新增 Pinterest 参考模式：独立于 Google Trends 的完整链路（12国种子词池 / LLM搜索词json_schema+防重复+已用词限100 / 并发爬图 / 多模态分析→原创简报 / 生图带爬取图参考图生图 / UI流程选择）

graph/nodes/pinterest_search_node.py

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+"""Pinterest 参考模式节点 1/3：LLM 生成搜索词（pinterest_search）。
+
+流程：国家 Pinterest 种子词池 → LLM 生成搜索词（json_schema 结构化 + 动态注入已用词防重复）
+      → 全局过滤（已用/黑名单/不适合T恤/去重）→ 持久化已用词。
+
+兜底链：LLM json_schema → json_object → 解析失败/调用失败 → 回退种子词池随机抽样。
+带 with_fallback：任何异常都不中断，返回空列表由下游跳过。
+"""
+import random
+from typing import Any, Dict, List
+
+from graph.llms import get_backend
+from graph.pinterest import (
+    filter_search_terms,
+    load_used_terms,
+    merge_used,
+    sample_seeds,
+    save_used_terms,
+)
+from graph.validate import with_fallback
+
+
+@with_fallback("pinterest_search")
+def pinterest_search_node(state: Dict[str, Any]) -> Dict[str, Any]:
+    country = state["country"]
+    config = state["config"]
+    output_dir = state["output_dir"]
+    errors = list(state.get("errors") or [])
+
+    pcfg = config.get("pinterest") or {}
+    if not pcfg.get("enabled", True):
+        return {"pinterest_search_terms": [], "errors": errors}
+
+    provider = str(pcfg.get("provider") or "openai").strip().lower()
+    want = int(pcfg.get("search_terms_per_run", 10))
+    seed_sample = int(pcfg.get("seed_sample", 40))
+    max_used_in_prompt = int(pcfg.get("max_used_terms_in_prompt", 100))
+    blacklist = config.get("blacklist") or []
+
+    # 1) 种子词池（随机抽样）+ 已用搜索词
+    seeds = sample_seeds(country, seed_sample)
+    used = load_used_terms(output_dir, country)
+    if not seeds:
+        print(f"[pinterest_search] {country} 无种子词，跳过搜索词生成")
+        return {"pinterest_search_terms": [], "errors": errors}
+
+    # 2) LLM 生成（json_schema + 动态注入已用词）
+    #    已用词只取最近 N 个（默认 100）注入提示词，防 token 超限；过滤仍用全量。
+    used_llm = used[-max_used_in_prompt:] if max_used_in_prompt > 0 else []
+    terms: List[str] = []
+    llm = None
+    if provider != "static":
+        try:
+            llm = get_backend(provider)
+            if hasattr(llm, "bind_config"):
+                llm.bind_config(config.get("llm_screen") or {})
+            if provider not in ("mock",) and not getattr(llm, "has_key", False):
+                print(f"[pinterest_search] {provider} 未配置 API key，降级 mock")
+                llm = get_backend("mock")
+        except Exception as e:  # noqa: BLE001
+            print(f"[pinterest_search] LLM 初始化失败: {e}")
+            llm = None
+
+    if llm is not None and hasattr(llm, "generate_pinterest_terms"):
+        try:
+            ctx = {"country": country, "seeds": seeds, "used_terms": used_llm, "count": want}
+            res = llm.generate_pinterest_terms(ctx)
+            terms = [str(t).strip() for t in (res.get("search_terms") or []) if str(t).strip()]
+            print(f"[pinterest_search] LLM 生成搜索词 {len(terms)} 个（{country}，已用词注入 {len(used_llm)}/{len(used)}）")
+        except Exception as e:  # noqa: BLE001
+            print(f"[pinterest_search] LLM 生成失败，回退种子词池: {e}")
+            terms = []
+
+    # 3) 兜底：LLM 无结果 → 种子词池随机抽样
+    if not terms:
+        terms = random.sample(seeds, min(want, len(seeds))) if seeds else []
+        print(f"[pinterest_search] 兜底：从种子词池取 {len(terms)} 个")
+
+    # 4) 全局过滤（已用/黑名单/不适合T恤/去重）
+    filtered = filter_search_terms(terms, used, blacklist)
+    if len(filtered) < want and seeds:
+        # 不足时用种子词池补充（同样过滤），保证数量
+        extra = filter_search_terms(seeds, merge_used(used, filtered), blacklist)
+        for t in extra:
+            if len(filtered) >= want:
+                break
+            filtered.append(t)
+
+    # 5) 持久化已用词
+    new_used = merge_used(used, filtered)
+    save_used_terms(output_dir, country, new_used)
+
+    stats = dict(state.get("stats") or {})
+    stats["pinterest_search"] = {
+        "provider": provider,
+        "generated": len(terms),
+        "filtered": len(filtered),
+        "used_total": len(new_used),
+    }
+    print(f"[pinterest_search] 搜索词 {len(filtered)} 个（已用累计 {len(new_used)}）: "
+          f"{', '.join(filtered[:6])}{'...' if len(filtered) > 6 else ''}")
+
+    return {"pinterest_search_terms": filtered, "stats": stats, "errors": errors}