mirror of https://github.com/Mai-with-u/MaiBot.git
🤖 自动格式化代码 [skip ci]
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1e015aed9f
commit
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@ -2,9 +2,9 @@ from .observation import ChattingObservation
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from src.plugins.knowledge.knowledge_lib import qa_manager
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from src.plugins.models.utils_model import LLMRequest
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from src.config.config import global_config
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from src.plugins.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat,build_readable_messages
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from src.plugins.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, build_readable_messages
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import time
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import re
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import re
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import traceback
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from src.common.logger_manager import get_logger
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from src.individuality.individuality import Individuality
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@ -129,7 +129,7 @@ def parse_knowledge_and_get_max_relevance(knowledge_str: str) -> (str, float):
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max_relevance = max(float(score) for score in relevance_scores)
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except ValueError:
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logger.warning(f"解析相关性得分时出错: {relevance_scores}")
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return knowledge_str, 0.0 # 出错时返回0.0
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return knowledge_str, 0.0 # 出错时返回0.0
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else:
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# 如果没有找到 "该条知识对于问题的相关性：" 这样的模式，
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# 说明可能 qa_manager 返回的格式有变，或者没有有效的知识。
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@ -172,8 +172,6 @@ def calculate_replacement_probability(similarity: float) -> float:
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# p = s + 0.1
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probability = similarity + 0.1
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return min(1.0, max(0.0, probability))
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class SubMind:
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@ -199,12 +197,12 @@ class SubMind:
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name = chat_manager.get_stream_name(self.subheartflow_id)
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self.log_prefix = f"[{name}] "
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self._update_structured_info_str()
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# 阶梯式筛选
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# 阶梯式筛选
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self.knowledge_retrieval_steps = self.knowledge_retrieval_steps = [
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{"name": "latest_1_msg", "limit": 1, "relevance_threshold": 0.75}, # 新增：最新1条，极高阈值
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{"name": "latest_2_msgs", "limit": 2, "relevance_threshold": 0.65}, # 新增：最新2条，较高阈值
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{"name": "short_window_3_msgs", "limit": 3, "relevance_threshold": 0.50}, # 原有的3条，阈值可保持或微调
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{"name": "medium_window_8_msgs", "limit": 8, "relevance_threshold": 0.30}, # 原有的8条，阈值可保持或微调
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{"name": "latest_1_msg", "limit": 1, "relevance_threshold": 0.75}, # 新增：最新1条，极高阈值
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{"name": "latest_2_msgs", "limit": 2, "relevance_threshold": 0.65}, # 新增：最新2条，较高阈值
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{"name": "short_window_3_msgs", "limit": 3, "relevance_threshold": 0.50}, # 原有的3条，阈值可保持或微调
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{"name": "medium_window_8_msgs", "limit": 8, "relevance_threshold": 0.30}, # 原有的8条，阈值可保持或微调
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# 完整窗口的回退逻辑保持不变
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]
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@ -254,16 +252,16 @@ class SubMind:
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)
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# 筛选出所有不是 lpmm_knowledge 类型的条目，或者其他需要保留的条目
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info_to_keep = [item for item in self.structured_info if item.get("type") != "lpmm_knowledge"]
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# 针对我们仅希望 lpmm_knowledge "用完即弃" 的情况：
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processed_info_to_keep = []
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for item in info_to_keep: # info_to_keep 已经不包含 lpmm_knowledge
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for item in info_to_keep: # info_to_keep 已经不包含 lpmm_knowledge
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item["ttl"] -= 1
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if item["ttl"] > 0:
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processed_info_to_keep.append(item)
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else:
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logger.debug(f"{self.log_prefix} 移除过期的非lpmm_knowledge项: {item.get('id', '未知ID')}")
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self.structured_info = processed_info_to_keep
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logger.debug(
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f"{self.log_prefix} 清理后 structured_info (仅保留非lpmm_knowledge且TTL有效项): "
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@ -338,7 +336,6 @@ class SubMind:
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logger.error(f"{self.log_prefix} 获取记忆时出错: {e}")
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logger.error(traceback.format_exc())
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# ---------- 2.5 阶梯式获取知识库信息 ----------
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final_knowledge_to_add = None
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retrieval_source_info = "未进行知识检索"
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@ -352,91 +349,101 @@ class SubMind:
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step_name = step_config["name"]
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limit = step_config["limit"]
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threshold = step_config["relevance_threshold"]
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logger.info(f"{self.log_prefix} 尝试阶梯检索 - 阶段: {step_name} (最近{limit}条, 阈值>{threshold})")
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try:
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# 1. 获取当前阶段的聊天记录上下文
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# 我们需要从 observation 中获取原始消息列表来构建特定长度的上下文
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# get_raw_msg_before_timestamp_with_chat 在 observation.py 中被导入
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# from src.plugins.utils.chat_message_builder import get_raw_msg_before_timestamp_with_chat, build_readable_messages
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# 需要确保 ChattingObservation 的实例 (self.observations[0]) 能提供 chat_id
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# 并且 build_readable_messages 可用
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context_messages_dicts = get_raw_msg_before_timestamp_with_chat(
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chat_id=observation.chat_id,
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timestamp=time.time(),
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limit=limit
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chat_id=observation.chat_id, timestamp=time.time(), limit=limit
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)
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if not context_messages_dicts:
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logger.debug(f"{self.log_prefix} 阶段 '{step_name}' 未获取到聊天记录，跳过此阶段。")
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continue
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current_context_text = await build_readable_messages(
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messages=context_messages_dicts,
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timestamp_mode="lite" # 或者您认为适合知识检索的模式
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timestamp_mode="lite", # 或者您认为适合知识检索的模式
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)
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if not current_context_text:
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logger.debug(f"{self.log_prefix} 阶段 '{step_name}' 构建的上下文为空，跳过此阶段。")
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continue
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logger.debug(f"{self.log_prefix} 阶段 '{step_name}' 使用上下文: '{current_context_text[:150]}...'")
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# 2. 调用知识库进行检索
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raw_knowledge_str = qa_manager.get_knowledge(current_context_text)
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if raw_knowledge_str:
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# 3. 解析知识并检查相关性
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knowledge_content, max_relevance = parse_knowledge_and_get_max_relevance(raw_knowledge_str)
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logger.info(f"{self.log_prefix} 阶段 '{step_name}' 检索到知识，最高相关性: {max_relevance:.4f}")
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if max_relevance >= threshold:
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logger.info(f"{self.log_prefix} 阶段 '{step_name}' 满足阈值 ({max_relevance:.4f} >= {threshold})，采纳此知识。")
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logger.info(
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f"{self.log_prefix} 阶段 '{step_name}' 满足阈值 ({max_relevance:.4f} >= {threshold})，采纳此知识。"
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)
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final_knowledge_to_add = knowledge_content
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retrieval_source_info = f"阶段 '{step_name}' (最近{limit}条, 相关性 {max_relevance:.4f})"
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break # 找到符合条件的知识，跳出阶梯循环
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break # 找到符合条件的知识，跳出阶梯循环
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else:
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logger.info(f"{self.log_prefix} 阶段 '{step_name}' 未满足阈值 ({max_relevance:.4f} < {threshold})，继续下一阶段。")
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logger.info(
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f"{self.log_prefix} 阶段 '{step_name}' 未满足阈值 ({max_relevance:.4f} < {threshold})，继续下一阶段。"
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)
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else:
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logger.debug(f"{self.log_prefix} 阶段 '{step_name}' 未从知识库检索到任何内容。")
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except Exception as e_step:
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logger.error(f"{self.log_prefix} 阶梯检索阶段 '{step_name}' 发生错误: {e_step}")
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logger.error(traceback.format_exc())
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continue # 当前阶段出错，尝试下一阶段
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continue # 当前阶段出错，尝试下一阶段
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# 阶段3: 如果前面的阶梯都没有成功，则使用完整的 chat_observe_info (即您配置的20条)
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if not final_knowledge_to_add and chat_observe_info: # 确保 chat_observe_info 可用
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logger.info(f"{self.log_prefix} 前序阶梯均未满足条件，尝试使用完整观察窗口 ('{observation.max_now_obs_len}'条)进行检索。")
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if not final_knowledge_to_add and chat_observe_info: # 确保 chat_observe_info 可用
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logger.info(
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f"{self.log_prefix} 前序阶梯均未满足条件，尝试使用完整观察窗口 ('{observation.max_now_obs_len}'条)进行检索。"
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)
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try:
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raw_knowledge_str = qa_manager.get_knowledge(chat_observe_info)
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if raw_knowledge_str:
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# 对于完整窗口，我们可能不强制要求阈值，或者使用一个较低的阈值
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# 或者，您可以选择在这里仍然应用一个阈值，例如 self.knowledge_retrieval_steps 中最后一个的阈值，或一个特定值
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knowledge_content, max_relevance = parse_knowledge_and_get_max_relevance(raw_knowledge_str)
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logger.info(f"{self.log_prefix} 完整窗口检索到知识，（此处未设阈值，或相关性: {max_relevance:.4f}）。")
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final_knowledge_to_add = knowledge_content # 默认采纳
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retrieval_source_info = f"完整窗口 (最多{observation.max_now_obs_len}条, 相关性 {max_relevance:.4f})"
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logger.info(
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f"{self.log_prefix} 完整窗口检索到知识，（此处未设阈值，或相关性: {max_relevance:.4f}）。"
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)
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final_knowledge_to_add = knowledge_content # 默认采纳
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retrieval_source_info = (
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f"完整窗口 (最多{observation.max_now_obs_len}条, 相关性 {max_relevance:.4f})"
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)
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else:
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logger.debug(f"{self.log_prefix} 完整窗口检索也未找到知识。")
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except Exception as e_full:
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logger.error(f"{self.log_prefix} 完整窗口知识检索发生错误: {e_full}")
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logger.error(traceback.format_exc())
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# 将最终选定的知识（如果有）添加到 structured_info
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if final_knowledge_to_add:
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knowledge_item = {
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"type": "lpmm_knowledge",
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"id": f"lpmm_knowledge_{time.time()}",
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"content": final_knowledge_to_add,
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"ttl": 1 # 由于是当轮精心选择的，可以让TTL短一些，下次重新评估（或者按照您的意愿设为3）
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"ttl": 1, # 由于是当轮精心选择的，可以让TTL短一些，下次重新评估（或者按照您的意愿设为3）
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}
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# 我们在方法开头已经清理了旧的 lpmm_knowledge，这里直接添加新的
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self.structured_info.append(knowledge_item)
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logger.info(f"{self.log_prefix} 添加了来自 '{retrieval_source_info}' 的知识到 structured_info (ID: {knowledge_item['id']})")
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self._update_structured_info_str() # 更新字符串表示
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logger.info(
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f"{self.log_prefix} 添加了来自 '{retrieval_source_info}' 的知识到 structured_info (ID: {knowledge_item['id']})"
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)
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self._update_structured_info_str() # 更新字符串表示
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else:
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logger.info(f"{self.log_prefix} 经过所有阶梯检索后，没有最终采纳的知识。")
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