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Update planner.py

pull/998/head
2829798842 2025-05-28 21:21:49 +08:00 committed by GitHub
parent 53cb094c79
commit 073d156466
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1 changed files with 49 additions and 57 deletions
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106
src/chat/focus_chat/planners/planner.py
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@ -24,41 +24,39 @@ install(extra_lines=3)
def init_prompt(): def init_prompt():
Prompt( Prompt(
""" """
Your self-awareness is: 你的自我认知是：
{self_info_block} {self_info_block}
{extra_info_block} {extra_info_block}
你需要基于以下信息决定如何参与对话
You need to decide how to participate in the conversation based on the following information 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：
These information may conflict, please integrate these information, and choose the most suitable action:
{chat_content_block} {chat_content_block}
{mind_info_block} {mind_info_block}
{cycle_info_block} {cycle_info_block}
IMPORTANT: The following tool call information has the highest priority and should be heavily weighted in your decision-making: 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:
{structured_info_block} 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:
Please analyze the conversation content and new messages you see, refer to the conversation plan, and choose the appropriate action:
{action_options_text} {action_options_text}
You must choose one from the available actions above and explain why. 你必须从上面列出的可用action中选择一个，并说明原因。
Your decision must be output in strict JSON format, and only contain JSON content, no other text or explanation. 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。
Please output your decision JSON in the following format: {moderation_prompt}
请你以下面格式输出你选择的action：
{{ {{
"action": "action_name", "action": "action_name",
"reasoning": "Your decision reason", "reasoning": "你的决策理由",
"parameter1": "parameter1 value", "参数1": "参数1的值",
"parameter2": "parameter2 value", "参数2": "参数2的值",
"parameter3": "parameter3 value", "参数3": "参数3的值",
... ...
}} }}
Please output your decision JSON: """, 请输出你的决策 JSON：""",
"planner_prompt", "planner_prompt",
) )
@ -81,7 +79,7 @@ class ActionPlanner:
self.planner_llm = LLMRequest( self.planner_llm = LLMRequest(
model=global_config.model.focus_planner, model=global_config.model.focus_planner,
max_tokens=1000, max_tokens=1000,
request_type="action_planning", # 用于动作规划 request_type="focus_planner", # 用于动作规划
) )
self.action_manager = action_manager self.action_manager = action_manager
@ -98,20 +96,10 @@ class ActionPlanner:
action = "no_reply" # 默认动作 action = "no_reply" # 默认动作
reasoning = "规划器初始化默认" reasoning = "规划器初始化默认"
action_data = {} action_data = {}
# 初始化所有可能用到的变量，避免UnboundLocalError
observed_messages = []
observed_messages_str = ""
current_mind = ""
cycle_info = ""
self_info = ""
is_group_chat = True # 默认为群聊
prompt = "未生成prompt" # 初始化prompt变量
try: try:
# 获取观察信息 # 获取观察信息
extra_info: list[str] = [] extra_info: list[str] = []
_structured_info_list = []
# 首先处理动作变更 # 首先处理动作变更
for info in all_plan_info: for info in all_plan_info:
@ -119,6 +107,7 @@ class ActionPlanner:
add_actions = info.get_add_actions() add_actions = info.get_add_actions()
remove_actions = info.get_remove_actions() remove_actions = info.get_remove_actions()
reason = info.get_reason() reason = info.get_reason()
print(f"{self.log_prefix} 动作变更: {add_actions} {remove_actions} {reason}")
# 处理动作的增加 # 处理动作的增加
for action_name in add_actions: for action_name in add_actions:
@ -137,6 +126,10 @@ class ActionPlanner:
reasoning = f"之前选择的动作{action}已被移除，原因: {reason}" reasoning = f"之前选择的动作{action}已被移除，原因: {reason}"
# 继续处理其他信息 # 继续处理其他信息
self_info = ""
current_mind = ""
cycle_info = ""
structured_info = ""
for info in all_plan_info: for info in all_plan_info:
if isinstance(info, ObsInfo): if isinstance(info, ObsInfo):
observed_messages = info.get_talking_message() observed_messages = info.get_talking_message()
@ -150,24 +143,25 @@ class ActionPlanner:
elif isinstance(info, SelfInfo): elif isinstance(info, SelfInfo):
self_info = info.get_processed_info() self_info = info.get_processed_info()
elif isinstance(info, StructuredInfo): elif isinstance(info, StructuredInfo):
_structured_info = info.get_data() structured_info = info.get_processed_info()
# 收集工具调用的结构化信息 # print(f"structured_info: {structured_info}")
if _structured_info and isinstance(_structured_info, dict):
# StructuredInfo 的数据结构是 {tool_type: tool_content}
for tool_type, tool_content in _structured_info.items():
if tool_content: # 确保内容不为空
_structured_info_list.append(f"{tool_type}: {str(tool_content)}")
elif not isinstance(info, ActionInfo): # 跳过已处理的ActionInfo elif not isinstance(info, ActionInfo): # 跳过已处理的ActionInfo
extra_info.append(info.get_processed_info()) extra_info.append(info.get_processed_info())
# 获取当前可用的动作 # 获取当前可用的动作
current_available_actions = self.action_manager.get_using_actions() current_available_actions = self.action_manager.get_using_actions()
# 如果没有可用动作，直接返回no_reply # 如果没有可用动作或只有no_reply动作，直接返回no_reply
if not current_available_actions: if not current_available_actions or (
logger.warning(f"{self.log_prefix}没有可用的动作，将使用no_reply") len(current_available_actions) == 1 and "no_reply" in current_available_actions
):
action = "no_reply" action = "no_reply"
reasoning = "没有可用的动作" 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"
logger.info(f"{self.log_prefix}{reasoning}")
self.action_manager.restore_actions()
logger.debug(
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())}"
)
return { return {
"action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning}, "action_result": {"action_type": action, "action_data": action_data, "reasoning": reasoning},
"current_mind": current_mind, "current_mind": current_mind,
@ -175,20 +169,13 @@ class ActionPlanner:
} }
# --- 构建提示词 (调用修改后的 PromptBuilder 方法) --- # --- 构建提示词 (调用修改后的 PromptBuilder 方法) ---
# 构建工具信息块
structured_info_block_str = "\n".join(_structured_info_list)
if structured_info_block_str:
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}"
else:
structured_info_block_str = "No tool information available for reference."
prompt = await self.build_planner_prompt( prompt = await self.build_planner_prompt(
self_info_block=self_info, self_info_block=self_info,
is_group_chat=is_group_chat, # <-- Pass HFC state is_group_chat=is_group_chat, # <-- Pass HFC state
chat_target_info=None, chat_target_info=None,
observed_messages_str=observed_messages_str, # <-- Pass local variable observed_messages_str=observed_messages_str, # <-- Pass local variable
current_mind=current_mind, # <-- Pass argument current_mind=current_mind, # <-- Pass argument
structured_info_block=structured_info_block_str, structured_info=structured_info, # <-- Pass SubMind info
current_available_actions=current_available_actions, # <-- Pass determined actions current_available_actions=current_available_actions, # <-- Pass determined actions
cycle_info=cycle_info, # <-- Pass cycle info cycle_info=cycle_info, # <-- Pass cycle info
extra_info=extra_info, extra_info=extra_info,
@ -197,8 +184,9 @@ class ActionPlanner:
# --- 调用 LLM (普通文本生成) --- # --- 调用 LLM (普通文本生成) ---
llm_content = None llm_content = None
try: try:
llm_content, _, _ = await self.planner_llm.generate_response(prompt=prompt) llm_content, reasoning_content, _ = await self.planner_llm.generate_response(prompt=prompt)
logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}") logger.debug(f"{self.log_prefix}[Planner] LLM 原始 JSON 响应 (预期): {llm_content}")
logger.debug(f"{self.log_prefix}[Planner] LLM 原始理由 响应 (预期): {reasoning_content}")
except Exception as req_e: except Exception as req_e:
logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}") logger.error(f"{self.log_prefix}[Planner] LLM 请求执行失败: {req_e}")
reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}" reasoning = f"LLM 请求失败，你的模型出现问题: {req_e}"
@ -254,10 +242,10 @@ class ActionPlanner:
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}" 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}"
) )
# 恢复原始动作集 # 恢复到默认动作集
self.action_manager.restore_actions() self.action_manager.restore_actions()
logger.debug( logger.debug(
f"{self.log_prefix}恢复了原始动作集, 当前可用: {list(self.action_manager.get_using_actions().keys())}" 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())}"
) )
action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning} action_result = {"action_type": action, "action_data": action_data, "reasoning": reasoning}
@ -277,7 +265,7 @@ class ActionPlanner:
chat_target_info: Optional[dict], # Now passed as argument chat_target_info: Optional[dict], # Now passed as argument
observed_messages_str: str, observed_messages_str: str,
current_mind: Optional[str], current_mind: Optional[str],
structured_info_block: str, structured_info: Optional[str],
current_available_actions: Dict[str, ActionInfo], current_available_actions: Dict[str, ActionInfo],
cycle_info: Optional[str], cycle_info: Optional[str],
extra_info: list[str], extra_info: list[str],
@ -334,11 +322,14 @@ class ActionPlanner:
action_options_block += using_action_prompt action_options_block += using_action_prompt
extra_info_block = "\n".join(extra_info) # 将局部变量名从 extra_info_block_str 改为 extra_info_block extra_info_block = "\n".join(extra_info)
if extra_info_block: # 使用新的变量名进行检查 extra_info_block += f"\n{structured_info}"
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 if extra_info or structured_info:
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"
else: else:
extra_info_block = "No additional information available." # 使用新的变量名进行赋值 extra_info_block = ""
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。"
planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt") planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
prompt = planner_prompt_template.format( prompt = planner_prompt_template.format(
@ -348,10 +339,11 @@ class ActionPlanner:
chat_context_description=chat_context_description, chat_context_description=chat_context_description,
chat_content_block=chat_content_block, chat_content_block=chat_content_block,
mind_info_block=mind_info_block, mind_info_block=mind_info_block,
structured_info_block=structured_info_block,
cycle_info_block=cycle_info, cycle_info_block=cycle_info,
action_options_text=action_options_block, action_options_text=action_options_block,
extra_info_block=extra_info_block, # 确保这里传递的是修改后的变量名 # action_available_block=action_available_block,
extra_info_block=extra_info_block,
moderation_prompt=moderation_prompt_block,
) )
return prompt return prompt