359 lines
		
	
	
		
			11 KiB
		
	
	
	
		
			Markdown
		
	
	
	
			
		
		
	
	
			359 lines
		
	
	
		
			11 KiB
		
	
	
	
		
			Markdown
		
	
	
	
| # 防止推理模型独白泄露解决方案
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| 
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| ## 🎯 问题分析
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| 
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| ### 推理模型的"搞笑"表现
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| ```
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| 用户问题: "今天股市如何?"
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| 
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| 推理模型回答:
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| "让我想想...首先我需要分析当前的市场情况...
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| 嗯,从技术面来看...不对,我应该先看基本面...
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| 等等,用户问的是今天,我需要看今日数据...
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| 好的,我的分析是:今天股市表现良好。"
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| 
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| 正常回答应该是:
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| "今天股市表现良好,主要受益于..."
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| ```
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| 
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| ## 🔍 模型分类与选择策略
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| 
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| ### 1. 推理模型识别
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| ```python
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| # 已知的推理模型列表
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| REASONING_MODELS = {
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|     "openai": [
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|         "o1-preview", "o1-mini", "o1-pro"
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|     ],
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|     "anthropic": [
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|         "claude-3-opus-reasoning", "claude-3-sonnet-reasoning"
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|     ],
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|     "google": [
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|         "gemini-2.0-flash-thinking"
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|     ],
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|     "alibaba": [
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|         "qwen2.5-math-instruct", "qwen-reasoning"
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|     ],
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|     "deepseek": [
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|         "deepseek-r1", "deepseek-reasoning"
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|     ]
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| }
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| 
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| # 非推理模型(安全选择)
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| NON_REASONING_MODELS = {
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|     "openai": ["gpt-4o-mini", "gpt-4o", "gpt-3.5-turbo"],
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|     "anthropic": ["claude-3.5-sonnet", "claude-3-haiku"],
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|     "google": ["gemini-1.5-flash", "gemini-1.5-pro"],
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|     "mistral": ["mistral-7b-instruct", "mixtral-8x7b-instruct"],
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|     "meta": ["llama-3.1-8b-instruct", "llama-3.1-70b-instruct"]
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| }
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| ```
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| 
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| ### 2. 八仙模型重新配置(避免推理模型)
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| ```yaml
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| # 修正后的八仙配置
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| baxian_models_corrected:
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|   乾一_吕洞宾:
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|     model: "mistralai/mistral-7b-instruct:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   兑二_何仙姑:
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|     model: "google/gemini-1.5-flash:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   离三_铁拐李:
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|     model: "microsoft/phi-3.5-mini-instruct:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   震四_汉钟离:
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|     model: "meta-llama/llama-3.1-8b-instruct:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   巽五_蓝采和:
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|     model: "moonshot-v1-8k:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   坎六_张果老:
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|     model: "alibaba/qwen-2.5-7b-instruct:free"  # 避免推理版本 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   艮七_韩湘子:
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|     model: "deepseek-chat:free"  # 非推理版本 ✅
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|     reasoning_type: "non-reasoning"
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|     
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|   坤八_曹国舅:
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|     model: "zhipuai/glm-4-9b-chat:free"  # 非推理模型 ✅
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|     reasoning_type: "non-reasoning"
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| ```
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| 
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| ## 🛡️ 防独白泄露技术方案
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| 
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| ### 方案1: Prompt工程防护
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| ```python
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| class AntiMonologuePrompt:
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|     """防独白泄露的Prompt设计"""
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|     
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|     @staticmethod
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|     def create_clean_prompt(role, character, topic):
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|         return f"""你是{role},{character}。
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| 
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| 【重要规则】
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| 1. 直接给出你的观点,不要展示思考过程
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| 2. 不要说"让我想想"、"首先"、"然后"等思考词汇
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| 3. 不要暴露你的分析步骤
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| 4. 直接表达结论和建议
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| 5. 保持角色特色,简洁有力
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| 
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| 【话题】{topic}
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| 
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| 【你的发言】(直接开始,不超过100字):"""
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| 
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| # 示例对比
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| bad_prompt = "请分析一下今天的股市情况"
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| 
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| good_prompt = """你是吕洞宾,剑仙,理性分析师。
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| 
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| 【重要规则】
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| 1. 直接给出观点,不展示思考过程
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| 2. 不说"让我分析"、"首先"等词
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| 3. 直接表达结论
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| 4. 保持剑仙风格,简洁犀利
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| 
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| 【话题】今天股市情况
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| 
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| 【你的发言】(直接开始,不超过100字):"""
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| ```
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| 
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| ### 方案2: 输出过滤系统
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| ```python
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| class OutputFilter:
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|     """输出内容过滤器"""
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|     
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|     def __init__(self):
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|         # 需要过滤的思考词汇
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|         self.thinking_patterns = [
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|             r"让我想想.*?",
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|             r"首先.*?然后.*?",
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|             r"我需要分析.*?",
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|             r"让我考虑.*?",
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|             r"从.*?角度来看.*?",
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|             r"等等.*?",
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|             r"不对.*?我应该.*?",
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|             r"嗯.*?",
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|             r"好的,我的.*?是",
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|             r"经过思考.*?",
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|             r"分析如下.*?",
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|             r"我的思路是.*?"
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|         ]
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|         
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|         # 独白标识词
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|         self.monologue_indicators = [
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|             "让我", "我想", "我觉得需要", "我应该",
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|             "等等", "不对", "重新考虑", "换个角度"
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|         ]
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|     
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|     def filter_monologue(self, text):
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|         """过滤独白内容"""
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|         import re
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|         
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|         # 移除思考过程
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|         for pattern in self.thinking_patterns:
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|             text = re.sub(pattern, "", text, flags=re.IGNORECASE)
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|         
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|         # 移除独白句子
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|         sentences = text.split('。')
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|         filtered_sentences = []
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|         
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|         for sentence in sentences:
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|             has_monologue = any(indicator in sentence for indicator in self.monologue_indicators)
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|             if not has_monologue and sentence.strip():
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|                 filtered_sentences.append(sentence.strip())
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|         
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|         # 重新组合
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|         result = '。'.join(filtered_sentences)
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|         if result and not result.endswith('。'):
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|             result += '。'
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|             
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|         return result
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|     
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|     def clean_output(self, raw_output):
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|         """清理输出内容"""
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|         # 1. 过滤独白
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|         filtered = self.filter_monologue(raw_output)
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|         
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|         # 2. 移除多余空行
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|         filtered = re.sub(r'\n\s*\n', '\n', filtered)
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|         
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|         # 3. 确保简洁
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|         if len(filtered) > 200:  # 如果太长,取前200字
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|             filtered = filtered[:200] + "..."
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|         
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|         return filtered.strip()
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| ```
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| 
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| ### 方案3: 模型调用包装器
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| ```python
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| class SafeModelCaller:
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|     """安全模型调用器"""
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|     
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|     def __init__(self):
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|         self.output_filter = OutputFilter()
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|         self.retry_count = 3
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|     
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|     async def safe_call(self, model_name, prompt, max_tokens=150):
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|         """安全调用模型,确保无独白泄露"""
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|         
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|         for attempt in range(self.retry_count):
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|             try:
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|                 # 调用模型
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|                 raw_response = await self.call_model(model_name, prompt, max_tokens)
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|                 
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|                 # 过滤输出
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|                 clean_response = self.output_filter.clean_output(raw_response)
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|                 
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|                 # 验证输出质量
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|                 if self.is_valid_response(clean_response):
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|                     return clean_response
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|                 else:
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|                     # 如果输出质量不好,重试
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|                     continue
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|                     
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|             except Exception as e:
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|                 if attempt == self.retry_count - 1:
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|                     return f"系统错误,请稍后重试。"
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|                 continue
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|         
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|         return "无法生成有效回应。"
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|     
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|     def is_valid_response(self, response):
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|         """验证回应质量"""
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|         # 检查是否太短
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|         if len(response.strip()) < 10:
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|             return False
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|         
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|         # 检查是否还有独白痕迹
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|         monologue_signs = ["让我", "我想", "首先", "然后"]
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|         if any(sign in response for sign in monologue_signs):
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|             return False
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|         
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|         return True
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| ```
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| 
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| ## 🎭 八仙专用防独白配置
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| 
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| ### 针对性Prompt模板
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| ```python
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| class BaxianAntiMonologue:
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|     """八仙防独白专用配置"""
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|     
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|     def __init__(self):
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|         self.immortal_prompts = {
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|             "吕洞宾": """你是吕洞宾,剑仙,理性分析师。
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|             
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| 【发言规则】
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| - 直接表达观点,如剑出鞘般犀利
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| - 不展示分析过程,只给结论
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| - 语言简洁有力,不超过100字
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| - 保持剑仙风格:理性、犀利、直接
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| 
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| 【话题】{topic}
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| 
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| 【直接发言】:""",
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| 
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|             "何仙姑": """你是何仙姑,唯一女仙,情感洞察师。
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|             
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| 【发言规则】
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| - 直接表达直觉判断
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| - 不说"我感觉"、"让我想想"
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| - 语言优美但简洁,不超过100字
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| - 保持女性视角:敏锐、温和、智慧
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| 
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| 【话题】{topic}
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| 
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| 【直接发言】:""",
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| 
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|             # ... 其他六仙类似配置
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|         }
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|     
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|     def get_clean_prompt(self, immortal, topic):
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|         """获取无独白风险的prompt"""
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|         base_prompt = self.immortal_prompts.get(immortal, "")
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|         return base_prompt.format(topic=topic)
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| ```
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| 
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| ## 🔧 实施方案
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| 
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| ### 完整的防独白系统
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| ```python
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| class XiantianBaguaAntiMonologue:
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|     """先天八卦防独白辩论系统"""
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|     
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|     def __init__(self):
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|         self.safe_caller = SafeModelCaller()
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|         self.baxian_prompts = BaxianAntiMonologue()
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|         self.model_config = self.load_safe_models()
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|     
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|     def load_safe_models(self):
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|         """加载安全的非推理模型"""
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|         return {
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|             "吕洞宾": "mistralai/mistral-7b-instruct:free",
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|             "何仙姑": "google/gemini-1.5-flash:free",
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|             "铁拐李": "microsoft/phi-3.5-mini-instruct:free",
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|             "汉钟离": "meta-llama/llama-3.1-8b-instruct:free",
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|             "蓝采和": "moonshot-v1-8k:free",
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|             "张果老": "alibaba/qwen-2.5-7b-instruct:free",
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|             "韩湘子": "deepseek-chat:free",
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|             "曹国舅": "zhipuai/glm-4-9b-chat:free"
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|         }
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|     
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|     async def get_immortal_statement(self, immortal, topic):
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|         """获取仙人发言(无独白版本)"""
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|         # 获取安全prompt
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|         prompt = self.baxian_prompts.get_clean_prompt(immortal, topic)
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|         
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|         # 获取模型
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|         model = self.model_config[immortal]
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|         
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|         # 安全调用
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|         statement = await self.safe_caller.safe_call(model, prompt)
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|         
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|         return statement
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|     
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|     async def conduct_clean_debate(self, topic):
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|         """进行无独白泄露的辩论"""
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|         bagua_order = ["吕洞宾", "何仙姑", "铁拐李", "汉钟离",
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|                       "蓝采和", "张果老", "韩湘子", "曹国舅"]
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|         
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|         debate_results = []
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|         
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|         for immortal in bagua_order:
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|             statement = await self.get_immortal_statement(immortal, topic)
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|             debate_results.append({
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|                 "immortal": immortal,
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|                 "statement": statement,
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|                 "clean": True  # 标记为已清理
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|             })
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|         
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|         return debate_results
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| ```
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| 
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| ## 💡 最终建议
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| 
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| ### 推荐策略
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| 1. **优先使用非推理模型** - 从源头避免问题
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| 2. **强化Prompt设计** - 明确禁止展示思考过程
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| 3. **输出后处理** - 过滤可能的独白内容
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| 4. **质量验证** - 确保输出符合角色特征
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| 
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| ### 模型选择原则
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| ```
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| ✅ 选择: 标准对话模型 (gpt-4o-mini, claude-3.5-sonnet, mistral-7b等)
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| ❌ 避免: 推理模型 (o1系列, reasoning系列等)
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| ✅ 特征: 直接输出,无思考链暴露
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| ❌ 特征: 会显示"让我想想..."的模型
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| ```
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| 
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| 这样配置后,你的八仙就不会再说出搞笑的独白了,每个都会保持专业的角色形象!🎭
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| 
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| 需要我进一步优化某个具体方面吗?
 |