mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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277 lines
12 KiB
Python
277 lines
12 KiB
Python
# encoding:utf-8
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import time
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import json
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import openai
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import openai.error
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from bot.bot import Bot
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from bot.session_manager import SessionManager
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from bridge.context import ContextType
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from bridge.reply import Reply, ReplyType
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from common.log import logger
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from config import conf, load_config
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from .modelscope_session import ModelScopeSession
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import requests
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# ModelScope对话模型API
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class ModelScopeBot(Bot):
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def __init__(self):
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super().__init__()
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self.sessions = SessionManager(ModelScopeSession, model=conf().get("model") or "Qwen/Qwen2.5-7B-Instruct")
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model = conf().get("model") or "Qwen/Qwen2.5-7B-Instruct"
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if model == "modelscope":
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model = "Qwen/Qwen2.5-7B-Instruct"
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self.args = {
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"model": model, # 对话模型的名称
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"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。
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"top_p": conf().get("top_p", 1.0), # 使用默认值
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}
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self.api_key = conf().get("modelscope_api_key")
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self.base_url = conf().get("modelscope_base_url", "https://api-inference.modelscope.cn/v1/chat/completions")
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"""
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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。
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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commend/const.py文件也可以获取模型列表。
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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。
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"""
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def reply(self, query, context=None):
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# acquire reply content
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if context.type == ContextType.TEXT:
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logger.info("[MODELSCOPE_AI] query={}".format(query))
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session_id = context["session_id"]
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reply = None
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clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
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if query in clear_memory_commands:
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self.sessions.clear_session(session_id)
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reply = Reply(ReplyType.INFO, "记忆已清除")
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elif query == "#清除所有":
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self.sessions.clear_all_session()
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reply = Reply(ReplyType.INFO, "所有人记忆已清除")
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elif query == "#更新配置":
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load_config()
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reply = Reply(ReplyType.INFO, "配置已更新")
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if reply:
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return reply
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session = self.sessions.session_query(query, session_id)
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logger.debug("[MODELSCOPE_AI] session query={}".format(session.messages))
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model = context.get("modelscope_model")
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new_args = self.args.copy()
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if model:
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new_args["model"] = model
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if new_args["model"] == "Qwen/QwQ-32B":
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reply_content = self.reply_text_stream(session, args=new_args)
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else:
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reply_content = self.reply_text(session, args=new_args)
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logger.debug(
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"[MODELSCOPE_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
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session.messages,
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session_id,
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reply_content["content"],
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reply_content["completion_tokens"],
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)
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)
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if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
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# 只有当 content 为空且 completion_tokens 为 0 时才标记为错误
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if len(reply_content["content"]) == 0:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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else:
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reply = Reply(ReplyType.TEXT, reply_content["content"])
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elif reply_content["completion_tokens"] > 0:
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self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
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reply = Reply(ReplyType.TEXT, reply_content["content"])
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else:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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logger.debug("[MODELSCOPE_AI] reply {} used 0 tokens.".format(reply_content))
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return reply
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elif context.type == ContextType.IMAGE_CREATE:
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ok, retstring = self.create_img(query, 0)
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reply = None
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if ok:
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reply = Reply(ReplyType.IMAGE_URL, retstring)
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else:
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reply = Reply(ReplyType.ERROR, retstring)
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return reply
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else:
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reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
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return reply
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def reply_text(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
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"""
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call openai's ChatCompletion to get the answer
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:param session: a conversation session
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:param session_id: session id
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:param retry_count: retry count
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:return: {}
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"""
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer " + self.api_key
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}
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body = args
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body["messages"] = session.messages
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res = requests.post(
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self.base_url,
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headers=headers,
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data=json.dumps(body)
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)
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if res.status_code == 200:
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response = res.json()
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return {
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"total_tokens": response["usage"]["total_tokens"],
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"completion_tokens": response["usage"]["completion_tokens"],
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"content": response["choices"][0]["message"]["content"]
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}
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else:
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response = res.json()
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if "errors" in response:
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error = response.get("errors")
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elif "error" in response:
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error = response.get("error")
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else:
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error = "Unknown error"
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logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
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f"msg={error.get('message')}, type={error.get('type')}")
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result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
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need_retry = False
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if res.status_code >= 500:
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# server error, need retry
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logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
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need_retry = retry_count < 2
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elif res.status_code == 401:
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result["content"] = "授权失败，请检查API Key是否正确"
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elif res.status_code == 429:
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result["content"] = "请求过于频繁，请稍后再试"
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need_retry = retry_count < 2
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else:
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need_retry = False
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if need_retry:
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time.sleep(3)
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return self.reply_text(session, args, retry_count + 1)
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else:
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return result
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except Exception as e:
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logger.exception(e)
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need_retry = retry_count < 2
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result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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if need_retry:
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return self.reply_text(session, args, retry_count + 1)
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else:
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return result
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def reply_text_stream(self, session: ModelScopeSession, args=None, retry_count=0) -> dict:
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"""
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call ModelScope's ChatCompletion to get the answer with stream response
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:param session: a conversation session
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:param session_id: session id
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:param retry_count: retry count
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:return: {}
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"""
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try:
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer " + self.api_key
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}
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body = args
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body["messages"] = session.messages
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body["stream"] = True # 启用流式响应
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res = requests.post(
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self.base_url,
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headers=headers,
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data=json.dumps(body),
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stream=True
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)
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if res.status_code == 200:
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content = ""
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for line in res.iter_lines():
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if line:
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decoded_line = line.decode('utf-8')
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if decoded_line.startswith("data: "):
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try:
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json_data = json.loads(decoded_line[6:])
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delta_content = json_data.get("choices", [{}])[0].get("delta", {}).get("content", "")
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if delta_content:
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content += delta_content
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except json.JSONDecodeError as e:
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pass
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return {
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"total_tokens": 1, # 流式响应通常不返回token使用情况
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"completion_tokens": 1,
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"content": content
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}
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else:
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response = res.json()
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if "errors" in response:
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error = response.get("errors")
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elif "error" in response:
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error = response.get("error")
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else:
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error = "Unknown error"
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logger.error(f"[MODELSCOPE_AI] chat failed, status_code={res.status_code}, "
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f"msg={error.get('message')}, type={error.get('type')}")
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result = {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
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need_retry = False
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if res.status_code >= 500:
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# server error, need retry
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logger.warn(f"[MODELSCOPE_AI] do retry, times={retry_count}")
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need_retry = retry_count < 2
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elif res.status_code == 401:
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result["content"] = "授权失败，请检查API Key是否正确"
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elif res.status_code == 429:
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result["content"] = "请求过于频繁，请稍后再试"
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need_retry = retry_count < 2
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else:
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need_retry = False
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if need_retry:
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time.sleep(3)
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return self.reply_text_stream(session, args, retry_count + 1)
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else:
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return result
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except Exception as e:
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logger.exception(e)
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need_retry = retry_count < 2
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result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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if need_retry:
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return self.reply_text_stream(session, args, retry_count + 1)
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else:
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return result
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def create_img(self, query, retry_count=0):
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try:
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logger.info("[ModelScopeImage] image_query={}".format(query))
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headers = {
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"Content-Type": "application/json; charset=utf-8", # 明确指定编码
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"Authorization": f"Bearer {self.api_key}"
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}
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payload = {
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"prompt": query, # required
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"n": 1,
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"model": conf().get("text_to_image"),
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}
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url = "https://api-inference.modelscope.cn/v1/images/generations"
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# 手动序列化并保留中文（禁用 ASCII 转义）
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json_payload = json.dumps(payload, ensure_ascii=False).encode('utf-8')
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# 使用 data 参数发送原始字符串（requests 会自动处理编码）
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res = requests.post(url, headers=headers, data=json_payload)
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response_data = res.json()
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image_url = response_data['images'][0]['url']
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logger.info("[ModelScopeImage] image_url={}".format(image_url))
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return True, image_url
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except Exception as e:
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logger.error(format(e))
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return False, "画图出现问题，请休息一下再问我吧" |