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77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
from __future__ import annotations
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import requests
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from ...typing import Any, CreateResult, Messages
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from ..base_provider import AbstractProvider
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models = {
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"theb-ai": "TheB.AI",
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"gpt-3.5-turbo": "GPT-3.5",
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"gpt-3.5-turbo-16k": "GPT-3.5-16K",
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"gpt-4-turbo": "GPT-4 Turbo",
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"gpt-4": "GPT-4",
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"gpt-4-32k": "GPT-4 32K",
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"claude-2": "Claude 2",
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"claude-1": "Claude",
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"claude-1-100k": "Claude 100K",
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"claude-instant-1": "Claude Instant",
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"claude-instant-1-100k": "Claude Instant 100K",
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"palm-2": "PaLM 2",
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"palm-2-codey": "Codey",
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"vicuna-13b-v1.5": "Vicuna v1.5 13B",
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"llama-2-7b-chat": "Llama 2 7B",
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"llama-2-13b-chat": "Llama 2 13B",
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"llama-2-70b-chat": "Llama 2 70B",
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"code-llama-7b": "Code Llama 7B",
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"code-llama-13b": "Code Llama 13B",
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"code-llama-34b": "Code Llama 34B",
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"qwen-7b-chat": "Qwen 7B"
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}
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class ThebApi(AbstractProvider):
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url = "https://theb.ai"
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working = True
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needs_auth = True
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@staticmethod
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def create_completion(
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model: str,
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messages: Messages,
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stream: bool,
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auth: str,
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proxy: str = None,
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**kwargs
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) -> CreateResult:
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if model and model not in models:
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raise ValueError(f"Model are not supported: {model}")
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headers = {
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'accept': 'application/json',
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'authorization': f'Bearer {auth}',
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'content-type': 'application/json',
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}
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# response = requests.get("https://api.baizhi.ai/v1/models", headers=headers).json()["data"]
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# models = dict([(m["id"], m["name"]) for m in response])
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# print(json.dumps(models, indent=4))
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data: dict[str, Any] = {
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"model": model if model else "gpt-3.5-turbo",
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"messages": messages,
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"stream": False,
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"model_params": {
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"system_prompt": kwargs.get("system_message", "You are ChatGPT, a large language model trained by OpenAI, based on the GPT-3.5 architecture."),
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"temperature": 1,
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"top_p": 1,
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**kwargs
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}
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}
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response = requests.post(
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"https://api.theb.ai/v1/chat/completions",
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headers=headers,
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json=data,
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proxies={"https": proxy}
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)
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try:
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response.raise_for_status()
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yield response.json()["choices"][0]["message"]["content"]
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except:
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raise RuntimeError(f"Response: {next(response.iter_lines()).decode()}") |