mirror of https://github.com/Mai-with-u/MaiBot.git
修复参数错误
A0000Xz
GitHub
parent
3365f89bca
commit
f31424323a
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@ -1,185 +1,185 @@
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from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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import time
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from src.common.logger import get_logger
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from src.individuality.individuality import get_individuality
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.tools.tool_use import ToolUser
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from src.chat.utils.json_utils import process_llm_tool_calls
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from .base_processor import BaseProcessor
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from typing import List, Optional, Dict
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from src.chat.heart_flow.observation.observation import Observation
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from src.chat.focus_chat.info.structured_info import StructuredInfo
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from src.chat.heart_flow.observation.structure_observation import StructureObservation
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logger = get_logger("processor")
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def init_prompt():
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# ... 原有代码 ...
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# 添加工具执行器提示词
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tool_executor_prompt = """
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你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
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群里正在进行的聊天内容：
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{chat_observe_info}
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请仔细分析聊天内容，考虑以下几点：
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1. 内容中是否包含需要查询信息的问题
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2. 是否有明确的工具使用指令
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If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
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"""
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Prompt(tool_executor_prompt, "tool_executor_prompt")
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class ToolProcessor(BaseProcessor):
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log_prefix = "工具执行器"
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def __init__(self, subheartflow_id: str):
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super().__init__()
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self.subheartflow_id = subheartflow_id
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self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
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self.llm_model = LLMRequest(
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model=global_config.model.focus_tool_use,
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request_type="focus.processor.tool",
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)
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self.structured_info = []
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async def process_info(
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self, observations: Optional[List[Observation]] = None, running_memories: Optional[List[Dict]] = None, *infos
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) -> List[StructuredInfo]:
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"""处理信息对象
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Args:
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observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型
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running_memories: 可选的运行时记忆列表，包含字典类型的记忆信息
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*infos: 可变数量的InfoBase类型的信息对象
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Returns:
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list: 处理后的结构化信息列表
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"""
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working_infos = []
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result = []
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if observations:
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for observation in observations:
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if isinstance(observation, ChattingObservation):
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result, used_tools, prompt = await self.execute_tools(observation, running_memories)
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logger.debug(f"工具调用结果: {result}")
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# 更新WorkingObservation中的结构化信息
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for observation in observations:
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if isinstance(observation, StructureObservation):
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for structured_info in result:
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# logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
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observation.add_structured_info(structured_info)
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working_infos = observation.get_observe_info()
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logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}")
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structured_info = StructuredInfo()
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if working_infos:
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for working_info in working_infos:
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structured_info.set_info(key=working_info.get("type"), value=working_info.get("content"))
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return [structured_info]
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async def execute_tools(self, observation: ChattingObservation, running_memorys: Optional[List[Dict]] = None):
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"""
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并行执行工具，返回结构化信息
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参数:
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sub_mind: 子思维对象
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chat_target_name: 聊天目标名称，默认为"对方"
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is_group_chat: 是否为群聊，默认为False
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return_details: 是否返回详细信息，默认为False
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cycle_info: 循环信息对象，可用于记录详细执行信息
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返回:
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如果return_details为False:
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List[Dict]: 工具执行结果的结构化信息列表
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如果return_details为True:
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Tuple[List[Dict], List[str], str]: (工具执行结果列表, 使用的工具列表, 工具执行提示词)
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"""
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tool_instance = ToolUser()
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tools = tool_instance._define_tools()
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# logger.debug(f"observation: {observation}")
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# logger.debug(f"observation.chat_target_info: {observation.chat_target_info}")
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# logger.debug(f"observation.is_group_chat: {observation.is_group_chat}")
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# logger.debug(f"observation.person_list: {observation.person_list}")
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is_group_chat = observation.is_group_chat
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chat_observe_info = observation.get_observe_info()
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# person_list = observation.person_list
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memory_str = ""
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if running_memorys:
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memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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for running_memory in running_memorys:
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memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
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# 获取时间信息
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time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# 构建专用于工具调用的提示词
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prompt = await global_prompt_manager.format_prompt(
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"tool_executor_prompt",
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memory_str=memory_str,
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chat_observe_info=chat_observe_info,
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is_group_chat=is_group_chat,
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bot_name=get_individuality().name,
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time_now=time_now,
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)
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# 调用LLM，专注于工具使用
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# logger.info(f"开始执行工具调用{prompt}")
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response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
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if len(other_info) == 3:
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reasoning_content, model_name, tool_calls = other_info
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else:
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reasoning_content, model_name = other_info
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tool_calls = None
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# print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool")
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if tool_calls:
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logger.info(f"获取到工具原始输出:\n{tool_calls}")
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# 处理工具调用和结果收集，类似于SubMind中的逻辑
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new_structured_items = []
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used_tools = [] # 记录使用了哪些工具
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if tool_calls:
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success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
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if success and valid_tool_calls:
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for tool_call in valid_tool_calls:
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try:
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# 记录使用的工具名称
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tool_name = tool_call.get("name", "unknown_tool")
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used_tools.append(tool_name)
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result = await tool_instance._execute_tool_call(tool_call)
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name = result.get("type", "unknown_type")
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content = result.get("content", "")
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logger.info(f"工具{name}，获得信息:{content}")
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if result:
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new_item = {
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"type": result.get("type", "unknown_type"),
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"id": result.get("id", f"tool_exec_{time.time()}"),
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"content": result.get("content", ""),
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"ttl": 3,
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}
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new_structured_items.append(new_item)
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except Exception as e:
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logger.error(f"{self.log_prefix}工具执行失败: {e}")
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return new_structured_items, used_tools, prompt
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init_prompt()
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from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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import time
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from src.common.logger import get_logger
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from src.individuality.individuality import get_individuality
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.tools.tool_use import ToolUser
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from src.chat.utils.json_utils import process_llm_tool_calls
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from .base_processor import BaseProcessor
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from typing import List, Optional, Dict
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from src.chat.heart_flow.observation.observation import Observation
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from src.chat.focus_chat.info.structured_info import StructuredInfo
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from src.chat.heart_flow.observation.structure_observation import StructureObservation
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logger = get_logger("processor")
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def init_prompt():
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# ... 原有代码 ...
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# 添加工具执行器提示词
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tool_executor_prompt = """
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你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。
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群里正在进行的聊天内容：
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{chat_observe_info}
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请仔细分析聊天内容，考虑以下几点：
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1. 内容中是否包含需要查询信息的问题
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2. 是否有明确的工具使用指令
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If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
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"""
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Prompt(tool_executor_prompt, "tool_executor_prompt")
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class ToolProcessor(BaseProcessor):
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log_prefix = "工具执行器"
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def __init__(self, subheartflow_id: str):
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super().__init__()
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self.subheartflow_id = subheartflow_id
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self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
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self.llm_model = LLMRequest(
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model=global_config.model.focus_tool_use,
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request_type="focus.processor.tool",
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)
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self.structured_info = []
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async def process_info(
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self, observations: Optional[List[Observation]] = None, running_memories: Optional[List[Dict]] = None, *infos
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) -> List[StructuredInfo]:
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"""处理信息对象
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Args:
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observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型
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running_memories: 可选的运行时记忆列表，包含字典类型的记忆信息
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*infos: 可变数量的InfoBase类型的信息对象
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Returns:
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list: 处理后的结构化信息列表
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"""
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working_infos = []
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result = []
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if observations:
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for observation in observations:
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if isinstance(observation, ChattingObservation):
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result, used_tools, prompt = await self.execute_tools(observation, running_memories)
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logger.debug(f"工具调用结果: {result}")
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# 更新WorkingObservation中的结构化信息
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for observation in observations:
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if isinstance(observation, StructureObservation):
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for structured_info in result:
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# logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}")
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observation.add_structured_info(structured_info)
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working_infos = observation.get_observe_info()
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logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}")
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structured_info = StructuredInfo()
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if working_infos:
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for working_info in working_infos:
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structured_info.set_info(key=working_info.get("type"), value=working_info.get("content"))
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return [structured_info]
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async def execute_tools(self, observation: ChattingObservation, running_memorys: Optional[List[Dict]] = None):
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"""
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并行执行工具，返回结构化信息
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参数:
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sub_mind: 子思维对象
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chat_target_name: 聊天目标名称，默认为"对方"
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is_group_chat: 是否为群聊，默认为False
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return_details: 是否返回详细信息，默认为False
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cycle_info: 循环信息对象，可用于记录详细执行信息
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返回:
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如果return_details为False:
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List[Dict]: 工具执行结果的结构化信息列表
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如果return_details为True:
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Tuple[List[Dict], List[str], str]: (工具执行结果列表, 使用的工具列表, 工具执行提示词)
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"""
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tool_instance = ToolUser()
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tools = tool_instance._define_tools()
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# logger.debug(f"observation: {observation}")
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# logger.debug(f"observation.chat_target_info: {observation.chat_target_info}")
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# logger.debug(f"observation.is_group_chat: {observation.is_group_chat}")
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# logger.debug(f"observation.person_list: {observation.person_list}")
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is_group_chat = observation.is_group_chat
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chat_observe_info = observation.get_observe_info()
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# person_list = observation.person_list
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memory_str = ""
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if running_memorys:
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memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
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for running_memory in running_memorys:
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memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
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# 获取时间信息
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time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
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# 构建专用于工具调用的提示词
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prompt = await global_prompt_manager.format_prompt(
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"tool_executor_prompt",
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memory_str=memory_str,
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chat_observe_info=chat_observe_info,
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is_group_chat=is_group_chat,
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bot_name=get_individuality().name,
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time_now=time_now,
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)
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# 调用LLM，专注于工具使用
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# logger.info(f"开始执行工具调用{prompt}")
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response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools)
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if len(other_info) == 3:
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reasoning_content, model_name, tool_calls = other_info
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else:
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reasoning_content, model_name = other_info
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tool_calls = None
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# print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool")
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if tool_calls:
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logger.info(f"获取到工具原始输出:\n{tool_calls}")
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# 处理工具调用和结果收集，类似于SubMind中的逻辑
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new_structured_items = []
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used_tools = [] # 记录使用了哪些工具
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if tool_calls:
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success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
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if success and valid_tool_calls:
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for tool_call in valid_tool_calls:
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try:
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# 记录使用的工具名称
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tool_name = tool_call.get("name", "unknown_tool")
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used_tools.append(tool_name)
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result = await tool_instance._execute_tool_call(tool_call)
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name = result.get("type", "unknown_type")
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content = result.get("content", "")
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logger.info(f"工具{name}，获得信息:{content}")
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if result:
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new_item = {
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"type": result.get("type", "unknown_type"),
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"id": result.get("id", f"tool_exec_{time.time()}"),
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"content": result.get("content", ""),
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"ttl": 3,
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}
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new_structured_items.append(new_item)
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except Exception as e:
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logger.error(f"{self.log_prefix}工具执行失败: {e}")
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return new_structured_items, used_tools, prompt
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init_prompt()
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