190 lines
9.2 KiB
Python
190 lines
9.2 KiB
Python
from typing import Tuple, List, Dict, Any
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from src.common.logger import get_module_logger
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from ..models.utils_model import LLMRequest
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from ...config.config import global_config
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from .chat_observer import ChatObserver
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from .reply_checker import ReplyChecker
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from src.individuality.individuality import Individuality
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from .observation_info import ObservationInfo
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from .conversation_info import ConversationInfo
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from src.plugins.utils.chat_message_builder import build_readable_messages
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logger = get_module_logger("reply_generator")
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class ReplyGenerator:
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"""回复生成器"""
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def __init__(self, stream_id: str):
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self.llm = LLMRequest(
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model=global_config.llm_PFC_chat,
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temperature=global_config.llm_PFC_chat["temp"],
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max_tokens=300,
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request_type="reply_generation",
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)
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self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=3)
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self.identity_detail_info = Individuality.get_instance().get_prompt(type="identity", x_person=2, level=2)
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self.name = global_config.BOT_NICKNAME
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self.chat_observer = ChatObserver.get_instance(stream_id)
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self.reply_checker = ReplyChecker(stream_id)
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async def generate(self, observation_info: ObservationInfo, conversation_info: ConversationInfo) -> str:
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"""生成回复
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Args:
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goal: 对话目标
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chat_history: 聊天历史
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knowledge_cache: 知识缓存
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previous_reply: 上一次生成的回复（如果有）
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retry_count: 当前重试次数
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Returns:
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str: 生成的回复
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"""
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# 构建提示词
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logger.debug(f"开始生成回复：当前目标: {conversation_info.goal_list}")
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# 构建对话目标
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goals_str = ""
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if conversation_info.goal_list:
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for goal_reason in conversation_info.goal_list:
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# 处理字典或元组格式
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if isinstance(goal_reason, tuple):
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# 假设元组的第一个元素是目标，第二个元素是原因
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goal = goal_reason[0]
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reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
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elif isinstance(goal_reason, dict):
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goal = goal_reason.get("goal")
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reasoning = goal_reason.get("reasoning", "没有明确原因")
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else:
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# 如果是其他类型，尝试转为字符串
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goal = str(goal_reason)
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reasoning = "没有明确原因"
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goal_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
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goals_str += goal_str
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else:
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goal = "目前没有明确对话目标"
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reasoning = "目前没有明确对话目标，最好思考一个对话目标"
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goals_str = f"目标：{goal}，产生该对话目标的原因：{reasoning}\n"
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# 获取聊天历史记录
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chat_history_list = (
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observation_info.chat_history[-20:]
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if len(observation_info.chat_history) >= 20
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else observation_info.chat_history
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)
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chat_history_text = observation_info.chat_history_str
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if observation_info.new_messages_count > 0:
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new_messages_list = observation_info.unprocessed_messages
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new_messages_str = await build_readable_messages(
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new_messages_list,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="relative",
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read_mark=0.0,
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)
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chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
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# await observation_info.clear_unprocessed_messages()
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identity_details_only = self.identity_detail_info
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identity_addon = ""
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if isinstance(identity_details_only, str):
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pronouns = ["你", "我", "他"]
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for p in pronouns:
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if identity_details_only.startswith(p):
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identity_details_only = identity_details_only[len(p) :]
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break
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if identity_details_only.endswith("。"):
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identity_details_only = identity_details_only[:-1]
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cleaned_details = identity_details_only.strip(",， ")
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if cleaned_details:
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identity_addon = f"并且{cleaned_details}"
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persona_text = f"你的名字是{self.name}，{self.personality_info}{identity_addon}。"
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# 构建action历史文本
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action_history_list = (
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conversation_info.done_action[-10:]
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if len(conversation_info.done_action) >= 10
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else conversation_info.done_action
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)
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action_history_text = "你之前做的事情是："
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for action in action_history_list:
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if isinstance(action, dict):
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action_type = action.get("action")
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action_reason = action.get("reason")
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action_status = action.get("status")
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if action_status == "recall":
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action_history_text += (
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f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
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)
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elif action_status == "done":
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action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
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elif isinstance(action, tuple):
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# 假设元组的格式是(action_type, action_reason, action_status)
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action_type = action[0] if len(action) > 0 else "未知行动"
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action_reason = action[1] if len(action) > 1 else "未知原因"
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action_status = action[2] if len(action) > 2 else "done"
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if action_status == "recall":
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action_history_text += (
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f"原本打算：{action_type}，但是因为有新消息，你发现这个行动不合适，所以你没做\n"
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)
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elif action_status == "done":
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action_history_text += f"你之前做了：{action_type}，原因：{action_reason}\n"
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prompt = f"""{persona_text}。现在你在参与一场QQ私聊，请根据以下信息生成一条新消息：
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当前对话目标：{goals_str}
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最近的聊天记录：
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{chat_history_text}
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请根据上述信息，结合聊天记录，发一条消息（可以是回复，补充，深入话题，或追问等等）。该消息应该：
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1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
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2. 符合你的性格特征和身份细节
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3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况）
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4. 适当利用相关知识，但不要生硬引用
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5. 自然、得体，结合聊天记录逻辑合理，且没有重复表达同质内容
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请注意把握聊天内容，不要回复的太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
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可以回复得自然随意自然一些，就像真人一样，注意把握聊天内容，整体风格可以平和、简短，不要刻意突出自身学科背景，不要说你说过的话，可以简短，多简短都可以，但是避免冗长。
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请你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)，只输出回复内容。
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不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
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**注意：如果聊天记录中最新的消息是你自己发送的，那么你的思路不应该是“回复”，而是应该紧紧衔接你发送的消息，进行话题的深入，补充，或追问等等，避免与最新消息内容重叠；**
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请直接输出回复内容，不需要任何额外格式。"""
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try:
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content, _ = await self.llm.generate_response_async(prompt)
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logger.info(f"生成的回复: {content}")
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# is_new = self.chat_observer.check()
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# logger.debug(f"再看一眼聊天记录，{'有' if is_new else '没有'}新消息")
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# 如果有新消息,重新生成回复
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# if is_new:
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# logger.info("检测到新消息,重新生成回复")
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# return await self.generate(
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# goal, chat_history, knowledge_cache,
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# None, retry_count
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# )
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return content
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except Exception as e:
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logger.error(f"生成回复时出错: {e}")
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return "抱歉，我现在有点混乱，让我重新思考一下..."
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async def check_reply(
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self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_str: str, retry_count: int = 0
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) -> Tuple[bool, str, bool]:
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"""检查回复是否合适
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Args:
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reply: 生成的回复
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goal: 对话目标
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retry_count: 当前重试次数
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Returns:
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Tuple[bool, str, bool]: (是否合适, 原因, 是否需要重新规划)
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"""
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return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count) |