fix(llm): 策略稿润色预留 completion 预算，缓解输出截断

generate_strategy_draft_markdown_llm 按摘要/第九章节选/规则底稿分级收紧，满足最小 completion token 后再调用；底稿与 JSON 超限仍截断并提示。系统提示补充收束全文要求。

Made-with: Cursor

backend/pipeline/llm/generate_strategy.py

@@ -18,7 +18,8 @@ STRATEGY_SYSTEM = """你是市场策略顾问，根据**结构化监测摘要**
 - **不得编造**输入中不存在的销量、占比、价格数字；若底稿与摘要中有数字，须保持一致；表述集中度时用「第一大……份额」「前三家合计」等中文，**不要用** CR1、CR3 等缩写；
 - 若 `structured_brief` 含 `matrix_overview_for_llm` 或矩阵相关字段，策略中应**呼应**细分类目分组与竞品矩阵结论，不得无故删光矩阵相关建议；
 - 可调整段落衔接、标题层级、列表与表格呈现，使更易读；可补充「建议」「待业务确认」类表述，但不虚构竞品名称或数据；
-- **仅输出** Markdown 正文（不要 ``` 围栏包裹全文）。"""
+- **仅输出** Markdown 正文（不要 ``` 围栏包裹全文）；
+- **输出完整性**：须收束各小节与全文，勿在句子或列表中途戛然而止。"""
 
 STRATEGY_USER_PREFIX = """请基于以下 JSON 输出最终策略稿（Markdown）。\n\n"""
 
@@ -45,24 +46,86 @@ def generate_strategy_draft_markdown_llm(
         generated_at_iso=generated_at_iso,
         strategy_decisions=strategy_decisions,
     )
-    compact = compact_brief_for_llm(brief, max_chars=80_000)
+    excerpt_raw = (report_strategy_excerpt or "").strip()
-    excerpt = (report_strategy_excerpt or "").strip()
+    sys_prompt = STRATEGY_SYSTEM
-    payload = {
+    min_comp = _min_strategy_completion_tokens()
-        "job_id": job_id,
+    min_comp_relaxed = max(256, min_comp // 2)
-        "keyword": keyword,
+
-        "generated_at_iso": generated_at_iso,
+    def _payload_and_user(
-        "strategy_decisions": strategy_decisions,
+        *,
-        "business_notes": business_notes,
+        compact_max: int,
-        "structured_brief": compact,
+        excerpt_max: int,
-        "rules_draft_markdown": rules_md,
+        rules_max: int | None,
-        "report_strategy_excerpt": excerpt,
+    ) -> str:
-    }
+        compact = compact_brief_for_llm(brief, max_chars=compact_max)
-    raw = json.dumps(payload, ensure_ascii=False)
+        ex = (
-    if len(raw) > 500_000:
+            _truncate_strategy_narrative(excerpt_raw, excerpt_max)
-        payload["rules_draft_markdown"] = rules_md[:200_000] + "\n\n…（底稿过长已截断，请勿编造截断后内容）\n"
+            if excerpt_raw
+            else ""
+        )
+        if rules_max is None:
+            rd = rules_md
+        else:
+            rd = _truncate_rules_draft_md(rules_md, rules_max)
+        payload: dict[str, Any] = {
+            "job_id": job_id,
+            "keyword": keyword,
+            "generated_at_iso": generated_at_iso,
+            "strategy_decisions": strategy_decisions,
+            "business_notes": business_notes,
+            "structured_brief": compact,
+            "rules_draft_markdown": rd,
+            "report_strategy_excerpt": ex,
+        }
         raw = json.dumps(payload, ensure_ascii=False)
-    user = STRATEGY_USER_PREFIX + raw
+        if len(raw) > 500_000:
-    return call_llm(STRATEGY_SYSTEM, user)
+            payload["rules_draft_markdown"] = _truncate_rules_draft_md(rd, 200_000)
+            raw = json.dumps(payload, ensure_ascii=False)
+        return STRATEGY_USER_PREFIX + raw
+
+    for cap_brief, cap_excerpt, cap_rules in (
+        (80_000, 24_000, None),
+        (64_000, 20_000, None),
+        (48_000, 17_000, None),
+        (36_000, 14_000, None),
+        (28_000, 11_000, None),
+        (22_000, 9_000, None),
+        (18_000, 7_000, None),
+        (14_000, 5_000, None),
+        (12_000, 4_000, 220_000),
+        (10_000, 3_500, 180_000),
+        (10_000, 3_000, 150_000),
+        (9_000, 2_500, 120_000),
+        (8_000, 2_000, 100_000),
+        (8_000, 2_000, 70_000),
+    ):
+        user = _payload_and_user(
+            compact_max=cap_brief,
+            excerpt_max=cap_excerpt,
+            rules_max=cap_rules,
+        )
+        if _strategy_prompt_ok_for_call(
+            sys_prompt, user, min_completion_tokens=min_comp
+        ):
+            return call_llm(sys_prompt, user)
+
+    for cap_brief, cap_excerpt, cap_rules in (
+        (10_000, 2_000, 55_000),
+        (8_000, 1_500, 45_000),
+        (7_000, 1_200, 35_000),
+    ):
+        user = _payload_and_user(
+            compact_max=cap_brief,
+            excerpt_max=cap_excerpt,
+            rules_max=cap_rules,
+        )
+        if _strategy_prompt_ok_for_call(
+            sys_prompt, user, min_completion_tokens=min_comp_relaxed
+        ):
+            return call_llm(sys_prompt, user)
+
+    user = _payload_and_user(compact_max=6_000, excerpt_max=1_000, rules_max=28_000)
+    return call_llm(sys_prompt, user)
 
 
 STRATEGY_OPPORTUNITIES_SYSTEM = """你是 B 端市场与增长顾问。输入 JSON 含 ``keyword``、``competitor_brief``（与本任务规则报告同源的结构化摘要，可能经裁剪，并含 ``matrix_overview_for_llm``），以及可选 ``prior_chapter_llm_narratives``（本报告 **第五至第八章** 已生成的大模型归纳节选，与正文**同源**）。
@@ -97,6 +160,19 @@ STRATEGY_OPPORTUNITIES_USER_PREFIX = (
 )
 
 
+def _truncate_rules_draft_md(text: str, max_chars: int) -> str:
+    """规则策略底稿过长时截断，避免 JSON 与 completion 预算挤占输出。"""
+    s = (text or "").strip()
+    if not s:
+        return ""
+    if len(s) <= max_chars:
+        return s
+    return (
+        s[: max_chars - 80].rstrip()
+        + "\n\n…（规则底稿已截断，请勿编造截断后内容。）\n"
+    )
+
+
 def _truncate_strategy_narrative(text: str, max_chars: int) -> str:
     s = (text or "").strip()
     if not s: