feat(chat): 增强prompt构建功能并优化回复逻辑
- 为HfcContext和ChatStream添加focus_energy配置支持 - 修复默认回复生成器中识别自身消息的逻辑 - 完整实现prompt构建中的表达习惯、记忆、工具信息和知识模块 - 优化错误处理，使用原生异常链式传递 - 确保数据库操作中focus_energy字段的持久化 这些改进提升了聊天系统的上下文感知能力和回复质量，同时增强了模块的健壮性和可维护性。
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Windpicker-owo
Windpicker-owo
parent
bd8b6232ca
commit
14a70496f7
@@ -312,16 +312,15 @@ class Prompt:
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except asyncio.TimeoutError as e:
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logger.error(f"构建Prompt超时: {e}")
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raise TimeoutError(f"构建Prompt超时: {e}")
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raise TimeoutError(f"构建Prompt超时: {e}") from e
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except Exception as e:
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logger.error(f"构建Prompt失败: {e}")
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raise RuntimeError(f"构建Prompt失败: {e}")
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raise RuntimeError(f"构建Prompt失败: {e}") from e
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async def _build_context_data(self) -> Dict[str, Any]:
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"""构建智能上下文数据"""
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# 并行执行所有构建任务
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start_time = time.time()
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timing_logs = {}
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try:
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# 准备构建任务
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@@ -381,7 +380,6 @@ class Prompt:
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results = []
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for i in range(0, len(tasks), max_concurrent_tasks):
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batch_tasks = tasks[i : i + max_concurrent_tasks]
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batch_names = task_names[i : i + max_concurrent_tasks]
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batch_results = await asyncio.wait_for(
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asyncio.gather(*batch_tasks, return_exceptions=True), timeout=timeout_seconds
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@@ -520,13 +518,99 @@ class Prompt:
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async def _build_expression_habits(self) -> Dict[str, Any]:
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"""构建表达习惯"""
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# 简化的实现，完整实现需要导入相关模块
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return {"expression_habits_block": ""}
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if not global_config.expression.enable_expression:
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return {"expression_habits_block": ""}
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try:
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from src.chat.express.expression_selector import ExpressionSelector
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# 获取聊天历史用于表情选择
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chat_history = ""
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if self.parameters.message_list_before_now_long:
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recent_messages = self.parameters.message_list_before_now_long[-10:]
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chat_history = build_readable_messages(
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recent_messages,
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replace_bot_name=True,
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timestamp_mode="normal",
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truncate=True
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)
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# 创建表情选择器
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expression_selector = ExpressionSelector(self.parameters.chat_id)
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# 选择合适的表情
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selected_expressions = await expression_selector.select_suitable_expressions_llm(
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chat_history=chat_history,
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current_message=self.parameters.target,
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emotional_tone="neutral",
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topic_type="general"
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)
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# 构建表达习惯块
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if selected_expressions:
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style_habits_str = "\n".join([f"- {expr}" for expr in selected_expressions])
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expression_habits_block = f"你可以参考以下的语言习惯，当情景合适就使用，但不要生硬使用，以合理的方式结合到你的回复中：\n{style_habits_str}"
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else:
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expression_habits_block = ""
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return {"expression_habits_block": expression_habits_block}
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except Exception as e:
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logger.error(f"构建表达习惯失败: {e}")
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return {"expression_habits_block": ""}
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async def _build_memory_block(self) -> Dict[str, Any]:
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"""构建记忆块"""
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# 简化的实现
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return {"memory_block": ""}
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if not global_config.memory.enable_memory:
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return {"memory_block": ""}
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try:
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from src.chat.memory_system.memory_activator import MemoryActivator
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from src.chat.memory_system.async_instant_memory_wrapper import async_memory
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# 获取聊天历史
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chat_history = ""
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if self.parameters.message_list_before_now_long:
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recent_messages = self.parameters.message_list_before_now_long[-20:]
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chat_history = build_readable_messages(
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recent_messages,
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replace_bot_name=True,
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timestamp_mode="normal",
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truncate=True
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)
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# 激活长期记忆
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memory_activator = MemoryActivator()
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running_memories = await memory_activator.activate_memory_with_chat_history(
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chat_history=chat_history,
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target_user=self.parameters.sender,
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chat_id=self.parameters.chat_id
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)
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# 获取即时记忆
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instant_memory = await async_memory.get_memory_with_fallback(
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chat_id=self.parameters.chat_id,
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target_user=self.parameters.sender
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)
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# 构建记忆块
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memory_parts = []
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if running_memories:
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memory_parts.append("以下是当前在聊天中，你回忆起的记忆：")
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for memory in running_memories:
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memory_parts.append(f"- {memory['content']}")
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if instant_memory:
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memory_parts.append(f"- {instant_memory}")
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memory_block = "\n".join(memory_parts) if memory_parts else ""
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return {"memory_block": memory_block}
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except Exception as e:
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logger.error(f"构建记忆块失败: {e}")
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return {"memory_block": ""}
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async def _build_relation_info(self) -> Dict[str, Any]:
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"""构建关系信息"""
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@@ -539,13 +623,106 @@ class Prompt:
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async def _build_tool_info(self) -> Dict[str, Any]:
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"""构建工具信息"""
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# 简化的实现
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return {"tool_info_block": ""}
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if not global_config.tool.enable_tool:
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return {"tool_info_block": ""}
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try:
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from src.plugin_system.core.tool_use import ToolExecutor
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# 获取聊天历史
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chat_history = ""
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if self.parameters.message_list_before_now_long:
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recent_messages = self.parameters.message_list_before_now_long[-15:]
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chat_history = build_readable_messages(
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recent_messages,
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replace_bot_name=True,
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timestamp_mode="normal",
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truncate=True
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)
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# 创建工具执行器
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tool_executor = ToolExecutor()
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# 执行工具获取信息
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tool_results, _, _ = await tool_executor.execute_from_chat_message(
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sender=self.parameters.sender,
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target_message=self.parameters.target,
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chat_history=chat_history,
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return_details=False
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)
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# 构建工具信息块
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if tool_results:
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tool_info_parts = ["以下是你通过工具获取到的实时信息："]
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for tool_result in tool_results:
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tool_name = tool_result.get("tool_name", "unknown")
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content = tool_result.get("content", "")
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result_type = tool_result.get("type", "tool_result")
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tool_info_parts.append(f"- 【{tool_name}】{result_type}: {content}")
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tool_info_parts.append("以上是你获取到的实时信息，请在回复时参考这些信息。")
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tool_info_block = "\n".join(tool_info_parts)
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else:
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tool_info_block = ""
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return {"tool_info_block": tool_info_block}
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except Exception as e:
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logger.error(f"构建工具信息失败: {e}")
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return {"tool_info_block": ""}
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async def _build_knowledge_info(self) -> Dict[str, Any]:
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"""构建知识信息"""
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# 简化的实现
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return {"knowledge_prompt": ""}
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if not global_config.lpmm_knowledge.enable:
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return {"knowledge_prompt": ""}
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try:
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from src.chat.knowledge.knowledge_lib import QAManager
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# 获取问题文本（当前消息）
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question = self.parameters.target or ""
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if not question:
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return {"knowledge_prompt": ""}
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# 创建QA管理器
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qa_manager = QAManager()
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# 搜索相关知识
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knowledge_results = await qa_manager.get_knowledge(
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question=question,
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chat_id=self.parameters.chat_id,
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max_results=5,
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min_similarity=0.5
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)
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# 构建知识块
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if knowledge_results and knowledge_results.get("knowledge_items"):
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knowledge_parts = ["以下是与你当前对话相关的知识信息："]
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for item in knowledge_results["knowledge_items"]:
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content = item.get("content", "")
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source = item.get("source", "")
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relevance = item.get("relevance", 0.0)
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if content:
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if source:
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knowledge_parts.append(f"- [{relevance:.2f}] {content} (来源: {source})")
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else:
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knowledge_parts.append(f"- [{relevance:.2f}] {content}")
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if knowledge_results.get("summary"):
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knowledge_parts.append(f"\n知识总结: {knowledge_results['summary']}")
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knowledge_prompt = "\n".join(knowledge_parts)
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else:
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knowledge_prompt = ""
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return {"knowledge_prompt": knowledge_prompt}
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except Exception as e:
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logger.error(f"构建知识信息失败: {e}")
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return {"knowledge_prompt": ""}
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async def _build_cross_context(self) -> Dict[str, Any]:
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"""构建跨群上下文"""
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