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202 lines
6.8 KiB
Python
202 lines
6.8 KiB
Python
"""OpenAI 接口底层封装
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目前使用的对话接口有：
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ChatCompletion - gpt-3.5-turbo 等模型
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Completion - text-davinci-003 等模型
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此模块封装此两个接口的请求实现，为上层提供统一的调用方式
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"""
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import openai, logging, threading, asyncio
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import openai.error as aiE
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from pkg.openai.api.model import RequestBase
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from pkg.openai.api.completion import CompletionRequest
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from pkg.openai.api.chat_completion import ChatCompletionRequest
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COMPLETION_MODELS = {
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'text-davinci-003',
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'text-davinci-002',
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'code-davinci-002',
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'code-cushman-001',
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'text-curie-001',
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'text-babbage-001',
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'text-ada-001',
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}
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CHAT_COMPLETION_MODELS = {
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'gpt-3.5-turbo',
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'gpt-3.5-turbo-16k',
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'gpt-3.5-turbo-0613',
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'gpt-3.5-turbo-16k-0613',
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# 'gpt-3.5-turbo-0301',
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'gpt-4',
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'gpt-4-0613',
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'gpt-4-32k',
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'gpt-4-32k-0613'
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}
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EDIT_MODELS = {
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}
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IMAGE_MODELS = {
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}
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# class ModelRequest:
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# """模型接口请求父类"""
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# can_chat = False
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# runtime: threading.Thread = None
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# ret = {}
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# proxy: str = None
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# request_ready = True
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# error_info: str = "若在没有任何错误的情况下看到这句话，请带着配置文件上报Issues"
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# def __init__(self, model_name, user_name, request_fun, http_proxy:str = None, time_out = None):
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# self.model_name = model_name
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# self.user_name = user_name
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# self.request_fun = request_fun
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# self.time_out = time_out
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# if http_proxy != None:
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# self.proxy = http_proxy
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# openai.proxy = self.proxy
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# self.request_ready = False
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# async def __a_request__(self, **kwargs):
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# """异步请求"""
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# try:
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# self.ret: dict = await self.request_fun(**kwargs)
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# self.request_ready = True
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# except aiE.APIConnectionError as e:
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# self.error_info = "{}\n请检查网络连接或代理是否正常".format(e)
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# raise ConnectionError(self.error_info)
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# except ValueError as e:
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# self.error_info = "{}\n该错误可能是由于http_proxy格式设置错误引起的"
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# except Exception as e:
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# self.error_info = "{}\n由于请求异常产生的未知错误，请查看日志".format(e)
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# raise type(e)(self.error_info)
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# def request(self, **kwargs):
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# """向接口发起请求"""
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# if self.proxy != None: #异步请求
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# self.request_ready = False
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# loop = asyncio.new_event_loop()
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# self.runtime = threading.Thread(
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# target=loop.run_until_complete,
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# args=(self.__a_request__(**kwargs),)
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# )
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# self.runtime.start()
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# else: #同步请求
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# self.ret = self.request_fun(**kwargs)
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# def __msg_handle__(self, msg):
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# """将prompt dict转换成接口需要的格式"""
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# return msg
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# def ret_handle(self):
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# '''
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# API消息返回处理函数
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# 若重写该方法，应检查异步线程状态，或在需要检查处super该方法
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# '''
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# if self.runtime != None and isinstance(self.runtime, threading.Thread):
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# self.runtime.join(self.time_out)
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# if self.request_ready:
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# return
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# raise Exception(self.error_info)
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# def get_total_tokens(self):
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# try:
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# return self.ret['usage']['total_tokens']
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# except:
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# return 0
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# def get_message(self):
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# return self.message
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# def get_response(self):
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# return self.ret
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# class ChatCompletionModel(ModelRequest):
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# """ChatCompletion接口的请求实现"""
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# Chat_role = ['system', 'user', 'assistant']
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# def __init__(self, model_name, user_name, http_proxy:str = None, **kwargs):
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# if http_proxy == None:
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# request_fun = openai.ChatCompletion.create
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# else:
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# request_fun = openai.ChatCompletion.acreate
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# self.can_chat = True
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# super().__init__(model_name, user_name, request_fun, http_proxy, **kwargs)
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# def request(self, prompts, **kwargs):
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# prompts = self.__msg_handle__(prompts)
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# kwargs['messages'] = prompts
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# super().request(**kwargs)
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# self.ret_handle()
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# def __msg_handle__(self, msgs):
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# temp_msgs = []
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# # 把msgs拷贝进temp_msgs
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# for msg in msgs:
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# temp_msgs.append(msg.copy())
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# return temp_msgs
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# def get_message(self):
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# return self.ret["choices"][0]["message"]['content'] #需要时直接加载加快请求速度，降低内存消耗
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# class CompletionModel(ModelRequest):
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# """Completion接口的请求实现"""
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# def __init__(self, model_name, user_name, http_proxy:str = None, **kwargs):
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# if http_proxy == None:
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# request_fun = openai.Completion.create
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# else:
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# request_fun = openai.Completion.acreate
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# super().__init__(model_name, user_name, request_fun, http_proxy, **kwargs)
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# def request(self, prompts, **kwargs):
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# prompts = self.__msg_handle__(prompts)
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# kwargs['prompt'] = prompts
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# super().request(**kwargs)
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# self.ret_handle()
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# def __msg_handle__(self, msgs):
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# prompt = ''
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# for msg in msgs:
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# prompt = prompt + "{}: {}\n".format(msg['role'], msg['content'])
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# # for msg in msgs:
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# # if msg['role'] == 'assistant':
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# # prompt = prompt + "{}\n".format(msg['content'])
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# # else:
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# # prompt = prompt + "{}:{}\n".format(msg['role'] , msg['content'])
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# prompt = prompt + "assistant: "
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# return prompt
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# def get_message(self):
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# return self.ret["choices"][0]["text"]
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# def create_openai_model_request(model_name: str, user_name: str = 'user', http_proxy:str = None) -> ModelRequest:
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# """使用给定的模型名称创建模型请求对象"""
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# if model_name in CHAT_COMPLETION_MODELS:
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# model = ChatCompletionModel(model_name, user_name, http_proxy)
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# elif model_name in COMPLETION_MODELS:
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# model = CompletionModel(model_name, user_name, http_proxy)
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# else :
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# log = "找不到模型[{}]，请检查配置文件".format(model_name)
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# logging.error(log)
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# raise IndexError(log)
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# logging.debug("使用接口[{}]创建模型请求[{}]".format(model.__class__.__name__, model_name))
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# return model
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def select_request_cls(model_name: str, messages: list, args: dict) -> RequestBase:
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if model_name in CHAT_COMPLETION_MODELS:
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return ChatCompletionRequest(model_name, messages, **args)
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elif model_name in COMPLETION_MODELS:
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return CompletionRequest(model_name, messages, **args)
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raise ValueError("不支持模型[{}]，请检查配置文件".format(model_name)) |