89fad16e0e
引入了一个新的聊天交互机制：当机器人发送了包含“错别字”的消息后，会在短暂延迟后自动撤回该消息，并发送正确的版本。此功能旨在模拟更真实的人类打字行为，增加交互的趣味性和拟人化程度。 主要变更： - **错别字处理流程**: - `ResponseHandler`现在会识别出带有错别字的消息，并在发送后创建一个异步任务来处理后续的修正。 - 新增`handle_typo_correction`方法，该方法会随机延迟2-4秒，然后调用新的`recall_message` API撤回原消息，并重新发送修正后的内容。 - **API扩展**: - `send_api`中增加了`recall_message`函数，用于调用适配器执行消息撤回操作。 - `send_response`的返回值从单个字符串`reply_text`变更为元组`(reply_text, sent_messages)`，以便将已发送的消息信息（包括ID和类型）传递给上层调用者。 - **数据结构调整**: - `process_llm_response`的返回类型从`list[str]`调整为`list[dict[str, str]]`，以支持更复杂的响应类型，如包含原文、错别字和修正建议的`typo`类型。 - **代码优化与重构**: - 对`ChineseTypoGenerator`进行了大量的代码清理、注释补充和逻辑优化，使其代码更清晰、更易于维护。 - 修复了多处代码中的类型注解和潜在的空指针问题，提高了代码的健壮性。
190 lines
6.6 KiB
Python
190 lines
6.6 KiB
Python
import time
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import random
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import asyncio
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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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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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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, sent_messages = await self.send_response(response_set, loop_start_time, action_message)
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if sent_messages:
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asyncio.create_task(self.handle_typo_correction(sent_messages))
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person_info_manager = get_person_info_manager()
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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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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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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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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) -> tuple[str, list[dict[str, 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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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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tuple[str, list[dict[str, str]]]: (完整的回复文本, 已发送消息列表)
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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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new_message_count = 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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need_reply = new_message_count >= random.randint(2, 4)
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reply_text = ""
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sent_messages = []
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is_proactive_thinking = message_data.get("message_type") == "proactive_thinking"
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first_replied = False
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for reply_seg in reply_set:
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logger.debug(f"Processing reply_seg type: {type(reply_seg)}, content: {reply_seg}")
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if reply_seg["type"] == "typo":
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data = reply_seg["typo"]
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else:
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data = reply_seg["content"]
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reply_text += data
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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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if not first_replied:
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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=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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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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if sent_message and reply_seg["type"] == "typo":
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sent_messages.append(
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{
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"type": "typo",
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"message_id": sent_message,
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"original_message": message_data,
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"correction": reply_seg["correction"],
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}
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)
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return reply_text, sent_messages
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async def handle_typo_correction(self, sent_messages: list[dict[str, Any]]):
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"""处理错别字修正"""
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for msg in sent_messages:
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if msg["type"] == "typo":
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await asyncio.sleep(random.uniform(2, 4))
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recalled = await send_api.recall_message(str(msg["message_id"]), self.context.stream_id)
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if recalled:
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await send_api.text_to_stream(
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str(msg["correction"]), self.context.stream_id, reply_to_message=msg["original_message"]
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)
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