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				@@ -451,7 +451,7 @@ def fetch_llm_completion(prompt, output_type="text"): 
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				     return response 
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				-def evaluate_push_agent(task): 
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				+def evaluate_agent(task, task_type): 
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				     context = { 
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				         "output_dict": { 
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				             "1.1": {"score": 1, "reason": "识别到用户焦虑并先安抚"}, 
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				@@ -460,30 +460,17 @@ def evaluate_push_agent(task): 
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				             "7.5": {"score": 1, "reason": "2025-05-28 发端午祝福;端午=2025-05-31"}, 
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				         }, 
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				         "dialogue_history": format_dialogue_history(task["dialogue_history"]), 
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				-        "message": task["push_message"], 
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				-        "send_time": task["push_time"], 
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				+        "message": task["message"], 
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				+        "send_time": task["send_time"], 
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				         "agent_profile": format_agent_profile(task["agent_profile"]), 
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				         "user_profile": format_user_profile(task["user_profile"]), 
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				     } 
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				-    evaluate_prompt = PUSH_MESSAGE_EVALUATE_PROMPT.format(**context) 
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				-    response = fetch_llm_completion(evaluate_prompt, output_type="json") 
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				-    return response 
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				- 
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				- 
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				-def evaluate_reply_agent(task): 
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				-    context = { 
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				-        "output_dict": { 
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				-            "1.1": {"score": 1, "reason": "识别到用户焦虑并先安抚"}, 
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				-            "2.1": {"score": 0, "reason": "跳过健康话题改聊理财"}, 
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				-            "5.4": {"score": 1, "reason": "青年男性用词简洁,无女性化词汇"}, 
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				-            "7.5": {"score": 1, "reason": "2025-05-28 发端午祝福;端午=2025-05-31"}, 
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				-        }, 
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				-        "dialogue_history": format_dialogue_history(task["dialogue_history"]), 
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				-        "message": task["reply_message"], 
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				-        "send_time": task["reply_time"], 
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				-        "agent_profile": format_agent_profile(task["agent_profile"]), 
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				-        "user_profile": format_user_profile(task["user_profile"]), 
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				-    } 
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				-    evaluate_prompt = REPLY_MESSAGE_EVALUATE_PROMPT.format(**context) 
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				+    match task_type: 
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				+        case 0: 
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				+            evaluate_prompt = REPLY_MESSAGE_EVALUATE_PROMPT.format(**context) 
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				+        case 1: 
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				+            evaluate_prompt = PUSH_MESSAGE_EVALUATE_PROMPT.format(**context) 
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				+        case _: 
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				+            raise ValueError("task_type must be 0 or 1") 
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				     response = fetch_llm_completion(evaluate_prompt, output_type="json") 
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				     return response 
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