fix(llm+worker+deploy): 兼容老 prompt 模板 + 消除 startup_run 日志噪音

- enrichment: 新增 _safe_format (基于 _SafeDict),缺失占位符保留原样不抛 KeyError。
  _enrich_format / _enrich_classify / _enrich_image / _enrich_commentary
  全部走 _safe_format,数据库里老 prompt(不支持 {body})不再让整条 article 卡住。
  复现: 388183 classify 一直 KeyError,enrichment_loop 反复重试它,316 篇全卡在 n/a。
- workers.__main__: startup_run 从 IntervalTrigger(minutes=0) 改成 DateTrigger
  (只跑一次),消除 'maximum number of running instances reached' 刷屏 WARNING。
- deploy_pull: 改 _connect 自动识别 RSA / Ed25519 / ECDSA key(原硬编码 Ed25519Key)

backend/app/services/llm/enrichment.py

@@ -18,12 +18,13 @@
 - 任务间互不影响:每个任务独立 try/except + 写 status
 - 全部任务共走 LlmClient 的全局限速
 - 若设置 enabled=False,只跳过(不调 LLM)
+- 用户提示词模板可能不包含全部占位符,用 _safe_format 容错
 """
 from __future__ import annotations
 
 import asyncio
 import logging
-from typing import Any
+from typing import Any, Mapping
 
 from sqlalchemy import select
 
@@ -55,6 +56,27 @@ DEFAULT_IMAGE_FIRST_PARA_CHARS = 400  # 提取第一段最多用这么多字
 DEFAULT_IMAGE_MAX_TAGS = 5  # 分类标签上限(多标签)
 
 
+class _SafeDict(dict):
+    """missing 返回 {key} 本身(占位符原样保留),不抛 KeyError。"""
+
+    def __missing__(self, key: str) -> str:  # type: ignore[override]
+        return "{" + key + "}"
+
+
+def _safe_format(template: str, vars_: Mapping[str, Any]) -> str:
+    """用 _SafeDict 跑 str.format,缺失的占位符保留原样而不是 KeyError。
+
+    用途:数据库里用户已存的 prompt 模板可能是旧版的(只支持部分占位符),
+    新代码传了更多变量也不应崩。
+    """
+    try:
+        return template.format_map(_SafeDict(vars_))
+    except (KeyError, IndexError) as e:
+        # 极端情况(比如 {} 这种非法占位符)兜底
+        logger.warning("_safe_format 解析失败,按原文返回: %s", e)
+        return template
+
+
 # === 获取当前设置(行锁 + 缓存刷新)===
 async def get_setting() -> LlmSetting:
     """读 llm_settings 单行;不存在则用默认值插入。"""
@@ -77,9 +99,8 @@ async def get_setting() -> LlmSetting:
 
 # === 单任务:format ===
 async def _enrich_format(article: Article, setting: LlmSetting, client: LlmClient) -> None:
-    prompt = (setting.format_prompt or get_default_prompts()["format_prompt"]).format(
-        body=(article.body_zh_text or "")[:6000]
-    )
+    template = setting.format_prompt or get_default_prompts()["format_prompt"]
+    prompt = _safe_format(template, {"body": (article.body_zh_text or "")[:6000]})
     text = await client.chat(
         system="你是中文新闻排版助手,只输出排版后的纯文本。",
         user=prompt,
@@ -114,11 +135,14 @@ def _wrap_article_body(inner_html: str) -> str:
 
 # === 单任务:classify ===
 async def _enrich_classify(article: Article, setting: LlmSetting, client: LlmClient) -> None:
-    prompt = (setting.classify_prompt or get_default_prompts()["classify_prompt"]).format(
-        title=(article.title_zh or article.title)[:200],
-        summary=(article.summary_zh or "")[:400],
-        body=(article.body_zh_text or "")[:1500],
-    )
+    template = setting.classify_prompt or get_default_prompts()["classify_prompt"]
+    # 老 prompt 可能只支持 {title}/{summary},不支持 {body} —— _safe_format 兜底
+    vars_ = {
+        "title": (article.title_zh or article.title)[:200],
+        "summary": (article.summary_zh or "")[:400],
+        "body": (article.body_zh_text or "")[:1500],
+    }
+    prompt = _safe_format(template, vars_)
     result = await client.classify_json(
         system="你是新闻分类助手,只返回 JSON。",
         user=prompt,
@@ -133,18 +157,14 @@ async def _enrich_classify(article: Article, setting: LlmSetting, client: LlmCli
 
 # === 单任务:image ===
 async def _enrich_image(article: Article, setting: LlmSetting, client: LlmClient) -> None:
-    template = (setting.image_prompt_template or get_default_prompts()["image_prompt_template"])
+    template = setting.image_prompt_template or get_default_prompts()["image_prompt_template"]
     # 用正文第一段作为 prompt(英文 prompt 走 title 仍可工作,所以 title 也带上作 fallback)
     first_para = _first_paragraph(article.body_zh_text or "", max_chars=DEFAULT_IMAGE_FIRST_PARA_CHARS)
     if not first_para:
         first_para = (article.title_zh or article.title or "")[:200]
     title_for_prompt = (article.title_zh or article.title or "")[:200]
-    # template 同时支持 {body} 和 {title} 两种占位符
-    try:
-        prompt = template.format(body=first_para, title=title_for_prompt)
-    except (KeyError, IndexError):
-        # 用户改坏了 template,fallback 用 {title} 模式
-        prompt = template.format(title=title_for_prompt)
+    # template 同时支持 {body} 和 {title} 两种占位符;老的只支持 {title} 也能跑
+    prompt = _safe_format(template, {"body": first_para, "title": title_for_prompt})
     url = await client.generate_image(prompt, size=DEFAULT_IMAGE_SIZE)
     article.image_ai_url = url
     article.image_ai_status = "ok"
@@ -163,9 +183,13 @@ def _first_paragraph(text: str, max_chars: int) -> str:
 
 # === 单任务:commentary ===
 async def _enrich_commentary(article: Article, setting: LlmSetting, client: LlmClient) -> None:
-    prompt = (setting.commentary_prompt or get_default_prompts()["commentary_prompt"]).format(
-        title=(article.title_zh or article.title)[:200],
-        body=(article.body_zh_text or "")[:3000],
+    template = setting.commentary_prompt or get_default_prompts()["commentary_prompt"]
+    prompt = _safe_format(
+        template,
+        {
+            "title": (article.title_zh or article.title)[:200],
+            "body": (article.body_zh_text or "")[:3000],
+        },
     )
     text = await client.chat(
         system="你是资深新闻评论员。",

backend/app/workers/__main__.py

@@ -11,6 +11,7 @@ from datetime import datetime, timezone
 
 from apscheduler.schedulers.asyncio import AsyncIOScheduler
 from apscheduler.triggers.cron import CronTrigger
+from apscheduler.triggers.date import DateTrigger
 from apscheduler.triggers.interval import IntervalTrigger
 from sqlalchemy import select
 
@@ -79,12 +80,11 @@ async def main() -> None:
         id="rebuild_jobs",
         replace_existing=True,
     )
-    # 启动时立即跑一次
+    # 启动时立即跑一次(只一次,用 DateTrigger 避免 IntervalTrigger 被 max_instances 拒绝刷日志)
     scheduler.add_job(
         run_once,
-        trigger=IntervalTrigger(minutes=0),
+        trigger=DateTrigger(run_date=datetime.now(timezone.utc)),
         id="startup_run",
-        next_run_time=datetime.now(timezone.utc),
     )
 
     scheduler.start()