mirror of
https://github.com/hwchase17/langchain
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cfecbda48b
### Issue
Recently, the new `allow_dangerous_deserialization` flag was introduced
for preventing unsafe model deserialization that relies on pickle
without user's notice (#18696). Since then some LLMs like Databricks
requires passing in this flag with true to instantiate the model.
However, this breaks existing functionality to loading such LLMs within
a chain using `load_chain` method, because the underlying loader
function
[load_llm_from_config](f96dd57501/libs/langchain/langchain/chains/loading.py (L40))
(and load_llm) ignores keyword arguments passed in.
### Solution
This PR fixes this issue by propagating the
`allow_dangerous_deserialization` argument to the class loader iff the
LLM class has that field.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
"""Base interface for loading large language model APIs."""
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import json
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from pathlib import Path
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from typing import Any, Union
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import yaml
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from langchain_core.language_models.llms import BaseLLM
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from langchain_community.llms import get_type_to_cls_dict
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_ALLOW_DANGEROUS_DESERIALIZATION_ARG = "allow_dangerous_deserialization"
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def load_llm_from_config(config: dict, **kwargs: Any) -> BaseLLM:
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"""Load LLM from Config Dict."""
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if "_type" not in config:
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raise ValueError("Must specify an LLM Type in config")
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config_type = config.pop("_type")
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type_to_cls_dict = get_type_to_cls_dict()
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if config_type not in type_to_cls_dict:
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raise ValueError(f"Loading {config_type} LLM not supported")
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llm_cls = type_to_cls_dict[config_type]()
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load_kwargs = {}
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if _ALLOW_DANGEROUS_DESERIALIZATION_ARG in llm_cls.__fields__:
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load_kwargs[_ALLOW_DANGEROUS_DESERIALIZATION_ARG] = kwargs.get(
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_ALLOW_DANGEROUS_DESERIALIZATION_ARG, False
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)
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return llm_cls(**config, **load_kwargs)
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def load_llm(file: Union[str, Path], **kwargs: Any) -> BaseLLM:
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# Convert file to Path object.
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if isinstance(file, str):
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file_path = Path(file)
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else:
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file_path = file
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# Load from either json or yaml.
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if file_path.suffix == ".json":
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with open(file_path) as f:
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config = json.load(f)
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elif file_path.suffix.endswith((".yaml", ".yml")):
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with open(file_path, "r") as f:
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config = yaml.safe_load(f)
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else:
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raise ValueError("File type must be json or yaml")
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# Load the LLM from the config now.
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return load_llm_from_config(config, **kwargs)
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