vectorize_client.models.pipeline_configuration_schema

Vectorize API (Beta)

API for Vectorize services

The version of the OpenAPI document: 0.0.1 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 (Beta)
  5
  6    API for Vectorize services
  7
  8    The version of the OpenAPI document: 0.0.1
  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
 21from typing import Any, ClassVar, Dict, List
 22from typing_extensions import Annotated
 23from vectorize_client.models.ai_platform_schema import AIPlatformSchema
 24from vectorize_client.models.destination_connector_schema import DestinationConnectorSchema
 25from vectorize_client.models.schedule_schema import ScheduleSchema
 26from vectorize_client.models.source_connector_schema import SourceConnectorSchema
 27from typing import Optional, Set
 28from typing_extensions import Self
 29
 30class PipelineConfigurationSchema(BaseModel):
 31    """
 32    PipelineConfigurationSchema
 33    """ # noqa: E501
 34    source_connectors: Annotated[List[SourceConnectorSchema], Field(min_length=1)] = Field(alias="sourceConnectors")
 35    destination_connector: DestinationConnectorSchema = Field(alias="destinationConnector")
 36    ai_platform: AIPlatformSchema = Field(alias="aiPlatform")
 37    pipeline_name: Annotated[str, Field(min_length=1, strict=True)] = Field(alias="pipelineName")
 38    schedule: ScheduleSchema
 39    __properties: ClassVar[List[str]] = ["sourceConnectors", "destinationConnector", "aiPlatform", "pipelineName", "schedule"]
 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 PipelineConfigurationSchema 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        # override the default output from pydantic by calling `to_dict()` of each item in source_connectors (list)
 81        _items = []
 82        if self.source_connectors:
 83            for _item_source_connectors in self.source_connectors:
 84                if _item_source_connectors:
 85                    _items.append(_item_source_connectors.to_dict())
 86            _dict['sourceConnectors'] = _items
 87        # override the default output from pydantic by calling `to_dict()` of destination_connector
 88        if self.destination_connector:
 89            _dict['destinationConnector'] = self.destination_connector.to_dict()
 90        # override the default output from pydantic by calling `to_dict()` of ai_platform
 91        if self.ai_platform:
 92            _dict['aiPlatform'] = self.ai_platform.to_dict()
 93        # override the default output from pydantic by calling `to_dict()` of schedule
 94        if self.schedule:
 95            _dict['schedule'] = self.schedule.to_dict()
 96        return _dict
 97
 98    @classmethod
 99    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
100        """Create an instance of PipelineConfigurationSchema from a dict"""
101        if obj is None:
102            return None
103
104        if not isinstance(obj, dict):
105            return cls.model_validate(obj)
106
107        _obj = cls.model_validate({
108            "sourceConnectors": [SourceConnectorSchema.from_dict(_item) for _item in obj["sourceConnectors"]] if obj.get("sourceConnectors") is not None else None,
109            "destinationConnector": DestinationConnectorSchema.from_dict(obj["destinationConnector"]) if obj.get("destinationConnector") is not None else None,
110            "aiPlatform": AIPlatformSchema.from_dict(obj["aiPlatform"]) if obj.get("aiPlatform") is not None else None,
111            "pipelineName": obj.get("pipelineName"),
112            "schedule": ScheduleSchema.from_dict(obj["schedule"]) if obj.get("schedule") is not None else None
113        })
114        return _obj

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

 31class PipelineConfigurationSchema(BaseModel):
 32    """
 33    PipelineConfigurationSchema
 34    """ # noqa: E501
 35    source_connectors: Annotated[List[SourceConnectorSchema], Field(min_length=1)] = Field(alias="sourceConnectors")
 36    destination_connector: DestinationConnectorSchema = Field(alias="destinationConnector")
 37    ai_platform: AIPlatformSchema = Field(alias="aiPlatform")
 38    pipeline_name: Annotated[str, Field(min_length=1, strict=True)] = Field(alias="pipelineName")
 39    schedule: ScheduleSchema
 40    __properties: ClassVar[List[str]] = ["sourceConnectors", "destinationConnector", "aiPlatform", "pipelineName", "schedule"]
 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 PipelineConfigurationSchema 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        # override the default output from pydantic by calling `to_dict()` of each item in source_connectors (list)
 82        _items = []
 83        if self.source_connectors:
 84            for _item_source_connectors in self.source_connectors:
 85                if _item_source_connectors:
 86                    _items.append(_item_source_connectors.to_dict())
 87            _dict['sourceConnectors'] = _items
 88        # override the default output from pydantic by calling `to_dict()` of destination_connector
 89        if self.destination_connector:
 90            _dict['destinationConnector'] = self.destination_connector.to_dict()
 91        # override the default output from pydantic by calling `to_dict()` of ai_platform
 92        if self.ai_platform:
 93            _dict['aiPlatform'] = self.ai_platform.to_dict()
 94        # override the default output from pydantic by calling `to_dict()` of schedule
 95        if self.schedule:
 96            _dict['schedule'] = self.schedule.to_dict()
 97        return _dict
 98
 99    @classmethod
100    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
101        """Create an instance of PipelineConfigurationSchema from a dict"""
102        if obj is None:
103            return None
104
105        if not isinstance(obj, dict):
106            return cls.model_validate(obj)
107
108        _obj = cls.model_validate({
109            "sourceConnectors": [SourceConnectorSchema.from_dict(_item) for _item in obj["sourceConnectors"]] if obj.get("sourceConnectors") is not None else None,
110            "destinationConnector": DestinationConnectorSchema.from_dict(obj["destinationConnector"]) if obj.get("destinationConnector") is not None else None,
111            "aiPlatform": AIPlatformSchema.from_dict(obj["aiPlatform"]) if obj.get("aiPlatform") is not None else None,
112            "pipelineName": obj.get("pipelineName"),
113            "schedule": ScheduleSchema.from_dict(obj["schedule"]) if obj.get("schedule") is not None else None
114        })
115        return _obj

PipelineConfigurationSchema

source_connectors: Annotated[List[vectorize_client.models.source_connector_schema.SourceConnectorSchema], FieldInfo(annotation=NoneType, required=True, metadata=[MinLen(min_length=1)])]

destination_connector: vectorize_client.models.destination_connector_schema.DestinationConnectorSchema

ai_platform: vectorize_client.models.ai_platform_schema.AIPlatformSchema

pipeline_name: typing.Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[Strict(strict=True), MinLen(min_length=1)])]

schedule: vectorize_client.models.schedule_schema.ScheduleSchema

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

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 PipelineConfigurationSchema from a JSON string"""
61        return cls.from_dict(json.loads(json_str))

Create an instance of PipelineConfigurationSchema 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        # override the default output from pydantic by calling `to_dict()` of each item in source_connectors (list)
82        _items = []
83        if self.source_connectors:
84            for _item_source_connectors in self.source_connectors:
85                if _item_source_connectors:
86                    _items.append(_item_source_connectors.to_dict())
87            _dict['sourceConnectors'] = _items
88        # override the default output from pydantic by calling `to_dict()` of destination_connector
89        if self.destination_connector:
90            _dict['destinationConnector'] = self.destination_connector.to_dict()
91        # override the default output from pydantic by calling `to_dict()` of ai_platform
92        if self.ai_platform:
93            _dict['aiPlatform'] = self.ai_platform.to_dict()
94        # override the default output from pydantic by calling `to_dict()` of schedule
95        if self.schedule:
96            _dict['schedule'] = self.schedule.to_dict()
97        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

 99    @classmethod
100    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
101        """Create an instance of PipelineConfigurationSchema from a dict"""
102        if obj is None:
103            return None
104
105        if not isinstance(obj, dict):
106            return cls.model_validate(obj)
107
108        _obj = cls.model_validate({
109            "sourceConnectors": [SourceConnectorSchema.from_dict(_item) for _item in obj["sourceConnectors"]] if obj.get("sourceConnectors") is not None else None,
110            "destinationConnector": DestinationConnectorSchema.from_dict(obj["destinationConnector"]) if obj.get("destinationConnector") is not None else None,
111            "aiPlatform": AIPlatformSchema.from_dict(obj["aiPlatform"]) if obj.get("aiPlatform") is not None else None,
112            "pipelineName": obj.get("pipelineName"),
113            "schedule": ScheduleSchema.from_dict(obj["schedule"]) if obj.get("schedule") is not None else None
114        })
115        return _obj

Create an instance of PipelineConfigurationSchema from a dict