diff --git a/src/heart_flow/chat_state_info.py b/src/heart_flow/chat_state_info.py new file mode 100644 index 000000000..78fb4e8d1 --- /dev/null +++ b/src/heart_flow/chat_state_info.py @@ -0,0 +1,17 @@ +from src.plugins.moods.moods import MoodManager +import enum + + +class ChatState(enum.Enum): + ABSENT = "没在看群" + CHAT = "随便水群" + FOCUSED = "激情水群" + + +class ChatStateInfo: + def __init__(self): + self.chat_status: ChatState = ChatState.ABSENT + self.current_state_time = 120 + + self.mood_manager = MoodManager() + self.mood = self.mood_manager.get_prompt() \ No newline at end of file diff --git a/src/heart_flow/sub_heartflow.py b/src/heart_flow/sub_heartflow.py index 1aa6f9027..bb6719136 100644 --- a/src/heart_flow/sub_heartflow.py +++ b/src/heart_flow/sub_heartflow.py @@ -1,24 +1,20 @@ from .observation import Observation, ChattingObservation import asyncio -from src.plugins.moods.moods import MoodManager -from src.plugins.models.utils_model import LLMRequest from src.config.config import global_config import time from typing import Optional, List, Dict, Callable import traceback -import enum from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402 -from src.individuality.individuality import Individuality import random -from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager from src.plugins.chat.message import MessageRecv from src.plugins.chat.chat_stream import chat_manager import math from src.plugins.heartFC_chat.heartFC_chat import HeartFChatting from src.plugins.heartFC_chat.normal_chat import NormalChat -from src.do_tool.tool_use import ToolUser from src.heart_flow.mai_state_manager import MaiStateInfo -from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls +from src.heart_flow.chat_state_info import ChatState, ChatStateInfo +from src.heart_flow.sub_mind import SubMind + # 定义常量 (从 interest.py 移动过来) MAX_INTEREST = 15.0 @@ -37,42 +33,6 @@ interest_log_config = LogConfig( interest_logger = get_module_logger("InterestChatting", config=interest_log_config) -def init_prompt(): - prompt = "" - # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n" - prompt += "{extra_info}\n" - # prompt += "{prompt_schedule}\n" - # prompt += "{relation_prompt_all}\n" - prompt += "{prompt_personality}\n" - prompt += "刚刚你的想法是：\n我是{bot_name}，我想，{current_thinking_info}\n" - prompt += "-----------------------------------\n" - prompt += "现在是{time_now}，你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：\n{chat_observe_info}\n" - prompt += "\n你现在{mood_info}\n" - # prompt += "你注意到{sender_name}刚刚说：{message_txt}\n" - prompt += "现在请你根据刚刚的想法继续思考，思考时可以想想如何对群聊内容进行回复，要不要对群里的话题进行回复，关注新话题，可以适当转换话题，大家正在说的话才是聊天的主题。\n" - prompt += "回复的要求是：平淡一些，简短一些，说中文，如果你要回复，最好只回复一个人的一个话题\n" - prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写。不要回复自己的发言，尽量不要说你说过的话。\n" - prompt += "现在请你先{hf_do_next}，不要分点输出,生成内心想法，文字不要浮夸" - prompt += "在输出完想法后，请你思考应该使用什么工具。如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。\n" - - Prompt(prompt, "sub_heartflow_prompt_before") - - -class ChatState(enum.Enum): - ABSENT = "没在看群" - CHAT = "随便水群" - FOCUSED = "激情水群" - - -class ChatStateInfo: - def __init__(self): - self.chat_status: ChatState = ChatState.ABSENT - self.current_state_time = 120 - - self.mood_manager = MoodManager() - self.mood = self.mood_manager.get_prompt() - - base_reply_probability = 0.05 probability_increase_rate_per_second = 0.08 max_reply_probability = 1 @@ -112,7 +72,7 @@ class InterestChatting: self.above_threshold = False self.start_hfc_probability = 0.0 - + def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool): self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned) self.last_interaction_time = time.time() @@ -259,15 +219,11 @@ class SubHeartflow: self.mai_states = mai_states - # 思维状态相关 - self.current_mind = "什么也没想" # 当前想法 - self.past_mind = [] # 历史想法记录 - # 聊天状态管理 self.chat_state: ChatStateInfo = ChatStateInfo() # 该sub_heartflow的聊天状态信息 self.interest_chatting = InterestChatting( state_change_callback=self.set_chat_state - ) # 该sub_heartflow的兴趣系统 + ) # 活动状态管理 self.last_active_time = time.time() # 最后活跃时间 @@ -281,16 +237,15 @@ class SubHeartflow: self.running_knowledges = [] # 运行中的知识 # LLM模型配置 - self.llm_model = LLMRequest( - model=global_config.llm_sub_heartflow, - temperature=global_config.llm_sub_heartflow["temp"], - max_tokens=800, - request_type="sub_heart_flow", + self.sub_mind = SubMind( + subheartflow_id=self.subheartflow_id, + chat_state=self.chat_state, + observations=self.observations ) - self.log_prefix = chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id - self.structured_info = {} + self.log_prefix = chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id + async def add_time_current_state(self, add_time: float): self.current_state_time += add_time @@ -381,6 +336,8 @@ class SubHeartflow: try: self.heart_fc_instance = HeartFChatting( chat_id=self.chat_id, + sub_mind=self.sub_mind, + observations=self.observations ) if await self.heart_fc_instance._initialize(): await self.heart_fc_instance.start() # 初始化成功后启动循环 @@ -477,188 +434,9 @@ class SubHeartflow: logger.info(f"{self.log_prefix} 子心流后台任务已停止。") - async def do_thinking_before_reply(self): - """ - 在回复前进行思考，生成内心想法并收集工具调用结果 - - 返回: - tuple: (current_mind, past_mind) 当前想法和过去的想法列表 - """ - # 更新活跃时间 - self.last_active_time = time.time() - - # ---------- 1. 准备基础数据 ---------- - # 获取现有想法和情绪状态 - current_thinking_info = self.current_mind - mood_info = self.chat_state.mood - - # 获取观察对象 - observation = self._get_primary_observation() - if not observation: - logger.error(f"[{self.subheartflow_id}] 无法获取观察对象") - self.update_current_mind("(我没看到任何聊天内容...)") - return self.current_mind, self.past_mind - - # 获取观察内容 - chat_observe_info = observation.get_observe_info() - - # ---------- 2. 准备工具和个性化数据 ---------- - # 初始化工具 - tool_instance = ToolUser() - tools = tool_instance._define_tools() - - # 获取个性化信息 - individuality = Individuality.get_instance() - - # 构建个性部分 - prompt_personality = f"你的名字是{individuality.personality.bot_nickname}，你" - prompt_personality += individuality.personality.personality_core - - # 随机添加个性侧面 - if individuality.personality.personality_sides: - random_side = random.choice(individuality.personality.personality_sides) - prompt_personality += f"，{random_side}" - - # 随机添加身份细节 - if individuality.identity.identity_detail: - random_detail = random.choice(individuality.identity.identity_detail) - prompt_personality += f"，{random_detail}" - - # 获取当前时间 - time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) - - # ---------- 3. 构建思考指导部分 ---------- - # 创建本地随机数生成器，基于分钟数作为种子 - local_random = random.Random() - current_minute = int(time.strftime("%M")) - local_random.seed(current_minute) - - # 思考指导选项和权重 - hf_options = [ - ("继续生成你在这个聊天中的想法，在原来想法的基础上继续思考", 0.7), - ("生成你在这个聊天中的想法，在原来的想法上尝试新的话题", 0.1), - ("生成你在这个聊天中的想法，不要太深入", 0.1), - ("继续生成你在这个聊天中的想法，进行深入思考", 0.1), - ] - - # 加权随机选择思考指导 - hf_do_next = local_random.choices( - [option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1 - )[0] - - # ---------- 4. 构建最终提示词 ---------- - # 获取提示词模板并填充数据 - prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format( - extra_info="", # 可以在这里添加额外信息 - prompt_personality=prompt_personality, - bot_name=individuality.personality.bot_nickname, - current_thinking_info=current_thinking_info, - time_now=time_now, - chat_observe_info=chat_observe_info, - mood_info=mood_info, - hf_do_next=hf_do_next, - ) - - logger.debug(f"[{self.subheartflow_id}] 心流思考提示词构建完成") - - # ---------- 5. 执行LLM请求并处理响应 ---------- - content = "" # 初始化内容变量 - reasoning_content = "" # 初始化推理内容变量 - - try: - # 调用LLM生成响应 - response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools) - - # 标准化响应格式 - success, normalized_response, error_msg = normalize_llm_response( - response, log_prefix=f"[{self.subheartflow_id}] " - ) - - if not success: - # 处理标准化失败情况 - logger.warning(f"[{self.subheartflow_id}] {error_msg}") - content = "LLM响应格式无法处理" - else: - # 从标准化响应中提取内容 - if len(normalized_response) >= 2: - content = normalized_response[0] - _reasoning_content = normalized_response[1] if len(normalized_response) > 1 else "" - - # 处理可能的工具调用 - if len(normalized_response) == 3: - # 提取并验证工具调用 - success, valid_tool_calls, error_msg = process_llm_tool_calls( - normalized_response, log_prefix=f"[{self.subheartflow_id}] " - ) - - if success and valid_tool_calls: - # 记录工具调用信息 - tool_calls_str = ", ".join( - [call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls] - ) - logger.info( - f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}" - ) - - # 收集工具执行结果 - await self._execute_tool_calls(valid_tool_calls, tool_instance) - elif not success: - logger.warning(f"[{self.subheartflow_id}] {error_msg}") - except Exception as e: - # 处理总体异常 - logger.error(f"[{self.subheartflow_id}] 执行LLM请求或处理响应时出错: {e}") - logger.error(traceback.format_exc()) - content = "思考过程中出现错误" - - # 记录最终思考结果 - logger.debug(f"[{self.subheartflow_id}] 心流思考结果:\n{content}\n") - - # 处理空响应情况 - if not content: - content = "(不知道该想些什么...)" - logger.warning(f"[{self.subheartflow_id}] LLM返回空结果，思考失败。") - - # ---------- 6. 更新思考状态并返回结果 ---------- - # 更新当前思考内容 - self.update_current_mind(content) - - return self.current_mind, self.past_mind - - async def _execute_tool_calls(self, tool_calls, tool_instance): - """ - 执行一组工具调用并收集结果 - - 参数: - tool_calls: 工具调用列表 - tool_instance: 工具使用器实例 - """ - tool_results = [] - structured_info = {} # 动态生成键 - - # 执行所有工具调用 - for tool_call in tool_calls: - try: - result = await tool_instance._execute_tool_call(tool_call) - if result: - tool_results.append(result) - - # 使用工具名称作为键 - tool_name = result["name"] - if tool_name not in structured_info: - structured_info[tool_name] = [] - - structured_info[tool_name].append({"name": result["name"], "content": result["content"]}) - except Exception as tool_e: - logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}") - - # 如果有工具结果，记录并更新结构化信息 - if structured_info: - logger.debug(f"工具调用收集到结构化信息: {safe_json_dumps(structured_info, ensure_ascii=False)}") - self.structured_info = structured_info def update_current_mind(self, response): - self.past_mind.append(self.current_mind) - self.current_mind = response + self.sub_mind.update_current_mind(response) def add_observation(self, observation: Observation): for existing_obs in self.observations: @@ -705,7 +483,7 @@ class SubHeartflow: interest_state = await self.get_interest_state() return { "interest_state": interest_state, - "current_mind": self.current_mind, + "current_mind": self.sub_mind.current_mind, "chat_state": self.chat_state.chat_status.value, "last_active_time": self.last_active_time, } @@ -747,4 +525,4 @@ class SubHeartflow: logger.info(f"{self.log_prefix} 子心流关闭完成。") -init_prompt() + diff --git a/src/heart_flow/sub_mind.py b/src/heart_flow/sub_mind.py new file mode 100644 index 000000000..03a9997fc --- /dev/null +++ b/src/heart_flow/sub_mind.py @@ -0,0 +1,254 @@ +from .observation import Observation +from src.plugins.models.utils_model import LLMRequest +from src.config.config import global_config +import time +import traceback +from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402 +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, normalize_llm_response, process_llm_tool_calls +from src.heart_flow.chat_state_info import ChatStateInfo + + +subheartflow_config = LogConfig( + console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"], + file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"], +) +logger = get_module_logger("subheartflow", config=subheartflow_config) + + + +def init_prompt(): + prompt = "" + # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n" + prompt += "{extra_info}\n" + # prompt += "{prompt_schedule}\n" + # prompt += "{relation_prompt_all}\n" + prompt += "{prompt_personality}\n" + prompt += "刚刚你的想法是：\n我是{bot_name}，我想，{current_thinking_info}\n" + prompt += "-----------------------------------\n" + prompt += "现在是{time_now}，你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：\n{chat_observe_info}\n" + prompt += "\n你现在{mood_info}\n" + # prompt += "你注意到{sender_name}刚刚说：{message_txt}\n" + prompt += "现在请你根据刚刚的想法继续思考，思考时可以想想如何对群聊内容进行回复，要不要对群里的话题进行回复，关注新话题，可以适当转换话题，大家正在说的话才是聊天的主题。\n" + prompt += "回复的要求是：平淡一些，简短一些，说中文，如果你要回复，最好只回复一个人的一个话题\n" + prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写。不要回复自己的发言，尽量不要说你说过的话。\n" + prompt += "现在请你先{hf_do_next}，不要分点输出,生成内心想法，文字不要浮夸" + prompt += "在输出完想法后，请你思考应该使用什么工具。如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。\n" + + Prompt(prompt, "sub_heartflow_prompt_before") + + + +class SubMind: + def __init__( + self, + subheartflow_id: str, + chat_state: ChatStateInfo, + observations: Observation + ): + self.subheartflow_id = subheartflow_id + + self.llm_model = LLMRequest( + model=global_config.llm_sub_heartflow, + temperature=global_config.llm_sub_heartflow["temp"], + max_tokens=800, + request_type="sub_heart_flow", + ) + + self.chat_state = chat_state + self.observations = observations + + self.current_mind = "" + self.past_mind = [] + self.structured_info = {} + + async def do_thinking_before_reply(self): + """ + 在回复前进行思考，生成内心想法并收集工具调用结果 + + 返回: + tuple: (current_mind, past_mind) 当前想法和过去的想法列表 + """ + # 更新活跃时间 + self.last_active_time = time.time() + + # ---------- 1. 准备基础数据 ---------- + # 获取现有想法和情绪状态 + current_thinking_info = self.current_mind + mood_info = self.chat_state.mood + + # 获取观察对象 + observation = self.observations[0] + if not observation: + logger.error(f"[{self.subheartflow_id}] 无法获取观察对象") + self.update_current_mind("(我没看到任何聊天内容...)") + return self.current_mind, self.past_mind + + # 获取观察内容 + chat_observe_info = observation.get_observe_info() + + # ---------- 2. 准备工具和个性化数据 ---------- + # 初始化工具 + tool_instance = ToolUser() + tools = tool_instance._define_tools() + + # 获取个性化信息 + individuality = Individuality.get_instance() + + # 构建个性部分 + prompt_personality = f"你的名字是{individuality.personality.bot_nickname}，你" + prompt_personality += individuality.personality.personality_core + + # 随机添加个性侧面 + if individuality.personality.personality_sides: + random_side = random.choice(individuality.personality.personality_sides) + prompt_personality += f"，{random_side}" + + # 随机添加身份细节 + if individuality.identity.identity_detail: + random_detail = random.choice(individuality.identity.identity_detail) + prompt_personality += f"，{random_detail}" + + # 获取当前时间 + time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + + # ---------- 3. 构建思考指导部分 ---------- + # 创建本地随机数生成器，基于分钟数作为种子 + local_random = random.Random() + current_minute = int(time.strftime("%M")) + local_random.seed(current_minute) + + # 思考指导选项和权重 + hf_options = [ + ("继续生成你在这个聊天中的想法，在原来想法的基础上继续思考", 0.7), + ("生成你在这个聊天中的想法，在原来的想法上尝试新的话题", 0.1), + ("生成你在这个聊天中的想法，不要太深入", 0.1), + ("继续生成你在这个聊天中的想法，进行深入思考", 0.1), + ] + + # 加权随机选择思考指导 + hf_do_next = local_random.choices( + [option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1 + )[0] + + # ---------- 4. 构建最终提示词 ---------- + # 获取提示词模板并填充数据 + prompt = (await global_prompt_manager.get_prompt_async("sub_heartflow_prompt_before")).format( + extra_info="", # 可以在这里添加额外信息 + prompt_personality=prompt_personality, + bot_name=individuality.personality.bot_nickname, + current_thinking_info=current_thinking_info, + time_now=time_now, + chat_observe_info=chat_observe_info, + mood_info=mood_info, + hf_do_next=hf_do_next, + ) + + logger.debug(f"[{self.subheartflow_id}] 心流思考提示词构建完成") + + # ---------- 5. 执行LLM请求并处理响应 ---------- + content = "" # 初始化内容变量 + reasoning_content = "" # 初始化推理内容变量 + + try: + # 调用LLM生成响应 + response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools) + + # 标准化响应格式 + success, normalized_response, error_msg = normalize_llm_response( + response, log_prefix=f"[{self.subheartflow_id}] " + ) + + if not success: + # 处理标准化失败情况 + logger.warning(f"[{self.subheartflow_id}] {error_msg}") + content = "LLM响应格式无法处理" + else: + # 从标准化响应中提取内容 + if len(normalized_response) >= 2: + content = normalized_response[0] + _reasoning_content = normalized_response[1] if len(normalized_response) > 1 else "" + + # 处理可能的工具调用 + if len(normalized_response) == 3: + # 提取并验证工具调用 + success, valid_tool_calls, error_msg = process_llm_tool_calls( + normalized_response, log_prefix=f"[{self.subheartflow_id}] " + ) + + if success and valid_tool_calls: + # 记录工具调用信息 + tool_calls_str = ", ".join( + [call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls] + ) + logger.info( + f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}" + ) + + # 收集工具执行结果 + await self._execute_tool_calls(valid_tool_calls, tool_instance) + elif not success: + logger.warning(f"[{self.subheartflow_id}] {error_msg}") + except Exception as e: + # 处理总体异常 + logger.error(f"[{self.subheartflow_id}] 执行LLM请求或处理响应时出错: {e}") + logger.error(traceback.format_exc()) + content = "思考过程中出现错误" + + # 记录最终思考结果 + logger.debug(f"[{self.subheartflow_id}] 心流思考结果:\n{content}\n") + + # 处理空响应情况 + if not content: + content = "(不知道该想些什么...)" + logger.warning(f"[{self.subheartflow_id}] LLM返回空结果，思考失败。") + + # ---------- 6. 更新思考状态并返回结果 ---------- + # 更新当前思考内容 + self.update_current_mind(content) + + return self.current_mind, self.past_mind + + + async def _execute_tool_calls(self, tool_calls, tool_instance): + """ + 执行一组工具调用并收集结果 + + 参数: + tool_calls: 工具调用列表 + tool_instance: 工具使用器实例 + """ + tool_results = [] + structured_info = {} # 动态生成键 + + # 执行所有工具调用 + for tool_call in tool_calls: + try: + result = await tool_instance._execute_tool_call(tool_call) + if result: + tool_results.append(result) + + # 使用工具名称作为键 + tool_name = result["name"] + if tool_name not in structured_info: + structured_info[tool_name] = [] + + structured_info[tool_name].append({"name": result["name"], "content": result["content"]}) + except Exception as tool_e: + logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}") + + # 如果有工具结果，记录并更新结构化信息 + if structured_info: + logger.debug(f"工具调用收集到结构化信息: {safe_json_dumps(structured_info, ensure_ascii=False)}") + self.structured_info = structured_info + + + def update_current_mind(self, response): + self.past_mind.append(self.current_mind) + self.current_mind = response + + +init_prompt() \ No newline at end of file diff --git a/src/heart_flow/subheartflow_manager.py b/src/heart_flow/subheartflow_manager.py index bf473b781..b9703e53b 100644 --- a/src/heart_flow/subheartflow_manager.py +++ b/src/heart_flow/subheartflow_manager.py @@ -456,7 +456,7 @@ class SubHeartflowManager: for subheartflow in self.subheartflows.values(): # 检查子心流是否活跃(非ABSENT状态) if subheartflow.chat_state.chat_status != ChatState.ABSENT: - minds.append(subheartflow.current_mind) + minds.append(subheartflow.sub_mind.current_mind) return minds def update_main_mind_in_subflows(self, main_mind: str): diff --git a/src/plugins/heartFC_chat/heartFC_chat.py b/src/plugins/heartFC_chat/heartFC_chat.py index 41ea2711b..3229317fd 100644 --- a/src/plugins/heartFC_chat/heartFC_chat.py +++ b/src/plugins/heartFC_chat/heartFC_chat.py @@ -1,9 +1,7 @@ import asyncio import time import traceback -from typing import List, Optional, Dict, Any, TYPE_CHECKING - -# import json # 移除，因为使用了json_utils +from typing import List, Optional, Dict, Any from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending from src.plugins.chat.message import MessageSet, Seg # Local import needed after move from src.plugins.chat.chat_stream import ChatStream @@ -19,6 +17,8 @@ from src.do_tool.tool_use import ToolUser from ..chat.message_sender import message_manager # <-- Import the global manager from src.plugins.chat.emoji_manager import emoji_manager from src.plugins.utils.json_utils import process_llm_tool_response # 导入新的JSON工具 +from src.heart_flow.sub_mind import SubMind +from src.heart_flow.observation import Observation # --- End import --- @@ -33,13 +33,6 @@ interest_log_config = LogConfig( logger = get_module_logger("HeartFCLoop", config=interest_log_config) # Logger Name Changed -# Forward declaration for type hinting -if TYPE_CHECKING: - # Keep this if HeartFCController methods are still needed elsewhere, - # but the instance variable will be removed from HeartFChatting - # from .heartFC_controler import HeartFCController - from src.heart_flow.heartflow import SubHeartflow # <-- 同时导入 heartflow 实例用于类型检查 - PLANNER_TOOL_DEFINITION = [ { "type": "function", @@ -74,7 +67,12 @@ class HeartFChatting: 其生命周期现在由其关联的 SubHeartflow 的 FOCUSED 状态控制。 """ - def __init__(self, chat_id: str): + def __init__( + self, + chat_id: str, + sub_mind: SubMind, + observations: Observation + ): """ HeartFChatting 初始化函数 @@ -84,7 +82,8 @@ class HeartFChatting: # 基础属性 self.stream_id: str = chat_id # 聊天流ID self.chat_stream: Optional[ChatStream] = None # 关联的聊天流 - self.sub_hf: SubHeartflow = None # 关联的子心流 + self.sub_mind: SubMind = sub_mind # 关联的子思维 + self.observations: Observation = observations # 关联的观察 # 初始化状态控制 self._initialized = False # 是否已初始化标志 @@ -121,18 +120,10 @@ class HeartFChatting: log_prefix = self._get_log_prefix() # 获取前缀 try: self.chat_stream = chat_manager.get_stream(self.stream_id) - if not self.chat_stream: logger.error(f"{log_prefix} 获取ChatStream失败。") return False - # <-- 在这里导入 heartflow 实例 - from src.heart_flow.heartflow import heartflow - - self.sub_hf = heartflow.get_subheartflow(self.stream_id) - if not self.sub_hf: - logger.warning(f"{log_prefix} 获取SubHeartflow失败。一些功能可能受限。") - self._initialized = True logger.info(f"麦麦感觉到了，激发了HeartFChatting{log_prefix} 初始化成功。") return True @@ -321,8 +312,8 @@ class HeartFChatting: # --- 新增：等待新消息 --- logger.debug(f"{log_prefix} HeartFChatting: 开始等待新消息 (自 {planner_start_db_time})...") observation = None - if self.sub_hf: - observation = self.sub_hf._get_primary_observation() + + observation = self.observations[0] if observation: with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer @@ -427,7 +418,7 @@ class HeartFChatting: llm_error = False try: - observation = self.sub_hf._get_primary_observation() + observation = self.observations[0] await observation.observe() observed_messages = observation.talking_message observed_messages_str = observation.talking_message_str @@ -435,7 +426,7 @@ class HeartFChatting: logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}") try: - current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply() + current_mind, _past_mind = await self.sub_mind.do_thinking_before_reply() except Exception as e_subhf: logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}") current_mind = "[思考时出错]" @@ -447,7 +438,7 @@ class HeartFChatting: llm_error = False # LLM错误标志 try: - prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_hf.structured_info) + prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_mind.structured_info) payload = { "model": self.planner_llm.model_name, "messages": [{"role": "user", "content": prompt}], @@ -655,8 +646,8 @@ class HeartFChatting: response_set: Optional[List[str]] = None try: response_set = await self.gpt_instance.generate_response( - structured_info=self.sub_hf.structured_info, - current_mind_info=self.sub_hf.current_mind, + structured_info=self.sub_mind.structured_info, + current_mind_info=self.sub_mind.current_mind, reason=reason, message=anchor_message, # Pass anchor_message positionally (matches 'message' parameter) thinking_id=thinking_id, # Pass thinking_id positionally