468 lines
19 KiB
Python
468 lines
19 KiB
Python
import time
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import traceback
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import json
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import random
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from typing import List, Dict, Any
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from json_repair import repair_json
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from src.common.logger import get_logger
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.person_info.person_info import get_person_info_manager
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logger = get_logger("relationship_fetcher")
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def init_real_time_info_prompts():
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"""初始化实时信息提取相关的提示词"""
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relationship_prompt = """
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<聊天记录>
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{chat_observe_info}
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</聊天记录>
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{name_block}
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现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息：
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1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，你们之间的交流方式，最近发生的事等等。
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2.请注意，请不要重复调取相同的信息，已经调取的信息如下：
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{info_cache_block}
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3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}}
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请以json格式输出，例如：
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{{
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"info_type": "信息类型",
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}}
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请严格按照json输出格式，不要输出多余内容：
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"""
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Prompt(relationship_prompt, "real_time_info_identify_prompt")
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fetch_info_prompt = """
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{name_block}
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以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解：
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{person_impression_block}
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{points_text_block}
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请从中提取用户"{person_name}"的有关"{info_type}"信息
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请以json格式输出，例如：
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{{
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{info_json_str}
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}}
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请严格按照json输出格式，不要输出多余内容：
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"""
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Prompt(fetch_info_prompt, "real_time_fetch_person_info_prompt")
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class RelationshipFetcher:
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def __init__(self, chat_id):
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self.chat_id = chat_id
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# 信息获取缓存：记录正在获取的信息请求
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self.info_fetching_cache: List[Dict[str, Any]] = []
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# 信息结果缓存：存储已获取的信息结果，带TTL
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self.info_fetched_cache: Dict[str, Dict[str, Any]] = {}
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# 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknown": bool}}}
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# LLM模型配置
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self.llm_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="relation.fetcher"
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)
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# 小模型用于即时信息提取
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self.instant_llm_model = LLMRequest(
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model_set=model_config.model_task_config.utils_small, request_type="relation.fetch"
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)
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name = get_chat_manager().get_stream_name(self.chat_id)
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self.log_prefix = f"[{name}] 实时信息"
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def _cleanup_expired_cache(self):
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"""清理过期的信息缓存"""
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for person_id in list(self.info_fetched_cache.keys()):
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for info_type in list(self.info_fetched_cache[person_id].keys()):
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self.info_fetched_cache[person_id][info_type]["ttl"] -= 1
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if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0:
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del self.info_fetched_cache[person_id][info_type]
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if not self.info_fetched_cache[person_id]:
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del self.info_fetched_cache[person_id]
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async def build_relation_info(self, person_id, points_num=3):
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# 清理过期的信息缓存
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self._cleanup_expired_cache()
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person_info_manager = get_person_info_manager()
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person_name = await person_info_manager.get_value(person_id, "person_name")
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attitude_to_me = await person_info_manager.get_value(person_id, "attitude_to_me")
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neuroticism = await person_info_manager.get_value(person_id, "neuroticism")
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conscientiousness = await person_info_manager.get_value(person_id, "conscientiousness")
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likeness = await person_info_manager.get_value(person_id, "likeness")
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nickname_str = await person_info_manager.get_value(person_id, "nickname")
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platform = await person_info_manager.get_value(person_id, "platform")
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current_points = await person_info_manager.get_value(person_id, "points") or []
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# 按时间排序forgotten_points
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current_points.sort(key=lambda x: x[2])
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# 按权重加权随机抽取最多3个不重复的points，point[1]的值在1-10之间，权重越高被抽到概率越大
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if len(current_points) > points_num:
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# point[1] 取值范围1-10，直接作为权重
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weights = [max(1, min(10, int(point[1]))) for point in current_points]
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# 使用加权采样不放回，保证不重复
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indices = list(range(len(current_points)))
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points = []
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for _ in range(points_num):
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if not indices:
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break
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sub_weights = [weights[i] for i in indices]
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chosen_idx = random.choices(indices, weights=sub_weights, k=1)[0]
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points.append(current_points[chosen_idx])
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indices.remove(chosen_idx)
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else:
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points = current_points
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# 构建points文本
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points_text = "\n".join([f"{point[2]}：{point[0]}" for point in points])
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nickname_str = ""
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if person_name != nickname_str:
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nickname_str = f"(ta在{platform}上的昵称是{nickname_str})"
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relation_info = ""
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attitude_info = ""
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attitude_parts = attitude_to_me.split(',')
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current_attitude_score = float(attitude_parts[0]) if len(attitude_parts) > 0 else 0.0
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total_confidence = float(attitude_parts[1]) if len(attitude_parts) > 1 else 1.0
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if attitude_to_me:
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if current_attitude_score > 8:
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attitude_info = f"{person_name}对你的态度十分好,"
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elif current_attitude_score > 5:
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attitude_info = f"{person_name}对你的态度较好,"
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if current_attitude_score < -8:
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attitude_info = f"{person_name}对你的态度十分恶劣,"
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elif current_attitude_score < -4:
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attitude_info = f"{person_name}对你的态度不好,"
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elif current_attitude_score < 0:
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attitude_info = f"{person_name}对你的态度一般,"
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neuroticism_info = ""
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neuroticism_parts = neuroticism.split(',')
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current_neuroticism_score = float(neuroticism_parts[0]) if len(neuroticism_parts) > 0 else 0.0
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total_confidence = float(neuroticism_parts[1]) if len(neuroticism_parts) > 1 else 1.0
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if neuroticism:
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if current_neuroticism_score > 8:
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neuroticism_info = f"{person_name}的情绪十分活跃，容易情绪化,"
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elif current_neuroticism_score > 6:
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neuroticism_info = f"{person_name}的情绪比较活跃,"
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elif current_neuroticism_score > 4:
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neuroticism_info = ""
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elif current_neuroticism_score > 2:
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neuroticism_info = f"{person_name}的情绪比较稳定,"
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else:
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neuroticism_info = f"{person_name}的情绪非常稳定,毫无波动"
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if points_text:
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points_info = f"你还记得ta最近做的事：{points_text}"
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relation_info = f"{person_name}:{nickname_str}{attitude_info}{neuroticism_info}{points_info}"
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return relation_info
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async def _build_fetch_query(self, person_id, target_message, chat_history):
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nickname_str = ",".join(global_config.bot.alias_names)
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name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
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person_info_manager = get_person_info_manager()
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person_name: str = await person_info_manager.get_value(person_id, "person_name") # type: ignore
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info_cache_block = self._build_info_cache_block()
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prompt = (await global_prompt_manager.get_prompt_async("real_time_info_identify_prompt")).format(
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chat_observe_info=chat_history,
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name_block=name_block,
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info_cache_block=info_cache_block,
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person_name=person_name,
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target_message=target_message,
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)
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try:
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logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n")
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content, _ = await self.llm_model.generate_response_async(prompt=prompt)
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if content:
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content_json = json.loads(repair_json(content))
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# 检查是否返回了不需要查询的标志
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if "none" in content_json:
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logger.debug(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}")
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return None
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if info_type := content_json.get("info_type"):
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# 记录信息获取请求
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self.info_fetching_cache.append(
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{
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"person_id": get_person_info_manager().get_person_id_by_person_name(person_name),
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"person_name": person_name,
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"info_type": info_type,
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"start_time": time.time(),
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"forget": False,
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}
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)
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# 限制缓存大小
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if len(self.info_fetching_cache) > 10:
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self.info_fetching_cache.pop(0)
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logger.info(f"{self.log_prefix} 识别到需要调取用户 {person_name} 的[{info_type}]信息")
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return info_type
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else:
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logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}")
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}")
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logger.error(traceback.format_exc())
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return None
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def _build_info_cache_block(self) -> str:
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"""构建已获取信息的缓存块"""
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info_cache_block = ""
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if self.info_fetching_cache:
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# 对于每个(person_id, info_type)组合，只保留最新的记录
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latest_records = {}
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for info_fetching in self.info_fetching_cache:
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key = (info_fetching["person_id"], info_fetching["info_type"])
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if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]:
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latest_records[key] = info_fetching
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# 按时间排序并生成显示文本
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sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"])
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for info_fetching in sorted_records:
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info_cache_block += (
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f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n"
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)
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return info_cache_block
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async def _extract_single_info(self, person_id: str, info_type: str, person_name: str):
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"""提取单个信息类型
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Args:
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person_id: 用户ID
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info_type: 信息类型
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person_name: 用户名
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"""
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start_time = time.time()
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person_info_manager = get_person_info_manager()
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# 首先检查 info_list 缓存
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info_list = await person_info_manager.get_value(person_id, "info_list") or []
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cached_info = None
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# 查找对应的 info_type
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for info_item in info_list:
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if info_item.get("info_type") == info_type:
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cached_info = info_item.get("info_content")
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logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}")
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break
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# 如果缓存中有信息，直接使用
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if cached_info:
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if person_id not in self.info_fetched_cache:
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self.info_fetched_cache[person_id] = {}
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self.info_fetched_cache[person_id][info_type] = {
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"info": cached_info,
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"ttl": 2,
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"start_time": start_time,
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"person_name": person_name,
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"unknown": cached_info == "none",
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}
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logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}")
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return
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# 如果缓存中没有，尝试从用户档案中提取
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try:
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person_impression = await person_info_manager.get_value(person_id, "impression")
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points = await person_info_manager.get_value(person_id, "points")
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# 构建印象信息块
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if person_impression:
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person_impression_block = (
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f"<对{person_name}的总体了解>\n{person_impression}\n</对{person_name}的总体了解>"
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)
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else:
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person_impression_block = ""
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# 构建要点信息块
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if points:
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points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points])
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points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n</对{person_name}的近期了解>"
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else:
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points_text_block = ""
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# 如果完全没有用户信息
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if not points_text_block and not person_impression_block:
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if person_id not in self.info_fetched_cache:
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self.info_fetched_cache[person_id] = {}
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self.info_fetched_cache[person_id][info_type] = {
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"info": "none",
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"ttl": 2,
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"start_time": start_time,
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"person_name": person_name,
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"unknown": True,
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}
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logger.info(f"{self.log_prefix} 完全不认识 {person_name}")
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await self._save_info_to_cache(person_id, info_type, "none")
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return
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# 使用LLM提取信息
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nickname_str = ",".join(global_config.bot.alias_names)
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name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。"
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prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format(
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name_block=name_block,
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info_type=info_type,
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person_impression_block=person_impression_block,
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person_name=person_name,
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info_json_str=f'"{info_type}": "有关{info_type}的信息内容"',
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points_text_block=points_text_block,
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)
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# 使用小模型进行即时提取
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content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt)
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if content:
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content_json = json.loads(repair_json(content))
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if info_type in content_json:
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info_content = content_json[info_type]
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is_unknown = info_content == "none" or not info_content
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# 保存到运行时缓存
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if person_id not in self.info_fetched_cache:
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self.info_fetched_cache[person_id] = {}
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self.info_fetched_cache[person_id][info_type] = {
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"info": "unknown" if is_unknown else info_content,
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"ttl": 3,
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"start_time": start_time,
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"person_name": person_name,
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"unknown": is_unknown,
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}
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# 保存到持久化缓存 (info_list)
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await self._save_info_to_cache(person_id, info_type, "none" if is_unknown else info_content)
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if not is_unknown:
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logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {info_content}")
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else:
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logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息")
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else:
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logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。")
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}")
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logger.error(traceback.format_exc())
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async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
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# sourcery skip: use-next
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"""将提取到的信息保存到 person_info 的 info_list 字段中
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Args:
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person_id: 用户ID
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info_type: 信息类型
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info_content: 信息内容
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"""
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try:
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person_info_manager = get_person_info_manager()
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# 获取现有的 info_list
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info_list = await person_info_manager.get_value(person_id, "info_list") or []
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# 查找是否已存在相同 info_type 的记录
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found_index = -1
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for i, info_item in enumerate(info_list):
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if isinstance(info_item, dict) and info_item.get("info_type") == info_type:
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found_index = i
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break
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# 创建新的信息记录
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new_info_item = {
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"info_type": info_type,
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"info_content": info_content,
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}
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if found_index >= 0:
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# 更新现有记录
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info_list[found_index] = new_info_item
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logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存")
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else:
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# 添加新记录
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info_list.append(new_info_item)
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logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存")
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# 保存更新后的 info_list
|
||
await person_info_manager.update_one_field(person_id, "info_list", info_list)
|
||
|
||
except Exception as e:
|
||
logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}")
|
||
logger.error(traceback.format_exc())
|
||
|
||
|
||
class RelationshipFetcherManager:
|
||
"""关系提取器管理器
|
||
|
||
管理不同 chat_id 的 RelationshipFetcher 实例
|
||
"""
|
||
|
||
def __init__(self):
|
||
self._fetchers: Dict[str, RelationshipFetcher] = {}
|
||
|
||
def get_fetcher(self, chat_id: str) -> RelationshipFetcher:
|
||
"""获取或创建指定 chat_id 的 RelationshipFetcher
|
||
|
||
Args:
|
||
chat_id: 聊天ID
|
||
|
||
Returns:
|
||
RelationshipFetcher: 关系提取器实例
|
||
"""
|
||
if chat_id not in self._fetchers:
|
||
self._fetchers[chat_id] = RelationshipFetcher(chat_id)
|
||
return self._fetchers[chat_id]
|
||
|
||
def remove_fetcher(self, chat_id: str):
|
||
"""移除指定 chat_id 的 RelationshipFetcher
|
||
|
||
Args:
|
||
chat_id: 聊天ID
|
||
"""
|
||
if chat_id in self._fetchers:
|
||
del self._fetchers[chat_id]
|
||
|
||
def clear_all(self):
|
||
"""清空所有 RelationshipFetcher"""
|
||
self._fetchers.clear()
|
||
|
||
def get_active_chat_ids(self) -> List[str]:
|
||
"""获取所有活跃的 chat_id 列表"""
|
||
return list(self._fetchers.keys())
|
||
|
||
|
||
# 全局管理器实例
|
||
relationship_fetcher_manager = RelationshipFetcherManager()
|
||
|
||
|
||
init_real_time_info_prompts()
|