class allms.domain.input_data.InputData dataclass

@dataclass
class InputData:
    input_mappings: Dict[str, str]
    id: str

Fields

• input_mappings (Dict[str, str]): Contains mapping from symbolic variables used in the prompt to the actual data that will be injected in place of these variables. You have to provide a map for each of symbolic variable used in the prompt.
• id (str): Unique identifier. Requests are done in an async mode, so the order of the responses is not the same as the order of the input data, so this field can be used to identify them.

class allms.domain.response.ResponseData dataclass

@dataclass
class ResponseData:
    response: Union[str, BaseModel]
    input_data: Optional[InputData] = None

    number_of_prompt_tokens: Optional[int] = None
    number_of_generated_tokens: Optional[int] = None
    error: Optional[str] = None

Fields

• response (Union[str, BaseModel]): Contains response of the model. If output_data_model_class param was provided to the generate() method, it'll contain response parsed to the provided class. If output_data_model_class wasn't provided, it'll contain raw string returned from the model.
• input_data (Optional[InputData]): If input_data was provided to the generate() method, it'll copy-paste that data to this field.
• number_of_prompt_tokens (int): Number of tokens used in the prompt.
• number_of_generated_tokens (str): Number of tokens generated by the model.
• error (str): If any error that prevented from completing the generation pipeline fully occurred, it'll be listed here.