mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into gn-dev
Bakadax
commit
9df84d23c5
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@ -50,21 +50,18 @@ class MessageStorage(ABC):
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class MongoDBMessageStorage(MessageStorage):
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"""MongoDB消息存储实现"""
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def __init__(self):
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self.db = db
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async def get_messages_after(self, chat_id: str, message_time: float) -> List[Dict[str, Any]]:
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query = {"chat_id": chat_id}
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# print(f"storage_check_message: {message_time}")
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query["time"] = {"$gt": message_time}
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return list(self.db.messages.find(query).sort("time", 1))
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return list(db.messages.find(query).sort("time", 1))
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async def get_messages_before(self, chat_id: str, time_point: float, limit: int = 5) -> List[Dict[str, Any]]:
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query = {"chat_id": chat_id, "time": {"$lt": time_point}}
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messages = list(self.db.messages.find(query).sort("time", -1).limit(limit))
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messages = list(db.messages.find(query).sort("time", -1).limit(limit))
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# 将消息按时间正序排列
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messages.reverse()
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@ -73,7 +70,7 @@ class MongoDBMessageStorage(MessageStorage):
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async def has_new_messages(self, chat_id: str, after_time: float) -> bool:
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query = {"chat_id": chat_id, "time": {"$gt": after_time}}
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return self.db.messages.find_one(query) is not None
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return db.messages.find_one(query) is not None
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# # 创建一个内存消息存储实现，用于测试
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@ -269,7 +269,7 @@ class EmojiManager:
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"""
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try:
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self._ensure_db()
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time_start = time.time()
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_time_start = time.time()
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# 获取所有表情包
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all_emojis = self.emoji_objects
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@ -287,35 +287,41 @@ class EmojiManager:
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# 计算与每个emotion标签的相似度，取最大值
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max_similarity = 0
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best_matching_emotion = "" # 记录最匹配的 emotion 喵~
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for emotion in emotions:
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# 使用编辑距离计算相似度
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distance = self._levenshtein_distance(text_emotion, emotion)
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max_len = max(len(text_emotion), len(emotion))
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similarity = 1 - (distance / max_len if max_len > 0 else 0)
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max_similarity = max(max_similarity, similarity)
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if similarity > max_similarity: # 如果找到更相似的喵~
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max_similarity = similarity
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best_matching_emotion = emotion # 就记下这个 emotion 喵~
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emoji_similarities.append((emoji, max_similarity))
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if best_matching_emotion: # 确保有匹配的情感才添加喵~
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emoji_similarities.append((emoji, max_similarity, best_matching_emotion)) # 把 emotion 也存起来喵~
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# 按相似度降序排序
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emoji_similarities.sort(key=lambda x: x[1], reverse=True)
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# 获取前5个最相似的表情包
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top_5_emojis = emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
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# 获取前10个最相似的表情包
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top_emojis = (
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emoji_similarities[:10] if len(emoji_similarities) > 10 else emoji_similarities
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) # 改个名字，更清晰喵~
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if not top_5_emojis:
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if not top_emojis:
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logger.warning("未找到匹配的表情包")
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return None
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# 从前5个中随机选择一个
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selected_emoji, similarity = random.choice(top_5_emojis)
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# 从前几个中随机选择一个
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selected_emoji, similarity, matched_emotion = random.choice(top_emojis) # 把匹配的 emotion 也拿出来喵~
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# 更新使用次数
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self.record_usage(selected_emoji.hash)
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time_end = time.time()
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_time_end = time.time()
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logger.info(
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f"找到[{text_emotion}]表情包,用时:{time_end - time_start:.2f}秒: {selected_emoji.description} (相似度: {similarity:.4f})"
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logger.info( # 使用匹配到的 emotion 记录日志喵~
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f"为[{text_emotion}]找到表情包: {matched_emotion},({similarity:.4f})"
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)
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return selected_emoji.path, f"[ {selected_emoji.description} ]"
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@ -301,9 +301,9 @@ class PromptBuilder:
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relation_prompt = ""
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for person in who_chat_in_group:
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relation_prompt += await relationship_manager.build_relationship_info(person)
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print(f"relation_prompt: {relation_prompt}")
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# print(f"relation_prompt: {relation_prompt}")
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print(f"relat11111111ion_prompt: {relation_prompt}")
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# print(f"relat11111111ion_prompt: {relation_prompt}")
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# 心情
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mood_manager = MoodManager.get_instance()
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@ -1,6 +1,7 @@
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import json
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import logging
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from typing import Any, Dict, TypeVar, List, Union, Tuple
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import ast
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# 定义类型变量用于泛型类型提示
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T = TypeVar("T")
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@ -12,6 +13,7 @@ logger = logging.getLogger("json_utils")
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def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
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"""
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安全地解析JSON字符串，出错时返回默认值
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现在尝试处理单引号和标准JSON
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参数:
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json_str: 要解析的JSON字符串
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@ -20,16 +22,34 @@ def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
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返回:
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解析后的Python对象，或在解析失败时返回default_value
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"""
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if not json_str:
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if not json_str or not isinstance(json_str, str):
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logger.warning(f"safe_json_loads 接收到非字符串输入: {type(json_str)}, 值: {json_str}")
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return default_value
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try:
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# 尝试标准的 JSON 解析
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return json.loads(json_str)
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except json.JSONDecodeError as e:
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logger.error(f"JSON解析失败: {e}, JSON字符串: {json_str[:100]}...")
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return default_value
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except json.JSONDecodeError:
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# 如果标准解析失败，尝试将单引号替换为双引号再解析
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# （注意：这种替换可能不安全，如果字符串内容本身包含引号）
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# 更安全的方式是用 ast.literal_eval
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try:
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# logger.debug(f"标准JSON解析失败，尝试用 ast.literal_eval 解析: {json_str[:100]}...")
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result = ast.literal_eval(json_str)
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# 确保结果是字典（因为我们通常期望参数是字典）
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if isinstance(result, dict):
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return result
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else:
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logger.warning(f"ast.literal_eval 解析成功但结果不是字典: {type(result)}, 内容: {result}")
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return default_value
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except (ValueError, SyntaxError, MemoryError, RecursionError) as ast_e:
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logger.error(f"使用 ast.literal_eval 解析失败: {ast_e}, 字符串: {json_str[:100]}...")
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return default_value
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except Exception as e:
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logger.error(f"使用 ast.literal_eval 解析时发生意外错误: {e}, 字符串: {json_str[:100]}...")
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return default_value
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except Exception as e:
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logger.error(f"JSON解析过程中发生意外错误: {e}")
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logger.error(f"JSON解析过程中发生意外错误: {e}, 字符串: {json_str[:100]}...")
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return default_value
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@ -177,25 +197,27 @@ def process_llm_tool_calls(
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if "name" not in func_details or not isinstance(func_details.get("name"), str):
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'name'或类型不正确: {func_details}")
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continue
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if "arguments" not in func_details or not isinstance(
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func_details.get("arguments"), str
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): # 参数是字符串形式的JSON
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'或类型不正确: {func_details}")
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# 验证参数 'arguments'
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args_value = func_details.get("arguments")
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# 1. 检查 arguments 是否存在且是字符串
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if args_value is None or not isinstance(args_value, str):
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logger.warning(f"{log_prefix}工具调用[{i}]的'function'字段缺少'arguments'字符串: {func_details}")
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continue
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# 可选：尝试解析参数JSON，确保其有效
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args_str = func_details["arguments"]
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try:
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json.loads(args_str) # 尝试解析，但不存储结果
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except json.JSONDecodeError as e:
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# 2. 尝试安全地解析 arguments 字符串
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parsed_args = safe_json_loads(args_value, None)
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# 3. 检查解析结果是否为字典
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if parsed_args is None or not isinstance(parsed_args, dict):
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logger.warning(
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f"{log_prefix}工具调用[{i}]的'arguments'不是有效的JSON字符串: {e}, 内容: {args_str[:100]}..."
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f"{log_prefix}工具调用[{i}]的'arguments'无法解析为有效的JSON字典, "
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f"原始字符串: {args_value[:100]}..., 解析结果类型: {type(parsed_args).__name__}"
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)
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continue
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except Exception as e:
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logger.warning(f"{log_prefix}解析工具调用[{i}]的'arguments'时发生意外错误: {e}, 内容: {args_str[:100]}...")
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continue
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# 如果检查通过，将原始的 tool_call 加入有效列表
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valid_tool_calls.append(tool_call)
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if not valid_tool_calls and tool_calls: # 如果原始列表不为空，但验证后为空
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@ -64,6 +64,9 @@ class ClassicalWillingManager(BaseWillingManager):
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self.chat_reply_willing[chat_id] = max(0, current_willing - 1.8)
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async def after_generate_reply_handle(self, message_id):
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if message_id not in self.ongoing_messages:
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return
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chat_id = self.ongoing_messages[message_id].chat_id
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current_willing = self.chat_reply_willing.get(chat_id, 0)
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if current_willing < 1:
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@ -110,7 +110,7 @@ class BaseWillingManager(ABC):
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def delete(self, message_id: str):
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del_message = self.ongoing_messages.pop(message_id, None)
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if not del_message:
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logger.debug(f"删除异常，当前消息{message_id}不存在")
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logger.debug(f"尝试删除不存在的消息 ID: {message_id}，可能已被其他流程处理，喵~")
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@abstractmethod
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async def async_task_starter(self) -> None:
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