185 lines
6.2 KiB
Python
185 lines
6.2 KiB
Python
import time
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import random
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from typing import Dict, Any, Tuple
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from src.common.logger import get_logger
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from src.plugin_system.apis import send_api, message_api, database_api
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from src.person_info.person_info import get_person_info_manager
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from .hfc_context import HfcContext
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# 导入反注入系统
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# 日志记录器
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logger = get_logger("hfc")
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anti_injector_logger = get_logger("anti_injector")
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class ResponseHandler:
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"""
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响应处理器类，负责生成和发送机器人的回复。
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"""
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def __init__(self, context: HfcContext):
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"""
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初始化响应处理器
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Args:
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context: HFC聊天上下文对象
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功能说明:
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- 负责生成和发送机器人的回复
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- 处理回复的格式化和发送逻辑
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- 管理回复状态和日志记录
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"""
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self.context = context
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async def generate_and_send_reply(
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self,
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response_set,
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reply_to_str,
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loop_start_time,
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action_message,
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cycle_timers: Dict[str, float],
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thinking_id,
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plan_result,
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) -> Tuple[Dict[str, Any], str, Dict[str, float]]:
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"""
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生成并发送回复的主方法
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Args:
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response_set: 生成的回复内容集合
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reply_to_str: 回复目标字符串
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loop_start_time: 循环开始时间
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action_message: 动作消息数据
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cycle_timers: 循环计时器
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thinking_id: 思考ID
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plan_result: 规划结果
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Returns:
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tuple: (循环信息, 回复文本, 计时器信息)
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功能说明:
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- 发送生成的回复内容
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- 存储动作信息到数据库
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- 构建并返回完整的循环信息
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- 用于上级方法的状态跟踪
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"""
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reply_text = await self.send_response(response_set, loop_start_time, action_message)
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person_info_manager = get_person_info_manager()
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# 获取平台信息
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platform = "default"
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if self.context.chat_stream:
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platform = (
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action_message.get("chat_info_platform")
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or action_message.get("user_platform")
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or self.context.chat_stream.platform
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)
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# 获取用户信息并生成回复提示
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user_id = action_message.get("user_id", "")
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person_id = person_info_manager.get_person_id(platform, user_id)
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person_name = await person_info_manager.get_value(person_id, "person_name")
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action_prompt_display = f"你对{person_name}进行了回复：{reply_text}"
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# 存储动作信息到数据库
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await database_api.store_action_info(
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chat_stream=self.context.chat_stream,
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action_build_into_prompt=False,
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action_prompt_display=action_prompt_display,
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action_done=True,
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thinking_id=thinking_id,
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action_data={"reply_text": reply_text, "reply_to": reply_to_str},
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action_name="reply",
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)
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# 构建循环信息
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loop_info: Dict[str, Any] = {
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"loop_plan_info": {
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"action_result": plan_result.get("action_result", {}),
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},
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"loop_action_info": {
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"action_taken": True,
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"reply_text": reply_text,
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"command": "",
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"taken_time": time.time(),
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},
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}
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return loop_info, reply_text, cycle_timers
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async def send_response(self, reply_set, thinking_start_time, message_data) -> str:
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"""
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发送回复内容的具体实现
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Args:
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reply_set: 回复内容集合，包含多个回复段
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reply_to: 回复目标
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thinking_start_time: 思考开始时间
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message_data: 消息数据
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Returns:
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str: 完整的回复文本
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功能说明:
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- 检查是否有新消息需要回复
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- 处理主动思考的"沉默"决定
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- 根据消息数量决定是否添加回复引用
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- 逐段发送回复内容，支持打字效果
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- 正确处理元组格式的回复段
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"""
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current_time = time.time()
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# 计算新消息数量
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new_message_count = await message_api.count_new_messages(
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chat_id=self.context.stream_id, start_time=thinking_start_time, end_time=current_time
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)
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# 根据新消息数量决定是否需要引用回复
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need_reply = new_message_count >= random.randint(2, 4)
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reply_text = ""
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is_proactive_thinking = (message_data.get("message_type") == "proactive_thinking") if message_data else True
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first_replied = False
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for reply_seg in reply_set:
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# 调试日志：验证reply_seg的格式
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logger.debug(f"Processing reply_seg type: {type(reply_seg)}, content: {reply_seg}")
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# 修正：正确处理元组格式 (格式为: (type, content))
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if isinstance(reply_seg, tuple) and len(reply_seg) >= 2:
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_, data = reply_seg
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else:
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# 向下兼容：如果已经是字符串，则直接使用
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data = str(reply_seg)
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if isinstance(data, list):
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data = "".join(map(str, data))
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reply_text += data
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# 如果是主动思考且内容为“沉默”，则不发送
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if is_proactive_thinking and data.strip() == "沉默":
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logger.info(f"{self.context.log_prefix} 主动思考决定保持沉默，不发送消息")
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continue
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# 发送第一段回复
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if not first_replied:
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await send_api.text_to_stream(
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text=data,
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stream_id=self.context.stream_id,
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reply_to_message=message_data,
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set_reply=need_reply,
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typing=False,
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)
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first_replied = True
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else:
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# 发送后续回复
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sent_message = await send_api.text_to_stream(
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text=data,
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stream_id=self.context.stream_id,
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reply_to_message=None,
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set_reply=False,
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typing=True,
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)
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return reply_text
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