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utils 方法解释还原

pull/937/head
Bakadax 2025-05-10 14:08:44 +08:00
parent 28eb827c5f
commit cd0a41dec6
1 changed files with 28 additions and 11 deletions
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39
src/plugins/PFC/pfc_utils.py
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@ -63,9 +63,9 @@ async def find_most_relevant_historical_message(
log_source_of_limit = f"回退逻辑 (排除最近 {fallback_exclude_seconds} 秒)" log_source_of_limit = f"回退逻辑 (排除最近 {fallback_exclude_seconds} 秒)"
logger.debug(f"[{chat_id}] (私聊历史) find_most_relevant_historical_message: " logger.debug(f"[{chat_id}] (私聊历史) find_most_relevant_historical_message: "
f"将使用时间上限 {effective_search_upper_limit} " f"将使用时间上限 {effective_search_upper_limit} "
f"(可读: {datetime.fromtimestamp(effective_search_upper_limit).strftime('%Y-%m-%d %H:%M:%S')}) " f"(可读: {datetime.fromtimestamp(effective_search_upper_limit).strftime('%Y-%m-%d %H:%M:%S')}) "
f"进行历史消息锚点搜索。来源: {log_source_of_limit}") f"进行历史消息锚点搜索。来源: {log_source_of_limit}")
# --- [新代码结束] --- # --- [新代码结束] ---
pipeline = [ pipeline = [
@ -202,6 +202,13 @@ async def retrieve_contextual_info(
) -> Tuple[str, str, str]: # 返回: 全局记忆, 知识, 私聊历史回忆 ) -> Tuple[str, str, str]: # 返回: 全局记忆, 知识, 私聊历史回忆
""" """
检索三种类型的上下文信息：全局压缩记忆、知识库知识、当前私聊的特定历史对话。 检索三种类型的上下文信息：全局压缩记忆、知识库知识、当前私聊的特定历史对话。
Args:
text: 用于检索的上下文文本 (例如聊天记录)。
private_name: 私聊对象的名称，用于日志记录。
Returns:
Tuple[str, str]: (检索到的记忆字符串, 检索到的知识字符串)
""" """
# 初始化返回值 # 初始化返回值
retrieved_global_memory_str = "无相关全局记忆。" retrieved_global_memory_str = "无相关全局记忆。"
@ -294,9 +301,9 @@ async def retrieve_contextual_info(
log_earliest_time_str = str(current_short_term_history_earliest_time) log_earliest_time_str = str(current_short_term_history_earliest_time)
logger.debug(f"[{private_name}] (私聊历史) retrieve_contextual_info: " logger.debug(f"[{private_name}] (私聊历史) retrieve_contextual_info: "
f"最终用于历史搜索的时间上限: {final_search_upper_limit_time} " f"最终用于历史搜索的时间上限: {final_search_upper_limit_time} "
f"(可读: {datetime.fromtimestamp(final_search_upper_limit_time).strftime('%Y-%m-%d %H:%M:%S')}). " f"(可读: {datetime.fromtimestamp(final_search_upper_limit_time).strftime('%Y-%m-%d %H:%M:%S')}). "
f"基于默认排除 {default_search_exclude_seconds}s 和 '最近记录'片段开始时间: {log_earliest_time_str}") f"基于默认排除 {default_search_exclude_seconds}s 和 '最近记录'片段开始时间: {log_earliest_time_str}")
most_relevant_message_doc = await find_most_relevant_historical_message( most_relevant_message_doc = await find_most_relevant_historical_message(
@ -314,7 +321,7 @@ async def retrieve_contextual_info(
# 校验锚点时间是否真的符合我们的硬性上限 (理论上 find_most_relevant_historical_message 内部已保证) # 校验锚点时间是否真的符合我们的硬性上限 (理论上 find_most_relevant_historical_message 内部已保证)
if anchor_time is not None and anchor_time >= final_search_upper_limit_time: if anchor_time is not None and anchor_time >= final_search_upper_limit_time:
logger.warning(f"[{private_name}] (私聊历史) find_most_relevant_historical_message 返回的锚点时间 {anchor_time} " logger.warning(f"[{private_name}] (私聊历史) find_most_relevant_historical_message 返回的锚点时间 {anchor_time} "
f"并未严格小于最终搜索上限 {final_search_upper_limit_time}。可能导致重叠。跳过构建上下文。") f"并未严格小于最终搜索上限 {final_search_upper_limit_time}。可能导致重叠。跳过构建上下文。")
historical_chat_log_msg = "检索到的锚点不符合最终时间要求，可能导致重叠。" historical_chat_log_msg = "检索到的锚点不符合最终时间要求，可能导致重叠。"
# 直接进入下一个分支 (else)，使得 retrieved_historical_chat_str 保持默认值 # 直接进入下一个分支 (else)，使得 retrieved_historical_chat_str 保持默认值
elif anchor_id and anchor_time is not None: elif anchor_id and anchor_time is not None:
@ -323,8 +330,8 @@ async def retrieve_contextual_info(
time_limit_for_context_window_after = final_search_upper_limit_time time_limit_for_context_window_after = final_search_upper_limit_time
logger.debug(f"[{private_name}] (私聊历史) 调用 retrieve_chat_context_window " logger.debug(f"[{private_name}] (私聊历史) 调用 retrieve_chat_context_window "
f"with anchor_time: {anchor_time}, " f"with anchor_time: {anchor_time}, "
f"excluded_time_threshold_for_window: {time_limit_for_context_window_after}") f"excluded_time_threshold_for_window: {time_limit_for_context_window_after}")
context_window_messages = await retrieve_chat_context_window( context_window_messages = await retrieve_chat_context_window(
chat_id=chat_id, chat_id=chat_id,
@ -377,7 +384,17 @@ def get_items_from_json(
allow_array: bool = True, allow_array: bool = True,
) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: ) -> Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]:
"""从文本中提取JSON内容并获取指定字段 """从文本中提取JSON内容并获取指定字段
(保持你原始 pfc_utils.py 中的此函数代码不变)
Args:
content: 包含JSON的文本
private_name: 私聊名称
*items: 要提取的字段名
default_values: 字段的默认值，格式为 {字段名: 默认值}
required_types: 字段的必需类型，格式为 {字段名: 类型}
allow_array: 是否允许解析JSON数组
Returns:
Tuple[bool, Union[Dict[str, Any], List[Dict[str, Any]]]]: (是否成功, 提取的字段字典或字典列表)
""" """
cleaned_content = content.strip() cleaned_content = content.strip()
result: Union[Dict[str, Any], List[Dict[str, Any]]] = {} result: Union[Dict[str, Any], List[Dict[str, Any]]] = {}
@ -540,7 +557,7 @@ async def adjust_relationship_value_nonlinear(old_value: float, raw_adjustment:
async def build_chat_history_text(observation_info: ObservationInfo, private_name: str) -> str: async def build_chat_history_text(observation_info: ObservationInfo, private_name: str) -> str:
""" (保持你原始 pfc_utils.py 中的此函数代码不变) """ """构建聊天历史记录文本 (包含未处理消息)"""
chat_history_text = "" chat_history_text = ""
try: try:
if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str: if hasattr(observation_info, "chat_history_str") and observation_info.chat_history_str: