Ruff Format
This commit is contained in:
DrSmoothl
@@ -35,6 +35,7 @@ logger = get_logger("planner")
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install(extra_lines=3)
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class ActionPlanner:
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def __init__(self, chat_id: str, action_manager: ActionManager):
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self.chat_id = chat_id
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@@ -48,7 +49,7 @@ class ActionPlanner:
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self.last_obs_time_mark = 0.0
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self.plan_log: List[Tuple[str, float, Union[List[ActionPlannerInfo], str]]] = []
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# 黑话缓存：使用 OrderedDict 实现 LRU，最多缓存10个
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self.unknown_words_cache: OrderedDict[str, None] = OrderedDict()
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self.unknown_words_cache_limit = 10
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@@ -111,20 +112,29 @@ class ActionPlanner:
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# 替换 [picid:xxx] 为 [图片：描述]
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pic_pattern = r"\[picid:([^\]]+)\]"
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def replace_pic_id(pic_match: re.Match) -> str:
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pic_id = pic_match.group(1)
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description = translate_pid_to_description(pic_id)
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return f"[图片：{description}]"
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msg_text = re.sub(pic_pattern, replace_pic_id, msg_text)
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# 替换用户引用格式：回复<aaa:bbb> 和 @<aaa:bbb>
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platform = getattr(message, "user_info", None) and message.user_info.platform or getattr(message, "chat_info", None) and message.chat_info.platform or "qq"
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platform = (
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getattr(message, "user_info", None)
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and message.user_info.platform
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or getattr(message, "chat_info", None)
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and message.chat_info.platform
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or "qq"
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)
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msg_text = replace_user_references(msg_text, platform, replace_bot_name=True)
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# 替换单独的 <用户名:用户ID> 格式（replace_user_references 已处理回复<和@<格式）
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# 匹配所有 <aaa:bbb> 格式，由于 replace_user_references 已经替换了回复<和@<格式，
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# 这里匹配到的应该都是单独的格式
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user_ref_pattern = r"<([^:<>]+):([^:<>]+)>"
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def replace_user_ref(user_match: re.Match) -> str:
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user_name = user_match.group(1)
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user_id = user_match.group(2)
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@@ -137,6 +147,7 @@ class ActionPlanner:
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except Exception:
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# 如果解析失败，使用原始昵称
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return user_name
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msg_text = re.sub(user_ref_pattern, replace_user_ref, msg_text)
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preview = msg_text if len(msg_text) <= 100 else f"{msg_text[:97]}..."
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@@ -165,7 +176,7 @@ class ActionPlanner:
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else:
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reasoning = "未提供原因"
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action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
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# 非no_reply动作需要target_message_id
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target_message = None
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@@ -244,7 +255,7 @@ class ActionPlanner:
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def _update_unknown_words_cache(self, new_words: List[str]) -> None:
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"""
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更新黑话缓存，将新的黑话加入缓存
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Args:
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new_words: 新提取的黑话列表
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"""
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@@ -254,7 +265,7 @@ class ActionPlanner:
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word = word.strip()
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if not word:
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continue
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# 如果已存在，移到末尾（LRU）
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if word in self.unknown_words_cache:
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self.unknown_words_cache.move_to_end(word)
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@@ -269,10 +280,10 @@ class ActionPlanner:
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def _merge_unknown_words_with_cache(self, new_words: Optional[List[str]]) -> List[str]:
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"""
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合并新提取的黑话和缓存中的黑话
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Args:
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new_words: 新提取的黑话列表（可能为None）
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Returns:
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合并后的黑话列表（去重）
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"""
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@@ -284,31 +295,29 @@ class ActionPlanner:
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word = word.strip()
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if word:
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cleaned_new_words.append(word)
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# 获取缓存中的黑话列表
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cached_words = list(self.unknown_words_cache.keys())
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# 合并并去重（保留顺序：新提取的在前，缓存的在后）
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merged_words: List[str] = []
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seen = set()
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# 先添加新提取的
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for word in cleaned_new_words:
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if word not in seen:
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merged_words.append(word)
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seen.add(word)
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# 再添加缓存的（如果不在新提取的列表中）
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for word in cached_words:
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if word not in seen:
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merged_words.append(word)
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seen.add(word)
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return merged_words
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def _process_unknown_words_cache(
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self, actions: List[ActionPlannerInfo]
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) -> None:
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def _process_unknown_words_cache(self, actions: List[ActionPlannerInfo]) -> None:
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"""
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处理黑话缓存逻辑：
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1. 检查是否有 reply action 提取了 unknown_words
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@@ -316,7 +325,7 @@ class ActionPlanner:
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3. 如果缓存数量大于5，移除最老的2个
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4. 对于每个 reply action，合并缓存和新提取的黑话
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5. 更新缓存
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Args:
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actions: 解析后的动作列表
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"""
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@@ -330,7 +339,7 @@ class ActionPlanner:
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removed_count += 1
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if removed_count > 0:
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logger.debug(f"{self.log_prefix}缓存数量大于5，移除最老的{removed_count}个缓存")
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# 检查是否有 reply action 提取了 unknown_words
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has_extracted_unknown_words = False
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for action in actions:
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@@ -340,22 +349,22 @@ class ActionPlanner:
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if unknown_words and isinstance(unknown_words, list) and len(unknown_words) > 0:
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has_extracted_unknown_words = True
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break
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# 如果当前 plan 的 reply 没有提取，移除最老的1个
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if not has_extracted_unknown_words:
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if len(self.unknown_words_cache) > 0:
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self.unknown_words_cache.popitem(last=False)
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logger.debug(f"{self.log_prefix}当前 plan 的 reply 没有提取黑话，移除最老的1个缓存")
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# 对于每个 reply action，合并缓存和新提取的黑话
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for action in actions:
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if action.action_type == "reply":
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action_data = action.action_data or {}
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new_words = action_data.get("unknown_words")
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# 合并新提取的和缓存的黑话列表
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merged_words = self._merge_unknown_words_with_cache(new_words)
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# 更新 action_data
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if merged_words:
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action_data["unknown_words"] = merged_words
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@@ -366,7 +375,7 @@ class ActionPlanner:
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else:
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# 如果没有合并后的黑话，移除 unknown_words 字段
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action_data.pop("unknown_words", None)
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# 更新缓存（将新提取的黑话加入缓存）
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if new_words:
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self._update_unknown_words_cache(new_words)
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@@ -442,15 +451,19 @@ class ActionPlanner:
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# 检查是否已经有回复该消息的 action
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has_reply_to_force_message = False
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for action in actions:
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if action.action_type == "reply" and action.action_message and action.action_message.message_id == force_reply_message.message_id:
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if (
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action.action_type == "reply"
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and action.action_message
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and action.action_message.message_id == force_reply_message.message_id
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):
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has_reply_to_force_message = True
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break
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# 如果没有回复该消息，强制添加回复 action
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if not has_reply_to_force_message:
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# 移除所有 no_reply action（如果有）
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actions = [a for a in actions if a.action_type != "no_reply"]
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# 创建强制回复 action
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available_actions_dict = dict(current_available_actions)
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force_reply_action = ActionPlannerInfo(
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@@ -577,10 +590,11 @@ class ActionPlanner:
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if global_config.chat.think_mode == "classic":
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reply_action_example = ""
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if global_config.chat.llm_quote:
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reply_action_example += "5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
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reply_action_example += (
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"5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
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)
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reply_action_example += (
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'{{"action":"reply", "target_message_id":"消息id(m+数字)", '
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'"unknown_words":["词语1","词语2"]'
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'{{"action":"reply", "target_message_id":"消息id(m+数字)", "unknown_words":["词语1","词语2"]'
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)
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if global_config.chat.llm_quote:
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reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
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@@ -590,7 +604,9 @@ class ActionPlanner:
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"5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n"
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)
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if global_config.chat.llm_quote:
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reply_action_example += "6.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
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reply_action_example += (
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"6.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
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)
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reply_action_example += (
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'{{"action":"reply", "think_level":数值等级(0或1), '
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'"target_message_id":"消息id(m+数字)", '
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@@ -741,15 +757,21 @@ class ActionPlanner:
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except Exception as req_e:
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logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
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return f"LLM 请求失败，模型出现问题: {req_e}", [
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ActionPlannerInfo(
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action_type="no_reply",
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reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
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action_data={},
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action_message=None,
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available_actions=available_actions,
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)
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], llm_content, llm_reasoning, llm_duration_ms
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return (
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f"LLM 请求失败，模型出现问题: {req_e}",
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[
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ActionPlannerInfo(
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action_type="no_reply",
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reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
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action_data={},
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action_message=None,
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available_actions=available_actions,
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)
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],
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llm_content,
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llm_reasoning,
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llm_duration_ms,
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)
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# 解析LLM响应
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extracted_reasoning = ""
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