autogen.coding.jupyter.EmbeddedIPythonCodeExecutor

EmbeddedIPythonCodeExecutor

EmbeddedIPythonCodeExecutor(**kwargs: Any)

(Experimental) A code executor class that executes code statefully using an embedded IPython kernel managed by this class.
This will execute LLM generated code on the local machine.

Each execution is stateful and can access variables created from previous executions in the same session. The kernel must be installed before using this class. The kernel can be installed using the following command:
python -m ipykernel install --user --name \{kernel_name} where kernel_name is the name of the kernel to install.
Create a new model by parsing and validating input data from keyword arguments.
Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.
self is explicitly positional-only to allow self as a field name.

Parameters:

Name	Description
**kwargs	Type: Any

Class Attributes

kernel_name

model_computed_fields

model_config

model_fields

output_dir

timeout

Instance Attributes

code_extractor

(Experimental) Export a code extractor that can be used by an agent.

model_extra

Get extra fields set during validation.
Returns:
A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.
Returns:
A set of strings representing the fields that have been set, i.e. that were not filled from defaults.

Instance Methods

construct

construct(**values: Any) -> Self

Parameters:

Name	Description
**values	Type: Any

copy

copy(
    self,
    *,
    include: AbstractSetIntStr | MappingIntStrAny | None = None,
    exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
    update: Dict[str, Any] | None = None,
    deep: bool = False
) -> Self

Returns a copy of the model.
!!! warning “Deprecated” This method is now deprecated; use model_copy instead.
If you need include or exclude, use:

data = self.model_dump(include=include, exclude=exclude, round_trip=True)
data = \{**data, **(update or \{})}
copied = self.model_validate(data)

Parameters:

Name	Description
include	Optional set or mapping specifying which fields to include in the copied model. Type: AbstractSetIntStr | MappingIntStrAny | None Default: None
exclude	Optional set or mapping specifying which fields to exclude in the copied model. Type: AbstractSetIntStr | MappingIntStrAny | None Default: None
update	Optional dictionary of field-value pairs to override field values in the copied model. Type: Dict[str, Any] | None Default: None
deep	If True, the values of fields that are Pydantic models will be deep-copied. Type: bool Default: False

Returns:

Type	Description
Self	A copy of the model with included, excluded and updated fields as specified.

dict

dict(
    self,
    *,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False
) -> Dict[str, Any]

Parameters:

Name	Description
include	Type: IncEx | None Default: None
exclude	Type: IncEx | None Default: None
by_alias	Type: bool Default: False
exclude_unset	Type: bool Default: False
exclude_defaults	Type: bool Default: False
exclude_none	Type: bool Default: False

execute_code_blocks

execute_code_blocks(self, code_blocks: list[CodeBlock]) -> IPythonCodeResult

(Experimental) Execute a list of code blocks and return the result.
This method executes a list of code blocks as cells in an IPython kernel managed by this class.
See: https://jupyter-client.readthedocs.io/en/stable/messaging.html for the message protocol.

Parameters:

Name	Description
code_blocks	A list of code blocks to execute. Type: list[CodeBlock]

Returns:

Type	Description
autogen.coding.base.IPythonCodeResult	IPythonCodeResult: The result of the code execution.

from_orm

from_orm(obj: Any) -> Self

Parameters:

Name	Description
obj	Type: Any

json

json(
    self,
    *,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    by_alias: bool = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Callable[[Any], Any] | None = PydanticUndefined,
    models_as_dict: bool = PydanticUndefined,
    **dumps_kwargs: Any
) -> str

Parameters:

Name	Description
include	Type: IncEx | None Default: None
exclude	Type: IncEx | None Default: None
by_alias	Type: bool Default: False
exclude_unset	Type: bool Default: False
exclude_defaults	Type: bool Default: False
exclude_none	Type: bool Default: False
encoder	Type: Callable[[Any], Any] | None Default: PydanticUndefined
models_as_dict	Type: bool Default: PydanticUndefined
**dumps_kwargs	Type: Any

model_construct

model_construct(**values: Any) -> Self

Creates a new instance of the Model class with validated data.
Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
!!! note model_construct() generally respects the model_config.extra setting on the provided model.
That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored.
Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

Name	Description
**values	Type: Any

Returns:

Type	Description
Self	A new instance of the Model class with validated data.

model_copy

model_copy(
    self,
    *,
    update: Mapping[str, Any] | None = None,
    deep: bool = False
) -> Self

!!! abstract “Usage Documentation” model_copy

Returns a copy of the model.
!!! note The underlying instance’s [__dict__][object.dict] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

Name	Description
update	Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data. Type: Mapping[str, Any] | None Default: None
deep	Set to True to make a deep copy of the model. Type: bool Default: False

Returns:

Type	Description
Self	New model instance.

model_dump

model_dump(
    self,
    *,
    mode: "Literal['json', 'python'] | str" = 'python',
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False
) -> dict[str, Any]

!!! abstract “Usage Documentation” model_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

Name	Description
mode	The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects. Type: “Literal[‘json’, ‘python’] | str” Default: ‘python’
include	A set of fields to include in the output. Type: IncEx | None Default: None
exclude	A set of fields to exclude from the output. Type: IncEx | None Default: None
context	Additional context to pass to the serializer. Type: Any | None Default: None
by_alias	Whether to use the field’s alias in the dictionary key if defined. Type: bool | None Default: None
exclude_unset	Whether to exclude fields that have not been explicitly set. Type: bool Default: False
exclude_defaults	Whether to exclude fields that are set to their default value. Type: bool Default: False
exclude_none	Whether to exclude fields that have a value of None. Type: bool Default: False
round_trip	If True, dumped values should be valid as input for non-idempotent types such as Json[T]. Type: bool Default: False
warnings	How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError]. Type: “bool | Literal[‘none’, ‘warn’, ‘error’]“ Default: True
fallback	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. Type: Callable[[Any], Any] | None Default: None
serialize_as_any	Whether to serialize fields with duck-typing serialization behavior. Type: bool Default: False

Returns:

Type	Description
dict[str, typing.Any]	A dictionary representation of the model.

model_dump_json

model_dump_json(
    self,
    *,
    indent: int | None = None,
    include: IncEx | None = None,
    exclude: IncEx | None = None,
    context: Any | None = None,
    by_alias: bool | None = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    fallback: Callable[[Any], Any] | None = None,
    serialize_as_any: bool = False
) -> str

!!! abstract “Usage Documentation” model_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

Name	Description
indent	Indentation to use in the JSON output. If None is passed, the output will be compact. Type: int | None Default: None
include	Field(s) to include in the JSON output. Type: IncEx | None Default: None
exclude	Field(s) to exclude from the JSON output. Type: IncEx | None Default: None
context	Additional context to pass to the serializer. Type: Any | None Default: None
by_alias	Whether to serialize using field aliases. Type: bool | None Default: None
exclude_unset	Whether to exclude fields that have not been explicitly set. Type: bool Default: False
exclude_defaults	Whether to exclude fields that are set to their default value. Type: bool Default: False
exclude_none	Whether to exclude fields that have a value of None. Type: bool Default: False
round_trip	If True, dumped values should be valid as input for non-idempotent types such as Json[T]. Type: bool Default: False
warnings	How to handle serialization errors. False/“none” ignores them, True/“warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError]. Type: “bool | Literal[‘none’, ‘warn’, ‘error’]“ Default: True
fallback	A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised. Type: Callable[[Any], Any] | None Default: None
serialize_as_any	Whether to serialize fields with duck-typing serialization behavior. Type: bool Default: False

Returns:

Type	Description
str	A JSON string representation of the model.

model_json_schema

model_json_schema(
    by_alias: bool = True,
    ref_template: str = '#/$defs/{model}',
    schema_generator: type[GenerateJsonSchema] = pydantic.json_schema.GenerateJsonSchema,
    mode: JsonSchemaMode = 'validation'
) -> dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

Name	Description
by_alias	Whether to use attribute aliases or not. Type: bool Default: True
ref_template	The reference template. Type: str Default: ’#/$defs/{model}‘
schema_generator	To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications Type: type[GenerateJsonSchema] Default: pydantic.json_schema.GenerateJsonSchema
mode	The mode in which to generate the schema. Type: JsonSchemaMode Default: ‘validation’

Returns:

Type	Description
dict[str, typing.Any]	The JSON schema for the given model class.

model_parametrized_name

model_parametrized_name(params: tuple[type[Any], ...]) -> str

Compute the class name for parametrizations of generic classes.
This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

Name	Description
params	Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params. Type: tuple[type[Any], …]

Returns:

Type	Description
str	String representing the new class where params are passed to cls as type variables.

model_post_init

model_post_init(
    self,
    context: Any,
    /
) -> None

Override this method to perform additional initialization after __init__ and model_construct.
This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

Name	Description
context	Type: Any
/

model_rebuild

model_rebuild(
    *,
    force: bool = False,
    raise_errors: bool = True
) -> 

Try to rebuild the pydantic-core schema for the model.
This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

Name	Description
force	Whether to force the rebuilding of the model schema, defaults to False. Type: bool Default: False
raise_errors	Whether to raise errors, defaults to True. Type: bool Default: True

model_validate

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

Validate a pydantic model instance.

Parameters:

Name	Description
obj	The object to validate. Type: Any
strict	Whether to enforce types strictly. Type: bool | None Default: None
from_attributes	Whether to extract data from object attributes. Type: bool | None Default: None
context	Additional context 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 model instance.

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.

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.

parse_file

parse_file(
    path: str | Path,
    *,
    content_type: str | None = None,
    encoding: str = 'utf8',
    proto: DeprecatedParseProtocol | None = None,
    allow_pickle: bool = False
) -> 

Parameters:

Name	Description
path	Type: str | Path
content_type	Type: str | None Default: None
encoding	Type: str Default: ‘utf8’
proto	Type: DeprecatedParseProtocol | None Default: None
allow_pickle	Type: bool Default: False

parse_obj

parse_obj(obj: Any) -> Self

Parameters:

Name	Description
obj	Type: Any

parse_raw

parse_raw(
    b: str | bytes,
    *,
    content_type: str | None = None,
    encoding: str = 'utf8',
    proto: DeprecatedParseProtocol | None = None,
    allow_pickle: bool = False
) -> 

Parameters:

Name	Description
b	Type: str | bytes
content_type	Type: str | None Default: None
encoding	Type: str Default: ‘utf8’
proto	Type: DeprecatedParseProtocol | None Default: None
allow_pickle	Type: bool Default: False

restart

restart(self) -> None

(Experimental) Restart a new session.

schema

schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') -> Dict[str, Any]

Parameters:

Name	Description
by_alias	Type: bool Default: True
ref_template	Type: str Default: ’#/$defs/{model}‘

schema_json

schema_json(
    *,
    by_alias: bool = True,
    ref_template: str = '#/$defs/{model}',
    **dumps_kwargs: Any
) -> str

Parameters:

Name	Description
by_alias	Type: bool Default: True
ref_template	Type: str Default: ’#/$defs/{model}‘
**dumps_kwargs	Type: Any

update_forward_refs

update_forward_refs(**localns: Any) -> None

Parameters:

Name	Description
**localns	Type: Any

validate

validate(value: Any) -> Self

Parameters:

Name	Description
value	Type: Any