A8: 文案按A/B/C三套正交叙事生成,避免套路化重复

- constants: 新增 TEXT_NARRATIVE_BY_STRATEGY(A痛点/B场景/C成分),与图片侧同轴
- build_prompt: 加 strategy_narrative 参数并注入 prompt
- text_variants: 全链路透传(含优化轮)
- run_text_generation: 改循环三套,text_count均摊(divmod余前补),跨套去重,打_strategy标记
- TextCandidate: 加 strategy String(4) 字段 + 迁移021(已upgrade head)
- packaging: 打包按strategy精准配对文图(texts_by_strategy映射+三层兜底)
- SSE text_candidate 事件携带 strategy

独立agent交叉验证7改造点全过,边界(text_count<3/无别名/不截断)无must-fix

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/app/services/ai_engine/_text_prompt.py

@@ -163,11 +163,13 @@ def _build_benchmark_block(refs: list[dict]) -> str:
     )
 
 
-def build_prompt(product: dict, count: int, extra_rules: str = "") -> str:
+def build_prompt(product: dict, count: int, extra_rules: str = "",
+                 strategy_narrative: str = "") -> str:
     """
     组装文案生成 user_prompt。
     数据层：product 动态注入（name/selling_points/style_tone/text_angles/custom_prompt）
     方法层：已在 COPY_SYSTEM 固定，这里只注入产品数据+随机变量
+    strategy_narrative：三套正交叙事主线（A痛点/B场景/C成分），由调用方按套传入
     """
     name     = product.get("name", "产品")
     selling  = "、".join(product.get("selling_points") or ["核心卖点待录入"])
@@ -191,6 +193,7 @@ def build_prompt(product: dict, count: int, extra_rules: str = "") -> str:
         f"产品：{name}",
         f"核心卖点（必须翻译成用户能感知的生活化利益，禁止直接列功效词；翻译范例：'烟酰胺'→'熬夜后第二天脸不那么黄了'，'高保湿'→'涂上去一整天都没搓泥拔干'）：{selling}",
         f"风格调性：{style}",
+        strategy_narrative,
         angle_hint,
         brand_rule,
         custom,

backend/app/services/ai_engine/constants.py

@@ -125,8 +125,28 @@ NARRATIVE_BY_STRATEGY = {
     ),
 }
 
-# ── 生图通道 ──────────────────────────────────────────────
-IMAGE_RETRY_ATTEMPTS = 3
+# ── 文案3套正交叙事策略（倩倩姐2026-06-18过目版，与图片侧同A/B/C轴）──────
+# 注入 build_prompt，让三套文案各走不同叙事主线，避免套路化重复
+# 与 NARRATIVE_BY_STRATEGY（图片侧）同根：套A文案痛点先行↔套A图也痛点先行，同套内文图一致
+TEXT_NARRATIVE_BY_STRATEGY = {
+    "A": (
+        "【本条叙事主线·痛点先行】开篇直戳用户困扰（脸黄显疲惫/素颜不敢出门/早八顶着黄脸），"
+        "情绪强、句子短促带感叹，痛点贯穿全文到种草，结尾用'别再顶着黄脸早八'这类痛点收束促单。"
+        "基调：紧迫感、强对比、情绪共鸣。"
+    ),
+    "B": (
+        "【本条叙事主线·场景先行】用真实生活场景开篇（早八来不及/通勤手忙脚乱/赶时间出门），"
+        "轻松代入感，突出'快/省时/伪素颜自由'，点到平价性价比但不堆砌。"
+        "基调：轻松、生活化、像朋友随手分享。"
+    ),
+    "C": (
+        "【本条叙事主线·成分背书先行】用成分原理或测评视角开篇（核心成分为什么有用/亲测对比），"
+        "专业可信，带使用前后时间线对比，像真实用户实证背书，结尾用'成分党闭眼入'收束。"
+        "基调：专业、可信、真实测评感。"
+    ),
+}
+
+
 IMAGE_RETRY_BACKOFF_BASE = 2.0  # 指数退避底数（秒）
 IMAGE_SIZE_DEFAULT = "1024x1536"
 

backend/app/services/ai_engine/text_variants.py

@@ -70,12 +70,15 @@ async def _call_llm(client: Any, prompt: str, max_tokens: int = 8192) -> str:
 TEXT_BATCH_SIZE = int(os.environ.get("TEXT_BATCH_SIZE", "4"))
 
 
-async def _generate_one_batch(llm_client: Any, product: dict, batch_n: int, extra: str) -> list[dict]:
+async def _generate_one_batch(llm_client: Any, product: dict, batch_n: int, extra: str,
+                              strategy_narrative: str = "") -> list[dict]:
     """生成一批 batch_n 条，含解析重试（最多2次）。失败返回空列表。
     max_tokens 按条数缩放(每条约 1800 token，封顶 8192)，压进 apiports 60s 网关窗口。"""
     batch_max_tokens = min(8192, max(1800, batch_n * 1800))
     for attempt in range(2):
-        raw = await _call_llm(llm_client, build_prompt(product, batch_n, extra_rules=extra), batch_max_tokens)
+        raw = await _call_llm(llm_client, build_prompt(
+            product, batch_n, extra_rules=extra, strategy_narrative=strategy_narrative,
+        ), batch_max_tokens)
         parsed = parse_json_array(raw)
         if parsed:
             return parsed
@@ -84,7 +87,8 @@ async def _generate_one_batch(llm_client: Any, product: dict, batch_n: int, extr
     return []
 
 
-async def _generate_in_batches(llm_client: Any, product: dict, count: int, extra: str) -> list[dict]:
+async def _generate_in_batches(llm_client: Any, product: dict, count: int, extra: str,
+                               strategy_narrative: str = "") -> list[dict]:
     """把 count 条按 TEXT_BATCH_SIZE 分批，串行调用合并。
     串行而非并发：opus 单批就慢(~300s)且 apiports 限并发，多批 gather 会触发
     大面积 503 雪崩(task45 实测)。故改串行，墙钟换稳定。"""
@@ -96,7 +100,7 @@ async def _generate_in_batches(llm_client: Any, product: dict, count: int, extra
         remaining -= n
     collected: list[dict] = []
     for n in sizes:
-        r = await _generate_one_batch(llm_client, product, n, extra)
+        r = await _generate_one_batch(llm_client, product, n, extra, strategy_narrative)
         collected.extend(r)
     return collected
 
@@ -108,12 +112,16 @@ async def generate_text_variants(
     previous_copies: list[dict] | None = None,
     banned_word_rows: list[dict] | None = None,
     flywheel_context: str = "",
+    strategy_narrative: str = "",
 ) -> list[dict]:
-    """轨A：一次出 count 条不同角度文案，三层兜底，自动优化循环"""
+    """轨A：一次出 count 条不同角度文案，三层兜底，自动优化循环。
+    strategy_narrative：本套正交叙事主线(A痛点/B场景/C成分)，由调用方按套传入，
+    贯穿首批生成与优化轮，确保同套内文案同一叙事不串味。"""
     banned_entries = build_entries_from_db(banned_word_rows or [])
     extra = flywheel_context
 
-    copies: list[dict] = await _generate_in_batches(llm_client, product, count, extra)
+    copies: list[dict] = await _generate_in_batches(
+        llm_client, product, count, extra, strategy_narrative)
     if not copies:
         copies = list(build_local_drafts(product, count))  # generator → list
 
@@ -149,6 +157,7 @@ async def generate_text_variants(
         raw2 = await _call_llm(llm_client, build_prompt(
             product, len(batch_failed),
             extra_rules=f"以下文案未达标，请重新生成并改进：\n{hint}\n不要重复已有标题和角度。",
+            strategy_narrative=strategy_narrative,
         ), min(8192, max(1800, len(batch_failed) * 1800)))
         if not raw2:
             # LLM 失败（如 503/超时）：优化是锦上添花，原始候选已够用，不再耗时重试