weaviate.outputs

weaviate.outputs.aggregate

class weaviate.outputs.aggregate.AggregateBoolean(count, percentage_false, percentage_true, total_false, total_true)

Bases: object

The aggregation result for a boolean property.

Parameters:

• count (int | None)

• percentage_false (float | None)

• percentage_true (float | None)

• total_false (int | None)

• total_true (int | None)

count: int | None

percentage_false: float | None

percentage_true: float | None

total_false: int | None

total_true: int | None

class weaviate.outputs.aggregate.AggregateDate(count, maximum, median, minimum, mode)

Bases: object

The aggregation result for a date property.

Parameters:

• count (int | None)

• maximum (str | None)

• median (str | None)

• minimum (str | None)

• mode (str | None)

count: int | None

maximum: str | None

median: str | None

minimum: str | None

mode: str | None

class weaviate.outputs.aggregate.AggregateGroup(grouped_by, properties, total_count)

Bases: object

The aggregation result for a collection grouped by a property.

Parameters:

• grouped_by (GroupedBy)

• properties (Dict[str, AggregateInteger | AggregateNumber | AggregateText | AggregateBoolean | AggregateDate | AggregateReference])

• total_count (int | None)

grouped_by: GroupedBy

properties: Dict[str, AggregateInteger | AggregateNumber | AggregateText | AggregateBoolean | AggregateDate | AggregateReference]

total_count: int | None

class weaviate.outputs.aggregate.AggregateGroupByReturn(groups)

Bases: object

The aggregation results for a collection grouped by a property.

Parameters:

groups (List[AggregateGroup])

groups: List[AggregateGroup]

class weaviate.outputs.aggregate.AggregateInteger(count, maximum, mean, median, minimum, mode, sum_)

Bases: object

The aggregation result for an int property.

Parameters:

• count (int | None)

• maximum (int | None)

• mean (float | None)

• median (float | None)

• minimum (int | None)

• mode (int | None)

• sum_ (int | None)

count: int | None

maximum: int | None

mean: float | None

median: float | None

minimum: int | None

mode: int | None

sum_: int | None

class weaviate.outputs.aggregate.AggregateNumber(count, maximum, mean, median, minimum, mode, sum_)

Bases: object

The aggregation result for a number property.

Parameters:

• count (int | None)

• maximum (float | None)

• mean (float | None)

• median (float | None)

• minimum (float | None)

• mode (float | None)

• sum_ (float | None)

count: int | None

maximum: float | None

mean: float | None

median: float | None

minimum: float | None

mode: float | None

sum_: float | None

class weaviate.outputs.aggregate.AggregateReturn(properties, total_count)

Bases: object

The aggregation result for a collection.

Parameters:

• properties (Dict[str, AggregateInteger | AggregateNumber | AggregateText | AggregateBoolean | AggregateDate | AggregateReference])

• total_count (int | None)

properties: Dict[str, AggregateInteger | AggregateNumber | AggregateText | AggregateBoolean | AggregateDate | AggregateReference]

total_count: int | None

class weaviate.outputs.aggregate.AggregateText(count, top_occurrences)

Bases: object

The aggregation result for a text property.

Parameters:

• count (int | None)

• top_occurrences (List[TopOccurrence])

count: int | None

top_occurrences: List[TopOccurrence]

class weaviate.outputs.aggregate.GroupedBy(prop, value)

Bases: object

The property that the collection was grouped by.

Parameters:

• prop (str)

• value (str | int | float | bool | List[str] | List[int] | List[float] | List[bool] | GeoCoordinate | None)

prop: str

value: str | int | float | bool | List[str] | List[int] | List[float] | List[bool] | GeoCoordinate | None

weaviate.outputs.backup

class weaviate.outputs.backup.BackupStatus(*values)

Bases: str, Enum

The status of a backup.

STARTED = 'STARTED'

TRANSFERRING = 'TRANSFERRING'

TRANSFERRED = 'TRANSFERRED'

SUCCESS = 'SUCCESS'

FAILED = 'FAILED'

CANCELED = 'CANCELED'

pydantic model weaviate.outputs.backup.BackupStatusReturn

Bases: BaseModel

Return type of the backup status methods.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field backup_id: str [Required] (alias 'id')

field error: str | None = None

field path: str [Required]

field status: BackupStatus [Required]

_abc_impl = <_abc._abc_data object>

class weaviate.outputs.backup.BackupStorage(*values)

Bases: str, Enum

Which backend should be used to write the backup to.

FILESYSTEM = 'filesystem'

S3 = 's3'

GCS = 'gcs'

AZURE = 'azure'

pydantic model weaviate.outputs.backup.BackupReturn

Bases: BackupStatusReturn

Return type of the backup creation and restore methods.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field backup_id: str [Required] (alias 'id')

field collections: List[str] [Optional] (alias 'classes')

field error: str | None = None

field path: str [Required]

field status: BackupStatus [Required]

_abc_impl = <_abc._abc_data object>

weaviate.outputs.batch

class weaviate.outputs.batch.BatchObjectReturn(_all_responses=<factory>, elapsed_seconds=0.0, errors=<factory>, uuids=<factory>, has_errors=False)

Bases: object

This class contains the results of a batch insert_many operation.

Since the individual objects within the batch can error for differing reasons, the data is split up within this class for ease use when performing error checking, handling, and data revalidation.

Note

Due to concerns over memory usage, this object will only ever store the last MAX_STORED_RESULTS uuids in the uuids dictionary and MAX_STORED_RESULTS in the all_responses list. If more than MAX_STORED_RESULTS uuids are added to the dictionary, the oldest uuids will be removed. If the number of objects inserted in this batch exceeds MAX_STORED_RESULTS, the all_responses list will only contain the last MAX_STORED_RESULTS objects. The keys of the errors and uuids dictionaries will always be equivalent to the original_index of the objects as you added them to the batching loop but won’t necessarily be the same as the indices in the all_responses list because of this.

Parameters:

• _all_responses (List[UUID | ErrorObject])

• elapsed_seconds (float)

• errors (Dict[int, ErrorObject])

• uuids (Dict[int, UUID])

• has_errors (bool)

elapsed_seconds

The time taken to perform the batch operation.

Type:

float

errors

A dictionary of all the failed responses from the batch operation. The keys are the indices of the objects in the batch, and the values are the Error objects.

Type:

Dict[int, weaviate.collections.classes.batch.ErrorObject]

uuids

A dictionary of all the successful responses from the batch operation. The keys are the indices of the objects in the batch, and the values are the uuid_package.UUID objects.

Type:

Dict[int, uuid.UUID]

has_errors

A boolean indicating whether or not any of the objects in the batch failed to be inserted. If this is True, then the errors dictionary will contain at least one entry.

Type:

bool

property all_responses: List[UUID | ErrorObject]

A list of all the responses from the batch operation. Each response is either a uuid_package.UUID object or an Error object.

WARNING: This only stores the last MAX_STORED_RESULTS objects. If more than MAX_STORED_RESULTS objects are added to the batch, the oldest objects will be removed from this list.

Type:

@deprecated

elapsed_seconds: float = 0.0

has_errors: bool = False

_all_responses: List[UUID | ErrorObject]

errors: Dict[int, ErrorObject]

uuids: Dict[int, UUID]

class weaviate.outputs.batch.BatchReferenceReturn(elapsed_seconds=0.0, errors=<factory>, has_errors=False)

Bases: object

This class contains the results of a batch insert_many_references operation.

Since the individual references within the batch can error for differing reasons, the data is split up within this class for ease use when performing error checking, handling, and data revalidation.

Parameters:

• elapsed_seconds (float)

• errors (Dict[int, ErrorReference])

• has_errors (bool)

elapsed_seconds

The time taken to perform the batch operation.

Type:

float

errors

A dictionary of all the failed responses from the batch operation. The keys are the indices of the references in the batch, and the values are the Error objects.

Type:

Dict[int, weaviate.collections.classes.batch.ErrorReference]

has_errors

A boolean indicating whether or not any of the references in the batch failed to be inserted. If this is True, then the errors dictionary will contain at least one entry.

Type:

bool

elapsed_seconds: float = 0.0

has_errors: bool = False

errors: Dict[int, ErrorReference]

class weaviate.outputs.batch.BatchResult

Bases: object

This class contains the results of a batch operation.

Since the individual objects and references within the batch can error for differing reasons, the data is split up within this class for ease use when performing error checking, handling, and data revalidation.

objs

The results of the batch object operation.

refs

The results of the batch reference operation.

class weaviate.outputs.batch.ErrorObject(message, object_, original_uuid=None)

Bases: object

This class contains the error information for a single object in a batch operation.

Parameters:

• message (str)

• object_ (BatchObject)

• original_uuid (str | UUID | None)

original_uuid: str | UUID | None = None

message: str

object_: BatchObject

class weaviate.outputs.batch.ErrorReference(message, reference)

Bases: object

This class contains the error information for a single reference in a batch operation.

Parameters:

• message (str)

• reference (BatchReference)

message: str

reference: BatchReference

weaviate.outputs.cluster

class weaviate.outputs.cluster.Node(git_hash, name, shards, stats, status, version)

Bases: Generic[Sh, St]

The properties of a single node in the cluster.

Parameters:

• git_hash (str)

• name (str)

• shards (Sh)

• stats (St)

• status (str)

• version (str)

git_hash: str

name: str

shards: Sh

stats: St

status: str

version: str

class weaviate.outputs.cluster.Shard(collection, name, node, object_count, vector_indexing_status, vector_queue_length, compressed, loaded)

Bases: object

The properties of a single shard of a collection.

Parameters:

• collection (str)

• name (str)

• node (str)

• object_count (int)

• vector_indexing_status (Literal['READONLY', 'INDEXING', 'READY'])

• vector_queue_length (int)

• compressed (bool)

• loaded (bool | None)

collection: str

name: str

node: str

object_count: int

vector_indexing_status: Literal['READONLY', 'INDEXING', 'READY']

vector_queue_length: int

compressed: bool

loaded: bool | None

weaviate.outputs.config

weaviate.outputs.config.BM25Config

alias of _BM25Config

weaviate.outputs.config.CollectionConfig

alias of _CollectionConfig

weaviate.outputs.config.CollectionConfigSimple

alias of _CollectionConfigSimple

weaviate.outputs.config.GenerativeConfig

alias of _GenerativeConfig

class weaviate.outputs.config.GenerativeSearches(*values)

Bases: str, BaseEnum

The available generative search modules in Weaviate.

These modules generate text from text-based inputs. See the docs for more details.

AWS

Weaviate module backed by AWS Bedrock generative models.

ANTHROPIC

Weaviate module backed by Anthropic generative models.

ANYSCALE

Weaviate module backed by Anyscale generative models.

COHERE

Weaviate module backed by Cohere generative models.

DATABRICKS

Weaviate module backed by Databricks generative models.

FRIENDLIAI

Weaviate module backed by FriendliAI generative models.

MISTRAL

Weaviate module backed by Mistral generative models.

NVIDIA

Weaviate module backed by NVIDIA generative models.

OLLAMA

Weaviate module backed by generative models deployed on Ollama infrastructure.

OPENAI

Weaviate module backed by OpenAI and Azure-OpenAI generative models.

PALM

Weaviate module backed by PaLM generative models.

AWS = 'generative-aws'

ANTHROPIC = 'generative-anthropic'

ANYSCALE = 'generative-anyscale'

COHERE = 'generative-cohere'

DATABRICKS = 'generative-databricks'

DUMMY = 'generative-dummy'

FRIENDLIAI = 'generative-friendliai'

MISTRAL = 'generative-mistral'

NVIDIA = 'generative-nvidia'

OLLAMA = 'generative-ollama'

OPENAI = 'generative-openai'

PALM = 'generative-palm'

XAI = 'generative-xai'

weaviate.outputs.config.InvertedIndexConfig

alias of _InvertedIndexConfig

weaviate.outputs.config.MultiTenancyConfig

alias of _MultiTenancyConfig

class weaviate.outputs.config.ReplicationDeletionStrategy(*values)

Bases: str, BaseEnum

How object deletions in multi node environments should be resolved.

PERMANENT_DELETION

Once an object has been deleted on one node it will be deleted on all nodes in case of conflicts.

NO_AUTOMATED_RESOLUTION

No deletion resolution.

DELETE_ON_CONFLICT = 'DeleteOnConflict'

NO_AUTOMATED_RESOLUTION = 'NoAutomatedResolution'

TIME_BASED_RESOLUTION = 'TimeBasedResolution'

weaviate.outputs.config.PQConfig

alias of _PQConfig

weaviate.outputs.config.PQEncoderConfig

alias of _PQEncoderConfig

class weaviate.outputs.config.PQEncoderDistribution(*values)

Bases: str, BaseEnum

Distribution of the PQ encoder.

LOG_NORMAL

Log-normal distribution.

NORMAL

Normal distribution.

LOG_NORMAL = 'log-normal'

NORMAL = 'normal'

class weaviate.outputs.config.PQEncoderType(*values)

Bases: str, BaseEnum

Type of the PQ encoder.

KMEANS

K-means encoder.

TILE

Tile encoder.

KMEANS = 'kmeans'

TILE = 'tile'

weaviate.outputs.config.PropertyConfig

alias of _Property

weaviate.outputs.config.ReferencePropertyConfig

alias of _ReferenceProperty

weaviate.outputs.config.ReplicationConfig

alias of _ReplicationConfig

class weaviate.outputs.config.Rerankers(*values)

Bases: str, BaseEnum

The available reranker modules in Weaviate.

These modules rerank the results of a search query. See the docs for more details.

NONE

No reranker.

COHERE

Weaviate module backed by Cohere reranking models.

TRANSFORMERS

Weaviate module backed by Transformers reranking models.

VOYAGEAI

Weaviate module backed by VoyageAI reranking models.

JINAAI

Weaviate module backed by JinaAI reranking models.

NVIDIA

Weaviate module backed by NVIDIA reranking models.

NONE = 'none'

COHERE = 'reranker-cohere'

TRANSFORMERS = 'reranker-transformers'

VOYAGEAI = 'reranker-voyageai'

JINAAI = 'reranker-jinaai'

NVIDIA = 'reranker-nvidia'

weaviate.outputs.config.RerankerConfig

alias of _RerankerConfig

weaviate.outputs.config.ShardingConfig

alias of _ShardingConfig

weaviate.outputs.config.ShardStatus

alias of _ShardStatus

class weaviate.outputs.config.VectorDistances(*values)

Bases: str, Enum

Vector similarity distance metric to be used in the VectorIndexConfig class.

To ensure optimal search results, we recommend reviewing whether your model provider advises a specific distance metric and following their advice.

COSINE

Cosine distance: reference

DOT

Dot distance: reference

L2_SQUARED

L2 squared distance: reference

HAMMING

Hamming distance: reference

MANHATTAN

Manhattan distance: reference

COSINE = 'cosine'

DOT = 'dot'

L2_SQUARED = 'l2-squared'

HAMMING = 'hamming'

MANHATTAN = 'manhattan'

weaviate.outputs.config.VectorIndexConfigHNSW

alias of _VectorIndexConfigHNSW

weaviate.outputs.config.VectorIndexConfigFlat

alias of _VectorIndexConfigFlat

class weaviate.outputs.config.VectorIndexType(*values)

Bases: str, Enum

The available vector index types in Weaviate.

HNSW

Hierarchical Navigable Small World (HNSW) index.

FLAT

Flat index.

HNSW = 'hnsw'

FLAT = 'flat'

DYNAMIC = 'dynamic'

class weaviate.outputs.config.Vectorizers(*values)

Bases: str, Enum

The available vectorization modules in Weaviate.

These modules encode binary data into lists of floats called vectors. See the docs for more details.

NONE

No vectorizer.

TEXT2VEC_AWS

Weaviate module backed by AWS text-based embedding models.

TEXT2VEC_COHERE

Weaviate module backed by Cohere text-based embedding models.

TEXT2VEC_CONTEXTIONARY

Weaviate module backed by Contextionary text-based embedding models.

TEXT2VEC_GPT4ALL

Weaviate module backed by GPT-4-All text-based embedding models.

TEXT2VEC_HUGGINGFACE

Weaviate module backed by HuggingFace text-based embedding models.

TEXT2VEC_OPENAI

Weaviate module backed by OpenAI and Azure-OpenAI text-based embedding models.

TEXT2VEC_PALM

Weaviate module backed by PaLM text-based embedding models.

TEXT2VEC_TRANSFORMERS

Weaviate module backed by Transformers text-based embedding models.

TEXT2VEC_JINAAI

Weaviate module backed by Jina AI text-based embedding models.

TEXT2VEC_VOYAGEAI

Weaviate module backed by Voyage AI text-based embedding models.

TEXT2VEC_NVIDIA

Weaviate module backed by NVIDIA text-based embedding models.

TEXT2VEC_WEAVIATE

Weaviate module backed by Weaviate’s self-hosted text-based embedding models.

IMG2VEC_NEURAL

Weaviate module backed by a ResNet-50 neural network for images.

MULTI2VEC_CLIP

Weaviate module backed by a Sentence-BERT CLIP model for images and text.

MULTI2VEC_PALM

Weaviate module backed by a palm model for images and text.

MULTI2VEC_BIND

Weaviate module backed by the ImageBind model for images, text, audio, depth, IMU, thermal, and video.

MULTI2VEC_VOYAGEAI

Weaviate module backed by a Voyage AI multimodal embedding models.

MULTI2VEC_NVIDIA

Weaviate module backed by NVIDIA multimodal embedding models.

REF2VEC_CENTROID

Weaviate module backed by a centroid-based model that calculates an object’s vectors from its referenced vectors.

NONE = 'none'

TEXT2COLBERT_JINAAI = 'text2colbert-jinaai'

TEXT2VEC_AWS = 'text2vec-aws'

TEXT2VEC_COHERE = 'text2vec-cohere'

TEXT2VEC_CONTEXTIONARY = 'text2vec-contextionary'

TEXT2VEC_DATABRICKS = 'text2vec-databricks'

TEXT2VEC_GPT4ALL = 'text2vec-gpt4all'

TEXT2VEC_HUGGINGFACE = 'text2vec-huggingface'

TEXT2VEC_MISTRAL = 'text2vec-mistral'

TEXT2VEC_NVIDIA = 'text2vec-nvidia'

TEXT2VEC_OLLAMA = 'text2vec-ollama'

TEXT2VEC_OPENAI = 'text2vec-openai'

TEXT2VEC_PALM = 'text2vec-palm'

TEXT2VEC_TRANSFORMERS = 'text2vec-transformers'

TEXT2VEC_JINAAI = 'text2vec-jinaai'

TEXT2VEC_VOYAGEAI = 'text2vec-voyageai'

TEXT2VEC_WEAVIATE = 'text2vec-weaviate'

IMG2VEC_NEURAL = 'img2vec-neural'

MULTI2VEC_CLIP = 'multi2vec-clip'

MULTI2VEC_COHERE = 'multi2vec-cohere'

MULTI2VEC_JINAAI = 'multi2vec-jinaai'

MULTI2VEC_BIND = 'multi2vec-bind'

MULTI2VEC_PALM = 'multi2vec-palm'

MULTI2VEC_VOYAGEAI = 'multi2vec-voyageai'

MULTI2VEC_NVIDIA = 'multi2vec-nvidia'

REF2VEC_CENTROID = 'ref2vec-centroid'

weaviate.outputs.config.VectorizerConfig

alias of _VectorizerConfig

weaviate.outputs.data

class weaviate.outputs.data.DeleteManyObject(uuid, successful, error=None)

Bases: object

This class contains the objects of a delete_many operation.

Parameters:

• uuid (UUID)

• successful (bool)

• error (str | None)

error: str | None = None

uuid: UUID

successful: bool

class weaviate.outputs.data.DeleteManyReturn(failed, matches, objects, successful)

Bases: Generic[T]

This class contains the results of a delete_many operation..

Parameters:

• failed (int)

• matches (int)

• objects (T)

• successful (int)

failed: int

matches: int

objects: T

successful: int

class weaviate.outputs.data.Error(message, code=None, original_uuid=None)

Bases: object

This class represents an error that occurred when attempting to insert an object within a batch.

Parameters:

• message (str)

• code (int | None)

• original_uuid (str | UUID | None)

code: int | None = None

original_uuid: str | UUID | None = None

message: str

class weaviate.outputs.data.RefError(message)

Bases: object

This class represents an error that occurred when attempting to insert a reference between objects within a batch.

Parameters:

message (str)

message: str

weaviate.outputs.query

weaviate.outputs.query.FilterByCreationTime

alias of _FilterByCreationTime

weaviate.outputs.query.FilterById

alias of _FilterById

weaviate.outputs.query.FilterByProperty

alias of _FilterByProperty

weaviate.outputs.query.FilterByRef

alias of _FilterByRef

weaviate.outputs.query.FilterByUpdateTime

alias of _FilterByUpdateTime

weaviate.outputs.query.FilterReturn

alias of _Filters

pydantic model weaviate.outputs.query.GeoCoordinate

Bases: _WeaviateInput

Input for the geo-coordinate datatype.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field latitude: float [Required]

Constraints:

• ge = -90

• le = 90

field longitude: float [Required]

Constraints:

• ge = -180

• le = 180

_to_dict()

Return type:

Dict[str, float]

_abc_impl = <_abc._abc_data object>

class weaviate.outputs.query.BM25OperatorAnd

Bases: BM25OperatorOptions

Define the ‘And’ operator for keyword queries.

operator: ClassVar[Any] = 2

class weaviate.outputs.query.BM25OperatorOr(minimum_should_match)

Bases: BM25OperatorOptions

Define the ‘Or’ operator for keyword queries.

Parameters:

minimum_should_match (int)

operator: ClassVar[Any] = 1

minimum_should_match: int

weaviate.outputs.query.ListOfVectorsQuery

alias of _ListOfVectorsQuery

class weaviate.outputs.query.MetadataReturn(creation_time=None, last_update_time=None, distance=None, certainty=None, score=None, explain_score=None, is_consistent=None, rerank_score=None)

Bases: object

Metadata of an object returned by a query.

Parameters:

• creation_time (datetime | None)

• last_update_time (datetime | None)

• distance (float | None)

• certainty (float | None)

• score (float | None)

• explain_score (str | None)

• is_consistent (bool | None)

• rerank_score (float | None)

_is_empty()

Return type:

bool

certainty: float | None = None

creation_time: datetime | None = None

distance: float | None = None

explain_score: str | None = None

is_consistent: bool | None = None

last_update_time: datetime | None = None

rerank_score: float | None = None

score: float | None = None

class weaviate.outputs.query.MetadataSingleObjectReturn(creation_time, last_update_time, is_consistent)

Bases: object

Metadata of an object returned by the fetch_object_by_id query.

Parameters:

• creation_time (datetime)

• last_update_time (datetime)

• is_consistent (bool | None)

creation_time: datetime

last_update_time: datetime

is_consistent: bool | None

class weaviate.outputs.query.Object(uuid, metadata, properties, references, vector, collection)

Bases: Generic[P, R], _Object[P, R, MetadataReturn]

A single Weaviate object returned by a query within the .query namespace of a collection.

Parameters:

• uuid (UUID)

• metadata (M)

• properties (P)

• references (R)

• vector (Dict[str, List[float] | List[List[float]]])

• collection (str)

class weaviate.outputs.query.ObjectSingleReturn(uuid, metadata, properties, references, vector, collection)

Bases: Generic[P, R], _Object[P, R, MetadataSingleObjectReturn]

A single Weaviate object returned by the fetch_object_by_id query.

Parameters:

• uuid (UUID)

• metadata (M)

• properties (P)

• references (R)

• vector (Dict[str, List[float] | List[List[float]]])

• collection (str)

class weaviate.outputs.query.GroupByObject(uuid, metadata, properties, references, vector, collection, belongs_to_group)

Bases: Generic[P, R], _Object[P, R, GroupByMetadataReturn]

A single Weaviate object returned by a query with the group_by argument specified.

Parameters:

• uuid (UUID)

• metadata (M)

• properties (P)

• references (R)

• vector (Dict[str, List[float] | List[List[float]]])

• collection (str)

• belongs_to_group (str)

belongs_to_group: str

uuid: UUID

metadata: M

properties: P

references: R

vector: Dict[str, List[float] | List[List[float]]]

collection: str

class weaviate.outputs.query.GroupByReturn(objects, groups)

Bases: Generic[P, R]

The return type of a query within the .query namespace of a collection with the group_by argument specified.

Parameters:

• objects (List[GroupByObject[P, R]])

• groups (Dict[str, Group[P, R]])

objects: List[GroupByObject[P, R]]

groups: Dict[str, Group[P, R]]

class weaviate.outputs.query.Group(name, min_distance, max_distance, number_of_objects, objects, rerank_score)

Bases: Generic[P, R]

A group of objects returned in a group by query.

Parameters:

• name (str)

• min_distance (float)

• max_distance (float)

• number_of_objects (int)

• objects (List[GroupByObject[P, R]])

• rerank_score (float | None)

name: str

min_distance: float

max_distance: float

number_of_objects: int

objects: List[GroupByObject[P, R]]

rerank_score: float | None

class weaviate.outputs.query.GenerativeObject(generated, generative, uuid, metadata, properties, references, vector, collection)

Bases: Generic[P, R], Object[P, R]

A single Weaviate object returned by a query within the generate namespace of a collection.

Parameters:

• generated (str | None)

• generative (GenerativeSingle | None)

• uuid (UUID)

• metadata (M)

• properties (P)

• references (R)

• vector (Dict[str, List[float] | List[List[float]]])

• collection (str)

property generated: str | None

The single generated text of the object.

__generated: str | None

generative: GenerativeSingle | None

uuid: UUID

metadata: M

properties: P

references: R

vector: Dict[str, List[float] | List[List[float]]]

collection: str

class weaviate.outputs.query.GenerativeReturn(generated, objects, generative)

Bases: Generic[P, R]

The return type of a query within the generate namespace of a collection.

Parameters:

• generated (str | None)

• objects (List[GenerativeObject[P, R]])

• generative (GenerativeGrouped | None)

property generated: str | None

The grouped generated text of the objects.

__generated: str | None

objects: List[GenerativeObject[P, R]]

generative: GenerativeGrouped | None

class weaviate.outputs.query.GenerativeGroupByReturn(objects, groups, generated)

Bases: Generic[P, R]

The return type of a query within the .generate namespace of a collection with the group_by argument specified.

Parameters:

• objects (List[GroupByObject[P, R]])

• groups (Dict[str, GenerativeGroup[P, R]])

• generated (str | None)

objects: List[GroupByObject[P, R]]

groups: Dict[str, GenerativeGroup[P, R]]

generated: str | None

class weaviate.outputs.query.GenerativeGroup(name, min_distance, max_distance, number_of_objects, objects, rerank_score, generated)

Bases: Generic[P, R], Group[P, R]

A group of objects returned in a generative group by query.

Parameters:

• name (str)

• min_distance (float)

• max_distance (float)

• number_of_objects (int)

• objects (List[GroupByObject[P, R]])

• rerank_score (float | None)

• generated (str | None)

generated: str | None

name: str

min_distance: float

max_distance: float

number_of_objects: int

objects: List[GroupByObject[P, R]]

rerank_score: float | None

weaviate.outputs.query.PhoneNumberType

alias of _PhoneNumber

class weaviate.outputs.query.QueryReturn(objects)

Bases: Generic[P, R]

The return type of a query within the .query namespace of a collection.

Parameters:

objects (List[Object[P, R]])

objects: List[Object[P, R]]

weaviate.outputs.query.Sorting

alias of _Sorting

weaviate.outputs.rbac

pydantic model weaviate.outputs.rbac.BackupsPermissionOutput

Bases: _BackupsPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.outputs.rbac.ClusterPermissionOutput

Bases: _ClusterPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.outputs.rbac.CollectionsPermissionOutput

Bases: _CollectionsPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.outputs.rbac.DataPermissionOutput

Bases: _DataPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.outputs.rbac.NodesPermissionOutput

Bases: _NodesPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.outputs.rbac.RolesPermissionOutput

Bases: _RolesPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

class weaviate.outputs.rbac.RoleScope(*values)

Bases: str, BaseEnum

Scope of the role permission.

MATCH = 'match'

ALL = 'all'

pydantic model weaviate.outputs.rbac.UsersPermissionOutput

Bases: _UsersPermission

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

_abc_impl = <_abc._abc_data object>

class weaviate.outputs.rbac.UserAssignment(user_id: str, user_type: weaviate.rbac.models.UserTypes)

Bases: object

Parameters:

• user_id (str)

• user_type (UserTypes)

user_id: str

user_type: UserTypes

weaviate.outputs.tenants

pydantic model weaviate.outputs.tenants.Tenant

Bases: BaseModel

Tenant class used to describe a tenant in Weaviate.

name

The name of the tenant.

activity_status

TenantActivityStatus, default: “HOT”

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field activityStatus: _TenantActivistatusServerValues = _TenantActivistatusServerValues.HOT

field activityStatusInternal: TenantActivityStatus = TenantActivityStatus.ACTIVE (alias 'activity_status')

field name: str [Required]

_model_post_init(user_input)

Parameters:

user_input (bool)

Return type:

None

model_post_init(_Tenant__context)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Parameters:

_Tenant__context (Any)

Return type:

None

_abc_impl = <_abc._abc_data object>

property activity_status: TenantActivityStatus

Getter for the activity status of the tenant.

class weaviate.outputs.tenants.TenantActivityStatus(*values)

Bases: str, Enum

TenantActivityStatus class used to describe the activity status of a tenant in Weaviate.

ACTIVE

The tenant is fully active and can be used.

INACTIVE

The tenant is not active, files stored locally.

OFFLOADED

The tenant is not active, files stored on the cloud.

OFFLOADING

The tenant is in the process of being offloaded.

ONLOADING

The tenant is in the process of being activated.

HOT

DEPRECATED, please use ACTIVE. The tenant is fully active and can be used.

COLD

DEPRECATED, please use INACTIVE. The tenant is not active, files stored locally.

FROZEN

DEPRECATED, please use OFFLOADED. The tenant is not active, files stored on the cloud.

ACTIVE = 'ACTIVE'

INACTIVE = 'INACTIVE'

OFFLOADED = 'OFFLOADED'

OFFLOADING = 'OFFLOADING'

ONLOADING = 'ONLOADING'

HOT = 'HOT'

COLD = 'COLD'

FROZEN = 'FROZEN'

weaviate.outputs.tenants.TenantOutputType

alias of Tenant