mirror of https://github.com/Mai-with-u/MaiBot.git
Update planner.py
parent
d230c6ae51
commit
c1429717b9
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@ -24,39 +24,41 @@ install(extra_lines=3)
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def init_prompt():
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Prompt(
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"""
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你的自我认知是:
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Your self-awareness is:
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{self_info_block}
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{extra_info_block}
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你需要基于以下信息决定如何参与对话
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这些信息可能会有冲突,请你整合这些信息,并选择一个最合适的action:
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You need to decide how to participate in the conversation based on the following information
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These information may conflict, please integrate these information, and choose the most suitable action:
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{chat_content_block}
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{mind_info_block}
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{cycle_info_block}
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请综合分析聊天内容和你看到的新消息,参考聊天规划,选择合适的action:
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注意,除了下面动作选项之外,你在群聊里不能做其他任何事情,这是你能力的边界,现在请你选择合适的action:
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IMPORTANT: The following tool call information has the highest priority and should be heavily weighted in your decision-making:
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{structured_info_block}
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Please analyze the conversation content and new messages you see, refer to the conversation plan, and choose the appropriate action:
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{action_options_text}
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你必须从上面列出的可用action中选择一个,并说明原因。
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你的决策必须以严格的 JSON 格式输出,且仅包含 JSON 内容,不要有任何其他文字或解释。
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You must choose one from the available actions above and explain why.
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Your decision must be output in strict JSON format, and only contain JSON content, no other text or explanation.
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{moderation_prompt}
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请你以下面格式输出你选择的action:
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Please output your decision JSON in the following format:
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{{
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"action": "action_name",
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"reasoning": "你的决策理由",
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"参数1": "参数1的值",
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"参数2": "参数2的值",
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"参数3": "参数3的值",
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"reasoning": "Your decision reason",
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"parameter1": "parameter1 value",
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"parameter2": "parameter2 value",
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"parameter3": "parameter3 value",
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...
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}}
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请输出你的决策 JSON:""",
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Please output your decision JSON: """,
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"planner_prompt",
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)
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@ -79,7 +81,7 @@ class ActionPlanner:
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self.planner_llm = LLMRequest(
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model=global_config.model.focus_planner,
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max_tokens=1000,
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request_type="focus_planner", # 用于动作规划
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request_type="action_planning", # 用于动作规划
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)
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self.action_manager = action_manager
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@ -100,6 +102,7 @@ class ActionPlanner:
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try:
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# 获取观察信息
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extra_info: list[str] = []
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_structured_info_list = []
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# 首先处理动作变更
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for info in all_plan_info:
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@ -107,7 +110,6 @@ class ActionPlanner:
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add_actions = info.get_add_actions()
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remove_actions = info.get_remove_actions()
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reason = info.get_reason()
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print(f"{self.log_prefix} 动作变更: {add_actions} {remove_actions} {reason}")
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# 处理动作的增加
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for action_name in add_actions:
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@ -126,10 +128,6 @@ class ActionPlanner:
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reasoning = f"之前选择的动作{action}已被移除,原因: {reason}"
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# 继续处理其他信息
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self_info = ""
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current_mind = ""
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cycle_info = ""
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structured_info = ""
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for info in all_plan_info:
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if isinstance(info, ObsInfo):
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observed_messages = info.get_talking_message()
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@ -143,25 +141,24 @@ class ActionPlanner:
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elif isinstance(info, SelfInfo):
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self_info = info.get_processed_info()
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elif isinstance(info, StructuredInfo):
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structured_info = info.get_processed_info()
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# print(f"structured_info: {structured_info}")
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_structured_info = info.get_data()
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# 收集工具调用的结构化信息
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if _structured_info and isinstance(_structured_info, dict):
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# StructuredInfo 的数据结构是 {tool_type: tool_content}
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for tool_type, tool_content in _structured_info.items():
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if tool_content: # 确保内容不为空
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_structured_info_list.append(f"{tool_type}: {str(tool_content)}")
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elif not isinstance(info, ActionInfo): # 跳过已处理的ActionInfo
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extra_info.append(info.get_processed_info())
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# 获取当前可用的动作
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current_available_actions = self.action_manager.get_using_actions()
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# 如果没有可用动作或只有no_reply动作,直接返回no_reply
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if not current_available_actions or (
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len(current_available_actions) == 1 and "no_reply" in current_available_actions
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):
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# 如果没有可用动作,直接返回no_reply
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if not current_available_actions:
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logger.warning(f"{self.log_prefix}没有可用的动作,将使用no_reply")
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action = "no_reply"
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reasoning = "没有可用的动作" if not current_available_actions else "只有no_reply动作可用,跳过规划"
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logger.info(f"{self.log_prefix}{reasoning}")
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self.action_manager.restore_actions()
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logger.debug(
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f"{self.log_prefix}沉默后恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
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)
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reasoning = "没有可用的动作"
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return {
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"action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
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"current_mind": current_mind,
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@ -169,13 +166,20 @@ class ActionPlanner:
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}
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# --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
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# 构建工具信息块
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structured_info_block_str = "\n".join(_structured_info_list)
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if structured_info_block_str:
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structured_info_block_str = f"The following is basic information returned by tool calls. Please use this information as the basis for subsequent decision-making and actions:\n{structured_info_block_str}"
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else:
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structured_info_block_str = "No tool information available for reference."
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prompt = await self.build_planner_prompt(
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self_info_block=self_info,
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is_group_chat=is_group_chat, # <-- Pass HFC state
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chat_target_info=None,
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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=structured_info, # <-- Pass SubMind info
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structured_info_block=structured_info_block_str,
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current_available_actions=current_available_actions, # <-- Pass determined actions
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cycle_info=cycle_info, # <-- Pass cycle info
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extra_info=extra_info,
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@ -184,9 +188,8 @@ class ActionPlanner:
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# --- 调用 LLM (普通文本生成) ---
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llm_content = None
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try:
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llm_content, reasoning_content, _ = await self.planner_llm.generate_response(prompt=prompt)
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llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt)
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logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
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logger.debug(f"{self.log_prefix}[Planner] LLM 原始理由 响应 (预期): {reasoning_content}")
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except Exception as req_e:
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logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
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reasoning = f"LLM 请求失败,你的模型出现问题: {req_e}"
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@ -242,10 +245,10 @@ class ActionPlanner:
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f"{self.log_prefix}规划器Prompt:\n{prompt}\n\n决策动作:{action},\n动作信息: '{action_data}'\n理由: {reasoning}"
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)
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# 恢复到默认动作集
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# 恢复原始动作集
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self.action_manager.restore_actions()
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logger.debug(
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f"{self.log_prefix}规划后恢复到默认动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
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f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}"
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)
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action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
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@ -265,7 +268,7 @@ class ActionPlanner:
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chat_target_info: Optional[dict], # Now passed as argument
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observed_messages_str: str,
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current_mind: Optional[str],
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structured_info: Optional[str],
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structured_info_block: str,
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current_available_actions: Dict[str, ActionInfo],
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cycle_info: Optional[str],
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extra_info: list[str],
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@ -322,14 +325,11 @@ class ActionPlanner:
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action_options_block += using_action_prompt
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extra_info_block = "\n".join(extra_info)
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extra_info_block += f"\n{structured_info}"
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if extra_info or structured_info:
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extra_info_block = f"以下是一些额外的信息,现在请你阅读以下内容,进行决策\n{extra_info_block}\n以上是一些额外的信息,现在请你阅读以下内容,进行决策"
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extra_info_block = "\n".join(extra_info) # 将局部变量名从 extra_info_block_str 改为 extra_info_block
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if extra_info_block: # 使用新的变量名进行检查
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extra_info_block = f"The following is some additional information. Please read the following content to make a decision:\n{extra_info_block}\nEnd of additional information." # 使用新的变量名进行格式化
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else:
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extra_info_block = ""
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moderation_prompt_block = "请不要输出违法违规内容,不要输出色情,暴力,政治相关内容,如有敏感内容,请规避。"
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extra_info_block = "No additional information available." # 使用新的变量名进行赋值
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planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
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prompt = planner_prompt_template.format(
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@ -339,11 +339,10 @@ class ActionPlanner:
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chat_context_description=chat_context_description,
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chat_content_block=chat_content_block,
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mind_info_block=mind_info_block,
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structured_info_block=structured_info_block,
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cycle_info_block=cycle_info,
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action_options_text=action_options_block,
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# action_available_block=action_available_block,
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extra_info_block=extra_info_block,
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moderation_prompt=moderation_prompt_block,
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extra_info_block=extra_info_block, # 确保这里传递的是修改后的变量名
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)
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return prompt
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