from __future__ import annotations from typing import TYPE_CHECKING, List import logging import json import commentjson as cjson import os import sys import requests import urllib3 from tqdm import tqdm import colorama from duckduckgo_search import ddg import asyncio import aiohttp from enum import Enum from .presets import * from .llama_func import * from .utils import * from . import shared from .config import retrieve_proxy from .base_model import BaseLLMModel, ModelType class OpenAIClient(BaseLLMModel): def __init__( self, model_name, api_key, system_prompt=INITIAL_SYSTEM_PROMPT, temperature=1.0, top_p=1.0, ) -> None: super().__init__( model_name=model_name, temperature=temperature, top_p=top_p, system_prompt=system_prompt, ) self.api_key = api_key self.headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}", } def get_answer_stream_iter(self): response = self._get_response(stream=True) if response is not None: iter = self._decode_chat_response(response) partial_text = "" for i in iter: partial_text += i yield partial_text else: yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG def get_answer_at_once(self): response = self._get_response() response = json.loads(response.text) content = response["choices"][0]["message"]["content"] total_token_count = response["usage"]["total_tokens"] return content, total_token_count def count_token(self, user_input): input_token_count = count_token(construct_user(user_input)) if self.system_prompt is not None and len(self.all_token_counts) == 0: system_prompt_token_count = count_token( construct_system(self.system_prompt) ) return input_token_count + system_prompt_token_count return input_token_count def billing_info(self): try: curr_time = datetime.datetime.now() last_day_of_month = get_last_day_of_month(curr_time).strftime("%Y-%m-%d") first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d") usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}" try: usage_data = self._get_billing_data(usage_url) except Exception as e: logging.error(f"获取API使用情况失败:" + str(e)) return f"**获取API使用情况失败**" rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100) return f"**本月使用金额** \u3000 ${rounded_usage}" except requests.exceptions.ConnectTimeout: status_text = ( STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG ) return status_text except requests.exceptions.ReadTimeout: status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG return status_text except Exception as e: logging.error(f"获取API使用情况失败:" + str(e)) return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG def set_token_upper_limit(self, new_upper_limit): pass @shared.state.switching_api_key # 在不开启多账号模式的时候，这个装饰器不会起作用 def _get_response(self, stream=False): openai_api_key = self.api_key system_prompt = self.system_prompt history = self.history logging.debug(colorama.Fore.YELLOW + f"{history}" + colorama.Fore.RESET) headers = { "Content-Type": "application/json", "Authorization": f"Bearer {openai_api_key}", } if system_prompt is not None: history = [construct_system(system_prompt), *history] payload = { "model": self.model_name, "messages": history, "temperature": self.temperature, "top_p": self.top_p, "n": self.n_choices, "stream": stream, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, } if self.max_generation_token is not None: payload["max_tokens"] = self.max_generation_token if self.stop_sequence is not None: payload["stop"] = self.stop_sequence if self.logit_bias is not None: payload["logit_bias"] = self.logit_bias if self.user_identifier is not None: payload["user"] = self.user_identifier if stream: timeout = TIMEOUT_STREAMING else: timeout = TIMEOUT_ALL # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求 if shared.state.completion_url != COMPLETION_URL: logging.info(f"使用自定义API URL: {shared.state.completion_url}") with retrieve_proxy(): try: response = requests.post( shared.state.completion_url, headers=headers, json=payload, stream=stream, timeout=timeout, ) except: return None return response def _get_billing_data(self, usage_url): with retrieve_proxy(): response = requests.get( usage_url, headers=self.headers, timeout=TIMEOUT_ALL, ) if response.status_code == 200: data = response.json() return data else: raise Exception( f"API request failed with status code {response.status_code}: {response.text}" ) def _decode_chat_response(self, response): for chunk in response.iter_lines(): if chunk: chunk = chunk.decode() chunk_length = len(chunk) try: chunk = json.loads(chunk[6:]) except json.JSONDecodeError: print(f"JSON解析错误,收到的内容: {chunk}") continue if chunk_length > 6 and "delta" in chunk["choices"][0]: if chunk["choices"][0]["finish_reason"] == "stop": break try: yield chunk["choices"][0]["delta"]["content"] except Exception as e: # logging.error(f"Error: {e}") continue def get_model( model_name, access_key=None, temperature=None, top_p=None, system_prompt=None ) -> BaseLLMModel: msg = f"模型设置为了： {model_name}" logging.info(msg) model_type = ModelType.get_type(model_name) if model_type == ModelType.OpenAI: model = OpenAIClient( model_name=model_name, api_key=access_key, system_prompt=system_prompt, temperature=temperature, top_p=top_p, ) return model, msg if __name__ == "__main__": with open("config.json", "r") as f: openai_api_key = cjson.load(f)["openai_api_key"] client = OpenAIClient("gpt-3.5-turbo", openai_api_key) chatbot = [] stream = False # 测试账单功能 print(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET) print(client.billing_info()) # 测试问答 print(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET) question = "巴黎是中国的首都吗？" for i in client.predict(inputs=question, chatbot=chatbot, stream=stream): print(i) print(f"测试问答后history : {client.history}") # 测试记忆力 print(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET) question = "我刚刚问了你什么问题？" for i in client.predict(inputs=question, chatbot=chatbot, stream=stream): print(i) print(f"测试记忆力后history : {client.history}") # 测试重试功能 print(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET) for i in client.retry(chatbot=chatbot, stream=stream): print(i) print(f"重试后history : {client.history}") # # 测试总结功能 # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET) # chatbot, msg = client.reduce_token_size(chatbot=chatbot) # print(chatbot, msg) # print(f"总结后history: {client.history}")