backend 去ai化

backend/app/services/summary_service.py

@@ -1,8 +1,3 @@
-# app/services/summary_service.py
-"""
-摘要服务：调用 LLM 生成统一标题、综合摘要、话题标签
-定时任务：对热度达标且未摘要的事件批量处理
-"""
 import json
 import os
 from datetime import timedelta
@@ -26,12 +21,16 @@ from app.prompts.summary_prompts import (
 )
 from app.services.fetcher_service import embedder_model
 
+# AI辅助生成：deepseek-v3-2，2026年3月20日
+
 HOT_SCORE_THRESHOLD = int(os.getenv("HOT_SCORE_THRESHOLD", 3))
 TOPIC_TAG_MIN_HOT_SCORE = int(os.getenv("TOPIC_TAG_MIN_HOT_SCORE", HOT_SCORE_THRESHOLD))
 TOPIC_SIMILARITY_THRESHOLD = float(os.getenv("TOPIC_SIMILARITY_THRESHOLD", 0.82))
 TOPIC_TAG_MAX_COUNT = int(os.getenv("TOPIC_TAG_MAX_COUNT", 8))
 AI_API_KEY = os.getenv("AI_API_KEY", "")
 
+# AI生成结束
+
 
 deepseek_client = AsyncOpenAI(
     api_key=AI_API_KEY,
@@ -184,7 +183,6 @@ async def generate_unified_summaries():
     """定时任务：对热度达标且未摘要的事件刷新标题、摘要、标签"""
     print(f"[{utcnow()}] Start unified summary generation task...")
 
-    # 先提取需要处理的事件 ID，尽早释放 session，不长期占用 db session
     with SessionLocal() as db:
         recent_threshold = utcnow() - timedelta(days=3)
         events = db.query(UnifiedEvent).filter(
@@ -197,11 +195,9 @@ async def generate_unified_summaries():
             print("No events require summary update in this round.")
             return
             
-        # 复制出需要的信息，脱离 session
         event_ids = [e.id for e in events]
         event_hot_scores = {e.id: e.hot_score for e in events}
 
-    # 外层循环：针对每个 event_id 开启一个极短生命周期的 session 获取依赖数据
     for event_id in event_ids:
         platform_dict: dict[str, set[str]] = {}
         with SessionLocal() as db: