# 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, field_validator
from typing import Any, ClassVar, Dict, List, Optional, Union
from typing_extensions import Annotated
from arize._generated.api_client.models.invocation_params import InvocationParams
from typing import Optional, Set
from typing_extensions import Self
[docs]
class TemplateEvaluationRunConfig(BaseModel):
"""
Configuration for running a template-based LLM evaluator against each dataset example.
""" # noqa: E501
experiment_type: StrictStr = Field(description="Discriminator. Must be `\"template_evaluation\"`.")
ai_integration_id: StrictStr = Field(description="AI integration identifier (base64). The LLM that judges each example.")
model_name: Optional[StrictStr] = Field(default=None, description="Model name (e.g. `gpt-4o`). Falls back to the integration's default if omitted.")
template: Annotated[str, Field(min_length=1, strict=True)] = Field(description="The evaluation prompt template. Use `{{variable}}` placeholders that map to dataset column paths via `column_mapping`. ")
provide_explanation: StrictBool = Field(description="Whether to ask the LLM to include a written explanation alongside the score/label.")
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}`).")
column_mapping: Optional[Dict[str, StrictStr]] = Field(default=None, description="Maps template variable names to dataset column paths.")
evaluator_version_id: Optional[StrictStr] = Field(default=None, description="EvaluatorVersion identifier (base64). Links this run to an Eval Hub evaluator version.")
invocation_parameters: Optional[InvocationParams] = None
provider_parameters: Optional[Dict[str, Any]] = Field(default=None, description="Provider-specific parameters. Defaults to `{}` (no overrides) if omitted.")
__properties: ClassVar[List[str]] = ["experiment_type", "ai_integration_id", "model_name", "template", "provide_explanation", "classification_choices", "column_mapping", "evaluator_version_id", "invocation_parameters", "provider_parameters"]
[docs]
@field_validator('experiment_type')
def experiment_type_validate_enum(cls, value):
"""Validates the enum"""
if value not in set(['template_evaluation']):
raise ValueError("must be one of enum values ('template_evaluation')")
return value
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
protected_namespaces=(),
)
[docs]
def to_str(self) -> str:
"""Returns the string representation of the model using alias"""
return pprint.pformat(self.model_dump(by_alias=True))
[docs]
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())
[docs]
@classmethod
def from_json(cls, json_str: str) -> Optional[Self]:
"""Create an instance of TemplateEvaluationRunConfig from a JSON string"""
return cls.from_dict(json.loads(json_str))
[docs]
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 invocation_parameters
if self.invocation_parameters:
_dict['invocation_parameters'] = self.invocation_parameters.to_dict()
# set to None if evaluator_version_id (nullable) is None
# and model_fields_set contains the field
if self.evaluator_version_id is None and "evaluator_version_id" in self.model_fields_set:
_dict['evaluator_version_id'] = None
return _dict
[docs]
@classmethod
def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
"""Create an instance of TemplateEvaluationRunConfig 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 TemplateEvaluationRunConfig) in the input: " + _key)
_obj = cls.model_validate({
"experiment_type": obj.get("experiment_type"),
"ai_integration_id": obj.get("ai_integration_id"),
"model_name": obj.get("model_name"),
"template": obj.get("template"),
"provide_explanation": obj.get("provide_explanation"),
"classification_choices": obj.get("classification_choices"),
"column_mapping": obj.get("column_mapping"),
"evaluator_version_id": obj.get("evaluator_version_id"),
"invocation_parameters": InvocationParams.from_dict(obj["invocation_parameters"]) if obj.get("invocation_parameters") is not None else None,
"provider_parameters": obj.get("provider_parameters")
})
return _obj
