Source code for langchain_community.llms.loading

"""Base interface for loading large language model APIs."""
import json
from pathlib import Path
from typing import Any, Union

import yaml
from langchain_core.language_models.llms import BaseLLM

from langchain_community.llms import get_type_to_cls_dict

_ALLOW_DANGEROUS_DESERIALIZATION_ARG = "allow_dangerous_deserialization"


[docs]def load_llm_from_config(config: dict, **kwargs: Any) -> BaseLLM: """Load LLM from Config Dict.""" if "_type" not in config: raise ValueError("Must specify an LLM Type in config") config_type = config.pop("_type") type_to_cls_dict = get_type_to_cls_dict() if config_type not in type_to_cls_dict: raise ValueError(f"Loading {config_type} LLM not supported") llm_cls = type_to_cls_dict[config_type]() load_kwargs = {} if _ALLOW_DANGEROUS_DESERIALIZATION_ARG in llm_cls.__fields__: load_kwargs[_ALLOW_DANGEROUS_DESERIALIZATION_ARG] = kwargs.get( _ALLOW_DANGEROUS_DESERIALIZATION_ARG, False ) return llm_cls(**config, **load_kwargs)
[docs]def load_llm(file: Union[str, Path], **kwargs: Any) -> BaseLLM: """Load LLM from a file.""" # Convert file to Path object. if isinstance(file, str): file_path = Path(file) else: file_path = file # Load from either json or yaml. if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) elif file_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "r") as f: config = yaml.safe_load(f) else: raise ValueError("File type must be json or yaml") # Load the LLM from the config now. return load_llm_from_config(config, **kwargs)