langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator

class langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator[source]

Bases: BaseModel

Generate synthetic data using the given LLM and few-shot template.

Utilizes the provided LLM to produce synthetic data based on the few-shot prompt template.

template

Template for few-shot prompting.

Type

FewShotPromptTemplate

llm

Large Language Model to use for generation.

Type

Optional[BaseLanguageModel]

llm_chain

LLM chain with the LLM and few-shot template.

Type

Optional[Chain]

example_input_key

Key to use for storing example inputs.

Type

str

Usage Example:
>>> template = FewShotPromptTemplate(...)
>>> llm = BaseLanguageModel(...)
>>> generator = SyntheticDataGenerator(template=template, llm=llm)
>>> results = generator.generate(subject="climate change", runs=5)

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param example_input_key: str = 'example'
param llm: Optional[BaseLanguageModel] = None
param llm_chain: Optional[Chain] = None
param results: list = []
param template: FewShotPromptTemplate [Required]
async agenerate(subject: str, runs: int, extra: str = '', *args: Any, **kwargs: Any) → List[str][source]

Generate synthetic data using the given subject asynchronously.

Note: Since the LLM calls run concurrently, you may have fewer duplicates by adding specific instructions to the “extra” keyword argument.

Parameters

  • subject (str) – The subject the synthetic data will be about.

  • runs (int) – Number of times to generate the data asynchronously.

  • extra (str) – Extra instructions for steerability in data generation.

  • args (Any) –

  • kwargs (Any) –

Returns

List of generated synthetic data for the given subject.

Return type

List[str]

Usage Example:
>>> results = await generator.agenerate(subject="climate change", runs=5,
extra="Focus on env impacts.")
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters

  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

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

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

Return type

DictStrAny

classmethod from_orm(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

generate(subject: str, runs: int, *args: Any, **kwargs: Any) → List[str][source]

Generate synthetic data using the given subject string.

Parameters

  • subject (str) – The subject the synthetic data will be about.

  • runs (int) – Number of times to generate the data.

  • extra (str) – Extra instructions for steerability in data generation.

  • args (Any) –

  • kwargs (Any) –

Returns

List of generated synthetic data.

Return type

List[str]

Usage Example:
>>> results = generator.generate(subject="climate change", runs=5,
extra="Focus on environmental impacts.")
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model
Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
Parameters

  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
Parameters

  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model
Parameters

value (Any) –

Return type

Model