From 11807fda38f0d341fa19838149a959dee4310606 Mon Sep 17 00:00:00 2001 From: KawaiiYusora Date: Thu, 6 Mar 2025 23:50:14 +0800 Subject: [PATCH] =?UTF-8?q?refactor(models)=EF=BC=9A=E7=BB=9F=E4=B8=80?= =?UTF-8?q?=E8=AF=B7=E6=B1=82=E5=A4=84=E7=90=86=E5=B9=B6=E4=BC=98=E5=8C=96?= =?UTF-8?q?=E5=93=8D=E5=BA=94=E5=A4=84=E7=90=86=20(refactor/unified=5Frequ?= =?UTF-8?q?est)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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 --- config/bot_config_template.toml | 1 + src/plugins/chat/config.py | 3 + src/plugins/chat/cq_code.py | 24 +- src/plugins/chat/prompt_builder.py | 8 +- src/plugins/chat/utils.py | 122 +++-- src/plugins/memory_system/memory.py | 2 +- src/plugins/models/utils_model.py | 706 +++++++--------------------- 7 files changed, 243 insertions(+), 623 deletions(-) diff --git a/config/bot_config_template.toml b/config/bot_config_template.toml index 28ffb0ce3..f3582de12 100644 --- a/config/bot_config_template.toml +++ b/config/bot_config_template.toml @@ -28,6 +28,7 @@ enable_pic_translate = false model_r1_probability = 0.8 # 麦麦回答时选择R1模型的概率 model_v3_probability = 0.1 # 麦麦回答时选择V3模型的概率 model_r1_distill_probability = 0.1 # 麦麦回答时选择R1蒸馏模型的概率 +max_response_length = 1024 # 麦麦回答的最大token数 [memory] build_memory_interval = 300 # 记忆构建间隔 单位秒 diff --git a/src/plugins/chat/config.py b/src/plugins/chat/config.py index d5ee364ce..ba1ca0b71 100644 --- a/src/plugins/chat/config.py +++ b/src/plugins/chat/config.py @@ -32,6 +32,8 @@ class BotConfig: EMOJI_REGISTER_INTERVAL: int = 10 # 表情包注册间隔（分钟） ban_words = set() + + max_response_length: int = 1024 # 最大回复长度 # 模型配置 llm_reasoning: Dict[str, str] = field(default_factory=lambda: {}) @@ -113,6 +115,7 @@ class BotConfig: config.MODEL_R1_DISTILL_PROBABILITY = response_config.get("model_r1_distill_probability", config.MODEL_R1_DISTILL_PROBABILITY) config.API_USING = response_config.get("api_using", config.API_USING) config.API_PAID = response_config.get("api_paid", config.API_PAID) + config.max_response_length = response_config.get("max_response_length", config.max_response_length) # 加载模型配置 if "model" in toml_dict: diff --git a/src/plugins/chat/cq_code.py b/src/plugins/chat/cq_code.py index 4d70736cd..df93c6fa2 100644 --- a/src/plugins/chat/cq_code.py +++ b/src/plugins/chat/cq_code.py @@ -64,15 +64,15 @@ class CQCode: """初始化LLM实例""" self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300) - def translate(self): + async def translate(self): """根据CQ码类型进行相应的翻译处理""" if self.type == 'text': self.translated_plain_text = self.params.get('text', '') elif self.type == 'image': if self.params.get('sub_type') == '0': - self.translated_plain_text = self.translate_image() + self.translated_plain_text = await self.translate_image() else: - self.translated_plain_text = self.translate_emoji() + self.translated_plain_text = await self.translate_emoji() elif self.type == 'at': user_nickname = get_user_nickname(self.params.get('qq', '')) if user_nickname: @@ -158,7 +158,7 @@ class CQCode: return None - def translate_emoji(self) -> str: + async def translate_emoji(self) -> str: """处理表情包类型的CQ码""" if 'url' not in self.params: return '[表情包]' @@ -167,12 +167,12 @@ class CQCode: # 将 base64 字符串转换为字节类型 image_bytes = base64.b64decode(base64_str) storage_emoji(image_bytes) - return self.get_emoji_description(base64_str) + return await self.get_emoji_description(base64_str) else: return '[表情包]' - def translate_image(self) -> str: + async def translate_image(self) -> str: """处理图片类型的CQ码，区分普通图片和表情包""" #没有url，直接返回默认文本 if 'url' not in self.params: @@ -181,25 +181,27 @@ class CQCode: if base64_str: image_bytes = base64.b64decode(base64_str) storage_image(image_bytes) - return self.get_image_description(base64_str) + return await self.get_image_description(base64_str) else: return '[图片]' - def get_emoji_description(self, image_base64: str) -> str: + async def get_emoji_description(self, image_base64: str) -> str: """调用AI接口获取表情包描述""" try: prompt = "这是一个表情包，请用简短的中文描述这个表情包传达的情感和含义。最多20个字。" - description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + description, _ = await self._llm.generate_response_for_image(prompt, image_base64) return f"[表情包：{description}]" except Exception as e: print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") return "[表情包]" - def get_image_description(self, image_base64: str) -> str: + async def get_image_description(self, image_base64: str) -> str: """调用AI接口获取普通图片描述""" try: prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。" - description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + # description, _ = self._llm.generate_response_for_image_sync(prompt, image_base64) + description, _ = await self._llm.generate_response_for_image(prompt, image_base64) return f"[图片：{description}]" except Exception as e: print(f"\033[1;31m[错误]\033[0m AI接口调用失败: {str(e)}") diff --git a/src/plugins/chat/prompt_builder.py b/src/plugins/chat/prompt_builder.py index 1c510e251..1c1431577 100644 --- a/src/plugins/chat/prompt_builder.py +++ b/src/plugins/chat/prompt_builder.py @@ -2,7 +2,7 @@ import time import random from ..schedule.schedule_generator import bot_schedule import os -from .utils import get_embedding, combine_messages, get_recent_group_detailed_plain_text,find_similar_topics +from .utils import get_embedding, combine_messages, get_recent_group_detailed_plain_text from ...common.database import Database from .config import global_config from .topic_identifier import topic_identifier @@ -60,7 +60,7 @@ class PromptBuilder: prompt_info = '' promt_info_prompt = '' - prompt_info = self.get_prompt_info(message_txt,threshold=0.5) + prompt_info = await self.get_prompt_info(message_txt,threshold=0.5) if prompt_info: prompt_info = f'''\n----------------------------------------------------\n你有以下这些[知识]：\n{prompt_info}\n请你记住上面的[知识]，之后可能会用到\n----------------------------------------------------\n''' @@ -214,10 +214,10 @@ class PromptBuilder: return prompt_for_initiative - def get_prompt_info(self,message:str,threshold:float): + async def get_prompt_info(self,message:str,threshold:float): related_info = '' print(f"\033[1;34m[调试]\033[0m 获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") - embedding = get_embedding(message) + embedding = await get_embedding(message) related_info += self.get_info_from_db(embedding,threshold=threshold) return related_info diff --git a/src/plugins/chat/utils.py b/src/plugins/chat/utils.py index 63daf6680..38aeefd21 100644 --- a/src/plugins/chat/utils.py +++ b/src/plugins/chat/utils.py @@ -32,16 +32,18 @@ def combine_messages(messages: List[Message]) -> str: time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message.time)) name = message.user_nickname or f"用户{message.user_id}" content = message.processed_plain_text or message.plain_text - + result += f"[{time_str}] {name}: {content}\n" - + return result -def db_message_to_str (message_dict: Dict) -> str: + +def db_message_to_str(message_dict: Dict) -> str: print(f"message_dict: {message_dict}") time_str = time.strftime("%m-%d %H:%M:%S", time.localtime(message_dict["time"])) try: - name="[(%s)%s]%s" % (message_dict['user_id'],message_dict.get("user_nickname", ""),message_dict.get("user_cardname", "")) + name = "[(%s)%s]%s" % ( + message_dict['user_id'], message_dict.get("user_nickname", ""), message_dict.get("user_cardname", "")) except: name = message_dict.get("user_nickname", "") or f"用户{message_dict['user_id']}" content = message_dict.get("processed_plain_text", "") @@ -58,6 +60,7 @@ def is_mentioned_bot_in_message(message: Message) -> bool: return True return False + def is_mentioned_bot_in_txt(message: str) -> bool: """检查消息是否提到了机器人""" keywords = [global_config.BOT_NICKNAME] @@ -66,10 +69,13 @@ def is_mentioned_bot_in_txt(message: str) -> bool: return True return False -def get_embedding(text): + +async def get_embedding(text): """获取文本的embedding向量""" llm = LLM_request(model=global_config.embedding) - return llm.get_embedding_sync(text) + # return llm.get_embedding_sync(text) + return await llm.get_embedding(text) + def cosine_similarity(v1, v2): dot_product = np.dot(v1, v2) @@ -77,51 +83,54 @@ def cosine_similarity(v1, v2): norm2 = np.linalg.norm(v2) return dot_product / (norm1 * norm2) + def calculate_information_content(text): """计算文本的信息量（熵）""" char_count = Counter(text) total_chars = len(text) - + entropy = 0 for count in char_count.values(): probability = count / total_chars entropy -= probability * math.log2(probability) - + return entropy + def get_cloest_chat_from_db(db, length: int, timestamp: str): """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""" chat_text = '' closest_record = db.db.messages.find_one({"time": {"$lte": timestamp}}, sort=[('time', -1)]) - - if closest_record and closest_record.get('memorized', 0) < 4: + + if closest_record and closest_record.get('memorized', 0) < 4: closest_time = closest_record['time'] group_id = closest_record['group_id'] # 获取groupid # 获取该时间戳之后的length条消息，且groupid相同 chat_records = list(db.db.messages.find( {"time": {"$gt": closest_time}, "group_id": group_id} ).sort('time', 1).limit(length)) - + # 更新每条消息的memorized属性 for record in chat_records: # 检查当前记录的memorized值 current_memorized = record.get('memorized', 0) - if current_memorized > 3: + if current_memorized > 3: # print(f"消息已读取3次，跳过") return '' - + # 更新memorized值 db.db.messages.update_one( {"_id": record["_id"]}, {"$set": {"memorized": current_memorized + 1}} ) - + chat_text += record["detailed_plain_text"] - + return chat_text # print(f"消息已读取3次，跳过") return '' + def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: """从数据库获取群组最近的消息记录 @@ -134,7 +143,7 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: list: Message对象列表，按时间正序排列 """ - # 从数据库获取最近消息 + # 从数据库获取最近消息 recent_messages = list(db.db.messages.find( {"group_id": group_id}, # { @@ -149,7 +158,7 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: if not recent_messages: return [] - + # 转换为 Message对象列表 from .message import Message message_objects = [] @@ -168,12 +177,13 @@ def get_recent_group_messages(db, group_id: int, limit: int = 12) -> list: except KeyError: print("[WARNING] 数据库中存在无效的消息") continue - + # 按时间正序排列 message_objects.reverse() return message_objects -def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,combine = False): + +def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12, combine=False): recent_messages = list(db.db.messages.find( {"group_id": group_id}, { @@ -187,16 +197,16 @@ def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,comb if not recent_messages: return [] - + message_detailed_plain_text = '' message_detailed_plain_text_list = [] - + # 反转消息列表，使最新的消息在最后 recent_messages.reverse() - + if combine: for msg_db_data in recent_messages: - message_detailed_plain_text+=str(msg_db_data["detailed_plain_text"]) + message_detailed_plain_text += str(msg_db_data["detailed_plain_text"]) return message_detailed_plain_text else: for msg_db_data in recent_messages: @@ -204,7 +214,6 @@ def get_recent_group_detailed_plain_text(db, group_id: int, limit: int = 12,comb return message_detailed_plain_text_list - def split_into_sentences_w_remove_punctuation(text: str) -> List[str]: """将文本分割成句子，但保持书名号中的内容完整 Args: @@ -224,30 +233,30 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]: split_strength = 0.7 else: split_strength = 0.9 - #先移除换行符 + # 先移除换行符 # print(f"split_strength: {split_strength}") - + # print(f"处理前的文本: {text}") - + # 统一将英文逗号转换为中文逗号 text = text.replace(',', '，') text = text.replace('\n', ' ') - + # print(f"处理前的文本: {text}") - + text_no_1 = '' for letter in text: # print(f"当前字符: {letter}") - if letter in ['!','！','?','？']: + if letter in ['!', '！', '?', '？']: # print(f"当前字符: {letter}, 随机数: {random.random()}") if random.random() < split_strength: letter = '' - if letter in ['。','…']: + if letter in ['。', '…']: # print(f"当前字符: {letter}, 随机数: {random.random()}") if random.random() < 1 - split_strength: letter = '' text_no_1 += letter - + # 对每个逗号单独判断是否分割 sentences = [text_no_1] new_sentences = [] @@ -276,15 +285,16 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]: sentences_done = [] for sentence in sentences: sentence = sentence.rstrip('，,') - if random.random() < split_strength*0.5: + if random.random() < split_strength * 0.5: sentence = sentence.replace('，', '').replace(',', '') elif random.random() < split_strength: sentence = sentence.replace('，', ' ').replace(',', ' ') sentences_done.append(sentence) - + print(f"处理后的句子: {sentences_done}") return sentences_done + # 常见的错别字映射 TYPO_DICT = { '的': '地得', @@ -355,6 +365,7 @@ TYPO_DICT = { '嘻': '嘻西希' } + def random_remove_punctuation(text: str) -> str: """随机处理标点符号，模拟人类打字习惯 @@ -366,7 +377,7 @@ def random_remove_punctuation(text: str) -> str: """ result = '' text_len = len(text) - + for i, char in enumerate(text): if char == '。' and i == text_len - 1: # 结尾的句号 if random.random() > 0.4: # 80%概率删除结尾句号 @@ -381,6 +392,7 @@ def random_remove_punctuation(text: str) -> str: result += char return result + def add_typos(text: str) -> str: TYPO_RATE = 0.02 # 控制错别字出现的概率(2%) result = "" @@ -393,20 +405,22 @@ def add_typos(text: str) -> str: result += char return result + def process_llm_response(text: str) -> List[str]: # processed_response = process_text_with_typos(content) if len(text) > 300: - print(f"回复过长 ({len(text)} 字符)，返回默认回复") - return ['懒得说'] + print(f"回复过长 ({len(text)} 字符)，返回默认回复") + return ['懒得说'] # 处理长消息 sentences = split_into_sentences_w_remove_punctuation(add_typos(text)) # 检查分割后的消息数量是否过多（超过3条） if len(sentences) > 4: print(f"分割后消息数量过多 ({len(sentences)} 条)，返回默认回复") return [f'{global_config.BOT_NICKNAME}不知道哦'] - + return sentences + def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_time: float = 0.1) -> float: """ 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 @@ -419,32 +433,10 @@ def calculate_typing_time(input_string: str, chinese_time: float = 0.2, english_ if '\u4e00' <= char <= '\u9fff': # 判断是否为中文字符 total_time += chinese_time else: # 其他字符（如英文） - total_time += english_time + total_time += english_time return total_time -def find_similar_topics(message_txt: str, all_memory_topic: list, top_k: int = 5) -> list: - """使用重排序API找出与输入文本最相似的话题 - - Args: - message_txt: 输入文本 - all_memory_topic: 所有记忆主题列表 - top_k: 返回最相似的话题数量 - - Returns: - list: 最相似话题列表及其相似度分数 - """ - - if not all_memory_topic: - return [] - - try: - llm = LLM_request(model=global_config.rerank) - return llm.rerank_sync(message_txt, all_memory_topic, top_k) - except Exception as e: - print(f"重排序API调用出错: {str(e)}") - return [] - def cosine_similarity(v1, v2): """计算余弦相似度""" dot_product = np.dot(v1, v2) @@ -454,6 +446,7 @@ def cosine_similarity(v1, v2): return 0 return dot_product / (norm1 * norm2) + def text_to_vector(text): """将文本转换为词频向量""" # 分词 @@ -462,11 +455,12 @@ def text_to_vector(text): word_freq = Counter(words) return word_freq + def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list: """使用简单的余弦相似度计算文本相似度""" # 将输入文本转换为词频向量 text_vector = text_to_vector(text) - + # 计算每个主题的相似度 similarities = [] for topic in topics: @@ -479,6 +473,6 @@ def find_similar_topics_simple(text: str, topics: list, top_k: int = 5) -> list: # 计算相似度 similarity = cosine_similarity(v1, v2) similarities.append((topic, similarity)) - + # 按相似度降序排序并返回前k个 - return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k] \ No newline at end of file + return sorted(similarities, key=lambda x: x[1], reverse=True)[:top_k] diff --git a/src/plugins/memory_system/memory.py b/src/plugins/memory_system/memory.py index cdb6e6e1b..43db3729d 100644 --- a/src/plugins/memory_system/memory.py +++ b/src/plugins/memory_system/memory.py @@ -11,7 +11,7 @@ from ..chat.config import global_config from ...common.database import Database # 使用正确的导入语法 from ..models.utils_model import LLM_request import math -from ..chat.utils import calculate_information_content, get_cloest_chat_from_db ,find_similar_topics,text_to_vector,cosine_similarity +from ..chat.utils import calculate_information_content, get_cloest_chat_from_db ,text_to_vector,cosine_similarity diff --git a/src/plugins/models/utils_model.py b/src/plugins/models/utils_model.py index 2801a3553..3e4d7f1a2 100644 --- a/src/plugins/models/utils_model.py +++ b/src/plugins/models/utils_model.py @@ -25,354 +25,195 @@ class LLM_request: self.model_name = model["name"] self.params = kwargs - async def generate_response(self, prompt: str) -> Tuple[str, str]: - """根据输入的提示生成模型的异步响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" + async def _execute_request( + self, + endpoint: str, + prompt: str = None, + image_base64: str = None, + payload: dict = None, + retry_policy: dict = None, + response_handler: callable = None, + ): + """统一请求执行入口 + Args: + endpoint: API端点路径 (如 "chat/completions") + prompt: prompt文本 + image_base64: 图片的base64编码 + payload: 请求体数据 + is_async: 是否异步 + retry_policy: 自定义重试策略 + (示例: {"max_retries":3, "base_wait":15, "retry_codes":[429,500]}) + response_handler: 自定义响应处理器 + """ + # 合并重试策略 + default_retry = { + "max_retries": 3, "base_wait": 15, + "retry_codes": [429, 413, 500, 503], + "abort_codes": [400, 401, 402, 403]} + policy = {**default_retry, **(retry_policy or {})} + + # 常见Error Code Mapping + error_code_mapping = { + 400: "参数不正确", + 401: "API key 错误，认证失败", + 402: "账号余额不足", + 403: "需要实名,或余额不足", + 404: "Not Found", + 429: "请求过于频繁，请稍后再试", + 500: "服务器内部故障", + 503: "服务器负载过高" } + api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}" + logger.info(f"发送请求到URL: {api_url}{self.model_name}") + # 构建请求体 - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - **self.params - } + if image_base64: + payload = await self._build_payload(prompt, image_base64) + elif payload is None: + payload = await self._build_payload(prompt) - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - logger.info(f"发送请求到URL: {api_url}{self.model_name}") # 记录请求的URL + session_method = aiohttp.ClientSession() - max_retries = 3 - base_wait_time = 15 - - for retry in range(max_retries): + for retry in range(policy["max_retries"]): try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue + # 使用上下文管理器处理会话 + headers = await self._build_headers() - if response.status in [500, 503]: - logger.error(f"服务器错误: {response.status}") - raise RuntimeError("服务器负载过高，模型恢复失败QAQ") + async with session_method as session: + response = await session.post(api_url, headers=headers, json=payload) - response.raise_for_status() # 检查其他响应状态 + # 处理需要重试的状态码 + if response.status in policy["retry_codes"]: + wait_time = policy["base_wait"] * (2 ** retry) + logger.warning(f"错误码: {response.status}, 等待 {wait_time}秒后重试") + if response.status == 413: + logger.warning("请求体过大，尝试压缩...") + image_base64 = compress_base64_image_by_scale(image_base64) + payload = await self._build_payload(prompt, image_base64) + elif response.status in [500, 503]: + logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}") + raise RuntimeError("服务器负载过高，模型恢复失败QAQ") + else: + logger.warning(f"请求限制(429)，等待{wait_time}秒后重试...") - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - message = result["choices"][0]["message"] - content = message.get("content", "") - think_match = None - reasoning_content = message.get("reasoning_content", "") - if not reasoning_content: - think_match = re.search(r'(?:)?(.*?)', content, re.DOTALL) - if think_match: - reasoning_content = think_match.group(1).strip() - content = re.sub(r'(?:)?.*?', '', content, flags=re.DOTALL, count=1).strip() - return content, reasoning_content - return "没有返回结果", "" + await asyncio.sleep(wait_time) + continue + elif response.status in policy["abort_codes"]: + logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}") + raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}") + + response.raise_for_status() + result = await response.json() + + # 使用自定义处理器或默认处理 + return response_handler(result) if response_handler else self._default_response_handler(result) except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) + if retry < policy["max_retries"] - 1: + wait_time = policy["base_wait"] * (2 ** retry) + logger.error(f"请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") await asyncio.sleep(wait_time) else: - logger.critical(f"请求失败: {str(e)}", exc_info=True) - logger.critical(f"请求头: {headers} 请求体: {data}") + logger.critical(f"请求失败: {str(e)}") + logger.critical(f"请求头: {self._build_headers()} 请求体: {payload}") raise RuntimeError(f"API请求失败: {str(e)}") logger.error("达到最大重试次数，请求仍然失败") raise RuntimeError("达到最大重试次数，API请求仍然失败") - async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]: - """根据输入的提示和图片生成模型的异步响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - # 构建请求体 - def build_request_data(img_base64: str): + async def _build_payload(self, prompt: str, image_base64: str = None) -> dict: + """构建请求体""" + if image_base64: return { "model": self.model_name, "messages": [ { "role": "user", "content": [ - { - "type": "text", - "text": prompt - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{img_base64}" - } - } + {"type": "text", "text": prompt}, + {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}} ] } ], + "max_tokens": global_config.max_response_length, + **self.params + } + else: + return { + "model": self.model_name, + "messages": [{"role": "user", "content": prompt}], + "max_tokens": global_config.max_response_length, **self.params } + def _default_response_handler(self, result: dict) -> Tuple: + """默认响应解析""" + if "choices" in result and result["choices"]: + message = result["choices"][0]["message"] + content = message.get("content", "") + content, reasoning = self._extract_reasoning(content) + reasoning_content = message.get("model_extra", {}).get("reasoning_content", "") + if not reasoning_content: + reasoning_content = reasoning - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - logger.info(f"发送请求到URL: {api_url}{self.model_name}") # 记录请求的URL + return content, reasoning_content - max_retries = 3 - base_wait_time = 15 + return "没有返回结果", "" - current_image_base64 = image_base64 - current_image_base64 = compress_base64_image_by_scale(current_image_base64) + def _extract_reasoning(self, content: str) -> tuple[str, str]: + """CoT思维链提取""" + match = re.search(r'(?:)?(.*?)', content, re.DOTALL) + content = re.sub(r'(?:)?.*?', '', content, flags=re.DOTALL, count=1).strip() + if match: + reasoning = match.group(1).strip() + else: + reasoning = "" + return content, reasoning - for retry in range(max_retries): - try: - data = build_request_data(current_image_base64) - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - elif response.status == 413: - logger.warning("图片太大(413)，尝试压缩...") - current_image_base64 = compress_base64_image_by_scale(current_image_base64) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - message = result["choices"][0]["message"] - content = message.get("content", "") - think_match = None - reasoning_content = message.get("reasoning_content", "") - if not reasoning_content: - think_match = re.search(r'(?:)?(.*?)', content, re.DOTALL) - if think_match: - reasoning_content = think_match.group(1).strip() - content = re.sub(r'(?:)?.*?', '', content, flags=re.DOTALL, count=1).strip() - return content, reasoning_content - return "没有返回结果", "" - - except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[image回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - await asyncio.sleep(wait_time) - else: - logger.critical(f"请求失败: {str(e)}", exc_info=True) - logger.critical(f"请求头: {headers} 请求体: {data}") - raise RuntimeError(f"API请求失败: {str(e)}") - - logger.error("达到最大重试次数，请求仍然失败") - raise RuntimeError("达到最大重试次数，API请求仍然失败") - - async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: - """异步方式根据输入的提示生成模型的响应""" - headers = { + async def _build_headers(self) -> dict: + """构建请求头""" + return { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" } + async def generate_response(self, prompt: str) -> Tuple[str, str]: + """根据输入的提示生成模型的异步响应""" + + content, reasoning_content = await self._execute_request( + endpoint="/chat/completions", + prompt=prompt + ) + return content, reasoning_content + + async def generate_response_for_image(self, prompt: str, image_base64: str) -> Tuple[str, str]: + """根据输入的提示和图片生成模型的异步响应""" + + content, reasoning_content = await self._execute_request( + endpoint="/chat/completions", + prompt=prompt, + image_base64=image_base64 + ) + return content, reasoning_content + + async def generate_response_async(self, prompt: str) -> Union[str, Tuple[str, str]]: + """异步方式根据输入的提示生成模型的响应""" # 构建请求体 data = { "model": self.model_name, "messages": [{"role": "user", "content": prompt}], "temperature": 0.5, + "max_tokens": global_config.max_response_length, **self.params } - # 发送请求到完整的 chat/completions 端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - logger.info(f"Request URL: {api_url}") # 记录请求的 URL - - max_retries = 3 - base_wait_time = 15 - - async with aiohttp.ClientSession() as session: - for retry in range(max_retries): - try: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) # 指数退避 - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = await response.json() - if "choices" in result and len(result["choices"]) > 0: - message = result["choices"][0]["message"] - content = message.get("content", "") - think_match = None - reasoning_content = message.get("reasoning_content", "") - if not reasoning_content: - think_match = re.search(r'(?:)?(.*?)', content, re.DOTALL) - if think_match: - reasoning_content = think_match.group(1).strip() - content = re.sub(r'(?:)?.*?', '', content, flags=re.DOTALL, count=1).strip() - return content, reasoning_content - return "没有返回结果", "" - - except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}") - await asyncio.sleep(wait_time) - else: - logger.error(f"请求失败: {str(e)}") - logger.critical(f"请求头: {headers} 请求体: {data}") - return f"请求失败: {str(e)}", "" - - logger.error("达到最大重试次数，请求仍然失败") - return "达到最大重试次数，请求仍然失败", "" - - - - def generate_response_for_image_sync(self, prompt: str, image_base64: str) -> Tuple[str, str]: - """同步方法：根据输入的提示和图片生成模型的响应""" - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - image_base64=compress_base64_image_by_scale(image_base64) - - # 构建请求体 - data = { - "model": self.model_name, - "messages": [ - { - "role": "user", - "content": [ - { - "type": "text", - "text": prompt - }, - { - "type": "image_url", - "image_url": { - "url": f"data:image/jpeg;base64,{image_base64}" - } - } - ] - } - ], - **self.params - } - - # 发送请求到完整的chat/completions端点 - api_url = f"{self.base_url.rstrip('/')}/chat/completions" - logger.info(f"发送请求到URL: {api_url}{self.model_name}") # 记录请求的URL - - max_retries = 2 - base_wait_time = 6 - - for retry in range(max_retries): - try: - response = requests.post(api_url, headers=headers, json=data, timeout=30) - - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() # 检查其他响应状态 - - result = response.json() - if "choices" in result and len(result["choices"]) > 0: - message = result["choices"][0]["message"] - content = message.get("content", "") - think_match = None - reasoning_content = message.get("reasoning_content", "") - if not reasoning_content: - think_match = re.search(r'(?:)?(.*?)', content, re.DOTALL) - if think_match: - reasoning_content = think_match.group(1).strip() - content = re.sub(r'(?:)?.*?', '', content, flags=re.DOTALL, count=1).strip() - return content, reasoning_content - return "没有返回结果", "" - - except Exception as e: - if retry < max_retries - 1: # 如果还有重试机会 - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[image_sync回复]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - time.sleep(wait_time) - else: - logger.critical(f"请求失败: {str(e)}", exc_info=True) - logger.critical(f"请求头: {headers} 请求体: {data}") - raise RuntimeError(f"API请求失败: {str(e)}") - - logger.error("达到最大重试次数，请求仍然失败") - raise RuntimeError("达到最大重试次数，API请求仍然失败") - - def get_embedding_sync(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]: - """同步方法：获取文本的embedding向量 - - Args: - text: 需要获取embedding的文本 - model: 使用的模型名称，默认为"BAAI/bge-m3" - - Returns: - list: embedding向量，如果失败则返回None - """ - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - data = { - "model": model, - "input": text, - "encoding_format": "float" - } - - api_url = f"{self.base_url.rstrip('/')}/embeddings" - logger.info(f"发送请求到URL: {api_url}{self.model_name}") # 记录请求的URL - - max_retries = 2 - base_wait_time = 6 - - for retry in range(max_retries): - try: - response = requests.post(api_url, headers=headers, json=data, timeout=30) - - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - response.raise_for_status() - - result = response.json() - if 'data' in result and len(result['data']) > 0: - return result['data'][0]['embedding'] - return None - - except Exception as e: - if retry < max_retries - 1: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[embedding_sync]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - time.sleep(wait_time) - else: - logger.critical(f"embedding请求失败: {str(e)}", exc_info=True) - logger.critical(f"请求头: {headers} 请求体: {data}") - return None - - logger.error("达到最大重试次数，embedding请求仍然失败") - return None + content, reasoning_content = await self._execute_request( + endpoint="/chat/completions", + payload=data, + prompt=prompt + ) + return content, reasoning_content async def get_embedding(self, text: str, model: str = "BAAI/bge-m3") -> Union[list, None]: """异步方法：获取文本的embedding向量 @@ -384,245 +225,24 @@ class LLM_request: Returns: list: embedding向量，如果失败则返回None """ - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } + def embedding_handler(result): + """处理响应""" + if "data" in result and len(result["data"]) > 0: + return result["data"][0].get("embedding", None) + return None - data = { - "model": model, - "input": text, - "encoding_format": "float" - } - - api_url = f"{self.base_url.rstrip('/')}/embeddings" - logger.info(f"发送请求到URL: {api_url}{self.model_name}") # 记录请求的URL - - max_retries = 3 - base_wait_time = 15 - - for retry in range(max_retries): - try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - response.raise_for_status() - - result = await response.json() - if 'data' in result and len(result['data']) > 0: - return result['data'][0]['embedding'] - return None - - except Exception as e: - if retry < max_retries - 1: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[embedding]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - await asyncio.sleep(wait_time) - else: - logger.critical(f"embedding请求失败: {str(e)}", exc_info=True) - logger.critical(f"请求头: {headers} 请求体: {data}") - return None - - logger.error("达到最大重试次数，embedding请求仍然失败") - return None - - def rerank_sync(self, query: str, documents: list, top_k: int = 5) -> list: - """同步方法：使用重排序API对文档进行排序 - - Args: - query: 查询文本 - documents: 待排序的文档列表 - top_k: 返回前k个结果 - - Returns: - list: [(document, score), ...] 格式的结果列表 - """ - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - data = { - "model": self.model_name, - "query": query, - "documents": documents, - "top_n": top_k, - "return_documents": True, - } - - api_url = f"{self.base_url.rstrip('/')}/rerank" - logger.info(f"发送请求到URL: {api_url}") - - max_retries = 2 - base_wait_time = 6 - - for retry in range(max_retries): - try: - response = requests.post(api_url, headers=headers, json=data, timeout=30) - - if response.status_code == 429: - wait_time = base_wait_time * (2 ** retry) - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - time.sleep(wait_time) - continue - - if response.status_code in [500, 503]: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"服务器错误({response.status_code})，等待{wait_time}秒后重试...") - if retry < max_retries - 1: - time.sleep(wait_time) - continue - else: - # 如果是最后一次重试，尝试使用chat/completions作为备选方案 - return self._fallback_rerank_with_chat(query, documents, top_k) - - response.raise_for_status() - - result = response.json() - if 'results' in result: - return [(item["document"], item["score"]) for item in result["results"]] - return [] - - except Exception as e: - if retry < max_retries - 1: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[rerank_sync]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - time.sleep(wait_time) - else: - logger.critical(f"重排序请求失败: {str(e)}", exc_info=True) - - logger.error("达到最大重试次数，重排序请求仍然失败") - return [] - - async def rerank(self, query: str, documents: list, top_k: int = 5) -> list: - """异步方法：使用重排序API对文档进行排序 - - Args: - query: 查询文本 - documents: 待排序的文档列表 - top_k: 返回前k个结果 - - Returns: - list: [(document, score), ...] 格式的结果列表 - """ - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - data = { - "model": self.model_name, - "query": query, - "documents": documents, - "top_n": top_k, - "return_documents": True, - } - - api_url = f"{self.base_url.rstrip('/')}/v1/rerank" - logger.info(f"发送请求到URL: {api_url}") - - max_retries = 3 - base_wait_time = 15 - - for retry in range(max_retries): - try: - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - if response.status == 429: - wait_time = base_wait_time * (2 ** retry) - logger.warning(f"遇到请求限制(429)，等待{wait_time}秒后重试...") - await asyncio.sleep(wait_time) - continue - - if response.status in [500, 503]: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"服务器错误({response.status})，等待{wait_time}秒后重试...") - if retry < max_retries - 1: - await asyncio.sleep(wait_time) - continue - else: - # 如果是最后一次重试，尝试使用chat/completions作为备选方案 - return await self._fallback_rerank_with_chat_async(query, documents, top_k) - - response.raise_for_status() - - result = await response.json() - if 'results' in result: - return [(item["document"], item["score"]) for item in result["results"]] - return [] - - except Exception as e: - if retry < max_retries - 1: - wait_time = base_wait_time * (2 ** retry) - logger.error(f"[rerank]请求失败，等待{wait_time}秒后重试... 错误: {str(e)}", exc_info=True) - await asyncio.sleep(wait_time) - else: - logger.critical(f"重排序请求失败: {str(e)}", exc_info=True) - # 作为最后的备选方案，尝试使用chat/completions - return await self._fallback_rerank_with_chat_async(query, documents, top_k) - - logger.error("达到最大重试次数，重排序请求仍然失败") - return [] - - async def _fallback_rerank_with_chat_async(self, query: str, documents: list, top_k: int = 5) -> list: - """当rerank API失败时的备选方案，使用chat/completions异步实现重排序 - - Args: - query: 查询文本 - documents: 待排序的文档列表 - top_k: 返回前k个结果 - - Returns: - list: [(document, score), ...] 格式的结果列表 - """ - try: - logger.info("使用chat/completions作为重排序的备选方案") - - # 构建提示词 - prompt = f"""请对以下文档列表进行重排序，按照与查询的相关性从高到低排序。 -查询: {query} - -文档列表: -{documents} - -请以JSON格式返回排序结果，格式为： -[{{"document": "文档内容", "score": 相关性分数}}, ...] -只返回JSON，不要其他任何文字。""" - - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } - - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - **self.params - } - - api_url = f"{self.base_url.rstrip('/')}/v1/chat/completions" - - async with aiohttp.ClientSession() as session: - async with session.post(api_url, headers=headers, json=data) as response: - response.raise_for_status() - result = await response.json() - - if "choices" in result and len(result["choices"]) > 0: - message = result["choices"][0]["message"] - content = message.get("content", "") - try: - import json - parsed_content = json.loads(content) - if isinstance(parsed_content, list): - return [(item["document"], item["score"]) for item in parsed_content] - except: - pass - return [] - except Exception as e: - logger.error(f"备选方案也失败了: {str(e)}") - return [] + embedding = await self._execute_request( + endpoint="/embeddings", + prompt=text, + payload={ + "model": model, + "input": text, + "encoding_format": "float" + }, + retry_policy={ + "max_retries": 2, + "base_wait": 6 + }, + response_handler=embedding_handler + ) + return embedding