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from typing import Any, List, Mapping, Optional |
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from g4f.Provider import ( |
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Ails, |
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You, |
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Bing, |
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Yqcloud, |
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Theb, |
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Aichat, |
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Bard, |
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Vercel, |
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Forefront, |
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Lockchat, |
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Liaobots, |
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H2o, |
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ChatgptLogin, |
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DeepAi, |
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GetGpt |
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) |
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import g4f |
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from langchain.callbacks.manager import CallbackManagerForLLMRun |
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from langchain.llms.base import LLM |
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provider_dict = { |
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'Ails': Ails, |
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'You': You, |
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'Bing': Bing, |
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'Yqcloud': Yqcloud, |
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'Theb': Theb, |
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'Aichat': Aichat, |
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'Bard': Bard, |
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'Vercel': Vercel, |
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'Forefront': Forefront, |
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'Lockchat': Lockchat, |
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'Liaobots': Liaobots, |
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'H2o': H2o, |
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'ChatgptLogin': ChatgptLogin, |
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'DeepAi': DeepAi, |
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'GetGpt': GetGpt |
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} |
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class CustomLLM(LLM): |
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model_name: str="gpt-3.5-turbo" |
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provider_name: str="GetGpt" |
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@property |
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def _llm_type(self) -> str: |
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return "custom" |
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def _call( |
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self, |
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prompt: str, |
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stop: Optional[List[str]] = None, |
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run_manager: Optional[CallbackManagerForLLMRun] = None, |
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model_name = 'gpt-3.5-turbo', |
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provider = GetGpt |
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) -> str: |
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if stop is not None: |
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raise ValueError("stop kwargs are not permitted.") |
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bot_msg = g4f.ChatCompletion.create(model=self.model_name, |
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provider=provider_dict[self.provider_name], |
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messages=[{"role": "user", |
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"content": prompt}], |
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stream=False) |
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return bot_msg |
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@property |
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def _identifying_params(self) -> Mapping[str, Any]: |
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"""Get the identifying parameters.""" |
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return {"model:": "gpt-3.5-turbo"} |
