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.

Parameters:

Name	Description
`**kwargs`	Type: Any

Class Attributes

kernel_name

model_config

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

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#model_copy

Returns a copy of the model.

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 = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: bool = False
) -> dict[str, Any]

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_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 Default: False
`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
`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 = False,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    round_trip: bool = False,
    warnings: "bool | Literal['none', 'warn', 'error']" = True,
    serialize_as_any: bool = False
) -> str

Usage docs: https://docs.pydantic.dev/2.10/concepts/serialization/#modelmodel_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 Default: False
`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
`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, _BaseModel__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
`_BaseModel__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
) -> 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

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
) -> Self

Usage docs: https://docs.pydantic.dev/2.10/concepts/json/#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

Returns:

Type	Description
Self	The validated Pydantic model.

model_validate_strings

model_validate_strings(
    obj: Any,
    *,
    strict: bool | None = None,
    context: Any | 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

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

DockerJupyterServer JupyterClient

On this page

EmbeddedIPythonCodeExecutor
Class Attributes
kernel_name
model_config
output_dir
timeout
Instance Attributes
code_extractor
model_extra
model_fields_set
Instance Methods
construct
copy
dict
execute_code_blocks
from_orm
json
model_construct
model_copy
model_dump
model_dump_json
model_json_schema
model_parametrized_name
model_post_init
model_rebuild
model_validate
model_validate_json
model_validate_strings
parse_file
parse_obj
parse_raw
restart
schema
schema_json
update_forward_refs
validate

API Reference

​EmbeddedIPythonCodeExecutor

​Class Attributes

​kernel_name

​model_config

​output_dir

​timeout

​Instance Attributes

​code_extractor

​model_extra

​model_fields_set

​Instance Methods

​construct

​copy

​dict

​execute_code_blocks

​from_orm

​json

​model_construct

​model_copy

​model_dump

​model_dump_json

​model_json_schema

​model_parametrized_name

​model_post_init

​model_rebuild

​model_validate

​model_validate_json

​model_validate_strings

​parse_file

​parse_obj

​parse_raw

​restart

​schema

​schema_json

​update_forward_refs

​validate

EmbeddedIPythonCodeExecutor

Class Attributes

kernel_name

model_config

output_dir

timeout

Instance Attributes

code_extractor

model_extra

model_fields_set

Instance Methods

construct

copy

dict

execute_code_blocks

from_orm

json

model_construct

model_copy

model_dump

model_dump_json

model_json_schema

model_parametrized_name

model_post_init

model_rebuild

model_validate

model_validate_json

model_validate_strings

parse_file

parse_obj

parse_raw

restart

schema

schema_json

update_forward_refs

validate