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# `langchain_experimental.tabular_synthetic_data.base`.SyntheticDataGenerator

class langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator

  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]

    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]

    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