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