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90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
from __future__ import annotations
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import json
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import requests
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from ...typing import CreateResult, Messages
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from ..base_provider import ProviderModelMixin, AbstractProvider
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from ..helper import format_prompt
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class NexraChatGPT(AbstractProvider, ProviderModelMixin):
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label = "Nexra ChatGPT"
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url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
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api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt"
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working = True
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default_model = 'gpt-3.5-turbo'
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models = ['gpt-4', 'gpt-4-0613', 'gpt-4-0314', 'gpt-4-32k-0314', default_model, 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0301', 'text-davinci-003', 'text-davinci-002', 'code-davinci-002', 'gpt-3', 'text-curie-001', 'text-babbage-001', 'text-ada-001', 'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002']
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model_aliases = {
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"gpt-4": "gpt-4-0613",
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"gpt-4": "gpt-4-32k",
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"gpt-4": "gpt-4-0314",
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"gpt-4": "gpt-4-32k-0314",
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"gpt-3.5-turbo": "gpt-3.5-turbo-16k",
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"gpt-3.5-turbo": "gpt-3.5-turbo-0613",
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"gpt-3.5-turbo": "gpt-3.5-turbo-16k-0613",
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"gpt-3.5-turbo": "gpt-3.5-turbo-0301",
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"gpt-3": "text-davinci-003",
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"gpt-3": "text-davinci-002",
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"gpt-3": "code-davinci-002",
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"gpt-3": "text-curie-001",
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"gpt-3": "text-babbage-001",
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"gpt-3": "text-ada-001",
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"gpt-3": "text-ada-001",
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"gpt-3": "davinci",
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"gpt-3": "curie",
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"gpt-3": "babbage",
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"gpt-3": "ada",
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"gpt-3": "babbage-002",
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"gpt-3": "davinci-002",
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}
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@classmethod
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def get_model(cls, model: str) -> str:
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if model in cls.models:
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return model
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elif model in cls.model_aliases:
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return cls.model_aliases[model]
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else:
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return cls.default_model
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@classmethod
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def create_completion(
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cls,
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model: str,
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messages: Messages,
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proxy: str = None,
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markdown: bool = False,
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**kwargs
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) -> CreateResult:
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model = cls.get_model(model)
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headers = {
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'Content-Type': 'application/json'
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}
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data = {
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"messages": [],
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"prompt": format_prompt(messages),
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"model": model,
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"markdown": markdown
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}
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response = requests.post(cls.api_endpoint, headers=headers, json=data)
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return cls.process_response(response)
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@classmethod
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def process_response(cls, response):
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if response.status_code == 200:
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try:
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data = response.json()
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return data.get('gpt', '')
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except json.JSONDecodeError:
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return "Error: Unable to decode JSON response"
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else:
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return f"Error: {response.status_code}"
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