feat：重新设计HFC逻辑，现转为并行思考模式

src/heart_flow/tool_user.py

@@ -0,0 +1,137 @@
+from .observation import ChattingObservation
+from src.plugins.models.utils_model import LLMRequest
+from src.config.config import global_config
+import time
+import traceback
+from src.common.logger_manager import get_logger
+from src.individuality.individuality import Individuality
+import random
+from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
+from src.do_tool.tool_use import ToolUser
+from src.plugins.utils.json_utils import safe_json_dumps, process_llm_tool_calls
+from src.heart_flow.chat_state_info import ChatStateInfo
+from src.plugins.chat.chat_stream import chat_manager
+from src.plugins.heartFC_chat.heartFC_Cycleinfo import CycleInfo
+import difflib
+from src.plugins.person_info.relationship_manager import relationship_manager
+from src.plugins.memory_system.Hippocampus import HippocampusManager
+import jieba
+from src.common.logger_manager import get_logger
+from src.heart_flow.sub_mind import SubMind
+logger = get_logger("tool_use")
+
+def init_prompt():
+    # ... 原有代码 ...
+    
+    # 添加工具执行器提示词
+    tool_executor_prompt = """
+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}。
+
+你要在群聊中扮演以下角色：
+{prompt_personality}
+
+你当前的额外信息：
+{extra_info}
+
+你的心情是：{mood_info}
+
+{relation_prompt}
+
+群里正在进行的聊天内容：
+{chat_observe_info}
+
+请仔细分析聊天内容，考虑以下几点：
+1. 内容中是否包含需要查询信息的问题
+2. 是否需要执行特定操作
+3. 是否有明确的工具使用指令
+4. 考虑用户与你的关系以及当前的对话氛围
+
+如果需要使用工具，请直接调用相应的工具函数。如果不需要使用工具，请简单输出"无需使用工具"。
+尽量只在确实必要时才使用工具。
+"""
+    Prompt(tool_executor_prompt, "tool_executor_prompt")
+
+class ToolExecutor:
+    def __init__(self, subheartflow_id: str):
+        self.subheartflow_id = subheartflow_id
+        self.log_prefix = f"[{subheartflow_id}:ToolExecutor] "
+        self.llm_model = LLMRequest(
+            model=global_config.llm_sub_heartflow,  # 为工具执行器配置单独的模型
+            temperature=global_config.llm_sub_heartflow["temp"],
+            max_tokens=800,
+            request_type="tool_execution",
+        )
+        self.structured_info = []
+        
+    async def execute_tools(self, sub_mind: SubMind, chat_target_name="对方", is_group_chat=False):
+        """并行执行工具，返回结构化信息"""
+        # 初始化工具
+        tool_instance = ToolUser()
+        tools = tool_instance._define_tools()
+        
+        observation: ChattingObservation = sub_mind.observations[0] if sub_mind.observations else None
+        
+        # 获取观察内容
+        chat_observe_info = observation.get_observe_info()
+        person_list = observation.person_list
+        
+        # extra structured info
+        extra_structured_info = sub_mind.structured_info_str
+        
+        # 构建关系信息
+        relation_prompt = "【关系信息】\n"
+        for person in person_list:
+            relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
+        
+        # 获取个性信息
+        individuality = Individuality.get_instance()
+        prompt_personality = individuality.get_prompt(x_person=2, level=2)
+        
+        # 获取心情信息
+        mood_info = observation.chat_state.mood if hasattr(observation, "chat_state") else ""
+        
+        # 获取时间信息
+        time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
+        
+        # 构建专用于工具调用的提示词
+        prompt = await global_prompt_manager.format_prompt(
+            "tool_executor_prompt",
+            extra_info=extra_structured_info,
+            chat_observe_info=chat_observe_info,
+            chat_target_name=chat_target_name,
+            is_group_chat=is_group_chat,
+            relation_prompt=relation_prompt,
+            prompt_personality=prompt_personality,
+            mood_info=mood_info,
+            bot_name=individuality.name,
+            time_now=time_now
+        )
+        
+        # 调用LLM，专注于工具使用
+        response, _, tool_calls = await self.llm_model.generate_response_tool_async(
+            prompt=prompt, tools=tools
+        )
+        
+        # 处理工具调用和结果收集，类似于SubMind中的逻辑
+        new_structured_items = []
+        if tool_calls:
+            success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls)
+            if success and valid_tool_calls:
+                for tool_call in valid_tool_calls:
+                    try:
+                        result = await tool_instance._execute_tool_call(tool_call)
+                        if result:
+                            new_item = {
+                                "type": result.get("type", "unknown_type"),
+                                "id": result.get("id", f"tool_exec_{time.time()}"),
+                                "content": result.get("content", ""),
+                                "ttl": 3,
+                            }
+                            new_structured_items.append(new_item)
+                    except Exception as e:
+                        logger.error(f"{self.log_prefix}工具执行失败: {e}")
+        
+        return new_structured_items
+
+
+init_prompt()