Source code for arize._generated.api_client.models.template_config

# coding: utf-8

"""
    Arize REST API

    API specification for the backend data server. The API is hosted globally at https://api.arize.com/v2 or in your own environment. 

    The version of the OpenAPI document: 2.0.0
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json

from pydantic import BaseModel, ConfigDict, Field, StrictBool, StrictFloat, StrictInt, StrictStr
from typing import Any, ClassVar, Dict, List, Optional, Union
from arize._generated.api_client.models.data_granularity import DataGranularity
from arize._generated.api_client.models.evaluator_llm_config import EvaluatorLlmConfig
from arize._generated.api_client.models.optimization_direction import OptimizationDirection
from typing import Optional, Set
from typing_extensions import Self



class TemplateConfig(BaseModel):
    """
    TemplateConfig
    """ # noqa: E501
    name: StrictStr = Field(description="Eval column name. Must match ^[a-zA-Z0-9_\\s\\-&()]+$")
    template: StrictStr = Field(description="The prompt template with variable placeholders")
    include_explanations: StrictBool = Field(description="Whether to include explanations in the evaluation output")
    use_function_calling_if_available: StrictBool = Field(description="Whether to use function calling if the model supports it")
    classification_choices: Optional[Dict[str, Union[StrictFloat, StrictInt]]] = Field(default=None, description="Map of choice label to numeric score (e.g. {\"relevant\": 1, \"irrelevant\": 0}). When omitted, the evaluator produces freeform (non-classification) output.")
    direction: Optional[OptimizationDirection] = OptimizationDirection.NONE
    data_granularity: Optional[DataGranularity] = Field(default=None, description="Data granularity level. Defaults to null when omitted.")
    llm_config: EvaluatorLlmConfig
    __properties: ClassVar[List[str]] = ["name", "template", "include_explanations", "use_function_calling_if_available", "classification_choices", "direction", "data_granularity", "llm_config"]

    model_config = ConfigDict(
        populate_by_name=True,
        validate_assignment=True,
        protected_namespaces=(),
    )




    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))




    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())




    @classmethod
    def from_json(cls, json_str: str) -> Optional[Self]:
        """Create an instance of TemplateConfig from a JSON string"""
        return cls.from_dict(json.loads(json_str))




    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        excluded_fields: Set[str] = set([
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        # override the default output from pydantic by calling `to_dict()` of llm_config
        if self.llm_config:
            _dict['llm_config'] = self.llm_config.to_dict()
        # set to None if classification_choices (nullable) is None
        # and model_fields_set contains the field
        if self.classification_choices is None and "classification_choices" in self.model_fields_set:
            _dict['classification_choices'] = None

        # set to None if data_granularity (nullable) is None
        # and model_fields_set contains the field
        if self.data_granularity is None and "data_granularity" in self.model_fields_set:
            _dict['data_granularity'] = None

        return _dict




    @classmethod
    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
        """Create an instance of TemplateConfig from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        # raise errors for additional fields in the input
        for _key in obj.keys():
            if _key not in cls.__properties:
                raise ValueError("Error due to additional fields (not defined in TemplateConfig) in the input: " + _key)

        _obj = cls.model_validate({
            "name": obj.get("name"),
            "template": obj.get("template"),
            "include_explanations": obj.get("include_explanations"),
            "use_function_calling_if_available": obj.get("use_function_calling_if_available"),
            "classification_choices": obj.get("classification_choices"),
            "direction": obj.get("direction") if obj.get("direction") is not None else OptimizationDirection.NONE,
            "data_granularity": obj.get("data_granularity"),
            "llm_config": EvaluatorLlmConfig.from_dict(obj["llm_config"]) if obj.get("llm_config") is not None else None
        })
        return _obj