refactor(models)：统一请求处理并优化响应处理 (refactor/unified_request)
对 `utils_model.py` 中的请求处理逻辑进行重构，创建统一的请求执行方法 `_execute_request`。该方法集中处理请求构建、重试逻辑和响应处理，替代了 `generate_response`、`generate_response_for_image` 和 `generate_response_async` 中的冗余代码。 关键变更： - 引入 `_execute_request` 作为 API 请求的单一入口 - 新增支持自定义重试策略和响应处理器 - 通过 `_build_payload` 简化图像和文本载荷构建 - 改进错误处理和日志记录 - 移除已弃用的同步方法 - 加入了`max_response_length`以兼容koboldcpp硬编码的默认值500 此次重构在保持现有功能的同时提高了代码可维护性，减少了重复代码
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KawaiiYusora
@@ -32,16 +32,18 @@ def combine_messages(messages: List[Message]) -> str:
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message.time))
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name = message.user_nickname or f"用户{message.user_id}"
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content = message.processed_plain_text or message.plain_text
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result += f"[{time_str}] {name}: {content}\n"
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return result
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def db_message_to_str (message_dict: Dict) -> str:
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def db_message_to_str(message_dict: Dict) -> str:
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print(f"message_dict: {message_dict}")
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time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"]))
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try:
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name="[(%s)%s]%s" % (message_dict['user_id'],message_dict.get("user_nickname", ""),message_dict.get("user_cardname", ""))
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name = "[(%s)%s]%s" % (
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message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", ""))
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except:
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name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}"
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content = message_dict.get("processed_plain_text", "")
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@@ -58,6 +60,7 @@ def is_mentioned_bot_in_message(message: Message) -> bool:
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return True
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return False
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def is_mentioned_bot_in_txt(message: str) -> bool:
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"""检查消息是否提到了机器人"""
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keywords = [global_config.BOT_NICKNAME]
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@@ -66,10 +69,13 @@ def is_mentioned_bot_in_txt(message: str) -> bool:
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return True
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return False
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def get_embedding(text):
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async def get_embedding(text):
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"""获取文本的embedding向量"""
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llm = LLM_request(model=global_config.embedding)
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return llm.get_embedding_sync(text)
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# return llm.get_embedding_sync(text)
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return await llm.get_embedding(text)
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def cosine_similarity(v1, v2):
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dot_product = np.dot(v1, v2)
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@@ -77,51 +83,54 @@ def cosine_similarity(v1, v2):
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norm2 = np.linalg.norm(v2)
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return dot_product / (norm1 * norm2)
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def calculate_information_content(text):
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"""计算文本的信息量（熵）"""
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char_count = Counter(text)
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total_chars = len(text)
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entropy = 0
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for count in char_count.values():
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probability = count / total_chars
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entropy -= probability * math.log2(probability)
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return entropy
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def get_cloest_chat_from_db(db, length: int, timestamp: str):
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"""从数据库中获取最接近指定时间戳的聊天记录，并记录读取次数"""
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chat_text = ''
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closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)])
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if closest_record and closest_record.get('memorized', 0) < 4:
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if closest_record and closest_record.get('memorized', 0) < 4:
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closest_time = closest_record['time']
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group_id = closest_record['group_id'] # 获取groupid
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# 获取该时间戳之后的length条消息，且groupid相同
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chat_records = list(db.db.messages.find(
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{"time": {"$gt": closest_time}, "group_id": group_id}
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).sort('time', 1).limit(length))
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# 更新每条消息的memorized属性
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for record in chat_records:
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# 检查当前记录的memorized值
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current_memorized = record.get('memorized', 0)
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if current_memorized > 3:
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if current_memorized > 3:
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# print(f"消息已读取3次，跳过")
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return ''
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# 更新memorized值
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db.db.messages.update_one(
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{"_id": record["_id"]},
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{"$set": {"memorized": current_memorized + 1}}
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)
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chat_text += record["detailed_plain_text"]
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return chat_text
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# print(f"消息已读取3次，跳过")
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return ''
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def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
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"""从数据库获取群组最近的消息记录
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@@ -134,7 +143,7 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
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list: Message对象列表，按时间正序排列
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"""
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# 从数据库获取最近消息
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# 从数据库获取最近消息
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recent_messages = list(db.db.messages.find(
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{"group_id": group_id},
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# {
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@@ -149,7 +158,7 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
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if not recent_messages:
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return []
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# 转换为 Message对象列表
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from .message import Message
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message_objects = []
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@@ -168,12 +177,13 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list:
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except KeyError:
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print("[WARNING] 数据库中存在无效的消息")
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continue
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# 按时间正序排列
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message_objects.reverse()
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return message_objects
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def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,combine = False):
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def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12, combine=False):
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recent_messages = list(db.db.messages.find(
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{"group_id": group_id},
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{
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@@ -187,16 +197,16 @@ def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,comb
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if not recent_messages:
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return []
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message_detailed_plain_text = ''
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message_detailed_plain_text_list = []
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# 反转消息列表，使最新的消息在最后
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recent_messages.reverse()
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if combine:
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for msg_db_data in recent_messages:
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message_detailed_plain_text+=str(msg_db_data["detailed_plain_text"])
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message_detailed_plain_text += str(msg_db_data["detailed_plain_text"])
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return message_detailed_plain_text
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else:
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for msg_db_data in recent_messages:
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@@ -204,7 +214,6 @@ def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,comb
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return message_detailed_plain_text_list
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def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
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"""将文本分割成句子，但保持书名号中的内容完整
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Args:
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@@ -224,30 +233,30 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
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split_strength = 0.7
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else:
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split_strength = 0.9
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#先移除换行符
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# 先移除换行符
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# print(f"split_strength: {split_strength}")
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# print(f"处理前的文本: {text}")
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# 统一将英文逗号转换为中文逗号
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text = text.replace(',', '，')
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text = text.replace('\n', ' ')
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# print(f"处理前的文本: {text}")
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text_no_1 = ''
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for letter in text:
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# print(f"当前字符: {letter}")
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if letter in ['!','！','?','？']:
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if letter in ['!', '！', '?', '？']:
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# print(f"当前字符: {letter}, 随机数: {random.random()}")
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if random.random() < split_strength:
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letter = ''
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if letter in ['。','…']:
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if letter in ['。', '…']:
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# print(f"当前字符: {letter}, 随机数: {random.random()}")
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if random.random() < 1 - split_strength:
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letter = ''
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text_no_1 += letter
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# 对每个逗号单独判断是否分割
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sentences = [text_no_1]
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new_sentences = []
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@@ -276,15 +285,16 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
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sentences_done = []
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for sentence in sentences:
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sentence = sentence.rstrip('，,')
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if random.random() < split_strength*0.5:
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if random.random() < split_strength * 0.5:
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sentence = sentence.replace('，', '').replace(',', '')
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elif random.random() < split_strength:
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sentence = sentence.replace('，', ' ').replace(',', ' ')
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sentences_done.append(sentence)
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print(f"处理后的句子: {sentences_done}")
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return sentences_done
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# 常见的错别字映射
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TYPO_DICT = {
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'的': '地得',
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@@ -355,6 +365,7 @@ TYPO_DICT = {
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'嘻': '嘻西希'
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}
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def random_remove_punctuation(text: str) -> str:
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"""随机处理标点符号，模拟人类打字习惯
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@@ -366,7 +377,7 @@ def random_remove_punctuation(text: str) -> str:
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"""
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result = ''
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text_len = len(text)
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for i, char in enumerate(text):
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if char == '。' and i == text_len - 1: # 结尾的句号
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if random.random() > 0.4: # 80%概率删除结尾句号
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@@ -381,6 +392,7 @@ def random_remove_punctuation(text: str) -> str:
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result += char
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return result
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def add_typos(text: str) -> str:
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TYPO_RATE = 0.02 # 控制错别字出现的概率(2%)
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result = ""
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@@ -393,20 +405,22 @@ def add_typos(text: str) -> str:
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result += char
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return result
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def process_llm_response(text: str) -> List[str]:
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# processed_response = process_text_with_typos(content)
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if len(text) > 300:
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print(f"回复过长 ({len(text)} 字符)，返回默认回复")
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return ['懒得说']
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print(f"回复过长 ({len(text)} 字符)，返回默认回复")
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return ['懒得说']
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# 处理长消息
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sentences = split_into_sentences_w_remove_punctuation(add_typos(text))
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# 检查分割后的消息数量是否过多（超过3条）
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if len(sentences) > 4:
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print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复")
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return [f'{global_config.BOT_NICKNAME}不知道哦']
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return sentences
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def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_time: float = 0.1) -> float:
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"""
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计算输入字符串所需的时间，中文和英文字符有不同的输入时间
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@@ -419,32 +433,10 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_
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if '\u4e00' <= char <= '\u9fff': # 判断是否为中文字符
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total_time += chinese_time
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else: # 其他字符（如英文）
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total_time += english_time
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total_time += english_time
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return total_time
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def find_similar_topics(message_txt: str, all_memory_topic: list, top_k: int = 5) -> list:
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"""使用重排序API找出与输入文本最相似的话题
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Args:
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message_txt: 输入文本
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all_memory_topic: 所有记忆主题列表
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top_k: 返回最相似的话题数量
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Returns:
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list: 最相似话题列表及其相似度分数
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"""
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if not all_memory_topic:
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return []
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try:
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llm = LLM_request(model=global_config.rerank)
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return llm.rerank_sync(message_txt, all_memory_topic, top_k)
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except Exception as e:
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print(f"重排序API调用出错: {str(e)}")
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return []
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def cosine_similarity(v1, v2):
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"""计算余弦相似度"""
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dot_product = np.dot(v1, v2)
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@@ -454,6 +446,7 @@ def cosine_similarity(v1, v2):
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return 0
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return dot_product / (norm1 * norm2)
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def text_to_vector(text):
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"""将文本转换为词频向量"""
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# 分词
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@@ -462,11 +455,12 @@ def text_to_vector(text):
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word_freq = Counter(words)
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return word_freq
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def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list:
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"""使用简单的余弦相似度计算文本相似度"""
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# 将输入文本转换为词频向量
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text_vector = text_to_vector(text)
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# 计算每个主题的相似度
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similarities = []
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for topic in topics:
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@@ -479,6 +473,6 @@ def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list:
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# 计算相似度
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similarity = cosine_similarity(v1, v2)
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similarities.append((topic, similarity))
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# 按相似度降序排序并返回前k个
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return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]
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return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k]
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Reference in New Issue
Block a user