vectorize_client.models.pipeline_metrics

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, StrictFloat, StrictInt, StrictStr
21from typing import Any, ClassVar, Dict, List, Optional, Union
22from typing import Optional, Set
23from typing_extensions import Self
24
25class PipelineMetrics(BaseModel):
26    """
27    PipelineMetrics
28    """ # noqa: E501
29    timestamp: Optional[StrictStr]
30    new_objects: Union[StrictFloat, StrictInt] = Field(alias="newObjects")
31    changed_objects: Union[StrictFloat, StrictInt] = Field(alias="changedObjects")
32    deleted_objects: Union[StrictFloat, StrictInt] = Field(alias="deletedObjects")
33    __properties: ClassVar[List[str]] = ["timestamp", "newObjects", "changedObjects", "deletedObjects"]
34
35    model_config = ConfigDict(
36        populate_by_name=True,
37        validate_assignment=True,
38        protected_namespaces=(),
39    )
40
41
42    def to_str(self) -> str:
43        """Returns the string representation of the model using alias"""
44        return pprint.pformat(self.model_dump(by_alias=True))
45
46    def to_json(self) -> str:
47        """Returns the JSON representation of the model using alias"""
48        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
49        return json.dumps(self.to_dict())
50
51    @classmethod
52    def from_json(cls, json_str: str) -> Optional[Self]:
53        """Create an instance of PipelineMetrics from a JSON string"""
54        return cls.from_dict(json.loads(json_str))
55
56    def to_dict(self) -> Dict[str, Any]:
57        """Return the dictionary representation of the model using alias.
58
59        This has the following differences from calling pydantic's
60        `self.model_dump(by_alias=True)`:
61
62        * `None` is only added to the output dict for nullable fields that
63          were set at model initialization. Other fields with value `None`
64          are ignored.
65        """
66        excluded_fields: Set[str] = set([
67        ])
68
69        _dict = self.model_dump(
70            by_alias=True,
71            exclude=excluded_fields,
72            exclude_none=True,
73        )
74        # set to None if timestamp (nullable) is None
75        # and model_fields_set contains the field
76        if self.timestamp is None and "timestamp" in self.model_fields_set:
77            _dict['timestamp'] = None
78
79        return _dict
80
81    @classmethod
82    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
83        """Create an instance of PipelineMetrics from a dict"""
84        if obj is None:
85            return None
86
87        if not isinstance(obj, dict):
88            return cls.model_validate(obj)
89
90        _obj = cls.model_validate({
91            "timestamp": obj.get("timestamp"),
92            "newObjects": obj.get("newObjects"),
93            "changedObjects": obj.get("changedObjects"),
94            "deletedObjects": obj.get("deletedObjects")
95        })
96        return _obj

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

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

PipelineMetrics

timestamp: Optional[Annotated[str, Strict(strict=True)]]

new_objects: Union[Annotated[float, Strict(strict=True)], Annotated[int, Strict(strict=True)]]

changed_objects: Union[Annotated[float, Strict(strict=True)], Annotated[int, Strict(strict=True)]]

deleted_objects: Union[Annotated[float, Strict(strict=True)], Annotated[int, Strict(strict=True)]]

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

43    def to_str(self) -> str:
44        """Returns the string representation of the model using alias"""
45        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

47    def to_json(self) -> str:
48        """Returns the JSON representation of the model using alias"""
49        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
50        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

52    @classmethod
53    def from_json(cls, json_str: str) -> Optional[Self]:
54        """Create an instance of PipelineMetrics from a JSON string"""
55        return cls.from_dict(json.loads(json_str))

Create an instance of PipelineMetrics from a JSON string

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

57    def to_dict(self) -> Dict[str, Any]:
58        """Return the dictionary representation of the model using alias.
59
60        This has the following differences from calling pydantic's
61        `self.model_dump(by_alias=True)`:
62
63        * `None` is only added to the output dict for nullable fields that
64          were set at model initialization. Other fields with value `None`
65          are ignored.
66        """
67        excluded_fields: Set[str] = set([
68        ])
69
70        _dict = self.model_dump(
71            by_alias=True,
72            exclude=excluded_fields,
73            exclude_none=True,
74        )
75        # set to None if timestamp (nullable) is None
76        # and model_fields_set contains the field
77        if self.timestamp is None and "timestamp" in self.model_fields_set:
78            _dict['timestamp'] = None
79
80        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

82    @classmethod
83    def from_dict(cls, obj: Optional[Dict[str, Any]]) -> Optional[Self]:
84        """Create an instance of PipelineMetrics from a dict"""
85        if obj is None:
86            return None
87
88        if not isinstance(obj, dict):
89            return cls.model_validate(obj)
90
91        _obj = cls.model_validate({
92            "timestamp": obj.get("timestamp"),
93            "newObjects": obj.get("newObjects"),
94            "changedObjects": obj.get("changedObjects"),
95            "deletedObjects": obj.get("deletedObjects")
96        })
97        return _obj

Create an instance of PipelineMetrics from a dict