autogen.llm_config.LLMConfig

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LLMConfig

LLMConfig(**kwargs: Any)

Parameters:

Name	Description
**kwargs	Type: Any

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Static Methods

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from_json

from_json(
    *,
    env: str | None = None,
    path: str | Path | None = None,
    file_location: str | None = None,
    **kwargs: Any
) -> LLMConfig

Parameters:

Name	Description
env	Type: str | None Default: None
path	Type: str | pathlib.Path | None Default: None
file_location	Type: str | None Default: None
**kwargs	Type: Any

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get_current_llm_config

get_current_llm_config(llm_config: Optional[LLMConfig] = None) -> LLMConfig | None

Parameters:

Name	Description
llm_config	Type: Optional[LLMConfig] Default: None

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Instance Methods

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copy

copy(self) -> LLMConfig

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deepcopy

deepcopy(self, memo: dict[int, Any] | None = None) -> LLMConfig

Parameters:

Name	Description
memo	Type: dict[int, typing.Any] | None Default: None

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get

get(
    self,
    key: str,
    default: Any | None = None
) -> Any

Parameters:

Name	Description
key	Type: str
default	Type: Any | None Default: None

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items

items(self) -> Iterable[tuple[str, Any]]

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keys

keys(self) -> Iterable[str]

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model_dump

model_dump(
    self,
    *args: Any,
    exclude_none: bool = True,
    **kwargs: Any
) -> dict[str, Any]

Parameters:

Name	Description
*args	Type: Any
exclude_none	Type: bool Default: True
**kwargs	Type: Any

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model_dump_json

model_dump_json(
    self,
    *args: Any,
    exclude_none: bool = True,
    **kwargs: Any
) -> str

Parameters:

Name	Description
*args	Type: Any
exclude_none	Type: bool Default: True
**kwargs	Type: Any

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model_validate

model_validate(
    self,
    *args: Any,
    **kwargs: Any
) -> Any

Parameters:

Name	Description
*args	Type: Any
**kwargs	Type: Any

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model_validate_json

model_validate_json(
    json_data: str | bytes | bytearray,
    *,
    strict: bool | None = None,
    context: Any | None = None,
    by_alias: bool | None = None,
    by_name: bool | None = None
) -> Self

!!! abstract “Usage Documentation” JSON Parsing

Validate the given JSON data against the Pydantic model.

Parameters:

Name	Description
json_data	The JSON data to validate. Type: str | bytes | bytearray
strict	Whether to enforce types strictly. Type: bool | None Default: None
context	Extra variables to pass to the validator. Type: Any | None Default: None
by_alias	Whether to use the field’s alias when validating against the provided input data. Type: bool | None Default: None
by_name	Whether to use the field’s name when validating against the provided input data. Type: bool | None Default: None

Returns:

Type	Description
Self	The validated Pydantic model.

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model_validate_strings

model_validate_strings(
    obj: Any,
    *,
    strict: bool | None = None,
    context: Any | None = None,
    by_alias: bool | None = None,
    by_name: bool | None = None
) -> Self

Validate the given object with string data against the Pydantic model.

Parameters:

Name	Description
obj	The object containing string data to validate. Type: Any
strict	Whether to enforce types strictly. Type: bool | None Default: None
context	Extra variables to pass to the validator. Type: Any | None Default: None
by_alias	Whether to use the field’s alias when validating against the provided input data. Type: bool | None Default: None
by_name	Whether to use the field’s name when validating against the provided input data. Type: bool | None Default: None

Returns:

Type	Description
Self	The validated Pydantic model.

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values

values(self) -> Iterable[Any]

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where

where(
    self,
    *,
    exclude: bool = False,
    **kwargs: Any
) -> LLMConfig

Parameters:

Name	Description
exclude	Type: bool Default: False
**kwargs	Type: Any