feat：拆分关系构建和关系信息提取
This commit is contained in:
SengokuCola
@@ -13,7 +13,8 @@ from src.chat.heart_flow.observation.observation import Observation
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from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor
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from src.chat.focus_chat.info_processors.relationship_processor import PersonImpressionpProcessor
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from src.chat.focus_chat.info_processors.relationship_processor import RelationshipBuildProcessor
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from src.chat.focus_chat.info_processors.real_time_info_processor import RealTimeInfoProcessor
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from src.chat.focus_chat.info_processors.working_memory_processor import WorkingMemoryProcessor
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from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
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from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation
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@@ -56,7 +57,8 @@ PROCESSOR_CLASSES = {
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# 定义后期处理器映射：在规划后、动作执行前运行的处理器
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POST_PLANNING_PROCESSOR_CLASSES = {
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"ToolProcessor": (ToolProcessor, "tool_use_processor"),
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"PersonImpressionpProcessor": (PersonImpressionpProcessor, "person_impression_processor"),
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"RelationshipBuildProcessor": (RelationshipBuildProcessor, "relationship_build_processor"),
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"RealTimeInfoProcessor": (RealTimeInfoProcessor, "real_time_info_processor"),
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}
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logger = get_logger("hfc") # Logger Name Changed
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@@ -132,11 +134,20 @@ class HeartFChatting:
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# 初始化后期处理器（规划后执行的处理器）
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self.enabled_post_planning_processor_names = []
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for proc_name, (_proc_class, config_key) in POST_PLANNING_PROCESSOR_CLASSES.items():
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# 对于关系处理器，需要同时检查两个配置项
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if proc_name == "PersonImpressionpProcessor":
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if global_config.relationship.enable_relationship and getattr(
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config_processor_settings, config_key, True
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):
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# 对于关系相关处理器，需要同时检查关系配置项
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if proc_name in ["RelationshipBuildProcessor", "RealTimeInfoProcessor"]:
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# 检查全局关系开关
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if not global_config.relationship.enable_relationship:
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continue
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# 检查处理器特定配置，同时支持向后兼容
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processor_enabled = getattr(config_processor_settings, config_key, True)
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# 向后兼容：如果旧的person_impression_processor为True，则启用两个新处理器
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if not processor_enabled and getattr(config_processor_settings, "person_impression_processor", True):
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processor_enabled = True
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if processor_enabled:
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self.enabled_post_planning_processor_names.append(proc_name)
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else:
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# 其他后期处理器的逻辑
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@@ -258,7 +269,8 @@ class HeartFChatting:
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# 根据处理器类名判断是否需要 subheartflow_id
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if name in [
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"ToolProcessor",
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"PersonImpressionpProcessor",
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"RelationshipBuildProcessor",
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"RealTimeInfoProcessor",
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"ExpressionSelectorProcessor",
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]:
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self.post_planning_processors.append(processor_actual_class(subheartflow_id=self.stream_id))
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552
src/chat/focus_chat/info_processors/real_time_info_processor.py
Normal file
552
src/chat/focus_chat/info_processors/real_time_info_processor.py
Normal file
@@ -0,0 +1,552 @@
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from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
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from src.chat.heart_flow.observation.observation import Observation
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import global_config
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import time
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import traceback
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from src.common.logger import get_logger
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.person_info.person_info import get_person_info_manager
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from .base_processor import BaseProcessor
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from typing import List, Dict
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from src.chat.focus_chat.info.info_base import InfoBase
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from src.chat.focus_chat.info.relation_info import RelationInfo
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from json_repair import repair_json
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import json
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logger = get_logger("real_time_info_processor")
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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，对ta的印象，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 RealTimeInfoProcessor(BaseProcessor):
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"""实时信息提取处理器
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负责从对话中识别需要的用户信息，并从用户档案中实时提取相关信息
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"""
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log_prefix = "实时信息"
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def __init__(self, subheartflow_id: str):
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super().__init__()
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self.subheartflow_id = subheartflow_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, "unknow": bool}}}
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# LLM模型配置
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self.llm_model = LLMRequest(
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model=global_config.model.relation,
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request_type="focus.real_time_info",
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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=global_config.model.utils_small,
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request_type="focus.real_time_info.instant",
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)
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from src.chat.message_receive.chat_stream import get_chat_manager
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name = get_chat_manager().get_stream_name(self.subheartflow_id)
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self.log_prefix = f"[{name}] 实时信息"
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async def process_info(
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self,
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observations: List[Observation] = None,
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action_type: str = None,
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action_data: dict = None,
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**kwargs,
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) -> List[InfoBase]:
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"""处理信息对象
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Args:
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observations: 观察对象列表
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action_type: 动作类型
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action_data: 动作数据
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Returns:
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List[InfoBase]: 处理后的结构化信息列表
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"""
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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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# 执行实时信息识别和提取
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relation_info_str = await self._identify_and_extract_info(observations, action_type, action_data)
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if relation_info_str:
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relation_info = RelationInfo()
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relation_info.set_relation_info(relation_info_str)
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return [relation_info]
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else:
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return []
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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 _identify_and_extract_info(
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self,
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observations: List[Observation] = None,
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action_type: str = None,
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action_data: dict = None,
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) -> str:
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"""识别并提取用户信息
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Args:
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observations: 观察对象列表
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action_type: 动作类型
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action_data: 动作数据
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Returns:
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str: 提取到的用户信息字符串
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"""
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# 只处理回复动作
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if action_type != "reply":
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return None
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# 解析回复目标
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target_message = action_data.get("reply_to", "")
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sender, text = self._parse_reply_target(target_message)
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if not sender or not text:
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return None
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# 获取用户ID
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person_info_manager = get_person_info_manager()
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person_id = person_info_manager.get_person_id_by_person_name(sender)
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if not person_id:
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logger.warning(f"{self.log_prefix} 未找到用户 {sender} 的ID，跳过信息提取")
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return None
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# 获取聊天观察信息
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chat_observe_info = self._extract_chat_observe_info(observations)
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if not chat_observe_info:
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logger.debug(f"{self.log_prefix} 没有聊天观察信息，跳过信息提取")
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return None
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# 识别需要提取的信息类型
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info_type = await self._identify_needed_info(chat_observe_info, sender, text)
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# 如果需要提取新信息，执行提取
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if info_type:
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await self._extract_single_info(person_id, info_type, sender)
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# 组织并返回已知信息
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return self._organize_known_info()
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def _parse_reply_target(self, target_message: str) -> tuple:
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"""解析回复目标消息
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Args:
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target_message: 目标消息，格式为 "用户名:消息内容"
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Returns:
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tuple: (发送者, 消息内容)
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"""
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if ":" in target_message:
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parts = target_message.split(":", 1)
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elif "：" in target_message:
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parts = target_message.split("：", 1)
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else:
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logger.warning(f"{self.log_prefix} reply_to格式不正确: {target_message}")
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return None, None
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if len(parts) != 2:
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logger.warning(f"{self.log_prefix} reply_to格式不正确: {target_message}")
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return None, None
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sender = parts[0].strip()
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text = parts[1].strip()
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return sender, text
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def _extract_chat_observe_info(self, observations: List[Observation]) -> str:
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"""从观察对象中提取聊天信息
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Args:
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observations: 观察对象列表
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Returns:
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str: 聊天观察信息
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"""
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if not observations:
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return ""
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for observation in observations:
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if isinstance(observation, ChattingObservation):
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return observation.get_observe_info()
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return ""
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async def _identify_needed_info(self, chat_observe_info: str, sender: str, text: str) -> str:
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"""识别需要提取的信息类型
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Args:
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chat_observe_info: 聊天观察信息
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sender: 发送者
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text: 消息内容
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Returns:
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str: 需要提取的信息类型，如果不需要则返回None
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"""
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# 构建名称信息块
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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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# 构建已获取信息缓存块
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info_cache_block = self._build_info_cache_block()
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# 构建提示词
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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_observe_info,
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name_block=name_block,
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info_cache_block=info_cache_block,
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person_name=sender,
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target_message=text,
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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.info(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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info_type = content_json.get("info_type")
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if 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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"person_id": get_person_info_manager().get_person_id_by_person_name(sender),
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"person_name": sender,
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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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# 限制缓存大小
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if len(self.info_fetching_cache) > 20:
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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 {sender} 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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"unknow": 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")
|
||||
points = await person_info_manager.get_value(person_id, "points")
|
||||
|
||||
# 构建印象信息块
|
||||
if person_impression:
|
||||
person_impression_block = (
|
||||
f"<对{person_name}的总体了解>\n{person_impression}\n</对{person_name}的总体了解>"
|
||||
)
|
||||
else:
|
||||
person_impression_block = ""
|
||||
|
||||
# 构建要点信息块
|
||||
if points:
|
||||
points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points])
|
||||
points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n</对{person_name}的近期了解>"
|
||||
else:
|
||||
points_text_block = ""
|
||||
|
||||
# 如果完全没有用户信息
|
||||
if not points_text_block and not person_impression_block:
|
||||
if person_id not in self.info_fetched_cache:
|
||||
self.info_fetched_cache[person_id] = {}
|
||||
self.info_fetched_cache[person_id][info_type] = {
|
||||
"info": "none",
|
||||
"ttl": 2,
|
||||
"start_time": start_time,
|
||||
"person_name": person_name,
|
||||
"unknow": True,
|
||||
}
|
||||
logger.info(f"{self.log_prefix} 完全不认识 {person_name}")
|
||||
await self._save_info_to_cache(person_id, info_type, "none")
|
||||
return
|
||||
|
||||
# 使用LLM提取信息
|
||||
nickname_str = ",".join(global_config.bot.alias_names)
|
||||
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。"
|
||||
|
||||
prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format(
|
||||
name_block=name_block,
|
||||
info_type=info_type,
|
||||
person_impression_block=person_impression_block,
|
||||
person_name=person_name,
|
||||
info_json_str=f'"{info_type}": "有关{info_type}的信息内容"',
|
||||
points_text_block=points_text_block,
|
||||
)
|
||||
|
||||
# 使用小模型进行即时提取
|
||||
content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt)
|
||||
|
||||
if content:
|
||||
content_json = json.loads(repair_json(content))
|
||||
if info_type in content_json:
|
||||
info_content = content_json[info_type]
|
||||
is_unknown = info_content == "none" or not info_content
|
||||
|
||||
# 保存到运行时缓存
|
||||
if person_id not in self.info_fetched_cache:
|
||||
self.info_fetched_cache[person_id] = {}
|
||||
self.info_fetched_cache[person_id][info_type] = {
|
||||
"info": "unknow" if is_unknown else info_content,
|
||||
"ttl": 3,
|
||||
"start_time": start_time,
|
||||
"person_name": person_name,
|
||||
"unknow": is_unknown,
|
||||
}
|
||||
|
||||
# 保存到持久化缓存 (info_list)
|
||||
await self._save_info_to_cache(person_id, info_type, info_content if not is_unknown else "none")
|
||||
|
||||
if not is_unknown:
|
||||
logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {info_content}")
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息")
|
||||
else:
|
||||
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。")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
|
||||
"""将提取到的信息保存到 person_info 的 info_list 字段中
|
||||
|
||||
Args:
|
||||
person_id: 用户ID
|
||||
info_type: 信息类型
|
||||
info_content: 信息内容
|
||||
"""
|
||||
try:
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
# 获取现有的 info_list
|
||||
info_list = await person_info_manager.get_value(person_id, "info_list") or []
|
||||
|
||||
# 查找是否已存在相同 info_type 的记录
|
||||
found_index = -1
|
||||
for i, info_item in enumerate(info_list):
|
||||
if isinstance(info_item, dict) and info_item.get("info_type") == info_type:
|
||||
found_index = i
|
||||
break
|
||||
|
||||
# 创建新的信息记录
|
||||
new_info_item = {
|
||||
"info_type": info_type,
|
||||
"info_content": info_content,
|
||||
}
|
||||
|
||||
if found_index >= 0:
|
||||
# 更新现有记录
|
||||
info_list[found_index] = new_info_item
|
||||
logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存")
|
||||
else:
|
||||
# 添加新记录
|
||||
info_list.append(new_info_item)
|
||||
logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存")
|
||||
|
||||
# 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())
|
||||
|
||||
def _organize_known_info(self) -> str:
|
||||
"""组织已知的用户信息为字符串
|
||||
|
||||
Returns:
|
||||
str: 格式化的用户信息字符串
|
||||
"""
|
||||
persons_infos_str = ""
|
||||
|
||||
if self.info_fetched_cache:
|
||||
persons_with_known_info = [] # 有已知信息的人员
|
||||
persons_with_unknown_info = [] # 有未知信息的人员
|
||||
|
||||
for person_id in self.info_fetched_cache:
|
||||
person_known_infos = []
|
||||
person_unknown_infos = []
|
||||
person_name = ""
|
||||
|
||||
for info_type in self.info_fetched_cache[person_id]:
|
||||
person_name = self.info_fetched_cache[person_id][info_type]["person_name"]
|
||||
if not self.info_fetched_cache[person_id][info_type]["unknow"]:
|
||||
info_content = self.info_fetched_cache[person_id][info_type]["info"]
|
||||
person_known_infos.append(f"[{info_type}]：{info_content}")
|
||||
else:
|
||||
person_unknown_infos.append(info_type)
|
||||
|
||||
# 如果有已知信息，添加到已知信息列表
|
||||
if person_known_infos:
|
||||
known_info_str = "；".join(person_known_infos) + "；"
|
||||
persons_with_known_info.append((person_name, known_info_str))
|
||||
|
||||
# 如果有未知信息，添加到未知信息列表
|
||||
if person_unknown_infos:
|
||||
persons_with_unknown_info.append((person_name, person_unknown_infos))
|
||||
|
||||
# 先输出有已知信息的人员
|
||||
for person_name, known_info_str in persons_with_known_info:
|
||||
persons_infos_str += f"你对 {person_name} 的了解：{known_info_str}\n"
|
||||
|
||||
# 统一处理未知信息，避免重复的警告文本
|
||||
if persons_with_unknown_info:
|
||||
unknown_persons_details = []
|
||||
for person_name, unknown_types in persons_with_unknown_info:
|
||||
unknown_types_str = "、".join(unknown_types)
|
||||
unknown_persons_details.append(f"{person_name}的[{unknown_types_str}]")
|
||||
|
||||
if len(unknown_persons_details) == 1:
|
||||
persons_infos_str += (
|
||||
f"你不了解{unknown_persons_details[0]}信息，不要胡乱回答，可以直接说不知道或忘记了；\n"
|
||||
)
|
||||
else:
|
||||
unknown_all_str = "、".join(unknown_persons_details)
|
||||
persons_infos_str += f"你不了解{unknown_all_str}等信息，不要胡乱回答，可以直接说不知道或忘记了；\n"
|
||||
|
||||
return persons_infos_str
|
||||
|
||||
def get_cache_status(self) -> str:
|
||||
"""获取缓存状态信息，用于调试和监控"""
|
||||
status_lines = [f"{self.log_prefix} 实时信息缓存状态："]
|
||||
status_lines.append(f"获取请求缓存数：{len(self.info_fetching_cache)}")
|
||||
status_lines.append(f"结果缓存用户数：{len(self.info_fetched_cache)}")
|
||||
|
||||
if self.info_fetched_cache:
|
||||
for person_id, info_types in self.info_fetched_cache.items():
|
||||
person_name = list(info_types.values())[0]["person_name"] if info_types else person_id
|
||||
status_lines.append(f" 用户 {person_name}: {len(info_types)} 个信息类型")
|
||||
for info_type, info_data in info_types.items():
|
||||
ttl = info_data["ttl"]
|
||||
unknow = info_data["unknow"]
|
||||
status = "未知" if unknow else "已知"
|
||||
status_lines.append(f" {info_type}: {status} (TTL: {ttl})")
|
||||
|
||||
return "\n".join(status_lines)
|
||||
|
||||
|
||||
# 初始化提示词
|
||||
init_real_time_info_prompts()
|
||||
@@ -5,18 +5,13 @@ from src.config.config import global_config
|
||||
import time
|
||||
import traceback
|
||||
from src.common.logger import get_logger
|
||||
from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
|
||||
from src.chat.message_receive.chat_stream import get_chat_manager
|
||||
from src.person_info.relationship_manager import get_relationship_manager
|
||||
from .base_processor import BaseProcessor
|
||||
from typing import List
|
||||
from typing import Dict
|
||||
from src.chat.focus_chat.info.info_base import InfoBase
|
||||
from src.chat.focus_chat.info.relation_info import RelationInfo
|
||||
from json_repair import repair_json
|
||||
from src.person_info.person_info import get_person_info_manager
|
||||
import json
|
||||
import asyncio
|
||||
from src.chat.utils.chat_message_builder import (
|
||||
get_raw_msg_by_timestamp_with_chat,
|
||||
get_raw_msg_by_timestamp_with_chat_inclusive,
|
||||
@@ -36,62 +31,21 @@ SEGMENT_CLEANUP_CONFIG = {
|
||||
}
|
||||
|
||||
|
||||
logger = get_logger("processor")
|
||||
logger = get_logger("relationship_build_processor")
|
||||
|
||||
|
||||
def init_prompt():
|
||||
relationship_prompt = """
|
||||
<聊天记录>
|
||||
{chat_observe_info}
|
||||
</聊天记录>
|
||||
class RelationshipBuildProcessor(BaseProcessor):
|
||||
"""关系构建处理器
|
||||
|
||||
{name_block}
|
||||
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：
|
||||
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，对ta的印象，最近发生的事等等。
|
||||
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：
|
||||
{info_cache_block}
|
||||
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"}}
|
||||
负责跟踪用户消息活动、管理消息段、触发关系构建和印象更新
|
||||
"""
|
||||
|
||||
请以json格式输出，例如：
|
||||
|
||||
{{
|
||||
"info_type": "信息类型",
|
||||
}}
|
||||
|
||||
请严格按照json输出格式，不要输出多余内容：
|
||||
"""
|
||||
Prompt(relationship_prompt, "relationship_prompt")
|
||||
|
||||
fetch_info_prompt = """
|
||||
|
||||
{name_block}
|
||||
以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解：
|
||||
{person_impression_block}
|
||||
{points_text_block}
|
||||
|
||||
请从中提取用户"{person_name}"的有关"{info_type}"信息
|
||||
请以json格式输出，例如：
|
||||
|
||||
{{
|
||||
{info_json_str}
|
||||
}}
|
||||
|
||||
请严格按照json输出格式，不要输出多余内容：
|
||||
"""
|
||||
Prompt(fetch_info_prompt, "fetch_person_info_prompt")
|
||||
|
||||
|
||||
class PersonImpressionpProcessor(BaseProcessor):
|
||||
log_prefix = "关系"
|
||||
log_prefix = "关系构建"
|
||||
|
||||
def __init__(self, subheartflow_id: str):
|
||||
super().__init__()
|
||||
|
||||
self.subheartflow_id = subheartflow_id
|
||||
self.info_fetching_cache: List[Dict[str, any]] = []
|
||||
self.info_fetched_cache: Dict[
|
||||
str, Dict[str, any]
|
||||
] = {} # {person_id: {"info": str, "ttl": int, "start_time": float}}
|
||||
|
||||
# 新的消息段缓存结构：
|
||||
# {person_id: [{"start_time": float, "end_time": float, "last_msg_time": float, "message_count": int}, ...]}
|
||||
@@ -107,19 +61,8 @@ class PersonImpressionpProcessor(BaseProcessor):
|
||||
# 最后清理时间，用于定期清理老消息段
|
||||
self.last_cleanup_time = 0.0
|
||||
|
||||
self.llm_model = LLMRequest(
|
||||
model=global_config.model.relation,
|
||||
request_type="focus.relationship",
|
||||
)
|
||||
|
||||
# 小模型用于即时信息提取
|
||||
self.instant_llm_model = LLMRequest(
|
||||
model=global_config.model.utils_small,
|
||||
request_type="focus.relationship.instant",
|
||||
)
|
||||
|
||||
name = get_chat_manager().get_stream_name(self.subheartflow_id)
|
||||
self.log_prefix = f"[{name}] "
|
||||
self.log_prefix = f"[{name}] 关系构建"
|
||||
|
||||
# 加载持久化的缓存
|
||||
self._load_cache()
|
||||
@@ -444,17 +387,7 @@ class PersonImpressionpProcessor(BaseProcessor):
|
||||
List[InfoBase]: 处理后的结构化信息列表
|
||||
"""
|
||||
await self.build_relation(observations)
|
||||
|
||||
relation_info_str = await self.relation_identify(observations, action_type, action_data)
|
||||
|
||||
if relation_info_str:
|
||||
relation_info = RelationInfo()
|
||||
relation_info.set_relation_info(relation_info_str)
|
||||
else:
|
||||
relation_info = None
|
||||
return None
|
||||
|
||||
return [relation_info]
|
||||
return [] # 关系构建处理器不返回信息，只负责后台构建关系
|
||||
|
||||
async def build_relation(self, observations: List[Observation] = None):
|
||||
"""构建关系"""
|
||||
@@ -512,208 +445,12 @@ class PersonImpressionpProcessor(BaseProcessor):
|
||||
for person_id in users_to_build_relationship:
|
||||
segments = self.person_engaged_cache[person_id]
|
||||
# 异步执行关系构建
|
||||
import asyncio
|
||||
asyncio.create_task(self.update_impression_on_segments(person_id, self.subheartflow_id, segments))
|
||||
# 移除已处理的用户缓存
|
||||
del self.person_engaged_cache[person_id]
|
||||
self._save_cache()
|
||||
|
||||
async def relation_identify(
|
||||
self,
|
||||
observations: List[Observation] = None,
|
||||
action_type: str = None,
|
||||
action_data: dict = None,
|
||||
):
|
||||
"""
|
||||
从人物获取信息
|
||||
"""
|
||||
|
||||
chat_observe_info = ""
|
||||
current_time = time.time()
|
||||
if observations:
|
||||
for observation in observations:
|
||||
if isinstance(observation, ChattingObservation):
|
||||
chat_observe_info = observation.get_observe_info()
|
||||
# latest_message_time = observation.last_observe_time
|
||||
# 从聊天观察中提取用户信息并更新消息段
|
||||
# 获取最新的非bot消息来更新消息段
|
||||
latest_messages = get_raw_msg_by_timestamp_with_chat(
|
||||
self.subheartflow_id,
|
||||
self.last_processed_message_time,
|
||||
current_time,
|
||||
limit=50, # 获取自上次处理后的消息
|
||||
)
|
||||
if latest_messages:
|
||||
# 处理所有新的非bot消息
|
||||
for latest_msg in latest_messages:
|
||||
user_id = latest_msg.get("user_id")
|
||||
platform = latest_msg.get("user_platform") or latest_msg.get("chat_info_platform")
|
||||
msg_time = latest_msg.get("time", 0)
|
||||
|
||||
if (
|
||||
user_id
|
||||
and platform
|
||||
and user_id != global_config.bot.qq_account
|
||||
and msg_time > self.last_processed_message_time
|
||||
):
|
||||
from src.person_info.person_info import PersonInfoManager
|
||||
|
||||
person_id = PersonInfoManager.get_person_id(platform, user_id)
|
||||
self._update_message_segments(person_id, msg_time)
|
||||
logger.debug(
|
||||
f"{self.log_prefix} 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}"
|
||||
)
|
||||
self.last_processed_message_time = max(self.last_processed_message_time, msg_time)
|
||||
break
|
||||
|
||||
for person_id in list(self.info_fetched_cache.keys()):
|
||||
for info_type in list(self.info_fetched_cache[person_id].keys()):
|
||||
self.info_fetched_cache[person_id][info_type]["ttl"] -= 1
|
||||
if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0:
|
||||
del self.info_fetched_cache[person_id][info_type]
|
||||
if not self.info_fetched_cache[person_id]:
|
||||
del self.info_fetched_cache[person_id]
|
||||
|
||||
if action_type != "reply":
|
||||
return None
|
||||
|
||||
target_message = action_data.get("reply_to", "")
|
||||
|
||||
if ":" in target_message:
|
||||
parts = target_message.split(":", 1)
|
||||
elif "：" in target_message:
|
||||
parts = target_message.split("：", 1)
|
||||
else:
|
||||
logger.warning(f"reply_to格式不正确: {target_message}，跳过关系识别")
|
||||
return None
|
||||
|
||||
if len(parts) != 2:
|
||||
logger.warning(f"reply_to格式不正确: {target_message}，跳过关系识别")
|
||||
return None
|
||||
|
||||
sender = parts[0].strip()
|
||||
text = parts[1].strip()
|
||||
|
||||
person_info_manager = get_person_info_manager()
|
||||
person_id = person_info_manager.get_person_id_by_person_name(sender)
|
||||
|
||||
if not person_id:
|
||||
logger.warning(f"未找到用户 {sender} 的ID，跳过关系识别")
|
||||
return None
|
||||
|
||||
nickname_str = ",".join(global_config.bot.alias_names)
|
||||
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。"
|
||||
|
||||
info_cache_block = ""
|
||||
if self.info_fetching_cache:
|
||||
# 对于每个(person_id, info_type)组合，只保留最新的记录
|
||||
latest_records = {}
|
||||
for info_fetching in self.info_fetching_cache:
|
||||
key = (info_fetching["person_id"], info_fetching["info_type"])
|
||||
if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]:
|
||||
latest_records[key] = info_fetching
|
||||
|
||||
# 按时间排序并生成显示文本
|
||||
sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"])
|
||||
for info_fetching in sorted_records:
|
||||
info_cache_block += (
|
||||
f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n"
|
||||
)
|
||||
|
||||
prompt = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format(
|
||||
chat_observe_info=chat_observe_info,
|
||||
name_block=name_block,
|
||||
info_cache_block=info_cache_block,
|
||||
person_name=sender,
|
||||
target_message=text,
|
||||
)
|
||||
|
||||
try:
|
||||
logger.info(f"{self.log_prefix} 人物信息prompt: \n{prompt}\n")
|
||||
content, _ = await self.llm_model.generate_response_async(prompt=prompt)
|
||||
if content:
|
||||
# print(f"content: {content}")
|
||||
content_json = json.loads(repair_json(content))
|
||||
|
||||
# 检查是否返回了不需要查询的标志
|
||||
if "none" in content_json:
|
||||
logger.info(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}")
|
||||
# 跳过新的信息提取，但仍会处理已有缓存
|
||||
else:
|
||||
info_type = content_json.get("info_type")
|
||||
if info_type:
|
||||
self.info_fetching_cache.append(
|
||||
{
|
||||
"person_id": person_id,
|
||||
"person_name": sender,
|
||||
"info_type": info_type,
|
||||
"start_time": time.time(),
|
||||
"forget": False,
|
||||
}
|
||||
)
|
||||
if len(self.info_fetching_cache) > 20:
|
||||
self.info_fetching_cache.pop(0)
|
||||
|
||||
logger.info(f"{self.log_prefix} 调取用户 {sender} 的[{info_type}]信息。")
|
||||
|
||||
# 执行信息提取
|
||||
await self._fetch_single_info_instant(person_id, info_type, time.time())
|
||||
else:
|
||||
logger.warning(f"{self.log_prefix} LLM did not return a valid info_type. Response: {content}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
# 7. 合并缓存和新处理的信息
|
||||
persons_infos_str = ""
|
||||
# 处理已获取到的信息
|
||||
if self.info_fetched_cache:
|
||||
persons_with_known_info = [] # 有已知信息的人员
|
||||
persons_with_unknown_info = [] # 有未知信息的人员
|
||||
|
||||
for person_id in self.info_fetched_cache:
|
||||
person_known_infos = []
|
||||
person_unknown_infos = []
|
||||
person_name = ""
|
||||
|
||||
for info_type in self.info_fetched_cache[person_id]:
|
||||
person_name = self.info_fetched_cache[person_id][info_type]["person_name"]
|
||||
if not self.info_fetched_cache[person_id][info_type]["unknow"]:
|
||||
info_content = self.info_fetched_cache[person_id][info_type]["info"]
|
||||
person_known_infos.append(f"[{info_type}]：{info_content}")
|
||||
else:
|
||||
person_unknown_infos.append(info_type)
|
||||
|
||||
# 如果有已知信息，添加到已知信息列表
|
||||
if person_known_infos:
|
||||
known_info_str = "；".join(person_known_infos) + "；"
|
||||
persons_with_known_info.append((person_name, known_info_str))
|
||||
|
||||
# 如果有未知信息，添加到未知信息列表
|
||||
if person_unknown_infos:
|
||||
persons_with_unknown_info.append((person_name, person_unknown_infos))
|
||||
|
||||
# 先输出有已知信息的人员
|
||||
for person_name, known_info_str in persons_with_known_info:
|
||||
persons_infos_str += f"你对 {person_name} 的了解：{known_info_str}\n"
|
||||
|
||||
# 统一处理未知信息，避免重复的警告文本
|
||||
if persons_with_unknown_info:
|
||||
unknown_persons_details = []
|
||||
for person_name, unknown_types in persons_with_unknown_info:
|
||||
unknown_types_str = "、".join(unknown_types)
|
||||
unknown_persons_details.append(f"{person_name}的[{unknown_types_str}]")
|
||||
|
||||
if len(unknown_persons_details) == 1:
|
||||
persons_infos_str += (
|
||||
f"你不了解{unknown_persons_details[0]}信息，不要胡乱回答，可以直接说不知道或忘记了；\n"
|
||||
)
|
||||
else:
|
||||
unknown_all_str = "、".join(unknown_persons_details)
|
||||
persons_infos_str += f"你不了解{unknown_all_str}等信息，不要胡乱回答，可以直接说不知道或忘记了；\n"
|
||||
|
||||
return persons_infos_str
|
||||
|
||||
# ================================
|
||||
# 关系构建模块
|
||||
# 负责触发关系构建、整合消息段、更新用户印象
|
||||
@@ -783,169 +520,3 @@ class PersonImpressionpProcessor(BaseProcessor):
|
||||
except Exception as e:
|
||||
logger.error(f"为 {person_id} 更新印象时发生错误: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
# ================================
|
||||
# 信息调取模块
|
||||
# 负责实时分析对话需求、提取用户信息、管理信息缓存
|
||||
# ================================
|
||||
|
||||
async def _fetch_single_info_instant(self, person_id: str, info_type: str, start_time: float):
|
||||
"""
|
||||
使用小模型提取单个信息类型
|
||||
"""
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
# 首先检查 info_list 缓存
|
||||
info_list = await person_info_manager.get_value(person_id, "info_list") or []
|
||||
cached_info = None
|
||||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||||
|
||||
# print(f"info_list: {info_list}")
|
||||
|
||||
# 查找对应的 info_type
|
||||
for info_item in info_list:
|
||||
if info_item.get("info_type") == info_type:
|
||||
cached_info = info_item.get("info_content")
|
||||
logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}")
|
||||
break
|
||||
|
||||
# 如果缓存中有信息，直接使用
|
||||
if cached_info:
|
||||
if person_id not in self.info_fetched_cache:
|
||||
self.info_fetched_cache[person_id] = {}
|
||||
|
||||
self.info_fetched_cache[person_id][info_type] = {
|
||||
"info": cached_info,
|
||||
"ttl": 2,
|
||||
"start_time": start_time,
|
||||
"person_name": person_name,
|
||||
"unknow": cached_info == "none",
|
||||
}
|
||||
logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}")
|
||||
return
|
||||
|
||||
try:
|
||||
person_name = await person_info_manager.get_value(person_id, "person_name")
|
||||
person_impression = await person_info_manager.get_value(person_id, "impression")
|
||||
if person_impression:
|
||||
person_impression_block = (
|
||||
f"<对{person_name}的总体了解>\n{person_impression}\n</对{person_name}的总体了解>"
|
||||
)
|
||||
else:
|
||||
person_impression_block = ""
|
||||
|
||||
points = await person_info_manager.get_value(person_id, "points")
|
||||
if points:
|
||||
points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points])
|
||||
points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n</对{person_name}的近期了解>"
|
||||
else:
|
||||
points_text_block = ""
|
||||
|
||||
if not points_text_block and not person_impression_block:
|
||||
if person_id not in self.info_fetched_cache:
|
||||
self.info_fetched_cache[person_id] = {}
|
||||
self.info_fetched_cache[person_id][info_type] = {
|
||||
"info": "none",
|
||||
"ttl": 2,
|
||||
"start_time": start_time,
|
||||
"person_name": person_name,
|
||||
"unknow": True,
|
||||
}
|
||||
logger.info(f"{self.log_prefix} 完全不认识 {person_name}")
|
||||
await self._save_info_to_cache(person_id, info_type, "none")
|
||||
return
|
||||
|
||||
nickname_str = ",".join(global_config.bot.alias_names)
|
||||
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。"
|
||||
prompt = (await global_prompt_manager.get_prompt_async("fetch_person_info_prompt")).format(
|
||||
name_block=name_block,
|
||||
info_type=info_type,
|
||||
person_impression_block=person_impression_block,
|
||||
person_name=person_name,
|
||||
info_json_str=f'"{info_type}": "有关{info_type}的信息内容"',
|
||||
points_text_block=points_text_block,
|
||||
)
|
||||
except Exception:
|
||||
logger.error(traceback.format_exc())
|
||||
return
|
||||
|
||||
try:
|
||||
# 使用小模型进行即时提取
|
||||
content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt)
|
||||
|
||||
if content:
|
||||
content_json = json.loads(repair_json(content))
|
||||
if info_type in content_json:
|
||||
info_content = content_json[info_type]
|
||||
is_unknown = info_content == "none" or not info_content
|
||||
|
||||
# 保存到运行时缓存
|
||||
if person_id not in self.info_fetched_cache:
|
||||
self.info_fetched_cache[person_id] = {}
|
||||
self.info_fetched_cache[person_id][info_type] = {
|
||||
"info": "unknow" if is_unknown else info_content,
|
||||
"ttl": 3,
|
||||
"start_time": start_time,
|
||||
"person_name": person_name,
|
||||
"unknow": is_unknown,
|
||||
}
|
||||
|
||||
# 保存到持久化缓存 (info_list)
|
||||
await self._save_info_to_cache(person_id, info_type, info_content if not is_unknown else "none")
|
||||
|
||||
if not is_unknown:
|
||||
logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {content}")
|
||||
else:
|
||||
logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息")
|
||||
else:
|
||||
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。")
|
||||
except Exception as e:
|
||||
logger.error(f"{self.log_prefix} 执行小模型请求获取用户信息时出错: {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
|
||||
async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str):
|
||||
"""
|
||||
将提取到的信息保存到 person_info 的 info_list 字段中
|
||||
|
||||
Args:
|
||||
person_id: 用户ID
|
||||
info_type: 信息类型
|
||||
info_content: 信息内容
|
||||
"""
|
||||
try:
|
||||
person_info_manager = get_person_info_manager()
|
||||
|
||||
# 获取现有的 info_list
|
||||
info_list = await person_info_manager.get_value(person_id, "info_list") or []
|
||||
|
||||
# 查找是否已存在相同 info_type 的记录
|
||||
found_index = -1
|
||||
for i, info_item in enumerate(info_list):
|
||||
if isinstance(info_item, dict) and info_item.get("info_type") == info_type:
|
||||
found_index = i
|
||||
break
|
||||
|
||||
# 创建新的信息记录
|
||||
new_info_item = {
|
||||
"info_type": info_type,
|
||||
"info_content": info_content,
|
||||
}
|
||||
|
||||
if found_index >= 0:
|
||||
# 更新现有记录
|
||||
info_list[found_index] = new_info_item
|
||||
logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存")
|
||||
else:
|
||||
# 添加新记录
|
||||
info_list.append(new_info_item)
|
||||
logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存")
|
||||
|
||||
# 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())
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
||||
@@ -236,14 +236,6 @@ class ActionPlanner(BasePlanner):
|
||||
|
||||
action_data["loop_start_time"] = loop_start_time
|
||||
|
||||
memory_str = ""
|
||||
if running_memorys:
|
||||
memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
|
||||
for running_memory in running_memorys:
|
||||
memory_str += f"{running_memory['content']}\n"
|
||||
if memory_str:
|
||||
action_data["memory_block"] = memory_str
|
||||
|
||||
# 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data
|
||||
|
||||
if extracted_action not in current_available_actions:
|
||||
|
||||
@@ -329,7 +329,13 @@ class FocusChatProcessorConfig(ConfigBase):
|
||||
"""专注聊天处理器配置类"""
|
||||
|
||||
person_impression_processor: bool = True
|
||||
"""是否启用关系识别处理器"""
|
||||
"""是否启用关系识别处理器（已废弃，为了兼容性保留）"""
|
||||
|
||||
relationship_build_processor: bool = True
|
||||
"""是否启用关系构建处理器"""
|
||||
|
||||
real_time_info_processor: bool = True
|
||||
"""是否启用实时信息提取处理器"""
|
||||
|
||||
tool_use_processor: bool = True
|
||||
"""是否启用工具使用处理器"""
|
||||
|
||||
Reference in New Issue
Block a user