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

# 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 datetime import datetime
from pydantic import BaseModel, ConfigDict, Field, StrictStr
from typing import Any, ClassVar, Dict, List, Optional
from typing import Optional, Set
from typing_extensions import Self

[docs] class Prompt(BaseModel): """ A prompt is a reusable template for LLM interactions. Prompts can be versioned and labeled to track changes over time. Use prompts to standardize how you interact with LLMs across your application. """ # noqa: E501 id: StrictStr = Field(description="The prompt ID") name: StrictStr = Field(description="The prompt name") description: Optional[StrictStr] = Field(default=None, description="The prompt description") space_id: StrictStr = Field(description="The space ID the prompt belongs to") created_at: datetime = Field(description="When the prompt was created") updated_at: datetime = Field(description="When the prompt was last updated") created_by_user_id: StrictStr = Field(description="The user ID of the user who created the prompt") __properties: ClassVar[List[str]] = ["id", "name", "description", "space_id", "created_at", "updated_at", "created_by_user_id"] 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 Prompt 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, ) # set to None if description (nullable) is None # and model_fields_set contains the field if self.description is None and "description" in self.model_fields_set: _dict['description'] = None return _dict
[docs] @classmethod def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: """Create an instance of Prompt 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 Prompt) in the input: " + _key) _obj = cls.model_validate({ "id": obj.get("id"), "name": obj.get("name"), "description": obj.get("description"), "space_id": obj.get("space_id"), "created_at": obj.get("created_at"), "updated_at": obj.get("updated_at"), "created_by_user_id": obj.get("created_by_user_id") }) return _obj