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@ -455,177 +455,7 @@ class DualAIDialogueEngine:
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print(f"⚠ 对话评分失败: {e}")
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return 0.0, "{}", f"评分失败: {str(e)}"
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def generate_character_prompt(self, character_name: str, context_info: List[Dict], dialogue_history: List[DialogueTurn],
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history_context_count: int = 3, context_info_count: int = 2) -> str:
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"""为角色生成对话提示
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Args:
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character_name: 角色名称
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context_info: 相关上下文信息
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dialogue_history: 对话历史
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history_context_count: 使用的历史对话轮数（默认3轮）
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context_info_count: 使用的上下文信息数量（默认2个）
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"""
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char_data = self.kb.character_data.get(character_name, {})
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# 基础角色设定
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prompt_parts = []
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prompt_parts.append(f"你是{character_name}，具有以下设定：")
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if char_data.get('personality', {}).get('core_traits'):
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traits = ", ".join(char_data['personality']['core_traits'])
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prompt_parts.append(f"性格特点：{traits}")
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if char_data.get('speech_patterns', {}).get('sample_phrases'):
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phrases = char_data['speech_patterns']['sample_phrases'][:3]
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prompt_parts.append(f"说话风格示例：{'; '.join(phrases)}")
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# 当前情境
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if char_data.get('current_situation'):
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situation = char_data['current_situation']
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prompt_parts.append(f"当前状态：{situation.get('current_mood', '')}")
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# 相关世界观信息（可控制数量）
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if context_info:
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prompt_parts.append("相关背景信息：")
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for info in context_info[:context_info_count]:
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content = info['content'][:200] + "..." if len(info['content']) > 200 else info['content']
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prompt_parts.append(f"- {content}")
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# 对话历史（可控制数量）
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if dialogue_history:
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prompt_parts.append("最近的对话：")
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# 使用参数控制历史对话轮数
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history_to_use = dialogue_history[-history_context_count:] if history_context_count > 0 else []
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for turn in history_to_use:
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prompt_parts.append(f"{turn.speaker}: {turn.content}")
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prompt_parts.append("\n请根据角色设定和上下文，生成符合角色特点的自然对话。回复应该在50-150字之间。")
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return "\n".join(prompt_parts)
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def generate_dialogue(self, session_id: str, current_speaker: str, topic_hint: str = "",
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history_context_count: int = 3, context_info_count: int = 2) -> Tuple[str, List[str]]:
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"""生成角色对话
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Args:
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session_id: 会话ID
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current_speaker: 当前说话者
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topic_hint: 话题提示
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history_context_count: 使用的历史对话轮数（默认3轮）
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context_info_count: 使用的上下文信息数量（默认2个）
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"""
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# 获取对话历史
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dialogue_history = self.conv_mgr.get_conversation_history(session_id)
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# 构建搜索查询
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if dialogue_history:
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# 基于最近的对话内容（可控制数量）
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recent_turns = dialogue_history[-history_context_count:] if history_context_count > 0 else []
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recent_content = " ".join([turn.content for turn in recent_turns])
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search_query = recent_content + " " + topic_hint
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else:
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# 首次对话
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search_query = f"{current_speaker} {topic_hint} introduction greeting"
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# 搜索相关上下文
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context_info = self.kb.search_relevant_context(search_query, current_speaker, context_info_count)
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# 生成提示（使用参数控制上下文数量）
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prompt = self.generate_character_prompt(
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current_speaker,
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context_info,
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dialogue_history,
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history_context_count,
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context_info_count
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)
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# 生成对话 - 使用双模型系统
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try:
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# 检查是否为双模型对话系统
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if hasattr(self.llm_generator, 'generate_dual_character_dialogue'):
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# 使用双模型系统
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response = self.llm_generator.generate_dual_character_dialogue(
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current_speaker,
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prompt,
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topic_hint or "请继续对话",
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temperature=0.8,
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max_new_tokens=150
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)
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else:
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# 兼容旧的单模型系统
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response = self.llm_generator.generate_character_dialogue(
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current_speaker,
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prompt,
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topic_hint or "请继续对话",
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temperature=0.8,
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max_new_tokens=150
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)
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# 记录使用的上下文
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context_used = [f"{info['section']}.{info['subsection']}" for info in context_info[:context_info_count]]
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avg_relevance = sum(info['relevance_score'] for info in context_info[:context_info_count]) / len(context_info[:context_info_count]) if context_info else 0.0
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# 对对话进行评分
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if self.enable_scoring:
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dialogue_score, score_details, score_feedback = self.score_dialogue_turn(response, current_speaker, dialogue_history)
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print(f" [评分: {dialogue_score:.2f}] {score_feedback}")
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else:
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dialogue_score, score_details, score_feedback = 0.0, "{}", ""
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# 保存对话轮次（包含评分信息）
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self.conv_mgr.add_dialogue_turn(
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session_id, current_speaker, response, context_used, avg_relevance,
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dialogue_score, score_details, score_feedback
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)
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return response, context_used
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except Exception as e:
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print(f"✗ 对话生成失败: {e}")
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return f"[{current_speaker}暂时无法回应]", []
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def run_conversation_turn(self, session_id: str, characters: List[str], turns_count: int = 1, topic: str = "",
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history_context_count: int = 3, context_info_count: int = 2):
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"""运行对话轮次
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Args:
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session_id: 会话ID
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characters: 角色列表
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turns_count: 对话轮数
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topic: 对话主题
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history_context_count: 使用的历史对话轮数（默认3轮）
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context_info_count: 使用的上下文信息数量（默认2个）
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"""
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results = []
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print(f" [上下文设置: 历史{history_context_count}轮, 信息{context_info_count}个]")
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for i in range(turns_count):
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for char in characters:
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response, context_used = self.generate_dialogue(
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session_id,
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char,
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topic,
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history_context_count,
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context_info_count
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)
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results.append({
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"speaker": char,
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"content": response,
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"context_used": context_used,
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"turn": i + 1,
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"context_settings": {
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"history_count": history_context_count,
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"context_info_count": context_info_count
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}
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})
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print(f"{char}: {response}")
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# if context_used:
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# print(f" [使用上下文: {', '.join(context_used)}]")
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print()
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return results
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def run_dual_model_conversation(self, session_id: str, topic: str = "", turns: int = 4,
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history_context_count: int = 3, context_info_count: int = 2):
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@ -713,232 +543,3 @@ class DualAIDialogueEngine:
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return conversation_results
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# def main():
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# """主函数 - 演示系统使用"""
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# print("=== RAG增强双AI角色对话系统 ===")
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# # 设置路径
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# knowledge_dir = "./knowledge_base" # 包含世界观和角色文档的目录
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# # 检查必要文件
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# required_dirs = [knowledge_dir]
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# for dir_path in required_dirs:
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# if not os.path.exists(dir_path):
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# print(f"✗ 目录不存在: {dir_path}")
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# print("请确保以下文件存在：")
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# print("- ./knowledge_base/worldview_template_coc.json")
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# print("- ./knowledge_base/character_template_detective.json")
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# print("- ./knowledge_base/character_template_professor.json")
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# return
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# try:
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# # 初始化系统组件
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# print("\n初始化系统...")
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# kb = RAGKnowledgeBase(knowledge_dir)
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# conv_mgr = ConversationManager()
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# # 这里需要你的LLM生成器，使用新的双模型对话系统
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# from npc_dialogue_generator import DualModelDialogueGenerator
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# base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-4B' # 根据你的路径调整
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# lora_model_path = './output/NPC_Dialogue_LoRA/final_model'
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# if not os.path.exists(lora_model_path):
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# lora_model_path = None
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# # 创建双模型对话生成器
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# if hasattr(kb, 'character_data') and len(kb.character_data) >= 2:
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# print("✓ 使用knowledge_base角色数据创建双模型对话系统")
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# # 获取前两个角色
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# character_names = list(kb.character_data.keys())[:2]
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# char1_name = character_names[0]
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# char2_name = character_names[1]
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# # 配置两个角色的模型
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# character1_config = {
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# "name": char1_name,
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# "lora_path": lora_model_path, # 可以为每个角色设置不同的LoRA
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# "character_data": kb.character_data[char1_name]
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# }
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# character2_config = {
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# "name": char2_name,
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# "lora_path": lora_model_path, # 可以为每个角色设置不同的LoRA
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# "character_data": kb.character_data[char2_name]
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# }
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# llm_generator = DualModelDialogueGenerator(
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# base_model_path,
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# character1_config,
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# character2_config
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# )
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# else:
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# print("⚠ 角色数据不足，无法创建双模型对话系统")
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# return
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# # 创建对话引擎
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# dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, llm_generator)
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# print("✓ 系统初始化完成")
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# # 交互式菜单
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# while True:
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# print("\n" + "="*50)
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# print("双AI角色对话系统")
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# print("1. 创建新对话")
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# print("2. 继续已有对话")
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# print("3. 查看对话历史")
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# print("4. 列出所有会话")
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# print("0. 退出")
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# print("="*50)
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# choice = input("请选择操作: ").strip()
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# if choice == '0':
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# break
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# elif choice == '1':
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# # 创建新对话
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# print(f"可用角色: {list(kb.character_data.keys())}")
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# characters = input("请输入两个角色名称（用空格分隔）: ").strip().split()
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# if len(characters) != 2:
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# print("❌ 请输入正好两个角色名称")
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# continue
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# worldview = kb.worldview_data.get('worldview_name', '未知世界观') if kb.worldview_data else '未知世界观'
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# session_id = conv_mgr.create_session(characters, worldview)
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# topic = input("请输入对话主题（可选）: ").strip()
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# turns = int(input("请输入对话轮次数量（默认2）: ").strip() or "2")
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# # 历史上下文控制选项
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# print("\n历史上下文设置:")
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# history_count = input("使用历史对话轮数（默认3，0表示不使用）: ").strip()
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# history_count = int(history_count) if history_count.isdigit() else 3
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# context_info_count = input("使用上下文信息数量（默认2）: ").strip()
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# context_info_count = int(context_info_count) if context_info_count.isdigit() else 2
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# print(f"\n开始对话 - 会话ID: {session_id}")
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# print(f"上下文设置: 历史{history_count}轮, 信息{context_info_count}个")
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# # 询问是否使用双模型对话
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# use_dual_model = input("是否使用双模型对话系统？(y/n，默认y): ").strip().lower()
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# if use_dual_model != 'n':
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# print("使用双模型对话系统...")
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# dialogue_engine.run_dual_model_conversation(session_id, topic, turns, history_count, context_info_count)
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# else:
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# print("使用传统对话系统...")
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# dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count)
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# elif choice == '2':
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# # 继续已有对话
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# sessions = conv_mgr.list_sessions()
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# if not sessions:
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# print("❌ 没有已有对话")
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# continue
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# print("已有会话:")
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# for i, session in enumerate(sessions[:5]):
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# chars = ", ".join(session['characters'])
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# print(f"{i+1}. {session['session_id'][:8]}... ({chars}) - {session['last_update'][:16]}")
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# try:
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# idx = int(input("请选择会话编号: ").strip()) - 1
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# if 0 <= idx < len(sessions):
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# session = sessions[idx]
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# session_id = session['session_id']
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# characters = session['characters']
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# # 显示最近的对话
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# history = conv_mgr.get_conversation_history(session_id, 4)
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# if history:
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# print("\n最近的对话:")
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# for turn in history:
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# print(f"{turn.speaker}: {turn.content}")
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# topic = input("请输入对话主题（可选）: ").strip()
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# turns = int(input("请输入对话轮次数量（默认1）: ").strip() or "1")
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# # 历史上下文控制选项
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# print("\n历史上下文设置:")
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# history_count = input("使用历史对话轮数（默认3，0表示不使用）: ").strip()
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# history_count = int(history_count) if history_count.isdigit() else 3
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# context_info_count = input("使用上下文信息数量（默认2）: ").strip()
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# context_info_count = int(context_info_count) if context_info_count.isdigit() else 2
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# print(f"\n继续对话 - 会话ID: {session_id}")
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# print(f"上下文设置: 历史{history_count}轮, 信息{context_info_count}个")
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# # 询问是否使用双模型对话
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# use_dual_model = input("是否使用双模型对话系统？(y/n，默认y): ").strip().lower()
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# if use_dual_model != 'n':
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# print("使用双模型对话系统...")
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# dialogue_engine.run_dual_model_conversation(session_id, topic, turns, history_count, context_info_count)
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# else:
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# print("使用传统对话系统...")
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# dialogue_engine.run_conversation_turn(session_id, characters, turns, topic, history_count, context_info_count)
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# else:
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# print("❌ 无效的会话编号")
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# except ValueError:
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# print("❌ 请输入有效的数字")
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# elif choice == '3':
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# # 查看对话历史
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# session_id = input("请输入会话ID（前8位即可）: ").strip()
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# # 查找匹配的会话
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# sessions = conv_mgr.list_sessions()
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# matching_session = None
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# for session in sessions:
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# if session['session_id'].startswith(session_id):
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# matching_session = session
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# break
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# if matching_session:
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# full_session_id = matching_session['session_id']
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# history = conv_mgr.get_conversation_history(full_session_id, 20)
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# if history:
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# print(f"\n对话历史 - {full_session_id}")
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# print(f"角色: {', '.join(matching_session['characters'])}")
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# print(f"世界观: {matching_session['worldview']}")
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# print("-" * 50)
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# for turn in history:
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# print(f"[{turn.timestamp[:16]}] {turn.speaker}:")
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# print(f" {turn.content}")
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# if turn.context_used:
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# print(f" 使用上下文: {', '.join(turn.context_used)}")
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# print()
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# else:
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# print("该会话暂无对话历史")
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# else:
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# print("❌ 未找到匹配的会话")
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# elif choice == '4':
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# # 列出所有会话
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# sessions = conv_mgr.list_sessions()
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# if sessions:
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# print(f"\n共有 {len(sessions)} 个对话会话:")
|
||||
# for session in sessions:
|
||||
# chars = ", ".join(session['characters'])
|
||||
# print(f"ID: {session['session_id']}")
|
||||
# print(f" 角色: {chars}")
|
||||
# print(f" 世界观: {session['worldview']}")
|
||||
# print(f" 最后更新: {session['last_update']}")
|
||||
# print()
|
||||
# else:
|
||||
# print("暂无对话会话")
|
||||
|
||||
# else:
|
||||
# print("❌ 无效选择")
|
||||
|
||||
# except Exception as e:
|
||||
# print(f"✗ 系统运行出错: {e}")
|
||||
# import traceback
|
||||
# traceback.print_exc()
|
||||
|
||||
# if __name__ == '__main__':
|
||||
# main()
|
||||
@ -121,7 +121,7 @@ def show_character_info():
|
||||
except Exception as e:
|
||||
print(f"✗ 读取角色文件失败: {char_file} - {e}")
|
||||
|
||||
def run_dialogue_system():
|
||||
def run_dialogue_system(enableScore: bool):
|
||||
"""运行双AI对话系统"""
|
||||
print("\n" + "="*60)
|
||||
print("启动双AI角色对话系统")
|
||||
@ -191,7 +191,7 @@ def run_dialogue_system():
|
||||
kb,
|
||||
conv_mgr,
|
||||
dual_generator,
|
||||
enable_scoring=True,
|
||||
enable_scoring=enableScore,
|
||||
base_model_path=base_model_path
|
||||
)
|
||||
|
||||
@ -260,68 +260,6 @@ def run_dialogue_system():
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
def create_demo_scenario():
|
||||
"""创建演示场景"""
|
||||
print("\n创建演示对话场景...")
|
||||
|
||||
try:
|
||||
from dual_ai_dialogue_system import RAGKnowledgeBase, ConversationManager, DualAIDialogueEngine
|
||||
from npc_dialogue_generator import NPCDialogueGenerator
|
||||
|
||||
# 初始化组件
|
||||
kb = RAGKnowledgeBase("./knowledge_base")
|
||||
conv_mgr = ConversationManager("./conversation_data/demo_conversations.db")
|
||||
|
||||
# 检查模型路径
|
||||
base_model_path = '/mnt/e/AI/Project02/AITrain/Qwen/Qwen3-4B'
|
||||
lora_model_path = './output/NPC_Dialogue_LoRA/final_model'
|
||||
|
||||
if not os.path.exists(base_model_path):
|
||||
print(f"✗ 基础模型路径不存在: {base_model_path}")
|
||||
print("请修改 main_controller.py 中的模型路径")
|
||||
return
|
||||
|
||||
if not os.path.exists(lora_model_path):
|
||||
lora_model_path = None
|
||||
print("⚠ LoRA模型不存在，使用基础模型")
|
||||
|
||||
llm_generator = NPCDialogueGenerator(base_model_path, lora_model_path, kb.character_data)
|
||||
dialogue_engine = DualAIDialogueEngine(kb, conv_mgr, llm_generator)
|
||||
|
||||
# 创建演示对话
|
||||
characters = ["维多利亚·布莱克伍德", "阿奇博尔德·韦恩"]
|
||||
worldview = "克苏鲁的呼唤"
|
||||
|
||||
session_id = conv_mgr.create_session(characters, worldview)
|
||||
print(f"✓ 创建演示会话: {session_id}")
|
||||
|
||||
# 运行几轮对话
|
||||
topic = "最近发生的神秘事件"
|
||||
print(f"\n开始演示对话 - 主题: {topic}")
|
||||
print("-" * 40)
|
||||
|
||||
# 演示不同的历史上下文设置
|
||||
# print("演示1: 使用默认上下文设置（历史3轮，信息2个）")
|
||||
# dialogue_engine.run_conversation_turn(session_id, characters, 6, topic)
|
||||
|
||||
|
||||
session_id = conv_mgr.create_session(characters, worldview)
|
||||
print(f"✓ 创建演示会话: {session_id}")
|
||||
print("\n演示3: 使用最少历史上下文（历史1轮，信息1个）")
|
||||
dialogue_engine.run_conversation_turn(session_id, characters, 6, topic, 1, 10)
|
||||
|
||||
session_id = conv_mgr.create_session(characters, worldview)
|
||||
print(f"✓ 创建演示会话: {session_id}")
|
||||
print("\n演示2: 使用更多历史上下文（历史10轮，信息10个）")
|
||||
dialogue_engine.run_conversation_turn(session_id, characters, 6, topic, 5, 10)
|
||||
|
||||
print(f"\n✓ 演示完成！会话ID: {session_id}")
|
||||
print("你可以通过主对话系统继续这个对话")
|
||||
|
||||
except Exception as e:
|
||||
print(f"✗ 演示场景创建失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
def analyze_model_performance():
|
||||
"""分析模型性能"""
|
||||
@ -1333,8 +1271,8 @@ def main():
|
||||
print("主菜单 - 请选择操作:")
|
||||
print("1. 处理PDF世界观文档 (转换为RAG格式)")
|
||||
print("2. 查看角色设定信息")
|
||||
print("3. 启动双AI对话系统 (支持双模型对话)")
|
||||
print("4. 创建演示对话场景")
|
||||
print("3. 启动双AI对话系统 (开启ai打分)")
|
||||
print("4. 启动双AI对话系统 (关闭ai打分)")
|
||||
print("5. 系统状态检查")
|
||||
print("6. 查看对话评分统计")
|
||||
print("7. 模型性能分析与优化")
|
||||
@ -1357,10 +1295,10 @@ def main():
|
||||
show_character_info()
|
||||
|
||||
elif choice == '3':
|
||||
run_dialogue_system()
|
||||
run_dialogue_system(enableScore = True)
|
||||
|
||||
elif choice == '4':
|
||||
create_demo_scenario()
|
||||
run_dialogue_system(enableScore = False)
|
||||
|
||||
elif choice == '5':
|
||||
show_system_status()
|
||||
|
||||
@ -471,101 +471,3 @@ class DualModelDialogueGenerator:
|
||||
"""列出两个角色名称"""
|
||||
return [self.character1_config['name'], self.character2_config['name']]
|
||||
|
||||
# def main():
|
||||
# """测试对话生成器"""
|
||||
# # 配置路径
|
||||
# base_model_path = '/mnt/g/Project02/AITrain/Qwen/Qwen3-8B-AWQ'
|
||||
# lora_model_path = './output/NPC_Dialogue_LoRA/final_model' # 如果没有训练LoRA，设为None
|
||||
|
||||
# # 检查LoRA模型是否存在
|
||||
# if not os.path.exists(lora_model_path):
|
||||
# print("LoRA模型不存在，使用基础模型")
|
||||
# lora_model_path = None
|
||||
|
||||
# # 创建对话生成器
|
||||
# generator = NPCDialogueGenerator(base_model_path, lora_model_path)
|
||||
|
||||
# print("=== 游戏NPC角色对话生成器 ===")
|
||||
# print(f"可用角色：{', '.join(generator.list_available_characters())}")
|
||||
|
||||
# # 测试单个角色对话生成
|
||||
# print("\n=== 单角色对话测试 ===")
|
||||
# test_scenarios = [
|
||||
# {
|
||||
# "character": "克莱恩",
|
||||
# "context": "玩家向你咨询神秘学知识",
|
||||
# "input": "请告诉我一些关于灵界的注意事项。"
|
||||
# },
|
||||
# {
|
||||
# "character": "阿兹克",
|
||||
# "context": "学生遇到了修炼瓶颈",
|
||||
# "input": "导师，我在修炼中遇到了困难。"
|
||||
# },
|
||||
# {
|
||||
# "character": "塔利姆",
|
||||
# "context": "在俱乐部偶遇老朋友",
|
||||
# "input": "好久不见，最近怎么样？"
|
||||
# }
|
||||
# ]
|
||||
|
||||
# for scenario in test_scenarios:
|
||||
# print(f"\n--- {scenario['character']} ---")
|
||||
# print(f"情境：{scenario['context']}")
|
||||
# print(f"输入：{scenario['input']}")
|
||||
|
||||
# dialogue = generator.generate_character_dialogue(
|
||||
# scenario["character"],
|
||||
# scenario["context"],
|
||||
# scenario["input"]
|
||||
# )
|
||||
# print(f"回复：{dialogue}")
|
||||
|
||||
# # 测试角色间对话
|
||||
# print("\n=== 角色间对话测试 ===")
|
||||
# conversation = generator.generate_dialogue_conversation(
|
||||
# "克莱恩", "塔利姆", "最近遇到的神秘事件", turns=4
|
||||
# )
|
||||
|
||||
# for turn in conversation:
|
||||
# print(f"{turn['speaker']}：{turn['dialogue']}")
|
||||
|
||||
# # 交互式对话模式
|
||||
# print("\n=== 交互式对话模式 ===")
|
||||
# print("输入格式：角色名 上下文 用户输入")
|
||||
# print("例如：克莱恩 在俱乐部 请给我一些建议")
|
||||
# print("输入'quit'退出")
|
||||
|
||||
# while True:
|
||||
# try:
|
||||
# user_command = input("\n请输入指令: ").strip()
|
||||
# if user_command.lower() == 'quit':
|
||||
# break
|
||||
|
||||
# parts = user_command.split(' ', 2)
|
||||
# if len(parts) < 2:
|
||||
# print("格式错误，请使用：角色名 上下文 [用户输入]")
|
||||
# continue
|
||||
|
||||
# character = parts[0]
|
||||
# context = parts[1]
|
||||
# user_input = parts[2] if len(parts) > 2 else ""
|
||||
|
||||
# if character not in generator.list_available_characters():
|
||||
# print(f"未知角色：{character}")
|
||||
# print(f"可用角色：{', '.join(generator.list_available_characters())}")
|
||||
# continue
|
||||
|
||||
# dialogue = generator.generate_character_dialogue(
|
||||
# character, context, user_input
|
||||
# )
|
||||
# print(f"\n{character}：{dialogue}")
|
||||
|
||||
# except KeyboardInterrupt:
|
||||
# break
|
||||
# except Exception as e:
|
||||
# print(f"生成对话时出错：{e}")
|
||||
|
||||
# print("\n对话生成器已退出")
|
||||
|
||||
# if __name__ == '__main__':
|
||||
# main()
|
||||
Loading…
x
Reference in New Issue
Block a user