")
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add(dyn_style, 1.0, "dynamic")
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add(dyn_related, 1.0, "dynamic")
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items = list(pool.values())
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limit_style = int(context.get("max_style_seeds") or 0)
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limit_related = int(context.get("max_related_seeds") or 0)
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# 2) 每次随机取（加权，不重复）；池不足 → 全部用
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style_pick = _weighted_sample(items, limit_style)
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style_keys = {id(it) for it in style_pick}
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remaining = [it for it in items if id(it) not in style_keys]
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related_pick = _weighted_sample(remaining, limit_related)
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return {
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"style_seeds": [it["word"] for it in style_pick],
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"related_seeds": [it["word"] for it in related_pick],
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"dynamic": True,
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"pool_size": len(items),
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"pool": [it["word"] for it in items],
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"llm_style_seeds": dyn_style,
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"llm_related_seeds": dyn_related,
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"holiday_style_seeds": holiday_style,
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"holiday_related_seeds": holiday_related,
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"static_style_seeds": base_style,
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"static_related_seeds": base_related,
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}
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