vectorize_client.models.ai_platform_connector_input

Vectorize API

API for Vectorize services (Beta)

The version of the OpenAPI document: 0.1.2 Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.

View Source

  1# coding: utf-8
  2
  3"""
  4    Vectorize API
  5
  6    API for Vectorize services (Beta)
  7
  8    The version of the OpenAPI document: 0.1.2
  9    Generated by OpenAPI Generator (https://openapi-generator.tech)
 10
 11    Do not edit the class manually.
 12"""  # noqa: E501
 13
 14
 15from __future__ import annotations
 16import pprint
 17import re  # noqa: F401
 18import json
 19
 20from pydantic import BaseModel, ConfigDict, Field, StrictStr, field_validator
 21from typing import Any, ClassVar, Dict, List, Optional
 22from typing import Optional, Set
 23from typing_extensions import Self
 24
 25class AIPlatformConnectorInput(BaseModel):
 26    """
 27    AI platform configuration
 28    """ # noqa: E501
 29    id: StrictStr = Field(description="Unique identifier for the AI platform")
 30    type: StrictStr = Field(description="Type of AI platform")
 31    config: Optional[Any] = Field(description="Configuration specific to the AI platform")
 32    __properties: ClassVar[List[str]] = ["id", "type", "config"]
 33
 34    @field_validator('type')
 35    def type_validate_enum(cls, value):
 36        """Validates the enum"""
 37        if value not in set(['BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE']):
 38            raise ValueError("must be one of enum values ('BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE')")
 39        return value
 40
 41    model_config = ConfigDict(
 42        populate_by_name=True,
 43        validate_assignment=True,
 44        protected_namespaces=(),
 45    )
 46
 47
 48    def to_str(self) -> str:
 49        """Returns the string representation of the model using alias"""
 50        return pprint.pformat(self.model_dump(by_alias=True))
 51
 52    def to_json(self) -> str:
 53        """Returns the JSON representation of the model using alias"""
 54        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
 55        return json.dumps(self.to_dict())
 56
 57    @classmethod
 58    def from_json(cls, json_str: str) -> Optional[Self]:
 59        """Create an instance of AIPlatformConnectorInput from a JSON string"""
 60        return cls.from_dict(json.loads(json_str))
 61
 62    def to_dict(self) -> Dict[str, Any]:
 63        """Return the dictionary representation of the model using alias.
 64
 65        This has the following differences from calling pydantic's
 66        `self.model_dump(by_alias=True)`:
 67
 68        * `None` is only added to the output dict for nullable fields that
 69          were set at model initialization. Other fields with value `None`
 70          are ignored.
 71        """
 72        excluded_fields: Set[str] = set([
 73        ])
 74
 75        _dict = self.model_dump(
 76            by_alias=True,
 77            exclude=excluded_fields,
 78            exclude_none=True,
 79        )
 80        # set to None if config (nullable) is None
 81        # and model_fields_set contains the field
 82        if self.config is None and "config" in self.model_fields_set:
 83            _dict['config'] = None
 84
 85        return _dict
 86
 87    @classmethod
 88    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
 89        """Create an instance of AIPlatformConnectorInput from a dict"""
 90        if obj is None:
 91            return None
 92
 93        if not isinstance(obj, dict):
 94            return cls.model_validate(obj)
 95
 96        _obj = cls.model_validate({
 97            "id": obj.get("id"),
 98            "type": obj.get("type"),
 99            "config": obj.get("config")
100        })
101        return _obj

class AIPlatformConnectorInput(pydantic.main.BaseModel): View Source

 26class AIPlatformConnectorInput(BaseModel):
 27    """
 28    AI platform configuration
 29    """ # noqa: E501
 30    id: StrictStr = Field(description="Unique identifier for the AI platform")
 31    type: StrictStr = Field(description="Type of AI platform")
 32    config: Optional[Any] = Field(description="Configuration specific to the AI platform")
 33    __properties: ClassVar[List[str]] = ["id", "type", "config"]
 34
 35    @field_validator('type')
 36    def type_validate_enum(cls, value):
 37        """Validates the enum"""
 38        if value not in set(['BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE']):
 39            raise ValueError("must be one of enum values ('BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE')")
 40        return value
 41
 42    model_config = ConfigDict(
 43        populate_by_name=True,
 44        validate_assignment=True,
 45        protected_namespaces=(),
 46    )
 47
 48
 49    def to_str(self) -> str:
 50        """Returns the string representation of the model using alias"""
 51        return pprint.pformat(self.model_dump(by_alias=True))
 52
 53    def to_json(self) -> str:
 54        """Returns the JSON representation of the model using alias"""
 55        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
 56        return json.dumps(self.to_dict())
 57
 58    @classmethod
 59    def from_json(cls, json_str: str) -> Optional[Self]:
 60        """Create an instance of AIPlatformConnectorInput from a JSON string"""
 61        return cls.from_dict(json.loads(json_str))
 62
 63    def to_dict(self) -> Dict[str, Any]:
 64        """Return the dictionary representation of the model using alias.
 65
 66        This has the following differences from calling pydantic's
 67        `self.model_dump(by_alias=True)`:
 68
 69        * `None` is only added to the output dict for nullable fields that
 70          were set at model initialization. Other fields with value `None`
 71          are ignored.
 72        """
 73        excluded_fields: Set[str] = set([
 74        ])
 75
 76        _dict = self.model_dump(
 77            by_alias=True,
 78            exclude=excluded_fields,
 79            exclude_none=True,
 80        )
 81        # set to None if config (nullable) is None
 82        # and model_fields_set contains the field
 83        if self.config is None and "config" in self.model_fields_set:
 84            _dict['config'] = None
 85
 86        return _dict
 87
 88    @classmethod
 89    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
 90        """Create an instance of AIPlatformConnectorInput from a dict"""
 91        if obj is None:
 92            return None
 93
 94        if not isinstance(obj, dict):
 95            return cls.model_validate(obj)
 96
 97        _obj = cls.model_validate({
 98            "id": obj.get("id"),
 99            "type": obj.get("type"),
100            "config": obj.get("config")
101        })
102        return _obj

AI platform configuration

id: typing.Annotated[str, Strict(strict=True)]

type: typing.Annotated[str, Strict(strict=True)]

config: Optional[Any]

@field_validator('type')

def type_validate_enum(cls, value): View Source

35    @field_validator('type')
36    def type_validate_enum(cls, value):
37        """Validates the enum"""
38        if value not in set(['BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE']):
39            raise ValueError("must be one of enum values ('BEDROCK', 'VERTEX', 'OPENAI', 'VOYAGE')")
40        return value

Validates the enum

model_config = {'populate_by_name': True, 'validate_assignment': True, 'protected_namespaces': (), 'validate_by_alias': True, 'validate_by_name': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

def to_str(self) -> str: View Source

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

Returns the string representation of the model using alias

def to_json(self) -> str: View Source

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

Returns the JSON representation of the model using alias

@classmethod

def from_json(cls, json_str: str) -> Optional[Self]: View Source

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

Create an instance of AIPlatformConnectorInput from a JSON string

def to_dict(self) -> Dict[str, Any]: View Source

63    def to_dict(self) -> Dict[str, Any]:
64        """Return the dictionary representation of the model using alias.
65
66        This has the following differences from calling pydantic's
67        `self.model_dump(by_alias=True)`:
68
69        * `None` is only added to the output dict for nullable fields that
70          were set at model initialization. Other fields with value `None`
71          are ignored.
72        """
73        excluded_fields: Set[str] = set([
74        ])
75
76        _dict = self.model_dump(
77            by_alias=True,
78            exclude=excluded_fields,
79            exclude_none=True,
80        )
81        # set to None if config (nullable) is None
82        # and model_fields_set contains the field
83        if self.config is None and "config" in self.model_fields_set:
84            _dict['config'] = None
85
86        return _dict

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.

@classmethod

def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]: View Source

 88    @classmethod
 89    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
 90        """Create an instance of AIPlatformConnectorInput from a dict"""
 91        if obj is None:
 92            return None
 93
 94        if not isinstance(obj, dict):
 95            return cls.model_validate(obj)
 96
 97        _obj = cls.model_validate({
 98            "id": obj.get("id"),
 99            "type": obj.get("type"),
100            "config": obj.get("config")
101        })
102        return _obj

Create an instance of AIPlatformConnectorInput from a dict