Source code for arize._generated.api_client.models.agent_call_run_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, StrictStr, field_validator
from typing import Any, ClassVar, Dict, List
from typing import Optional, Set
from typing_extensions import Self



class AgentCallRunConfig(BaseModel):
    """
    Configuration for running an agent integration against each dataset example. The `input_template` is sent to the agent after Mustache substitution. 
    """ # noqa: E501
    experiment_type: StrictStr = Field(description="Discriminator. Must be `\"AGENT_CALL\"`.")
    integration_id: StrictStr = Field(description="Agent integration identifier (base64). The agent invoked for each dataset example. Must reference an integration of `type` `AGENT`; other integration types are rejected. ")
    input_template: Dict[str, Any] = Field(description="JSON request body sent to the agent for each dataset example. Must be a JSON object whose values conform to the agent integration's input schema. Mustache placeholders (`{{column}}`) are substituted with each dataset row's values before the request is sent. The `dataset.` prefix is optional — `{{column}}` and `{{dataset.column}}` are equivalent, and responses (create, update, and read) always echo the normalized `{{column}}` form. ")
    additional_properties: Dict[str, Any] = {}
    __properties: ClassVar[List[str]] = ["experiment_type", "integration_id", "input_template"]



    @field_validator('experiment_type')
    def experiment_type_validate_enum(cls, value):
        """Validates the enum"""
        if value not in set(['AGENT_CALL']):
            raise ValueError("must be one of enum values ('AGENT_CALL')")
        return value


    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 AgentCallRunConfig 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.
        * Fields in `self.additional_properties` are added to the output dict.
        """
        excluded_fields: Set[str] = set([
            "additional_properties",
        ])

        _dict = self.model_dump(
            by_alias=True,
            exclude=excluded_fields,
            exclude_none=True,
        )
        # puts key-value pairs in additional_properties in the top level
        if self.additional_properties is not None:
            for _key, _value in self.additional_properties.items():
                _dict[_key] = _value

        return _dict




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

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

        _obj = cls.model_validate({
            "experiment_type": obj.get("experiment_type"),
            "integration_id": obj.get("integration_id"),
            "input_template": obj.get("input_template")
        })
        # store additional fields in additional_properties
        for _key in obj.keys():
            if _key not in cls.__properties:
                _obj.additional_properties[_key] = obj.get(_key)

        return _obj