langchain-gpt4free/langchain_g4f/G4FLLM.py

from types import ModuleType
from typing import Optional, List, Any, Mapping, Union

import g4f
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens


class G4FLLM(LLM):
    # Model.model or str
    model: Union[type, str]
    # Provider.Provider
    provider: Optional[ModuleType] = None
    auth: Optional[Union[str, bool]] = None
    create_kwargs: Optional[dict] = None

    @property
    def _llm_type(self) -> str:
        return 'custom'

    def _call(self, prompt: str, stop: Optional[List[str]] = None,
              run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> str:

        create_kwargs = {} if self.create_kwargs is None else self.create_kwargs.copy()
        if self.model is not None:
            create_kwargs['model'] = self.model
        if self.provider is not None:
            create_kwargs['provider'] = self.provider
        if self.auth is not None:
            create_kwargs['auth'] = self.auth

        text = g4f.ChatCompletion.create(
            messages=[{'role': 'user', 'content': prompt}],
            **create_kwargs,
        )
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            'model': self.model,
            'provider': self.provider,
            'auth': self.auth,
            'create_kwargs': self.create_kwargs,
        }