feat：进一步完善hfc私聊支持
This commit is contained in:
SengokuCola
@@ -851,18 +851,15 @@ class HeartFChatting:
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f"{self.log_prefix}[Planner] 临时移除的动作: {actions_to_remove_temporarily}, 当前可用: {list(current_available_actions.keys())}"
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)
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# --- 构建提示词 (调用修改后的 _build_planner_prompt) ---
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# replan_prompt_str = "" # 暂时简化
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# if is_re_planned:
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# replan_prompt_str = await self._build_replan_prompt(
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# self._current_cycle.action_type, self._current_cycle.reasoning
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# )
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prompt = await self._build_planner_prompt(
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observed_messages_str,
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current_mind,
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self.sub_mind.structured_info,
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"", # replan_prompt_str,
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current_available_actions, # <--- 传入当前可用动作
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# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
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prompt = await prompt_builder.build_planner_prompt(
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is_group_chat=self.is_group_chat, # <-- Pass HFC state
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chat_target_info=self.chat_target_info, # <-- Pass HFC state
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cycle_history=self._cycle_history, # <-- Pass HFC state
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observed_messages_str=observed_messages_str, # <-- Pass local variable
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current_mind=current_mind, # <-- Pass argument
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structured_info=self.sub_mind.structured_info, # <-- Pass SubMind info
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current_available_actions=current_available_actions # <-- Pass determined actions
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)
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# --- 调用 LLM (普通文本生成) ---
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@@ -1126,217 +1123,6 @@ class HeartFChatting:
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return prompt
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async def _build_planner_prompt(
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self,
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observed_messages_str: str,
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current_mind: Optional[str],
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structured_info: Dict[str, Any],
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replan_prompt: str,
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current_available_actions: Dict[str, str],
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) -> str:
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"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
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try:
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# 准备结构化信息块
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structured_info_block = ""
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if structured_info:
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structured_info_block = f"以下是一些额外的信息：\n{structured_info}\n"
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# 准备聊天内容块
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chat_content_block = ""
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if observed_messages_str:
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chat_content_block = "观察到的最新聊天内容如下：\n---\n"
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chat_content_block += observed_messages_str
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chat_content_block += "\n---"
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else:
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chat_content_block = "当前没有观察到新的聊天内容。\n"
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# 准备当前思维块 (修改以匹配模板)
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current_mind_block = ""
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if current_mind:
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# 模板中占位符是 {current_mind_block}，它期望包含"你的内心想法："的前缀
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current_mind_block = f"你的内心想法：\n{current_mind}"
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else:
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current_mind_block = "你的内心想法：\n[没有特别的想法]"
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# 准备循环信息块 (分析最近的活动循环)
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recent_active_cycles = []
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for cycle in reversed(self._cycle_history):
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# 只关心实际执行了动作的循环
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if cycle.action_taken:
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recent_active_cycles.append(cycle)
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# 最多找最近的3个活动循环
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if len(recent_active_cycles) == 3:
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break
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cycle_info_block = ""
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consecutive_text_replies = 0
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responses_for_prompt = []
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# 检查这最近的活动循环中有多少是连续的文本回复 (从最近的开始看)
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for cycle in recent_active_cycles:
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if cycle.action_type == "text_reply":
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consecutive_text_replies += 1
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# 获取回复内容，如果不存在则返回'[空回复]'
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response_text = cycle.response_info.get("response_text", [])
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# 使用简单的 join 来格式化回复内容列表
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formatted_response = "[空回复]" if not response_text else " ".join(response_text)
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responses_for_prompt.append(formatted_response)
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else:
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# 一旦遇到非文本回复，连续性中断
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break
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# 根据连续文本回复的数量构建提示信息
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# 注意: responses_for_prompt 列表是从最近到最远排序的
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if consecutive_text_replies >= 3: # 如果最近的三个活动都是文本回复
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cycle_info_block = f'你已经连续回复了三条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"，第三近: "{responses_for_prompt[2]}"）。你回复的有点多了，请注意'
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elif consecutive_text_replies == 2: # 如果最近的两个活动是文本回复
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cycle_info_block = f'你已经连续回复了两条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"），请注意'
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elif consecutive_text_replies == 1: # 如果最近的一个活动是文本回复
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cycle_info_block = f'你刚刚已经回复一条消息（内容: "{responses_for_prompt[0]}"）'
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# 包装提示块，增加可读性，即使没有连续回复也给个标记
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if cycle_info_block:
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# 模板中占位符是 {cycle_info_block}，它期望包含"【近期回复历史】"的前缀
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cycle_info_block = f"\n【近期回复历史】\n{cycle_info_block}\n"
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else:
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# 如果最近的活动循环不是文本回复，或者没有活动循环
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cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n"
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individuality = Individuality.get_instance()
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# 模板中占位符是 {prompt_personality}
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prompt_personality = individuality.get_prompt(x_person=2, level=2)
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# --- 构建可用动作描述 (用于填充模板中的 {action_options_text}) ---
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action_options_text = "当前你可以选择的行动有：\n"
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action_keys = list(current_available_actions.keys())
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for name in action_keys:
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desc = current_available_actions[name]
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action_options_text += f"- '{name}': {desc}\n"
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# --- 选择一个示例动作键 (用于填充模板中的 {example_action}) ---
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example_action_key = action_keys[0] if action_keys else "no_reply"
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# --- 获取提示词模板 ---
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planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
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# --- 填充模板 ---
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prompt = planner_prompt_template.format(
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bot_name=global_config.BOT_NICKNAME,
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prompt_personality=prompt_personality,
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structured_info_block=structured_info_block,
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chat_content_block=chat_content_block,
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current_mind_block=current_mind_block,
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replan="", # 暂时留空 replan 信息
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cycle_info_block=cycle_info_block,
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action_options_text=action_options_text, # 传入可用动作描述
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example_action=example_action_key, # 传入示例动作键
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)
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return prompt
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except Exception as e:
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logger.error(f"{self.log_prefix}[Planner] 构建提示词时出错: {e}")
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logger.error(traceback.format_exc())
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return "[构建 Planner Prompt 时出错]" # 返回错误提示，避免空字符串
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# --- 回复器 (Replier) 的定义 --- #
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async def _replier_work(
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self,
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reason: str,
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anchor_message: MessageRecv,
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thinking_id: str,
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) -> Optional[List[str]]:
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"""
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回复器 (Replier): 核心逻辑，负责生成回复文本。
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(已整合原 HeartFCGenerator 的功能)
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"""
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try:
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# 1. 获取情绪影响因子并调整模型温度
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arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
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current_temp = global_config.llm_normal["temp"] * arousal_multiplier
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self.model_normal.temperature = current_temp # 动态调整温度
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# 2. 获取信息捕捉器
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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# 3. 构建 Prompt
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with Timer("构建Prompt", {}): # 内部计时器，可选保留
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prompt = await prompt_builder.build_prompt(
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build_mode="focus",
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reason=reason,
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current_mind_info=self.sub_mind.current_mind,
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structured_info=self.sub_mind.structured_info,
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message_txt="", # 似乎是固定的空字符串
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sender_name="", # 似乎是固定的空字符串
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chat_stream=anchor_message.chat_stream,
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)
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# 4. 调用 LLM 生成回复
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content = None
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reasoning_content = None
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model_name = "unknown_model"
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try:
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with Timer("LLM生成", {}): # 内部计时器，可选保留
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content, reasoning_content, model_name = await self.model_normal.generate_response(prompt)
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# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\\nPrompt:\\n{prompt}\\n生成回复: {content}\\n")
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# 捕捉 LLM 输出信息
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info_catcher.catch_after_llm_generated(
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prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
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)
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except Exception as llm_e:
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# 精简报错信息
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成失败: {llm_e}")
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return None # LLM 调用失败则无法生成回复
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# 5. 处理 LLM 响应
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if not content:
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logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成了空内容。")
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return None
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with Timer("处理响应", {}): # 内部计时器，可选保留
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processed_response = process_llm_response(content)
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if not processed_response:
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logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] 处理后的回复为空。")
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return None
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return processed_response
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except Exception as e:
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# 更通用的错误处理，精简信息
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] 回复生成意外失败: {e}")
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# logger.error(traceback.format_exc()) # 可以取消注释这行以在调试时查看完整堆栈
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return None
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# --- Methods moved from HeartFCController start ---
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async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]) -> Optional[str]:
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"""创建思考消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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messageinfo = anchor_message.message_info
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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thinking_time_point = round(time.time(), 2)
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thinking_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=anchor_message, # 回复的是锚点消息
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thinking_start_time=thinking_time_point,
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)
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# Access MessageManager directly
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await self.heart_fc_sender.register_thinking(thinking_message)
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return thinking_id
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async def _send_response_messages(
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self, anchor_message: Optional[MessageRecv], response_set: List[str], thinking_id: str
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) -> Optional[MessageSending]:
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@@ -1472,3 +1258,114 @@ class HeartFChatting:
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if self._cycle_history:
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return self._cycle_history[-1].to_dict()
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return None
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# --- 回复器 (Replier) 的定义 --- #
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async def _replier_work(
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self,
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reason: str,
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anchor_message: MessageRecv,
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thinking_id: str,
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) -> Optional[List[str]]:
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"""
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回复器 (Replier): 核心逻辑，负责生成回复文本。
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(已整合原 HeartFCGenerator 的功能)
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"""
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try:
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# 1. 获取情绪影响因子并调整模型温度
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arousal_multiplier = MoodManager.get_instance().get_arousal_multiplier()
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current_temp = global_config.llm_normal["temp"] * arousal_multiplier
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self.model_normal.temperature = current_temp # 动态调整温度
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# 2. 获取信息捕捉器
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info_catcher = info_catcher_manager.get_info_catcher(thinking_id)
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# --- Determine sender_name for private chat ---
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sender_name_for_prompt = "某人" # Default for group or if info unavailable
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if not self.is_group_chat and self.chat_target_info:
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# Prioritize person_name, then nickname
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sender_name_for_prompt = self.chat_target_info.get('person_name') or self.chat_target_info.get('user_nickname') or sender_name_for_prompt
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# --- End determining sender_name ---
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# 3. 构建 Prompt
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with Timer("构建Prompt", {}): # 内部计时器，可选保留
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prompt = await prompt_builder.build_prompt(
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build_mode="focus",
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chat_stream=self.chat_stream, # Pass the stream object
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# Focus specific args:
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reason=reason,
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current_mind_info=self.sub_mind.current_mind,
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structured_info=self.sub_mind.structured_info,
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sender_name=sender_name_for_prompt, # Pass determined name
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# Normal specific args (not used in focus mode):
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# message_txt="",
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)
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# 4. 调用 LLM 生成回复
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content = None
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reasoning_content = None
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model_name = "unknown_model"
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if not prompt:
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] Prompt 构建失败，无法生成回复。")
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return None
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try:
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with Timer("LLM生成", {}): # 内部计时器，可选保留
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content, reasoning_content, model_name = await self.model_normal.generate_response(prompt)
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# logger.info(f"{self.log_prefix}[Replier-{thinking_id}]\nPrompt:\n{prompt}\n生成回复: {content}\n")
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# 捕捉 LLM 输出信息
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info_catcher.catch_after_llm_generated(
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prompt=prompt, response=content, reasoning_content=reasoning_content, model_name=model_name
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)
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except Exception as llm_e:
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# 精简报错信息
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成失败: {llm_e}")
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return None # LLM 调用失败则无法生成回复
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# 5. 处理 LLM 响应
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if not content:
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logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] LLM 生成了空内容。")
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return None
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with Timer("处理响应", {}): # 内部计时器，可选保留
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processed_response = process_llm_response(content)
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if not processed_response:
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logger.warning(f"{self.log_prefix}[Replier-{thinking_id}] 处理后的回复为空。")
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return None
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return processed_response
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except Exception as e:
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# 更通用的错误处理，精简信息
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logger.error(f"{self.log_prefix}[Replier-{thinking_id}] 回复生成意外失败: {e}")
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# logger.error(traceback.format_exc()) # 可以取消注释这行以在调试时查看完整堆栈
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return None
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# --- Methods moved from HeartFCController start ---
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async def _create_thinking_message(self, anchor_message: Optional[MessageRecv]) -> Optional[str]:
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"""创建思考消息 (尝试锚定到 anchor_message)"""
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if not anchor_message or not anchor_message.chat_stream:
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logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流。")
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return None
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chat = anchor_message.chat_stream
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messageinfo = anchor_message.message_info
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bot_user_info = UserInfo(
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user_id=global_config.BOT_QQ,
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user_nickname=global_config.BOT_NICKNAME,
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platform=messageinfo.platform,
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)
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thinking_time_point = round(time.time(), 2)
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thinking_id = "mt" + str(thinking_time_point)
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thinking_message = MessageThinking(
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message_id=thinking_id,
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chat_stream=chat,
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bot_user_info=bot_user_info,
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reply=anchor_message, # 回复的是锚点消息
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thinking_start_time=thinking_time_point,
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)
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# Access MessageManager directly (using heart_fc_sender)
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await self.heart_fc_sender.register_thinking(thinking_message)
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return thinking_id
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