fix:FFUF
This commit is contained in:
SengokuCola
@@ -82,29 +82,25 @@ class ChattingObservation(Observation):
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new_messages_list = get_raw_msg_by_timestamp_with_chat(
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chat_id=self.chat_id,
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timestamp_start=self.last_observe_time,
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timestamp_end=datetime.now().timestamp(),
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timestamp_end=datetime.now().timestamp(),
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limit=self.max_now_obs_len,
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limit_mode="latest",
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)
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last_obs_time_mark = self.last_observe_time
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if new_messages_list:
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self.last_observe_time = new_messages_list[-1]["time"]
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self.talking_message.extend(new_messages_list)
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if len(self.talking_message) > self.max_now_obs_len:
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# 计算需要移除的消息数量，保留最新的 max_now_obs_len 条
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messages_to_remove_count = len(self.talking_message) - self.max_now_obs_len
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oldest_messages = self.talking_message[:messages_to_remove_count]
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self.talking_message = self.talking_message[messages_to_remove_count:] # 保留后半部分，即最新的
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oldest_messages_str = await build_readable_messages(
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messages=oldest_messages,
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timestamp_mode="normal",
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read_mark=0
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messages=oldest_messages, timestamp_mode="normal", read_mark=0
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)
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# 调用 LLM 总结主题
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prompt = (
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@@ -145,7 +141,7 @@ class ChattingObservation(Observation):
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messages=self.talking_message,
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timestamp_mode="normal",
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read_mark=last_obs_time_mark,
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)
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)
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logger.trace(
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f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"
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@@ -6,12 +6,10 @@ from src.config.config import global_config
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import time
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from typing import Optional, List, Dict, Callable
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import traceback
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from src.plugins.chat.utils import parse_text_timestamps
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import enum
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from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
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from src.individuality.individuality import Individuality
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import random
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from src.plugins.person_info.relationship_manager import relationship_manager
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from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
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from src.plugins.chat.message import MessageRecv
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from src.plugins.chat.chat_stream import chat_manager
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@@ -20,7 +18,7 @@ from src.plugins.heartFC_chat.heartFC_chat import HeartFChatting
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from src.plugins.heartFC_chat.normal_chat import NormalChat
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from src.do_tool.tool_use import ToolUser
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from src.heart_flow.mai_state_manager import MaiStateInfo
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from src.plugins.utils.json_utils import safe_json_dumps, process_llm_tool_response, normalize_llm_response, process_llm_tool_calls
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from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls
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# 定义常量 (从 interest.py 移动过来)
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MAX_INTEREST = 15.0
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@@ -114,8 +112,6 @@ class InterestChatting:
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self.above_threshold = False
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self.start_hfc_probability = 0.0
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def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
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self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
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@@ -293,7 +289,7 @@ class SubHeartflow:
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)
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self.log_prefix = chat_manager.get_stream_name(self.subheartflow_id) or self.subheartflow_id
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self.structured_info = {}
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async def add_time_current_state(self, add_time: float):
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@@ -484,36 +480,36 @@ class SubHeartflow:
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async def do_thinking_before_reply(self):
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"""
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在回复前进行思考，生成内心想法并收集工具调用结果
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返回:
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tuple: (current_mind, past_mind) 当前想法和过去的想法列表
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"""
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# 更新活跃时间
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self.last_active_time = time.time()
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# ---------- 1. 准备基础数据 ----------
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# 获取现有想法和情绪状态
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current_thinking_info = self.current_mind
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mood_info = self.chat_state.mood
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# 获取观察对象
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observation = self._get_primary_observation()
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if not observation:
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logger.error(f"[{self.subheartflow_id}] 无法获取观察对象")
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self.update_current_mind("(我没看到任何聊天内容...)")
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return self.current_mind, self.past_mind
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# 获取观察内容
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chat_observe_info = observation.get_observe_info()
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# ---------- 2. 准备工具和个性化数据 ----------
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# 初始化工具
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tool_instance = ToolUser()
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tools = tool_instance._define_tools()
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# 获取个性化信息
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individuality = Individuality.get_instance()
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# 构建个性部分
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prompt_personality = f"你的名字是{individuality.personality.bot_nickname}，你"
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prompt_personality += individuality.personality.personality_core
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@@ -547,9 +543,7 @@ class SubHeartflow:
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# 加权随机选择思考指导
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hf_do_next = local_random.choices(
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[option[0] for option in hf_options],
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weights=[option[1] for option in hf_options],
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k=1
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[option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1
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)[0]
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# ---------- 4. 构建最终提示词 ----------
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@@ -570,16 +564,16 @@ class SubHeartflow:
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# ---------- 5. 执行LLM请求并处理响应 ----------
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content = "" # 初始化内容变量
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reasoning_content = "" # 初始化推理内容变量
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try:
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# 调用LLM生成响应
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response = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
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# 标准化响应格式
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success, normalized_response, error_msg = normalize_llm_response(
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response, log_prefix=f"[{self.subheartflow_id}] "
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)
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if not success:
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# 处理标准化失败情况
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logger.warning(f"[{self.subheartflow_id}] {error_msg}")
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@@ -588,23 +582,24 @@ class SubHeartflow:
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# 从标准化响应中提取内容
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if len(normalized_response) >= 2:
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content = normalized_response[0]
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reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
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_reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
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# 处理可能的工具调用
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if len(normalized_response) == 3:
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# 提取并验证工具调用
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success, valid_tool_calls, error_msg = process_llm_tool_calls(
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normalized_response, log_prefix=f"[{self.subheartflow_id}] "
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)
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if success and valid_tool_calls:
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# 记录工具调用信息
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tool_calls_str = ", ".join([
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call.get("function", {}).get("name", "未知工具")
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for call in valid_tool_calls
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])
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logger.info(f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}")
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tool_calls_str = ", ".join(
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[call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
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)
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logger.info(
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f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
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)
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# 收集工具执行结果
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await self._execute_tool_calls(valid_tool_calls, tool_instance)
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elif not success:
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@@ -628,37 +623,34 @@ class SubHeartflow:
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self.update_current_mind(content)
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return self.current_mind, self.past_mind
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async def _execute_tool_calls(self, tool_calls, tool_instance):
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"""
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执行一组工具调用并收集结果
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参数:
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tool_calls: 工具调用列表
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tool_instance: 工具使用器实例
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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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try:
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result = await tool_instance._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({
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"name": result["name"],
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"content": result["content"]
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})
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structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
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except Exception as tool_e:
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logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}")
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# 如果有工具结果，记录并更新结构化信息
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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: {safe_json_dumps(structured_info, ensure_ascii=False)}")
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@@ -290,9 +290,9 @@ class SubHeartflowManager:
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log_prefix_flow = f"[{stream_name}]"
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# 只处理 CHAT 状态的子心流
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# The code snippet is checking if the `chat_status` attribute of `sub_hf.chat_state` is not equal to
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# `ChatState.CHAT`. If the condition is met, the code will continue to the next iteration of the loop
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# or block of code where this snippet is located.
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# The code snippet is checking if the `chat_status` attribute of `sub_hf.chat_state` is not equal to
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# `ChatState.CHAT`. If the condition is met, the code will continue to the next iteration of the loop
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# or block of code where this snippet is located.
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# if sub_hf.chat_state.chat_status != ChatState.CHAT:
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# continue
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