feat: 新增HFC处理器自定义项和思考间隔项
新增了 HFC不同处理器的开启关闭可选项 新增了思考间隔调整 移除无用工具
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SengokuCola
@@ -1,40 +0,0 @@
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from src.tools.tool_can_use.base_tool import BaseTool
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from src.common.logger import get_module_logger
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from typing import Any
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logger = get_module_logger("get_mid_memory_tool")
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class GetMidMemoryTool(BaseTool):
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"""从记忆系统中获取相关记忆的工具"""
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name = "mid_chat_mem"
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description = "之前的聊天内容概述id中获取具体信息，如果没有聊天内容概述id，就不要使用"
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parameters = {
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"type": "object",
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"properties": {
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"id": {"type": "integer", "description": "要查询的聊天记录概述id"},
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},
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"required": ["id"],
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}
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async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
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"""执行记忆获取
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Args:
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function_args: 工具参数
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message_txt: 原始消息文本
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Returns:
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dict: 工具执行结果
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"""
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try:
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id = function_args.get("id")
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return {"name": "mid_chat_mem", "content": str(id)}
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except Exception as e:
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logger.error(f"聊天记录获取工具执行失败: {str(e)}")
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return {"name": "mid_chat_mem", "content": f"聊天记录获取失败: {str(e)}"}
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# 注册工具
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# register_tool(GetMemoryTool)
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@@ -1,25 +0,0 @@
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from src.tools.tool_can_use.base_tool import BaseTool
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from src.common.logger import get_module_logger
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from typing import Any
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logger = get_module_logger("send_emoji_tool")
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class SendEmojiTool(BaseTool):
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"""发送表情包的工具"""
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name = "send_emoji"
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description = "当你觉得需要表达情感，或者帮助表达，可以使用这个工具发送表情包"
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parameters = {
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"type": "object",
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"properties": {"text": {"type": "string", "description": "要发送的表情包描述"}},
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"required": ["text"],
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}
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async def execute(self, function_args: dict[str, Any], message_txt: str = "") -> dict[str, Any]:
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text = function_args.get("text", message_txt)
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return {
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"name": "send_emoji",
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"content": text,
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}
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@@ -1,39 +0,0 @@
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from src.tools.tool_can_use.base_tool import BaseTool
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from src.common.logger_manager import get_logger
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from typing import Dict, Any
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from datetime import datetime
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import time
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logger = get_logger("get_time_date")
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class GetCurrentDateTimeTool(BaseTool):
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"""获取当前时间、日期、年份和星期的工具"""
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name = "get_current_date_time"
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description = "当有人询问或者涉及到具体时间或者日期的时候，必须使用这个工具"
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parameters = {
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"type": "object",
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"properties": {},
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"required": [],
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}
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async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]:
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"""执行获取当前时间、日期、年份和星期
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Args:
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function_args: 工具参数（此工具不使用）
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Returns:
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Dict: 工具执行结果
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"""
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current_time = datetime.now().strftime("%H:%M:%S")
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current_date = datetime.now().strftime("%Y-%m-%d")
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current_year = datetime.now().strftime("%Y")
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current_weekday = datetime.now().strftime("%A")
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return {
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"type": "time_info",
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"id": f"time_info_{time.time()}",
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"content": f"当前时间: {current_time}, 日期: {current_date}, 年份: {current_year}, 星期: {current_weekday}",
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}
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@@ -3,56 +3,14 @@ from src.config.config import global_config
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import json
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from src.common.logger_manager import get_logger
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from src.tools.tool_can_use import get_all_tool_definitions, get_tool_instance
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import traceback
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from src.chat.person_info.relationship_manager import relationship_manager
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from src.chat.utils.utils import parse_text_timestamps
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from src.chat.message_receive.chat_stream import ChatStream
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from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
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logger = get_logger("tool_use")
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class ToolUser:
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def __init__(self):
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self.llm_model_tool = LLMRequest(
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model=global_config.model.tool_use, temperature=0.2, max_tokens=1000, request_type="tool_use"
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)
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@staticmethod
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async def _build_tool_prompt(
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message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None
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):
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"""构建工具使用的提示词
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Args:
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message_txt: 用户消息文本
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subheartflow: 子心流对象
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Returns:
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str: 构建好的提示词
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"""
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if observation:
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mid_memory_info = observation.mid_memory_info
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# print(f"intol111111111111111111111111111111111222222222222mid_memory_info：{mid_memory_info}")
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# 这些信息应该从调用者传入，而不是从self获取
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bot_name = global_config.bot.nickname
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prompt = ""
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prompt += mid_memory_info
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prompt += "你正在思考如何回复群里的消息。\n"
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prompt += "之前群里进行了如下讨论:\n"
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prompt += message_txt
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# prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n"
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prompt += f"注意你就是{bot_name}，{bot_name}是你的名字。根据之前的聊天记录补充问题信息，搜索时避开你的名字。\n"
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# prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
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prompt += "你现在需要对群里的聊天内容进行回复，请你思考应该使用什么工具，然后选择工具来对消息和你的回复进行处理，你是否需要额外的信息，比如回忆或者搜寻已有的知识，改变关系和情感，或者了解你现在正在做什么。"
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prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)
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prompt = parse_text_timestamps(prompt, mode="lite")
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return prompt
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@staticmethod
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def _define_tools():
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"""获取所有已注册工具的定义
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@@ -100,93 +58,3 @@ class ToolUser:
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except Exception as e:
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logger.error(f"执行工具调用时发生错误: {str(e)}")
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return None
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async def use_tool(self, message_txt: str, chat_stream: ChatStream = None, observation: ChattingObservation = None):
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"""使用工具辅助思考，判断是否需要额外信息
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Args:
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message_txt: 用户消息文本
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chat_stream: 聊天流对象
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observation: 观察对象（可选）
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Returns:
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dict: 工具使用结果，包含结构化的信息
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"""
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try:
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# 构建提示词
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prompt = await self._build_tool_prompt(
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message_txt=message_txt,
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chat_stream=chat_stream,
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observation=observation,
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)
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# 定义可用工具
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tools = self._define_tools()
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logger.trace(f"工具定义: {tools}")
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# 使用llm_model_tool发送带工具定义的请求
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payload = {
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"model": self.llm_model_tool.model_name,
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"messages": [{"role": "user", "content": prompt}],
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"tools": tools,
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"temperature": 0.2,
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}
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logger.trace(f"发送工具调用请求，模型: {self.llm_model_tool.model_name}")
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# 发送请求获取模型是否需要调用工具
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response = await self.llm_model_tool._execute_request(
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endpoint="/chat/completions", payload=payload, prompt=prompt
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)
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# 根据返回值数量判断是否有工具调用
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if len(response) == 3:
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content, reasoning_content, tool_calls = response
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# logger.info(f"工具思考: {tool_calls}")
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# logger.debug(f"工具思考: {content}")
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# 检查响应中工具调用是否有效
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if not tool_calls:
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logger.debug("模型返回了空的tool_calls列表")
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return {"used_tools": False}
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tool_calls_str = ""
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for tool_call in tool_calls:
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tool_calls_str += f"{tool_call['function']['name']}\n"
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logger.info(
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f"根据:\n{prompt}\n\n内容：{content}\n\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}"
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)
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tool_results = []
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structured_info = {} # 动态生成键
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# 执行所有工具调用
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for tool_call in tool_calls:
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result = await self._execute_tool_call(tool_call)
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if result:
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tool_results.append(result)
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# 使用工具名称作为键
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tool_name = result["name"]
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if tool_name not in structured_info:
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structured_info[tool_name] = []
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structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
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# 如果有工具结果，返回结构化的信息
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if structured_info:
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logger.debug(f"工具调用收集到结构化信息: {json.dumps(structured_info, ensure_ascii=False)}")
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return {"used_tools": True, "structured_info": structured_info}
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else:
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# 没有工具调用
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content, reasoning_content = response
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logger.debug("模型没有请求调用任何工具")
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# 如果没有工具调用或处理失败，直接返回原始思考
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return {
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"used_tools": False,
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}
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except Exception as e:
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logger.error(f"工具调用过程中出错: {str(e)}")
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logger.error(f"工具调用过程中出错: {traceback.format_exc()}")
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return {
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"used_tools": False,
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"error": str(e),
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}
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