# encoding:utf-8 import time import json import openai import openai.error from bot.bot import Bot from bot.session_manager import SessionManager from bridge.context import ContextType from bridge.reply import Reply, ReplyType from common.log import logger from config import conf, load_config from .modelscope_session import ModelScopeSession import requests # ModelScope对话模型API class ModelScopeBot(Bot): def __init__(self): super().__init__() self.sessions = SessionManager(ModelScopeSession, model=conf().get("model") or "Qwen/Qwen2.5-7B-Instruct") model = conf().get("model") or "Qwen/Qwen2.5-7B-Instruct" if model == "modelscope": model = "Qwen/Qwen2.5-7B-Instruct" self.args = { "model": model, # 对话模型的名称 "temperature": conf().get("temperature", 0.3), # 如果设置，值域须为 [0, 1] 我们推荐 0.3，以达到较合适的效果。 "top_p": conf().get("top_p", 1.0), # 使用默认值 } self.api_key = conf().get("modelscope_api_key") self.base_url = conf().get("modelscope_base_url", "https://api-inference.modelscope.cn/v1/chat/completions") """ 需要获取MOdelScope支持API-inference的模型名称列表，请到魔搭社区官网模型中心查看 https://modelscope.cn/models?filter=inference_type&page=1。 或者使用命令 curl https://api-inference.modelscope.cn/v1/models 对模型列表和ID进行获取。查看bridge.py文件也可以获取模型列表。 获取ModelScope的免费API Key，请到魔搭社区官网用户中心查看获取方式 https://modelscope.cn/docs/model-service/API-Inference/intro。 """ def reply(self, query, context=None): # acquire reply content if context.type == ContextType.TEXT: logger.info("[MODELSCOPE_AI] query={}".format(query)) session_id = context["session_id"] reply = None clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"]) if query in clear_memory_commands: self.sessions.clear_session(session_id) reply = Reply(ReplyType.INFO, "记忆已清除") elif query == "#清除所有": self.sessions.clear_all_session() reply = Reply(ReplyType.INFO, "所有人记忆已清除") elif query == "#更新配置": load_config() reply = Reply(ReplyType.INFO, "配置已更新") if reply: return reply session = self.sessions.session_query(query, session_id) logger.debug("[MODELSCOPE_AI] session query={}".format(session.messages)) model = context.get("modelscope_model") new_args = self.args.copy() if model: new_args["model"] = model if new_args["model"] == "Qwen/QwQ-32B": reply_content = self.reply_text_stream(session, args=new_args) else: reply_content = self.reply_text(session, args=new_args) logger.debug( "[MODELSCOPE_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format( session.messages, session_id, reply_content["content"], reply_content["completion_tokens"], ) ) if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0: # 只有当 content 为空且 completion_tokens 为 0 时才标记为错误 if len(reply_content["content"]) == 0: reply = Reply(ReplyType.ERROR, reply_content["content"]) else: reply = Reply(ReplyType.TEXT, reply_content["content"]) elif reply_content["completion_tokens"] > 0: self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"]) reply = Reply(ReplyType.TEXT, reply_content["content"]) else: reply = Reply(ReplyType.ERROR, reply_content["content"]) logger.debug("[MODELSCOPE_AI] reply {} used 0 tokens.".format(reply_content)) return reply else: reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type)) return reply def reply_text(self, session: ModelScopeSession, args=None, retry_count=0) -> dict: """ call openai's ChatCompletion to get the answer :param session: a conversation session :param session_id: session id :param retry_count: retry count :return: {} """ try: headers = { "Content-Type": "application/json", "Authorization": "Bearer " + self.api_key } body = args body["messages"] = session.messages res = requests.post( self.base_url, headers=headers, data=json.dumps(body) ) if res.status_code == 200: response = res.json() return { "total_tokens": response["usage"]["total_tokens"], "completion_tokens": response["usage"]["completion_tokens"], "content": response["choices"][0]["message"]["content"] } else: response = res.json() error = response.get("error") logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, " f"msg={error.get('message')}, type={error.get('type')}") result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"} need_retry = False if res.status_code >= 500: # server error, need retry logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}") need_retry = retry_count < 2 elif res.status_code == 401: result["content"] = "授权失败，请检查API Key是否正确" elif res.status_code == 429: result["content"] = "请求过于频繁，请稍后再试" need_retry = retry_count < 2 else: need_retry = False if need_retry: time.sleep(3) return self.reply_text(session, args, retry_count + 1) else: return result except Exception as e: logger.exception(e) need_retry = retry_count < 2 result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} if need_retry: return self.reply_text(session, args, retry_count + 1) else: return result def reply_text_stream(self, session: ModelScopeSession, args=None, retry_count=0) -> dict: """ call ModelScope's ChatCompletion to get the answer with stream response :param session: a conversation session :param session_id: session id :param retry_count: retry count :return: {} """ try: headers = { "Content-Type": "application/json", "Authorization": "Bearer " + self.api_key } body = args body["messages"] = session.messages body["stream"] = True # 启用流式响应 res = requests.post( self.base_url, headers=headers, data=json.dumps(body), stream=True ) print(res.status_code) if res.status_code == 200: content = "" for line in res.iter_lines(): if line: decoded_line = line.decode('utf-8') if decoded_line.startswith("data: "): try: json_data = json.loads(decoded_line[6:]) delta_content = json_data.get("choices", [{}])[0].get("delta", {}).get("content", "") if delta_content: content += delta_content except json.JSONDecodeError as e: pass return { "total_tokens": 1, # 流式响应通常不返回token使用情况 "completion_tokens": 1, "content": content } else: response = res.json() error = response.get("error") logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, " f"msg={error.get('message')}, type={error.get('type')}") result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"} need_retry = False if res.status_code >= 500: # server error, need retry logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}") need_retry = retry_count < 2 elif res.status_code == 401: result["content"] = "授权失败，请检查API Key是否正确" elif res.status_code == 429: result["content"] = "请求过于频繁，请稍后再试" need_retry = retry_count < 2 else: need_retry = False if need_retry: time.sleep(3) return self.reply_text_stream(session, args, retry_count + 1) else: return result except Exception as e: logger.exception(e) need_retry = retry_count < 2 result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"} if need_retry: return self.reply_text_stream(session, args, retry_count + 1) else: return result