weaviate.collections.classes

weaviate.collections.classes.aggregate

class weaviate.collections.classes.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.collections.classes.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.collections.classes.aggregate.TopOccurrence(count, value)

Bases: object

The top occurrence of a text property.

Parameters:

• count (int | None)

• value (str | None)

count: int | None

value: str | None

class weaviate.collections.classes.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.collections.classes.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.collections.classes.aggregate.AggregateReference(pointing_to)

Bases: object

The aggregation result for a cross-reference property.

Parameters:

pointing_to (List[str] | None)

pointing_to: List[str] | None

class weaviate.collections.classes.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.collections.classes.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.collections.classes.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

class weaviate.collections.classes.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.collections.classes.aggregate.AggregateGroupByReturn(groups)

Bases: object

The aggregation results for a collection grouped by a property.

Parameters:

groups (List[AggregateGroup])

groups: List[AggregateGroup]

pydantic model weaviate.collections.classes.aggregate._MetricsBase

Bases: BaseModel

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 count: bool [Required]

field property_name: str [Required]

to_gql()

Return type:

str

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsText

Bases: _MetricsBase

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 min_occurrences: int | None [Required]

field top_occurrences_count: bool [Required]

field top_occurrences_value: bool [Required]

to_gql()

Return type:

str

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsNum

Bases: _MetricsBase

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 maximum: bool [Required]

field mean: bool [Required]

field median: bool [Required]

field minimum: bool [Required]

field mode: bool [Required]

field sum_: bool [Required]

to_gql()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsInteger

Bases: _MetricsNum

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.

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsNumber

Bases: _MetricsNum

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.

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsBoolean

Bases: _MetricsBase

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 percentage_false: bool [Required]

field percentage_true: bool [Required]

field total_false: bool [Required]

field total_true: bool [Required]

to_gql()

Return type:

str

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsDate

Bases: _MetricsBase

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 maximum: bool [Required]

field median: bool [Required]

field minimum: bool [Required]

field mode: bool [Required]

to_gql()

Return type:

str

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate._MetricsReference

Bases: BaseModel

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 pointing_to: bool [Required]

field property_name: str [Required]

to_gql()

Return type:

str

to_grpc()

Return type:

Aggregation

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.aggregate.GroupByAggregate

Bases: _WeaviateInput

Define how the aggregations’s group-by operation should be performed.

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 limit: int | None = None

field prop: str [Required]

_to_grpc()

Return type:

GroupBy

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.aggregate.Metrics(property_)

Bases: object

Define the metrics to be returned based on a property when aggregating over a collection.

Use the __init__ method to define the name to the property to be aggregated on. Then use the text, integer, number, boolean, date_, or reference methods to define the metrics to be returned.

See the docs for more details!

Parameters:

property_ (str)

text(count=False, top_occurrences_count=False, top_occurrences_value=False, min_occurrences=None)

Define the metrics to be returned for a TEXT or TEXT_ARRAY property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

• count (bool) – Whether to include the number of objects that contain this property.

• top_occurrences_count (bool) – Whether to include the number of the top occurrences of a property’s value.

• top_occurrences_value (bool) – Whether to include the value of the top occurrences of a property’s value.

• min_occurrences (int | None) – Only include entries with more occurrences than the given limit.

Returns:

A _MetricsStr object that includes the metrics to be returned.

Return type:

_MetricsText

integer(count=False, maximum=False, mean=False, median=False, minimum=False, mode=False, sum_=False)

Define the metrics to be returned for an INT or INT_ARRAY property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

• count (bool) – Whether to include the number of objects that contain this property.

• maximum (bool) – Whether to include the maximum value of this property.

• mean (bool) – Whether to include the mean value of this property.

• median (bool) – Whether to include the median value of this property.

• minimum (bool) – Whether to include the minimum value of this property.

• mode (bool) – Whether to include the mode value of this property.

• sum – Whether to include the sum of this property.

• sum_ (bool)

Returns:

A _MetricsInteger object that includes the metrics to be returned.

Return type:

_MetricsInteger

number(count=False, maximum=False, mean=False, median=False, minimum=False, mode=False, sum_=False)

Define the metrics to be returned for a NUMBER or NUMBER_ARRAY property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

• count (bool) – Whether to include the number of objects that contain this property.

• maximum (bool) – Whether to include the maximum value of this property.

• mean (bool) – Whether to include the mean value of this property.

• median (bool) – Whether to include the median value of this property.

• minimum (bool) – Whether to include the minimum value of this property.

• mode (bool) – Whether to include the mode value of this property.

• sum – Whether to include the sum of this property.

• sum_ (bool)

Returns:

A _MetricsNumber object that includes the metrics to be returned.

Return type:

_MetricsNumber

boolean(count=False, percentage_false=False, percentage_true=False, total_false=False, total_true=False)

Define the metrics to be returned for a BOOL or BOOL_ARRAY property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

• count (bool) – Whether to include the number of objects that contain this property.

• percentage_false (bool) – Whether to include the percentage of objects that have a false value for this property.

• percentage_true (bool) – Whether to include the percentage of objects that have a true value for this property.

• total_false (bool) – Whether to include the total number of objects that have a false value for this property.

• total_true (bool) – Whether to include the total number of objects that have a true value for this property.

Returns:

A _MetricsBoolean object that includes the metrics to be returned.

Return type:

_MetricsBoolean

date_(count=False, maximum=False, median=False, minimum=False, mode=False)

Define the metrics to be returned for a DATE or DATE_ARRAY property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

• count (bool) – Whether to include the number of objects that contain this property.

• maximum (bool) – Whether to include the maximum value of this property.

• median (bool) – Whether to include the median value of this property.

• minimum (bool) – Whether to include the minimum value of this property.

• mode (bool) – Whether to include the mode value of this property.

Returns:

A _MetricsDate object that includes the metrics to be returned.

Return type:

_MetricsDate

reference(pointing_to=False)

Define the metrics to be returned for a cross-reference property when aggregating over a collection.

If none of the arguments are provided then all metrics will be returned.

Parameters:

pointing_to (bool) – The UUIDs of the objects that are being pointed to.

Returns:

A _MetricsReference object that includes the metrics to be returned.

Return type:

_MetricsReference

weaviate.collections.classes.batch

class weaviate.collections.classes.batch._BatchObject(collection: str, vector: Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | NoneType, uuid: str, properties: Dict[str, NoneType | str | bool | int | float | datetime.datetime | uuid.UUID | weaviate.collections.classes.types.GeoCoordinate | weaviate.collections.classes.types.PhoneNumber | weaviate.collections.classes.types._PhoneNumber | Mapping[str, ForwardRef('WeaviateField')] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime.datetime] | Sequence[uuid.UUID] | Sequence[Mapping[str, ForwardRef('WeaviateField')]]] | None, tenant: str | None, references: Mapping[str, str | uuid.UUID | Sequence[str | uuid.UUID] | weaviate.collections.classes.internal.ReferenceToMulti] | None, index: int, retry_count: int = 0)

Bases: object

Parameters:

• collection (str)

• vector (Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | None)

• uuid (str)

• properties (Dict[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]]]] | None)

• tenant (str | None)

• references (Mapping[str, str | UUID | Sequence[str | UUID] | ReferenceToMulti] | None)

• index (int)

• retry_count (int)

collection: str

vector: Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | None

uuid: str

properties: Dict[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]]]] | None

tenant: str | None

references: Mapping[str, str | UUID | Sequence[str | UUID] | ReferenceToMulti] | None

index: int

retry_count: int = 0

class weaviate.collections.classes.batch._BatchReference(from_: str, to: str, tenant: str | None, from_uuid: str, to_uuid: str | None)

Bases: object

Parameters:

• from_ (str)

• to (str)

• tenant (str | None)

• from_uuid (str)

• to_uuid (str | None)

from_: str

to: str

tenant: str | None

from_uuid: str

to_uuid: str | None

pydantic model weaviate.collections.classes.batch.BatchObject

Bases: BaseModel

A Weaviate object to be added to the database.

Performs validation on the class name and UUID, and automatically generates a UUID if one is not provided. Also converts the vector to a list of floats if it is provided as a numpy array.

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 collection: str [Required]

Constraints:

• min_length = 1

Validated by:

• _validate_collection

field index: int [Required]

field properties: Dict[str, Any] | None = None

field references: Mapping[str, str | UUID | Sequence[str | UUID] | ReferenceToMulti] | None = None

field retry_count: int = 0

field tenant: str | None = None

field uuid: str | UUID | None = None

field vector: Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | None = None

classmethod _from_internal(obj)

Parameters:

obj (_BatchObject)

Return type:

BatchObject

_to_internal()

Return type:

_BatchObject

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.batch.Shard

Bases: BaseModel

Use this class when defining a shard whose vector indexing process will be awaited for in a sync blocking fashion.

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 collection: str [Required]

field tenant: str | None = None

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.batch.BatchReference

Bases: BaseModel

A reference between two objects in Weaviate.

Performs validation on the class names and UUIDs.

Converts provided data to an internal object containing beacons for insertion into Weaviate.

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 from_object_collection: str [Required]

Constraints:

• min_length = 1

Validated by:

• _validate_from_object_collection

field from_object_uuid: str | UUID [Required]

Validated by:

• _validate_uuids

field from_property_name: str [Required]

field tenant: str | None = None

field to_object_collection: str | None = None

Validated by:

• _validate_to_object_collection

field to_object_uuid: str | UUID [Required]

Validated by:

• _validate_uuids

classmethod _from_internal(ref)

Parameters:

ref (_BatchReference)

Return type:

BatchReference

_to_internal()

Return type:

_BatchReference

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.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)

message: str

object_: BatchObject

original_uuid: str | UUID | None = None

class weaviate.collections.classes.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

class weaviate.collections.classes.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

_all_responses: List[UUID | ErrorObject]

elapsed_seconds: float = 0.0

errors: Dict[int, ErrorObject]

uuids: Dict[int, UUID]

has_errors: bool = False

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

class weaviate.collections.classes.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

errors: Dict[int, ErrorReference]

has_errors: bool = False

class weaviate.collections.classes.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.collections.classes.batch.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)

uuid: UUID

successful: bool

error: str | None = None

class weaviate.collections.classes.batch.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

weaviate.collections.classes.cluster

class weaviate.collections.classes.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

class weaviate.collections.classes.cluster.Stats(object_count, shard_count)

Bases: object

The statistics of a collection.

Parameters:

• object_count (int)

• shard_count (int)

object_count: int

shard_count: int

class weaviate.collections.classes.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.collections.classes.cluster._ConvertFromREST

Bases: object

static nodes_verbose(nodes)

Parameters:

nodes (List[Node])

Return type:

List[Node[List[Shard], Stats]]

static nodes_minimal(nodes)

Parameters:

nodes (List[Node])

Return type:

List[Node[None, None]]

weaviate.collections.classes.config

class weaviate.collections.classes.config.ConsistencyLevel(*values)

Bases: str, BaseEnum

The consistency levels when writing to Weaviate with replication enabled.

ALL

Wait for confirmation of write success from all, N, replicas.

ONE

Wait for confirmation of write success from only one replica.

QUORUM

Wait for confirmation of write success from a quorum: N/2+1, of replicas.

ALL = 'ALL'

ONE = 'ONE'

QUORUM = 'QUORUM'

class weaviate.collections.classes.config.DataType(*values)

Bases: str, BaseEnum

The available primitive data types in Weaviate.

TEXT

Text data type.

TEXT_ARRAY

Text array data type.

INT

Integer data type.

INT_ARRAY

Integer array data type.

BOOL

Boolean data type.

BOOL_ARRAY

Boolean array data type.

NUMBER

Number data type.

NUMBER_ARRAY

Number array data type.

DATE

Date data type.

DATE_ARRAY

Date array data type.

UUID

UUID data type.

UUID_ARRAY

UUID array data type.

GEO_COORDINATES

Geo coordinates data type.

BLOB

Blob data type.

PHONE_NUMBER

Phone number data type.

OBJECT

Object data type.

OBJECT_ARRAY

Object array data type.

TEXT = 'text'

TEXT_ARRAY = 'text[]'

INT = 'int'

INT_ARRAY = 'int[]'

BOOL = 'boolean'

BOOL_ARRAY = 'boolean[]'

NUMBER = 'number'

NUMBER_ARRAY = 'number[]'

DATE = 'date'

DATE_ARRAY = 'date[]'

UUID = 'uuid'

UUID_ARRAY = 'uuid[]'

GEO_COORDINATES = 'geoCoordinates'

BLOB = 'blob'

PHONE_NUMBER = 'phoneNumber'

OBJECT = 'object'

OBJECT_ARRAY = 'object[]'

class weaviate.collections.classes.config.Tokenization(*values)

Bases: str, BaseEnum

The available inverted index tokenization methods for text properties in Weaviate.

WORD

Tokenize by word.

WHITESPACE

Tokenize by whitespace.

LOWERCASE

Tokenize by lowercase.

FIELD

Tokenize by field.

GSE

Tokenize using GSE (for Chinese and Japanese).

TRIGRAM

Tokenize into trigrams.

KAGOME_JA

Tokenize using the ‘Kagome’ tokenizer (for Japanese).

KAGOME_KR

Tokenize using the ‘Kagome’ tokenizer and a Korean MeCab dictionary (for Korean).

WORD = 'word'

WHITESPACE = 'whitespace'

LOWERCASE = 'lowercase'

FIELD = 'field'

GSE = 'gse'

TRIGRAM = 'trigram'

KAGOME_JA = 'kagome_ja'

KAGOME_KR = 'kagome_kr'

class weaviate.collections.classes.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'

class weaviate.collections.classes.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'

class weaviate.collections.classes.config.StopwordsPreset(*values)

Bases: str, BaseEnum

Preset stopwords to use in the Stopwords class.

EN

English stopwords.

NONE

No stopwords.

NONE = 'none'

EN = 'en'

class weaviate.collections.classes.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'

class weaviate.collections.classes.config.PQEncoderType(*values)

Bases: str, BaseEnum

Type of the PQ encoder.

KMEANS

K-means encoder.

TILE

Tile encoder.

KMEANS = 'kmeans'

TILE = 'tile'

class weaviate.collections.classes.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.collections.classes.config.MultiVectorAggregation(*values)

Bases: str, BaseEnum

Aggregation type to use for multivector indices.

MAX_SIM

Maximum similarity.

MAX_SIM = 'maxSim'

pydantic model weaviate.collections.classes.config._PQEncoderConfigCreate

Bases: _ConfigCreateModel

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 distribution: PQEncoderDistribution | None [Required]

field type_: PQEncoderType | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._PQEncoderConfigUpdate

Bases: _ConfigUpdateModel

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 distribution: PQEncoderDistribution | None [Required]

field type_: PQEncoderType | None [Required]

merge_with_existing(schema)

Must be done manually since Pydantic does not work well with type and type_.

Errors shadowing type occur if we want to use type as a field name.

Parameters:

schema (Dict[str, Any])

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._PQConfigCreate

Bases: _QuantizerConfigCreate

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 bitCompression: bool | None = None

field centroids: int | None [Required]

field encoder: _PQEncoderConfigCreate [Required]

field segments: int | None [Required]

field trainingLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._BQConfigCreate

Bases: _QuantizerConfigCreate

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 cache: bool | None [Required]

field rescoreLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._SQConfigCreate

Bases: _QuantizerConfigCreate

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 cache: bool | None [Required]

field rescoreLimit: int | None [Required]

field trainingLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._PQConfigUpdate

Bases: _QuantizerConfigUpdate

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 bitCompression: bool | None = None

field centroids: int | None [Required]

field enabled: bool | None [Required]

field encoder: _PQEncoderConfigUpdate | None [Required]

field segments: int | None [Required]

field trainingLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._BQConfigUpdate

Bases: _QuantizerConfigUpdate

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 enabled: bool | None [Required]

field rescoreLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._SQConfigUpdate

Bases: _QuantizerConfigUpdate

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 enabled: bool | None [Required]

field rescoreLimit: int | None [Required]

field trainingLimit: int | None [Required]

static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._ShardingConfigCreate

Bases: _ConfigCreateModel

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 desiredCount: int | None [Required]

field desiredVirtualCount: int | None [Required]

field function: str = 'murmur3'

field key: str = '_id'

field strategy: str = 'hash'

field virtualPerPhysical: int | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._ReplicationConfigCreate

Bases: _ConfigCreateModel

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 asyncEnabled: bool | None [Required]

field deletionStrategy: ReplicationDeletionStrategy | None [Required]

field factor: int | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._ReplicationConfigUpdate

Bases: _ConfigUpdateModel

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 asyncEnabled: bool | None [Required]

field deletionStrategy: ReplicationDeletionStrategy | None [Required]

field factor: int | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._BM25ConfigCreate

Bases: _ConfigCreateModel

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 b: float [Required]

field k1: float [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._BM25ConfigUpdate

Bases: _ConfigUpdateModel

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 b: float | None [Required]

field k1: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._StopwordsCreate

Bases: _ConfigCreateModel

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 additions: List[str] | None [Required]

field preset: StopwordsPreset | None [Required]

field removals: List[str] | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._StopwordsUpdate

Bases: _ConfigUpdateModel

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 additions: List[str] | None [Required]

field preset: StopwordsPreset | None [Required]

field removals: List[str] | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._InvertedIndexConfigCreate

Bases: _ConfigCreateModel

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 bm25: _BM25ConfigCreate | None [Required]

field cleanupIntervalSeconds: int | None [Required]

field indexNullState: bool | None [Required]

field indexPropertyLength: bool | None [Required]

field indexTimestamps: bool | None [Required]

field stopwords: _StopwordsCreate [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._InvertedIndexConfigUpdate

Bases: _ConfigUpdateModel

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 bm25: _BM25ConfigUpdate | None [Required]

field cleanupIntervalSeconds: int | None [Required]

field stopwords: _StopwordsUpdate | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._MultiTenancyConfigCreate

Bases: _ConfigCreateModel

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 autoTenantActivation: bool | None [Required]

field autoTenantCreation: bool | None [Required]

field enabled: bool [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._MultiTenancyConfigUpdate

Bases: _ConfigUpdateModel

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 autoTenantActivation: bool | None [Required]

field autoTenantCreation: bool | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeProvider

Bases: _ConfigCreateModel

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 generative: GenerativeSearches | _EnumLikeStr [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeAnyscale

Bases: _GenerativeProvider

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 baseURL: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.ANYSCALE

field model: str | None [Required]

field temperature: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeCustom

Bases: _GenerativeProvider

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 module_config: Dict[str, Any] | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeDatabricks

Bases: _GenerativeProvider

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 endpoint: str [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.DATABRICKS

field maxTokens: int | None [Required]

field temperature: float | None [Required]

field topK: int | None [Required]

field topP: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeMistral

Bases: _GenerativeProvider

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 baseURL: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.MISTRAL

field maxTokens: int | None [Required]

field model: str | None [Required]

field temperature: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeNvidia

Bases: _GenerativeProvider

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 baseURL: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.NVIDIA

field maxTokens: int | None [Required]

field model: str | None [Required]

field temperature: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeXai

Bases: _GenerativeProvider

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 baseURL: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.XAI

field maxTokens: int | None [Required]

field model: str | None [Required]

field temperature: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeFriendliai

Bases: _GenerativeProvider

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 baseURL: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.FRIENDLIAI

field maxTokens: int | None [Required]

field model: str | None [Required]

field temperature: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeOllama

Bases: _GenerativeProvider

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 apiEndpoint: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.OLLAMA

field model: str | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeOpenAIConfigBase

Bases: _GenerativeProvider

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field frequencyPenaltyProperty: float | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.OPENAI

field maxTokensProperty: int | None [Required]

field presencePenaltyProperty: float | None [Required]

field temperatureProperty: float | None [Required]

field topPProperty: float | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeOpenAIConfig

Bases: _GenerativeOpenAIConfigBase

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 model: str | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeAzureOpenAIConfig

Bases: _GenerativeOpenAIConfigBase

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 deploymentId: str [Required]

field resourceName: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeCohereConfig

Bases: _GenerativeProvider

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.COHERE

field kProperty: int | None [Required]

field maxTokensProperty: int | None [Required]

field model: str | None [Required]

field returnLikelihoodsProperty: str | None [Required]

field stopSequencesProperty: List[str] | None [Required]

field temperatureProperty: float | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeGoogleConfig

Bases: _GenerativeProvider

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 apiEndpoint: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.PALM

field maxOutputTokens: int | None [Required]

field modelId: str | None [Required]

field projectId: str [Required]

field temperature: float | None [Required]

field topK: int | None [Required]

field topP: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeAWSConfig

Bases: _GenerativeProvider

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 endpoint: str | None [Required]

field generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.AWS

field model: str | None [Required]

field region: str [Required]

field service: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._GenerativeAnthropicConfig

Bases: _GenerativeProvider

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 generative: GenerativeSearches | _EnumLikeStr = GenerativeSearches.ANTHROPIC

field maxTokens: int | None [Required]

field model: str | None [Required]

field stopSequences: List[str] | None [Required]

field temperature: float | None [Required]

field topK: int | None [Required]

field topP: float | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerProvider

Bases: _ConfigCreateModel

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 reranker: Rerankers | _EnumLikeStr [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerCohereConfig

Bases: _RerankerProvider

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 model: Literal['rerank-english-v2.0', 'rerank-multilingual-v2.0'] | str | None = None

field reranker: Rerankers | _EnumLikeStr = Rerankers.COHERE

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerCustomConfig

Bases: _RerankerProvider

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 module_config: Dict[str, Any] | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerTransformersConfig

Bases: _RerankerProvider

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 reranker: Rerankers | _EnumLikeStr = Rerankers.TRANSFORMERS

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerJinaAIConfig

Bases: _RerankerProvider

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 model: Literal['jina-reranker-v2-base-multilingual', 'jina-reranker-v1-base-en', 'jina-reranker-v1-turbo-en', 'jina-reranker-v1-tiny-en', 'jina-colbert-v1-en'] | str | None = None

field reranker: Rerankers | _EnumLikeStr = Rerankers.JINAAI

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerVoyageAIConfig

Bases: _RerankerProvider

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 model: Literal['rerank-2', 'rerank-2-lite', 'rerank-lite-1', 'rerank-1'] | str | None = None

field reranker: Rerankers | _EnumLikeStr = Rerankers.VOYAGEAI

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._RerankerNvidiaConfig

Bases: _RerankerProvider

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None = None

field reranker: Rerankers | _EnumLikeStr = Rerankers.NVIDIA

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config._Generative

Bases: object

Use this factory class to create the correct object for the generative_config argument in the collections.create() method.

Each staticmethod provides options specific to the named generative search module in the function’s name. Under-the-hood data validation steps will ensure that any mis-specifications will be caught before the request is sent to Weaviate.

static anyscale(model=None, temperature=None, base_url=None)

Create a _GenerativeAnyscale object for use when generating using the generative-anyscale module.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• base_url (str | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static custom(module_name, module_config=None)

Create a _GenerativeCustom object for use when generating using a custom specification.

Parameters:

• module_name (str) – The name of the module to use, REQUIRED.

• module_config (Dict[str, Any] | None) – The configuration to use for the module. Defaults to None, which uses the server-defined default.

Return type:

_GenerativeProvider

static databricks(*, endpoint, max_tokens=None, temperature=None, top_k=None, top_p=None)

Create a _GenerativeDatabricks object for use when performing AI generation using the generative-databricks module.

Parameters:

• endpoint (str) – The URL where the API request should go. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_k (int | None) – The top K value to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P value to use. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static friendliai(*, base_url=None, model=None, temperature=None, max_tokens=None)

Create a _GenerativeFriendliai object for use when performing AI generation using the generative-friendliai module.

Parameters:

• base_url (str | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static mistral(model=None, temperature=None, max_tokens=None, base_url=None)

Create a _GenerativeMistral object for use when performing AI generation using the generative-mistral module.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• base_url (str | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static nvidia(*, base_url=None, model=None, temperature=None, max_tokens=None)

Create a _GenerativeNvidia object for use when performing AI generation using the generative-nvidia module.

Parameters:

• base_url (str | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static xai(*, base_url=None, model=None, temperature=None, max_tokens=None)

Create a _GenerativeXai object for use when performing AI generation using the generative-xai module.

Parameters:

• base_url (str | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static ollama(*, api_endpoint=None, model=None)

Create a _GenerativeOllama object for use when performing AI generation using the generative-ollama module.

Parameters:

• api_endpoint (str | None) – The API endpoint to use. Defaults to None, which uses the server-defined default Docker users may need to specify an alias, such as http://host.docker.internal:11434 so that the container can access the host machine.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static openai(model=None, frequency_penalty=None, max_tokens=None, presence_penalty=None, temperature=None, top_p=None, base_url=None)

Create a _GenerativeOpenAIConfig object for use when performing AI generation using the generative-openai module.

See the documentation for detailed usage.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• frequency_penalty (float | None) – The frequency penalty to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• presence_penalty (float | None) – The presence penalty to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P to use. Defaults to None, which uses the server-defined default

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static azure_openai(resource_name, deployment_id, frequency_penalty=None, max_tokens=None, presence_penalty=None, temperature=None, top_p=None, base_url=None)

Create a _GenerativeAzureOpenAIConfig object for use when performing AI generation using the generative-openai module.

See the documentation for detailed usage.

Parameters:

• resource_name (str) – The name of the Azure OpenAI resource to use.

• deployment_id (str) – The Azure OpenAI deployment ID to use.

• frequency_penalty (float | None) – The frequency penalty to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• presence_penalty (float | None) – The presence penalty to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P to use. Defaults to None, which uses the server-defined default

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static cohere(model=None, k=None, max_tokens=None, return_likelihoods=None, stop_sequences=None, temperature=None, base_url=None)

Create a _GenerativeCohereConfig object for use when performing AI generation using the generative-cohere module.

See the documentation for detailed usage.

Parameters:

• model (Literal['embed-v4.0', 'embed-multilingual-v2.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'small', 'medium', 'large', 'multilingual-22-12', 'embed-english-v2.0', 'embed-english-light-v2.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default

• k (int | None) – The number of sequences to generate. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• return_likelihoods (str | None) – Whether to return the likelihoods. Defaults to None, which uses the server-defined default

• stop_sequences (List[str] | None) – The stop sequences to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL where the API request should go. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static palm(project_id, api_endpoint=None, max_output_tokens=None, model_id=None, temperature=None, top_k=None, top_p=None)

Create a _GenerativePaLMConfig object for use when performing AI generation using the generative-palm module.

See the documentation for detailed usage.

Parameters:

• project_id (str) – The PalM project ID to use.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• max_output_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_k (int | None) – The top K to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P to use. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

Deprecated since version 4.9.0.

This method is deprecated and will be removed in Q2 ‘25. Please use google() instead.

static google(project_id, api_endpoint=None, max_output_tokens=None, model_id=None, temperature=None, top_k=None, top_p=None)

Create a _GenerativeGoogleConfig object for use when performing AI generation using the generative-google module.

See the documentation for detailed usage.

Parameters:

• project_id (str) – The PalM project ID to use.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• max_output_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_k (int | None) – The top K to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P to use. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

static aws(model=None, region='', endpoint=None, service='bedrock')

Create a _GenerativeAWSConfig object for use when performing AI generation using the generative-aws module.

See the documentation for detailed usage.

Parameters:

• model (str | None) – The model to use, REQUIRED for service “bedrock”.

• region (str) – The AWS region to run the model from, REQUIRED.

• endpoint (str | None) – The model to use, REQUIRED for service “sagemaker”.

• service (Literal['bedrock', 'sagemaker'] | str) – The AWS service to use, options are “bedrock” and “sagemaker”.

Return type:

_GenerativeProvider

static anthropic(model=None, max_tokens=None, stop_sequences=None, temperature=None, top_k=None, top_p=None)

Create a _GenerativeAnthropicConfig object for use when performing AI generation using the generative-anthropic module.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• max_tokens (int | None) – The maximum number of tokens to generate. Defaults to None, which uses the server-defined default

• stop_sequences (List[str] | None) – The stop sequences to use. Defaults to None, which uses the server-defined default

• temperature (float | None) – The temperature to use. Defaults to None, which uses the server-defined default

• top_k (int | None) – The top K to use. Defaults to None, which uses the server-defined default

• top_p (float | None) – The top P to use. Defaults to None, which uses the server-defined default

Return type:

_GenerativeProvider

class weaviate.collections.classes.config._Reranker

Bases: object

Use this factory class to create the correct object for the reranker_config argument in the collections.create() method.

Each staticmethod provides options specific to the named reranker in the function’s name. Under-the-hood data validation steps will ensure that any mis-specifications will be caught before the request is sent to Weaviate.

static transformers()

Create a _RerankerTransformersConfig object for use when reranking using the reranker-transformers module.

See the documentation for detailed usage.

Return type:

_RerankerProvider

static custom(module_name, module_config=None)

Create a _RerankerCustomConfig object for use when reranking using a custom module.

Parameters:

• module_name (str) – The name of the module to use, REQUIRED.

• module_config (Dict[str, Any] | None) – The configuration to use for the module. Defaults to None, which uses the server-defined default.

Return type:

_RerankerProvider

static cohere(model=None)

Create a _RerankerCohereConfig object for use when reranking using the reranker-cohere module.

See the documentation for detailed usage.

Parameters:

model (Literal['rerank-english-v2.0', 'rerank-multilingual-v2.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default

Return type:

_RerankerProvider

static jinaai(model=None)

Create a _RerankerJinaAIConfig object for use when reranking using the reranker-jinaai module.

See the documentation for detailed usage.

Parameters:

model (Literal['jina-reranker-v2-base-multilingual', 'jina-reranker-v1-base-en', 'jina-reranker-v1-turbo-en', 'jina-reranker-v1-tiny-en', 'jina-colbert-v1-en'] | str | None) – The model to use. Defaults to None, which uses the server-defined default

Return type:

_RerankerProvider

static voyageai(model=None)

Create a _RerankerVoyageAIConfig object for use when reranking using the reranker-voyageai module.

See the documentation for detailed usage.

Parameters:

model (Literal['rerank-2', 'rerank-2-lite', 'rerank-lite-1', 'rerank-1'] | str | None) – The model to use. Defaults to None, which uses the server-defined default

Return type:

_RerankerProvider

static nvidia(model=None, base_url=None)

Create a _RerankerNvidiaConfig object for use when reranking using the reranker-nvidia module.

See the documentation for detailed usage.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to send the reranker requests to. Defaults to None, which uses the server-defined default.

Return type:

_RerankerProvider

pydantic model weaviate.collections.classes.config._CollectionConfigCreateBase

Bases: _ConfigCreateModel

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 description: str | None = None

field generativeSearch: _GenerativeProvider | None = None (alias 'generative_config')

field invertedIndexConfig: _InvertedIndexConfigCreate | None = None (alias 'inverted_index_config')

field moduleConfig: _VectorizerConfigCreate = _VectorizerConfigCreate(vectorizer=<Vectorizers.NONE: 'none'>) (alias 'vectorizer_config')

field multiTenancyConfig: _MultiTenancyConfigCreate | None = None (alias 'multi_tenancy_config')

field replicationConfig: _ReplicationConfigCreate | None = None (alias 'replication_config')

field rerankerConfig: _RerankerProvider | None = None (alias 'reranker_config')

field shardingConfig: _ShardingConfigCreate | None = None (alias 'sharding_config')

field vectorIndexConfig: _VectorIndexConfigCreate | None = None (alias 'vector_index_config')

static _CollectionConfigCreateBase__add_to_module_config(return_dict, addition_key, addition_val)

Parameters:

• return_dict (Dict[str, Any])

• addition_key (str)

• addition_val (Dict[str, Any])

Return type:

None

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._CollectionConfigUpdate

Bases: _ConfigUpdateModel

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 description: str | None = None

field generativeConfig: _GenerativeProvider | None = None (alias 'generative_config')

field invertedIndexConfig: _InvertedIndexConfigUpdate | None = None (alias 'inverted_index_config')

field multiTenancyConfig: _MultiTenancyConfigUpdate | None = None (alias 'multi_tenancy_config')

field property_descriptions: Dict[str, str] | None = None

field replicationConfig: _ReplicationConfigUpdate | None = None (alias 'replication_config')

field rerankerConfig: _RerankerProvider | None = None (alias 'reranker_config')

field vectorIndexConfig: _VectorIndexConfigUpdate | None = None (alias 'vector_index_config')

field vectorizerConfig: _VectorIndexConfigUpdate | List[_NamedVectorConfigUpdate] | None = None (alias 'vectorizer_config')

static _CollectionConfigUpdate__add_to_module_config(return_dict, addition_key, addition_val)

Parameters:

• return_dict (Dict[str, Any])

• addition_key (str)

• addition_val (Dict[str, Any])

Return type:

None

_CollectionConfigUpdate__check_quantizers(quantizer, vector_index_config)

Parameters:

• quantizer (_QuantizerConfigUpdate | None)

• vector_index_config (dict)

Return type:

None

merge_with_existing(schema)

Parameters:

schema (Dict[str, Any])

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config._BM25Config(b: float, k1: float)

Bases: _ConfigBase

Parameters:

• b (float)

• k1 (float)

b: float

k1: float

weaviate.collections.classes.config.BM25Config

alias of _BM25Config

class weaviate.collections.classes.config._StopwordsConfig(preset: weaviate.collections.classes.config.StopwordsPreset, additions: List[str] | None, removals: List[str] | None)

Bases: _ConfigBase

Parameters:

• preset (StopwordsPreset)

• additions (List[str] | None)

• removals (List[str] | None)

preset: StopwordsPreset

additions: List[str] | None

removals: List[str] | None

weaviate.collections.classes.config.StopwordsConfig

alias of _StopwordsConfig

class weaviate.collections.classes.config._InvertedIndexConfig(bm25: weaviate.collections.classes.config._BM25Config, cleanup_interval_seconds: int, index_null_state: bool, index_property_length: bool, index_timestamps: bool, stopwords: weaviate.collections.classes.config._StopwordsConfig)

Bases: _ConfigBase

Parameters:

• bm25 (_BM25Config)

• cleanup_interval_seconds (int)

• index_null_state (bool)

• index_property_length (bool)

• index_timestamps (bool)

• stopwords (_StopwordsConfig)

bm25: _BM25Config

cleanup_interval_seconds: int

index_null_state: bool

index_property_length: bool

index_timestamps: bool

stopwords: _StopwordsConfig

weaviate.collections.classes.config.InvertedIndexConfig

alias of _InvertedIndexConfig

class weaviate.collections.classes.config._MultiTenancyConfig(enabled: bool, auto_tenant_creation: bool, auto_tenant_activation: bool)

Bases: _ConfigBase

Parameters:

• enabled (bool)

• auto_tenant_creation (bool)

• auto_tenant_activation (bool)

enabled: bool

auto_tenant_creation: bool

auto_tenant_activation: bool

weaviate.collections.classes.config.MultiTenancyConfig

alias of _MultiTenancyConfig

class weaviate.collections.classes.config._PropertyVectorizerConfig(skip: bool, vectorize_property_name: bool)

Bases: object

Parameters:

• skip (bool)

• vectorize_property_name (bool)

skip: bool

vectorize_property_name: bool

weaviate.collections.classes.config.PropertyVectorizerConfig

alias of _PropertyVectorizerConfig

class weaviate.collections.classes.config._NestedProperty(data_type: weaviate.collections.classes.config.DataType, description: str | None, index_filterable: bool, index_searchable: bool, name: str, nested_properties: List[ForwardRef('NestedProperty')] | None, tokenization: weaviate.collections.classes.config.Tokenization | None)

Bases: _ConfigBase

Parameters:

• data_type (DataType)

• description (str | None)

• index_filterable (bool)

• index_searchable (bool)

• name (str)

• nested_properties (List[_NestedProperty] | None)

• tokenization (Tokenization | None)

data_type: DataType

description: str | None

index_filterable: bool

index_searchable: bool

name: str

nested_properties: List[_NestedProperty] | None

tokenization: Tokenization | None

to_dict()

Return type:

Dict[str, Any]

weaviate.collections.classes.config.NestedProperty

alias of _NestedProperty

class weaviate.collections.classes.config._PropertyBase(name: str, description: str | None)

Bases: _ConfigBase

Parameters:

• name (str)

• description (str | None)

name: str

description: str | None

to_dict()

Return type:

Dict[str, Any]

class weaviate.collections.classes.config._Property(name: str, description: str | None, data_type: weaviate.collections.classes.config.DataType, index_filterable: bool, index_range_filters: bool, index_searchable: bool, nested_properties: List[weaviate.collections.classes.config._NestedProperty] | None, tokenization: weaviate.collections.classes.config.Tokenization | None, vectorizer_config: weaviate.collections.classes.config._PropertyVectorizerConfig | None, vectorizer: str | None, vectorizer_configs: Dict[str, weaviate.collections.classes.config._PropertyVectorizerConfig] | None)

Bases: _PropertyBase

Parameters:

• name (str)

• description (str | None)

• data_type (DataType)

• index_filterable (bool)

• index_range_filters (bool)

• index_searchable (bool)

• nested_properties (List[_NestedProperty] | None)

• tokenization (Tokenization | None)

• vectorizer_config (_PropertyVectorizerConfig | None)

• vectorizer (str | None)

• vectorizer_configs (Dict[str, _PropertyVectorizerConfig] | None)

data_type: DataType

index_filterable: bool

index_range_filters: bool

index_searchable: bool

nested_properties: List[_NestedProperty] | None

tokenization: Tokenization | None

vectorizer_config: _PropertyVectorizerConfig | None

vectorizer: str | None

vectorizer_configs: Dict[str, _PropertyVectorizerConfig] | None

to_dict()

Return type:

Dict[str, Any]

weaviate.collections.classes.config.PropertyConfig

alias of _Property

class weaviate.collections.classes.config._ReferenceProperty(name: str, description: str | None, target_collections: List[str])

Bases: _PropertyBase

Parameters:

• name (str)

• description (str | None)

• target_collections (List[str])

target_collections: List[str]

to_dict()

Return type:

Dict[str, Any]

weaviate.collections.classes.config.ReferencePropertyConfig

alias of _ReferenceProperty

class weaviate.collections.classes.config._ReplicationConfig(factor: int, async_enabled: bool, deletion_strategy: weaviate.collections.classes.config.ReplicationDeletionStrategy)

Bases: _ConfigBase

Parameters:

• factor (int)

• async_enabled (bool)

• deletion_strategy (ReplicationDeletionStrategy)

factor: int

async_enabled: bool

deletion_strategy: ReplicationDeletionStrategy

weaviate.collections.classes.config.ReplicationConfig

alias of _ReplicationConfig

class weaviate.collections.classes.config._ShardingConfig(virtual_per_physical: int, desired_count: int, actual_count: int, desired_virtual_count: int, actual_virtual_count: int, key: str, strategy: str, function: str)

Bases: _ConfigBase

Parameters:

• virtual_per_physical (int)

• desired_count (int)

• actual_count (int)

• desired_virtual_count (int)

• actual_virtual_count (int)

• key (str)

• strategy (str)

• function (str)

virtual_per_physical: int

desired_count: int

actual_count: int

desired_virtual_count: int

actual_virtual_count: int

key: str

strategy: str

function: str

weaviate.collections.classes.config.ShardingConfig

alias of _ShardingConfig

class weaviate.collections.classes.config._PQEncoderConfig(type_: weaviate.collections.classes.config.PQEncoderType, distribution: weaviate.collections.classes.config.PQEncoderDistribution)

Bases: _ConfigBase

Parameters:

• type_ (PQEncoderType)

• distribution (PQEncoderDistribution)

type_: PQEncoderType

distribution: PQEncoderDistribution

to_dict()

Return type:

Dict[str, Any]

weaviate.collections.classes.config.PQEncoderConfig

alias of _PQEncoderConfig

class weaviate.collections.classes.config._PQConfig(internal_bit_compression: bool, segments: int, centroids: int, training_limit: int, encoder: weaviate.collections.classes.config._PQEncoderConfig)

Bases: _ConfigBase

Parameters:

• internal_bit_compression (bool)

• segments (int)

• centroids (int)

• training_limit (int)

• encoder (_PQEncoderConfig)

internal_bit_compression: bool

segments: int

centroids: int

training_limit: int

encoder: _PQEncoderConfig

property bit_compression: bool

weaviate.collections.classes.config.PQConfig

alias of _PQConfig

class weaviate.collections.classes.config._BQConfig(cache: bool | None, rescore_limit: int)

Bases: _ConfigBase

Parameters:

• cache (bool | None)

• rescore_limit (int)

cache: bool | None

rescore_limit: int

class weaviate.collections.classes.config._SQConfig(cache: bool | None, rescore_limit: int, training_limit: int)

Bases: _ConfigBase

Parameters:

• cache (bool | None)

• rescore_limit (int)

• training_limit (int)

cache: bool | None

rescore_limit: int

training_limit: int

weaviate.collections.classes.config.BQConfig

alias of _BQConfig

weaviate.collections.classes.config.SQConfig

alias of _SQConfig

class weaviate.collections.classes.config._MuveraConfig(enabled: bool | None, ksim: int | None, dprojections: int | None, repetitions: int | None)

Bases: _ConfigBase

Parameters:

• enabled (bool | None)

• ksim (int | None)

• dprojections (int | None)

• repetitions (int | None)

enabled: bool | None

ksim: int | None

dprojections: int | None

repetitions: int | None

weaviate.collections.classes.config.MuveraConfig

alias of _MuveraConfig

class weaviate.collections.classes.config._MultiVectorConfig(encoding: weaviate.collections.classes.config._MuveraConfig | None, aggregation: str)

Bases: _ConfigBase

Parameters:

• encoding (_MuveraConfig | None)

• aggregation (str)

encoding: _MuveraConfig | None

aggregation: str

weaviate.collections.classes.config.MultiVector

alias of _MultiVectorConfig

class weaviate.collections.classes.config._VectorIndexConfig(multi_vector: weaviate.collections.classes.config._MultiVectorConfig | None, quantizer: weaviate.collections.classes.config._PQConfig | weaviate.collections.classes.config._BQConfig | weaviate.collections.classes.config._SQConfig | NoneType)

Bases: _ConfigBase

Parameters:

• multi_vector (_MultiVectorConfig | None)

• quantizer (_PQConfig | _BQConfig | _SQConfig | None)

multi_vector: _MultiVectorConfig | None

quantizer: _PQConfig | _BQConfig | _SQConfig | None

to_dict()

Return type:

Dict[str, Any]

class weaviate.collections.classes.config._VectorIndexConfigHNSW(multi_vector: weaviate.collections.classes.config._MultiVectorConfig | None, quantizer: weaviate.collections.classes.config._PQConfig | weaviate.collections.classes.config._BQConfig | weaviate.collections.classes.config._SQConfig | NoneType, cleanup_interval_seconds: int, distance_metric: weaviate.collections.classes.config_vectorizers.VectorDistances, dynamic_ef_min: int, dynamic_ef_max: int, dynamic_ef_factor: int, ef: int, ef_construction: int, filter_strategy: weaviate.collections.classes.config_vector_index.VectorFilterStrategy, flat_search_cutoff: int, max_connections: int, skip: bool, vector_cache_max_objects: int)

Bases: _VectorIndexConfig

Parameters:

• multi_vector (_MultiVectorConfig | None)

• quantizer (_PQConfig | _BQConfig | _SQConfig | None)

• cleanup_interval_seconds (int)

• distance_metric (VectorDistances)

• dynamic_ef_min (int)

• dynamic_ef_max (int)

• dynamic_ef_factor (int)

• ef (int)

• ef_construction (int)

• filter_strategy (VectorFilterStrategy)

• flat_search_cutoff (int)

• max_connections (int)

• skip (bool)

• vector_cache_max_objects (int)

cleanup_interval_seconds: int

distance_metric: VectorDistances

dynamic_ef_min: int

dynamic_ef_max: int

dynamic_ef_factor: int

ef: int

ef_construction: int

filter_strategy: VectorFilterStrategy

flat_search_cutoff: int

max_connections: int

skip: bool

vector_cache_max_objects: int

static vector_index_type()

Return type:

str

weaviate.collections.classes.config.VectorIndexConfigHNSW

alias of _VectorIndexConfigHNSW

class weaviate.collections.classes.config._VectorIndexConfigFlat(multi_vector: weaviate.collections.classes.config._MultiVectorConfig | None, quantizer: weaviate.collections.classes.config._PQConfig | weaviate.collections.classes.config._BQConfig | weaviate.collections.classes.config._SQConfig | NoneType, distance_metric: weaviate.collections.classes.config_vectorizers.VectorDistances, vector_cache_max_objects: int)

Bases: _VectorIndexConfig

Parameters:

• multi_vector (_MultiVectorConfig | None)

• quantizer (_PQConfig | _BQConfig | _SQConfig | None)

• distance_metric (VectorDistances)

• vector_cache_max_objects (int)

distance_metric: VectorDistances

vector_cache_max_objects: int

static vector_index_type()

Return type:

str

weaviate.collections.classes.config.VectorIndexConfigFlat

alias of _VectorIndexConfigFlat

class weaviate.collections.classes.config._VectorIndexConfigDynamic(distance_metric: weaviate.collections.classes.config_vectorizers.VectorDistances, hnsw: weaviate.collections.classes.config._VectorIndexConfigHNSW | None, flat: weaviate.collections.classes.config._VectorIndexConfigFlat | None, threshold: int | None)

Bases: _ConfigBase

Parameters:

• distance_metric (VectorDistances)

• hnsw (_VectorIndexConfigHNSW | None)

• flat (_VectorIndexConfigFlat | None)

• threshold (int | None)

distance_metric: VectorDistances

hnsw: _VectorIndexConfigHNSW | None

flat: _VectorIndexConfigFlat | None

threshold: int | None

static vector_index_type()

Return type:

str

weaviate.collections.classes.config.VectorIndexConfigDynamic

alias of _VectorIndexConfigDynamic

class weaviate.collections.classes.config._GenerativeConfig(generative: weaviate.collections.classes.config.GenerativeSearches | str, model: Dict[str, Any])

Bases: _ConfigBase

Parameters:

• generative (GenerativeSearches | str)

• model (Dict[str, Any])

generative: GenerativeSearches | str

model: Dict[str, Any]

weaviate.collections.classes.config.GenerativeConfig

alias of _GenerativeConfig

class weaviate.collections.classes.config._VectorizerConfig(vectorizer: weaviate.collections.classes.config_vectorizers.Vectorizers | str, model: Dict[str, Any], vectorize_collection_name: bool)

Bases: _ConfigBase

Parameters:

• vectorizer (Vectorizers | str)

• model (Dict[str, Any])

• vectorize_collection_name (bool)

vectorizer: Vectorizers | str

model: Dict[str, Any]

vectorize_collection_name: bool

weaviate.collections.classes.config.VectorizerConfig

alias of _VectorizerConfig

class weaviate.collections.classes.config._RerankerConfig(model: Dict[str, Any], reranker: weaviate.collections.classes.config.Rerankers | str)

Bases: _ConfigBase

Parameters:

• model (Dict[str, Any])

• reranker (Rerankers | str)

model: Dict[str, Any]

reranker: Rerankers | str

weaviate.collections.classes.config.RerankerConfig

alias of _RerankerConfig

class weaviate.collections.classes.config._NamedVectorizerConfig(vectorizer: weaviate.collections.classes.config_vectorizers.Vectorizers | str, model: Dict[str, Any], source_properties: List[str] | None)

Bases: _ConfigBase

Parameters:

• vectorizer (Vectorizers | str)

• model (Dict[str, Any])

• source_properties (List[str] | None)

vectorizer: Vectorizers | str

model: Dict[str, Any]

source_properties: List[str] | None

to_dict()

Return type:

Dict[str, Any]

class weaviate.collections.classes.config._NamedVectorConfig(vectorizer: weaviate.collections.classes.config._NamedVectorizerConfig, vector_index_config: weaviate.collections.classes.config._VectorIndexConfigHNSW | weaviate.collections.classes.config._VectorIndexConfigFlat | weaviate.collections.classes.config._VectorIndexConfigDynamic)

Bases: _ConfigBase

Parameters:

• vectorizer (_NamedVectorizerConfig)

• vector_index_config (_VectorIndexConfigHNSW | _VectorIndexConfigFlat | _VectorIndexConfigDynamic)

vectorizer: _NamedVectorizerConfig

vector_index_config: _VectorIndexConfigHNSW | _VectorIndexConfigFlat | _VectorIndexConfigDynamic

to_dict()

Return type:

Dict

weaviate.collections.classes.config.NamedVectorConfig

alias of _NamedVectorConfig

class weaviate.collections.classes.config._CollectionConfig(name: str, description: str | None, generative_config: weaviate.collections.classes.config._GenerativeConfig | None, inverted_index_config: weaviate.collections.classes.config._InvertedIndexConfig, multi_tenancy_config: weaviate.collections.classes.config._MultiTenancyConfig, properties: List[weaviate.collections.classes.config._Property], references: List[weaviate.collections.classes.config._ReferenceProperty], replication_config: weaviate.collections.classes.config._ReplicationConfig, reranker_config: weaviate.collections.classes.config._RerankerConfig | None, sharding_config: weaviate.collections.classes.config._ShardingConfig | None, vector_index_config: weaviate.collections.classes.config._VectorIndexConfigHNSW | weaviate.collections.classes.config._VectorIndexConfigFlat | weaviate.collections.classes.config._VectorIndexConfigDynamic | NoneType, vector_index_type: weaviate.collections.classes.config_vector_index.VectorIndexType | None, vectorizer_config: weaviate.collections.classes.config._VectorizerConfig | None, vectorizer: weaviate.collections.classes.config_vectorizers.Vectorizers | str | NoneType, vector_config: Dict[str, weaviate.collections.classes.config._NamedVectorConfig] | None)

Bases: _ConfigBase

Parameters:

• name (str)

• description (str | None)

• generative_config (_GenerativeConfig | None)

• inverted_index_config (_InvertedIndexConfig)

• multi_tenancy_config (_MultiTenancyConfig)

• properties (List[_Property])

• references (List[_ReferenceProperty])

• replication_config (_ReplicationConfig)

• reranker_config (_RerankerConfig | None)

• sharding_config (_ShardingConfig | None)

• vector_index_config (_VectorIndexConfigHNSW | _VectorIndexConfigFlat | _VectorIndexConfigDynamic | None)

• vector_index_type (VectorIndexType | None)

• vectorizer_config (_VectorizerConfig | None)

• vectorizer (Vectorizers | str | None)

• vector_config (Dict[str, _NamedVectorConfig] | None)

name: str

description: str | None

generative_config: _GenerativeConfig | None

inverted_index_config: _InvertedIndexConfig

multi_tenancy_config: _MultiTenancyConfig

properties: List[_Property]

references: List[_ReferenceProperty]

replication_config: _ReplicationConfig

reranker_config: _RerankerConfig | None

sharding_config: _ShardingConfig | None

vector_index_config: _VectorIndexConfigHNSW | _VectorIndexConfigFlat | _VectorIndexConfigDynamic | None

vector_index_type: VectorIndexType | None

vectorizer_config: _VectorizerConfig | None

vectorizer: Vectorizers | str | None

vector_config: Dict[str, _NamedVectorConfig] | None

to_dict()

Return type:

dict

weaviate.collections.classes.config.CollectionConfig

alias of _CollectionConfig

class weaviate.collections.classes.config._CollectionConfigSimple(name: str, description: str | None, generative_config: weaviate.collections.classes.config._GenerativeConfig | None, properties: List[weaviate.collections.classes.config._Property], references: List[weaviate.collections.classes.config._ReferenceProperty], reranker_config: weaviate.collections.classes.config._RerankerConfig | None, vectorizer_config: weaviate.collections.classes.config._VectorizerConfig | None, vectorizer: weaviate.collections.classes.config_vectorizers.Vectorizers | str | NoneType, vector_config: Dict[str, weaviate.collections.classes.config._NamedVectorConfig] | None)

Bases: _ConfigBase

Parameters:

• name (str)

• description (str | None)

• generative_config (_GenerativeConfig | None)

• properties (List[_Property])

• references (List[_ReferenceProperty])

• reranker_config (_RerankerConfig | None)

• vectorizer_config (_VectorizerConfig | None)

• vectorizer (Vectorizers | str | None)

• vector_config (Dict[str, _NamedVectorConfig] | None)

name: str

description: str | None

generative_config: _GenerativeConfig | None

properties: List[_Property]

references: List[_ReferenceProperty]

reranker_config: _RerankerConfig | None

vectorizer_config: _VectorizerConfig | None

vectorizer: Vectorizers | str | None

vector_config: Dict[str, _NamedVectorConfig] | None

weaviate.collections.classes.config.CollectionConfigSimple

alias of _CollectionConfigSimple

class weaviate.collections.classes.config._ShardStatus(name: str, status: Literal['READONLY', 'READY', 'INDEXING'], vector_queue_size: int)

Bases: object

Parameters:

• name (str)

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

• vector_queue_size (int)

name: str

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

vector_queue_size: int

weaviate.collections.classes.config.ShardStatus

alias of _ShardStatus

pydantic model weaviate.collections.classes.config.Property

Bases: _ConfigCreateModel

This class defines the structure of a data property that a collection can have within Weaviate.

name

The name of the property, REQUIRED.

data_type

The data type of the property, REQUIRED.

description

A description of the property.

index_filterable

Whether the property should be filterable in the inverted index.

index_range_filters

Whether the property should support range filters in the inverted index.

index_searchable

Whether the property should be searchable in the inverted index.

nested_properties

nested properties for data type OBJECT and OBJECT_ARRAY`.

skip_vectorization

Whether to skip vectorization of the property. Defaults to False.

tokenization

The tokenization method to use for the inverted index. Defaults to None.

vectorize_property_name

Whether to vectorize the property name. Defaults to True.

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 dataType: DataType [Required] (alias 'data_type')

field description: str | None = None

field indexFilterable: bool | None = None (alias 'index_filterable')

field indexRangeFilters: bool | None = None (alias 'index_range_filters')

field indexSearchable: bool | None = None (alias 'index_searchable')

field name: str [Required]

Validated by:

• _check_name

field nestedProperties: Property | List[Property] | None = None (alias 'nested_properties')

field skip_vectorization: bool = False

field tokenization: Tokenization | None = None

field vectorize_property_name: bool = True

_to_dict(vectorizers=None)

Parameters:

vectorizers (Sequence[Vectorizers | _EnumLikeStr] | None)

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._ReferencePropertyBase

Bases: _ConfigCreateModel

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 name: str [Required]

Validated by:

• check_name

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._ReferencePropertyMultiTarget

Bases: _ReferencePropertyBase

This class defines properties that are cross references to multiple target collections.

Use this class when you want to create a cross-reference in the collection’s config that is capable of having cross-references to multiple other collections at once.

name

The name of the property, REQUIRED.

target_collections

The names of the target collections, REQUIRED.

description

A description of the property.

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 description: str | None = None

field target_collections: List[str] [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config.ReferenceProperty

Bases: _ReferencePropertyBase

This class defines properties that are cross references to a single target collection.

Use this class when you want to create a cross-reference in the collection’s config that is capable of having only cross-references to a single other collection.

name

The name of the property, REQUIRED.

target_collection

The name of the target collection, REQUIRED.

description

A description of the property.

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 description: str | None = None

field name: str [Required]

Validated by:

• check_name

field target_collection: str [Required]

MultiTarget

alias of _ReferencePropertyMultiTarget

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config._CollectionConfigCreate

Bases: _ConfigCreateModel

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 description: str | None = None

field generativeSearch: _GenerativeProvider | None = None (alias 'generative_config')

field invertedIndexConfig: _InvertedIndexConfigCreate | None = None (alias 'inverted_index_config')

field multiTenancyConfig: _MultiTenancyConfigCreate | None = None (alias 'multi_tenancy_config')

field name: str [Required]

field properties: Sequence[Property] | None = None

field references: List[_ReferencePropertyBase] | None = None

field replicationConfig: _ReplicationConfigCreate | None = None (alias 'replication_config')

field rerankerConfig: _RerankerProvider | None = None (alias 'reranker_config')

field shardingConfig: _ShardingConfigCreate | None = None (alias 'sharding_config')

field vectorIndexConfig: _VectorIndexConfigCreate | None = None (alias 'vector_index_config')

field vectorizerConfig: _VectorizerConfigCreate | List[_NamedVectorConfigCreate] | None = _VectorizerConfigCreate(vectorizer=<Vectorizers.NONE: 'none'>) (alias 'vectorizer_config')

Validated by:

• validate_vector_names

static _CollectionConfigCreate__add_to_module_config(return_dict, addition_key, addition_val)

Parameters:

• return_dict (Dict[str, Any])

• addition_key (str)

• addition_val (Dict[str, Any])

Return type:

None

_CollectionConfigCreate__add_props(props, ret_dict)

Parameters:

• props (Sequence[Property | _ReferencePropertyBase] | List[_ReferencePropertyBase] | None)

• ret_dict (Dict[str, Any])

Return type:

None

_to_dict()

Return type:

Dict[str, Any]

model_post_init(_CollectionConfigCreate__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:

_CollectionConfigCreate__context (Any)

Return type:

None

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config._VectorIndexMultivectorEncoding

Bases: object

static muvera(ksim=None, dprojections=None, repetitions=None)

Parameters:

• ksim (int | None)

• dprojections (int | None)

• repetitions (int | None)

Return type:

_EncodingConfigCreate

class weaviate.collections.classes.config._VectorIndexMultiVector

Bases: object

Encoding

alias of _VectorIndexMultivectorEncoding

static multi_vector(encoding=None, aggregation=None)

Parameters:

• encoding (_EncodingConfigCreate | None)

• aggregation (MultiVectorAggregation | None)

Return type:

_MultiVectorConfigCreate

class weaviate.collections.classes.config._VectorIndexQuantizer

Bases: object

static pq(bit_compression=None, centroids=None, encoder_distribution=None, encoder_type=None, segments=None, training_limit=None)

Create a _PQConfigCreate object to be used when defining the product quantization (PQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration.

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#hnsw-with-compression>`_ for a more detailed view!

• bit_compression (bool | None)

• centroids (int | None)

• encoder_distribution (PQEncoderDistribution | None)

• encoder_type (PQEncoderType | None)

• segments (int | None)

• training_limit (int | None)

Return type:

_PQConfigCreate

static bq(cache=None, rescore_limit=None)

Create a _BQConfigCreate object to be used when defining the binary quantization (BQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration. Note that the arguments have no effect for HNSW.

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#binary-quantization>`_ for a more detailed view!

• cache (bool | None)

• rescore_limit (int | None)

Return type:

_BQConfigCreate

static sq(cache=None, rescore_limit=None, training_limit=None)

Create a _SQConfigCreate object to be used when defining the scalar quantization (SQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration. Note that the arguments have no effect for HNSW.

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#binary-quantization>`_ for a more detailed view!

• cache (bool | None)

• rescore_limit (int | None)

• training_limit (int | None)

Return type:

_SQConfigCreate

class weaviate.collections.classes.config._VectorIndex

Bases: object

MultiVector

alias of _VectorIndexMultiVector

Quantizer

alias of _VectorIndexQuantizer

static none()

Create a _VectorIndexConfigSkipCreate object to be used when configuring Weaviate to not index your vectors.

Use this method when defining the vector_index_config argument in collections.create().

Return type:

_VectorIndexConfigSkipCreate

static hnsw(cleanup_interval_seconds=None, distance_metric=None, dynamic_ef_factor=None, dynamic_ef_max=None, dynamic_ef_min=None, ef=None, ef_construction=None, filter_strategy=None, flat_search_cutoff=None, max_connections=None, vector_cache_max_objects=None, quantizer=None, multi_vector=None)

Create a _VectorIndexConfigHNSWCreate object to be used when defining the HNSW vector index configuration of Weaviate.

Use this method when defining the vector_index_config argument in collections.create().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#how-to-configure-hnsw>`_ for a more detailed view!

• cleanup_interval_seconds (int | None)

• distance_metric (VectorDistances | None)

• dynamic_ef_factor (int | None)

• dynamic_ef_max (int | None)

• dynamic_ef_min (int | None)

• ef (int | None)

• ef_construction (int | None)

• filter_strategy (VectorFilterStrategy | None)

• flat_search_cutoff (int | None)

• max_connections (int | None)

• vector_cache_max_objects (int | None)

• quantizer (_QuantizerConfigCreate | None)

• multi_vector (_MultiVectorConfigCreate | None)

Return type:

_VectorIndexConfigHNSWCreate

static flat(distance_metric=None, vector_cache_max_objects=None, quantizer=None)

Create a _VectorIndexConfigFlatCreate object to be used when defining the FLAT vector index configuration of Weaviate.

Use this method when defining the vector_index_config argument in collections.create().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#how-to-configure-hnsw>`_ for a more detailed view!

• distance_metric (VectorDistances | None)

• vector_cache_max_objects (int | None)

• quantizer (_BQConfigCreate | None)

Return type:

_VectorIndexConfigFlatCreate

static dynamic(distance_metric=None, threshold=None, hnsw=None, flat=None)

Create a _VectorIndexConfigDynamicCreate object to be used when defining the DYNAMIC vector index configuration of Weaviate.

Use this method when defining the vector_index_config argument in collections.create().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#how-to-configure-hnsw>`_ for a more detailed view!

• distance_metric (VectorDistances | None)

• threshold (int | None)

• hnsw (_VectorIndexConfigHNSWCreate | None)

• flat (_VectorIndexConfigFlatCreate | None)

Return type:

_VectorIndexConfigDynamicCreate

class weaviate.collections.classes.config.Configure

Bases: object

Use this factory class to generate the correct object for use when using the collections.create() method. E.g., .multi_tenancy() will return a MultiTenancyConfigCreate object to be used in the multi_tenancy_config argument.

Each class method provides options specific to the named configuration type in the function’s name. Under-the-hood data validation steps will ensure that any mis-specifications are caught before the request is sent to Weaviate.

Generative

alias of _Generative

Reranker

alias of _Reranker

Vectorizer

alias of _Vectorizer

VectorIndex

alias of _VectorIndex

NamedVectors

alias of _NamedVectors

static inverted_index(bm25_b=None, bm25_k1=None, cleanup_interval_seconds=None, index_timestamps=None, index_property_length=None, index_null_state=None, stopwords_preset=None, stopwords_additions=None, stopwords_removals=None)

Create an InvertedIndexConfigCreate object to be used when defining the configuration of the keyword searching algorithm of Weaviate.

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#configure-the-inverted-index>`_ for details!

• bm25_b (float | None)

• bm25_k1 (float | None)

• cleanup_interval_seconds (int | None)

• index_timestamps (bool | None)

• index_property_length (bool | None)

• index_null_state (bool | None)

• stopwords_preset (StopwordsPreset | None)

• stopwords_additions (List[str] | None)

• stopwords_removals (List[str] | None)

Return type:

_InvertedIndexConfigCreate

static multi_tenancy(enabled=True, auto_tenant_creation=None, auto_tenant_activation=None)

Create a MultiTenancyConfigCreate object to be used when defining the multi-tenancy configuration of Weaviate.

Parameters:

• enabled (bool) – Whether multi-tenancy is enabled. Defaults to True.

• auto_tenant_creation (bool | None) – Automatically create nonexistent tenants during object creation. Defaults to None, which uses the server-defined default.

• auto_tenant_activation (bool | None) – Automatically turn tenants implicitly HOT when they are accessed. Defaults to None, which uses the server-defined default.

Return type:

_MultiTenancyConfigCreate

static replication(factor=None, async_enabled=None, deletion_strategy=None)

Create a ReplicationConfigCreate object to be used when defining the replication configuration of Weaviate.

NOTE: async_enabled is only available with WeaviateDB >=v1.26.0

Parameters:

• factor (int | None) – The replication factor.

• async_enabled (bool | None) – Enabled async replication.

• deletion_strategy (ReplicationDeletionStrategy | None) – How conflicts between different nodes about deleted objects are resolved.

Return type:

_ReplicationConfigCreate

static sharding(virtual_per_physical=None, desired_count=None, actual_count=None, desired_virtual_count=None, actual_virtual_count=None)

Create a ShardingConfigCreate object to be used when defining the sharding configuration of Weaviate.

NOTE: You can only use one of Sharding or Replication, not both.

See the docs for more details.

Parameters:

• virtual_per_physical (int | None) – The number of virtual shards per physical shard.

• desired_count (int | None) – The desired number of physical shards.

• actual_count (int | None) – The actual number of physical shards. This is a read-only field so has no effect. It is kept for backwards compatibility but will be removed in a future release.

• desired_virtual_count (int | None) – The desired number of virtual shards.

• actual_virtual_count (int | None) – The actual number of virtual shards. This is a read-only field so has no effect. It is kept for backwards compatibility but will be removed in a future release.

Return type:

_ShardingConfigCreate

class weaviate.collections.classes.config._VectorIndexQuantizerUpdate

Bases: object

static pq(bit_compression=None, centroids=None, encoder_distribution=None, encoder_type=None, segments=None, training_limit=None, enabled=True)

Create a _PQConfigUpdate object to be used when updating the product quantization (PQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#hnsw-with-compression>`_ for a more detailed view!

• bit_compression (bool | None)

• centroids (int | None)

• encoder_distribution (PQEncoderDistribution | None)

• encoder_type (PQEncoderType | None)

• segments (int | None)

• training_limit (int | None)

• enabled (bool)

Return type:

_PQConfigUpdate

static bq(rescore_limit=None, enabled=True)

Create a _BQConfigUpdate object to be used when updating the binary quantization (BQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#hnsw-with-compression>`_ for a more detailed view!

• rescore_limit (int | None)

• enabled (bool)

Return type:

_BQConfigUpdate

static sq(rescore_limit=None, training_limit=None, enabled=True)

Create a _SQConfigUpdate object to be used when updating the scalar quantization (SQ) configuration of Weaviate.

Use this method when defining the quantizer argument in the vector_index configuration in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/concepts/vector-index#hnsw-with-compression>`_ for a more detailed view!

• rescore_limit (int | None)

• training_limit (int | None)

• enabled (bool)

Return type:

_SQConfigUpdate

class weaviate.collections.classes.config._VectorIndexUpdate

Bases: object

Quantizer

alias of _VectorIndexQuantizerUpdate

static hnsw(dynamic_ef_factor=None, dynamic_ef_min=None, dynamic_ef_max=None, ef=None, flat_search_cutoff=None, filter_strategy=None, vector_cache_max_objects=None, quantizer=None)

Create an _VectorIndexConfigHNSWUpdate object to update the configuration of the HNSW vector index.

Use this method when defining the vectorizer_config argument in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#configure-the-inverted-index>`_ for a more detailed view!

• dynamic_ef_factor (int | None)

• dynamic_ef_min (int | None)

• dynamic_ef_max (int | None)

• ef (int | None)

• flat_search_cutoff (int | None)

• filter_strategy (VectorFilterStrategy | None)

• vector_cache_max_objects (int | None)

• quantizer (_PQConfigUpdate | _BQConfigUpdate | _SQConfigUpdate | None)

Return type:

_VectorIndexConfigHNSWUpdate

static flat(vector_cache_max_objects=None, quantizer=None)

Create an _VectorIndexConfigFlatUpdate object to update the configuration of the FLAT vector index.

Use this method when defining the vectorizer_config argument in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#configure-the-inverted-index>`_ for a more detailed view!

• vector_cache_max_objects (int | None)

• quantizer (_BQConfigUpdate | None)

Return type:

_VectorIndexConfigFlatUpdate

static dynamic(*, threshold=None, hnsw=None, flat=None, quantizer=None)

Create an _VectorIndexConfigDynamicUpdate object to update the configuration of the Dynamic vector index.

Use this method when defining the vectorizer_config argument in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#configure-the-inverted-index>`_ for a more detailed view!

• threshold (int | None)

• hnsw (_VectorIndexConfigHNSWUpdate | None)

• flat (_VectorIndexConfigFlatUpdate | None)

• quantizer (_BQConfigUpdate | None)

Return type:

_VectorIndexConfigDynamicUpdate

class weaviate.collections.classes.config.Reconfigure

Bases: object

Use this factory class to generate the correct xxxConfig object for use when using the collection.update() method.

Each staticmethod provides options specific to the named configuration type in the function’s name. Under-the-hood data validation steps will ensure that any mis-specifications are caught before the request is sent to Weaviate. Only those configurations that are mutable are available in this class. If you wish to update the configuration of an immutable aspect of your collection then you will have to delete the collection and re-create it with the new configuration.

NamedVectors

alias of _NamedVectorsUpdate

VectorIndex

alias of _VectorIndexUpdate

Generative

alias of _Generative

Reranker

alias of _Reranker

static inverted_index(bm25_b=None, bm25_k1=None, cleanup_interval_seconds=None, stopwords_additions=None, stopwords_preset=None, stopwords_removals=None)

Create an InvertedIndexConfigUpdate object.

Use this method when defining the inverted_index_config argument in collection.update().

Parameters:

• <https (See `the docs) –

  //weaviate.io/developers/weaviate/configuration/indexes#configure-the-inverted-index>`_ for a more detailed view!

• bm25_b (float | None)

• bm25_k1 (float | None)

• cleanup_interval_seconds (int | None)

• stopwords_additions (List[str] | None)

• stopwords_preset (StopwordsPreset | None)

• stopwords_removals (List[str] | None)

Return type:

_InvertedIndexConfigUpdate

static replication(factor=None, async_enabled=None, deletion_strategy=None)

Create a ReplicationConfigUpdate object.

Use this method when defining the replication_config argument in collection.update().

Parameters:

• factor (int | None) – The replication factor.

• async_enabled (bool | None) – Enable async replication.

• deletion_strategy (ReplicationDeletionStrategy | None) – How conflicts between different nodes about deleted objects are resolved.

Return type:

_ReplicationConfigUpdate

static multi_tenancy(auto_tenant_creation=None, auto_tenant_activation=None)

Create a MultiTenancyConfigUpdate object.

Use this method when defining the multi_tenancy argument in collection.update().

Parameters:

• auto_tenant_creation (bool | None) – When set, implicitly creates nonexistent tenants during object creation

• auto_tenant_activation (bool | None) – Automatically turn tenants implicitly HOT when they are accessed. Defaults to None, which uses the server-defined default.

Return type:

_MultiTenancyConfigUpdate

weaviate.collections.classes.config_base

pydantic model weaviate.collections.classes.config_base._ConfigCreateModel

Bases: BaseModel

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.

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_base._ConfigUpdateModel

Bases: BaseModel

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.

merge_with_existing(schema)

Parameters:

schema (Dict[str, Any])

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config_base._ConfigBase

Bases: object

to_dict()

Return type:

dict

pydantic model weaviate.collections.classes.config_base._QuantizerConfigCreate

Bases: _ConfigCreateModel

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 enabled: bool = True

abstractmethod static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_base._QuantizerConfigUpdate

Bases: _ConfigUpdateModel

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.

abstractmethod static quantizer_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config_base._EnumLikeStr(string: str)

Bases: object

Parameters:

string (str)

string: str

property value: str

weaviate.collections.classes.config_methods

weaviate.collections.classes.config_methods._is_primitive(d_type)

Parameters:

d_type (str)

Return type:

bool

weaviate.collections.classes.config_methods.__get_rerank_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_RerankerConfig | None

weaviate.collections.classes.config_methods.__get_generative_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_GenerativeConfig | None

weaviate.collections.classes.config_methods.__get_vectorizer_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_VectorizerConfig | None

weaviate.collections.classes.config_methods.__is_vectorizer_present(schema)

Parameters:

schema (Dict[str, Any])

Return type:

bool

weaviate.collections.classes.config_methods.__get_vector_index_type(schema)

Parameters:

schema (Dict[str, Any])

Return type:

VectorIndexType | None

weaviate.collections.classes.config_methods.__get_quantizer_config(config)

Parameters:

config (Dict[str, Any])

Return type:

_PQConfig | _BQConfig | _SQConfig | None

weaviate.collections.classes.config_methods.__get_multivector_encoding(config)

Parameters:

config (Dict[str, Any])

Return type:

_MuveraConfig | None

weaviate.collections.classes.config_methods.__get_multivector(config)

Parameters:

config (Dict[str, Any])

Return type:

_MultiVectorConfig | None

weaviate.collections.classes.config_methods.__get_hnsw_config(config)

Parameters:

config (Dict[str, Any])

Return type:

_VectorIndexConfigHNSW

weaviate.collections.classes.config_methods.__get_flat_config(config)

Parameters:

config (Dict[str, Any])

Return type:

_VectorIndexConfigFlat

weaviate.collections.classes.config_methods.__get_vector_index_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_VectorIndexConfigHNSW | _VectorIndexConfigFlat | _VectorIndexConfigDynamic | None

weaviate.collections.classes.config_methods.__get_vector_config(schema, simple)

Parameters:

• schema (Dict[str, Any])

• simple (bool)

Return type:

Dict[str, _NamedVectorConfig] | None

weaviate.collections.classes.config_methods.__get_vectorizer(schema)

Parameters:

schema (Dict[str, Any])

Return type:

str | Vectorizers | None

weaviate.collections.classes.config_methods._collection_config_simple_from_json(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_CollectionConfigSimple

weaviate.collections.classes.config_methods._collection_config_from_json(schema)

Parameters:

schema (Dict[str, Any])

Return type:

_CollectionConfig

weaviate.collections.classes.config_methods._collection_configs_from_json(schema)

Parameters:

schema (Dict[str, Any])

Return type:

Dict[str, _CollectionConfig]

weaviate.collections.classes.config_methods._collection_configs_simple_from_json(schema)

Parameters:

schema (Dict[str, Any])

Return type:

Dict[str, _CollectionConfigSimple]

weaviate.collections.classes.config_methods._nested_properties_from_config(props)

Parameters:

props (List[Dict[str, Any]])

Return type:

List[_NestedProperty]

weaviate.collections.classes.config_methods._properties_from_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

List[_Property]

weaviate.collections.classes.config_methods._references_from_config(schema)

Parameters:

schema (Dict[str, Any])

Return type:

List[_ReferenceProperty]

weaviate.collections.classes.config_named_vectors

pydantic model weaviate.collections.classes.config_named_vectors._NamedVectorizerConfigCreate

Bases: _ConfigCreateModel

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 properties: List[str] | None = None (alias 'source_properties')

Constraints:

• min_length = 1

field vectorizer: Vectorizers [Required]

static _to_vectorizer_dict(vectorizer, values)

Parameters:

• vectorizer (Vectorizers)

• values (Dict[str, Any])

Return type:

Dict[str, Any]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_named_vectors._NamedVectorConfigCreate

Bases: _ConfigCreateModel

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 name: str [Required]

field properties: List[str] | None = None (alias 'source_properties')

Constraints:

• min_length = 1

field vectorIndexConfig: _VectorIndexConfigCreate | None = None (alias 'vector_index_config')

field vectorIndexType: VectorIndexType = VectorIndexType.HNSW

field vectorizer: _VectorizerConfigCreate [Required]

_NamedVectorConfigCreate__parse_vectorizer()

Return type:

Dict[str, Any]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_named_vectors._NamedVectorConfigUpdate

Bases: _ConfigUpdateModel

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 name: str [Required]

field vectorIndexConfig: _VectorIndexConfigUpdate [Required] (alias 'vector_index_config')

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.config_named_vectors._NamedVectors

Bases: object

static none(name, *, vector_index_config=None)

Create a named vector using no vectorizer. You will need to provide the vectors yourself.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

Return type:

_NamedVectorConfigCreate

static custom(name, *, module_name, module_config=None, source_properties=None, vector_index_config=None)

Create a named vector using no vectorizer. You will need to provide the vectors yourself.

Parameters:

• name (str) – The name of the named vector.

• module_name (str) – The name of the custom module to use.

• module_config (Dict[str, Any] | None) – The configuration of the custom module to use.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

Return type:

_NamedVectorConfigCreate

static text2colbert_jinaai(name, *, dimensions=None, model=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2colbert_jinaai module.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• vectorize_collection_name – Whether to vectorize the collection name. Defaults to True.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – Number of dimensions. Applicable to v3 OpenAI models only. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static text2vec_cohere(name, *, base_url=None, model=None, truncate=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_cohere model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['embed-v4.0', 'embed-multilingual-v2.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'small', 'medium', 'large', 'multilingual-22-12', 'embed-english-v2.0', 'embed-english-light-v2.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate (Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

Raises:

pydantic.ValidationError – If model is not a valid value from the CohereModel type or if truncate is not a valid value from the CohereTruncation type.

Return type:

_NamedVectorConfigCreate

static multi2vec_cohere(name, *, base_url=None, image_fields=None, model=None, text_fields=None, truncate=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_cohere model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['embed-v4.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate (Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

Raises:

pydantic.ValidationError – If model is not a valid value from the CohereMultimodalModel type or if truncate is not a valid value from the CohereTruncation type.

Return type:

_NamedVectorConfigCreate

static text2vec_contextionary(name, *, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_contextionary model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static text2vec_databricks(name, *, endpoint, instruction=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-databricks model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• endpoint (str) – The endpoint to use.

• instruction (str | None) – The instruction strategy to use. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static text2vec_mistral(name, *, base_url=None, model=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-mistral model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static text2vec_ollama(name, *, api_endpoint=None, model=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-ollama model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name – Whether to vectorize the collection name. Defaults to True.

• api_endpoint (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default. Docker users may need to specify an alias, such as http://host.docker.internal:11434 so that the container can access the host machine.

Return type:

_NamedVectorConfigCreate

static text2vec_openai(name, *, base_url=None, dimensions=None, model=None, model_version=None, type_=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_openai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['text-embedding-3-small', 'text-embedding-3-large', 'text-embedding-ada-002'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• model_version (str | None) – The model version to use. Defaults to None, which uses the server-defined default.

• type – The type of model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – Number of dimensions. Applicable to v3 OpenAI models only. Defaults to None, which uses the server-defined default.

• type_ (Literal['text', 'code'] | None)

Raises:

pydantic.ValidationError – If type_ is not a valid value from the OpenAIType type.

Return type:

_NamedVectorConfigCreate

static text2vec_aws(name, region, *, endpoint=None, model=None, service='bedrock', source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_aws model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• region (str) – The AWS region to run the model from, REQUIRED.

• endpoint (str | None) – The endpoint to use. Defaults to None, which uses the server-defined default.

• model (Literal['amazon.titan-embed-text-v1', 'cohere.embed-english-v3', 'cohere.embed-multilingual-v3'] | str | None) – The model to use.

• service (Literal['bedrock', 'sagemaker'] | str) – The AWS service to use. Defaults to bedrock.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static img2vec_neural(name, image_fields, *, vector_index_config=None)

Create a Img2VecNeuralConfig object for use when vectorizing using the img2vec-neural model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• image_fields (List[str]) – The image fields to use. This is a required field and must match the property fields of the collection that are defined as DataType.BLOB.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

Raises:

pydantic.ValidationError – If image_fields is not a list.

Return type:

_NamedVectorConfigCreate

static multi2vec_clip(name, *, inference_url=None, image_fields=None, text_fields=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_clip model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• inference_url (str | None) – The inference url to use where API requests should go. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static multi2vec_palm(name, *, vector_index_config=None, vectorize_collection_name=True, location, project_id, image_fields=None, text_fields=None, video_fields=None, dimensions=None, video_interval_seconds=None, model_id=None)

Create a named vector using the multi2vec_palm model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• location (str) – Where the model runs. REQUIRED.

• project_id (str) – The project ID to use, REQUIRED.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• dimensions (int | None) – The number of dimensions to use. Defaults to None, which uses the server-defined default.

• video_interval_seconds (int | None) – Length of a video interval. Defaults to None, which uses the server-defined default.

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

Deprecated since version 4.9.0.

This method is deprecated and will be removed in Q2 ‘25. Please use multi2vec_google() instead.

static multi2vec_google(name, *, location, project_id, image_fields=None, text_fields=None, video_fields=None, dimensions=None, video_interval_seconds=None, model_id=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_google model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• location (str) – Where the model runs. REQUIRED.

• project_id (str) – The project ID to use, REQUIRED.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• dimensions (int | None) – The number of dimensions to use. Defaults to None, which uses the server-defined default.

• video_interval_seconds (int | None) – Length of a video interval. Defaults to None, which uses the server-defined default.

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static multi2vec_bind(name, *, audio_fields=None, depth_fields=None, image_fields=None, imu_fields=None, text_fields=None, thermal_fields=None, video_fields=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_bind model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• audio_fields (List[str] | List[Multi2VecField] | None) – The audio fields to use in vectorization.

• depth_fields (List[str] | List[Multi2VecField] | None) – The depth fields to use in vectorization.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• imu_fields (List[str] | List[Multi2VecField] | None) – The IMU fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• thermal_fields (List[str] | List[Multi2VecField] | None) – The thermal fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static multi2vec_voyageai(name, *, base_url=None, model=None, truncation=None, output_encoding=None, image_fields=None, text_fields=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_voyageai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['voyage-multimodal-3'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncation (bool | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• output_encoding (str | None)

Raises:

pydantic.ValidationError – If model is not a valid value from the VoyageaiMultimodalModel type.

Return type:

_NamedVectorConfigCreate

static multi2vec_nvidia(name, *, base_url=None, model=None, truncation=None, output_encoding=None, image_fields=None, text_fields=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_nvidia model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncation (bool | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• output_encoding (str | None)

Raises:

pydantic.ValidationError – If model is not a valid value from the NvidiaMultimodalModel type.

Return type:

_NamedVectorConfigCreate

static ref2vec_centroid(name, reference_properties, *, method='mean', vector_index_config=None)

Create a named vector using the ref2vec_centroid model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• reference_properties (List[str]) – The reference properties to use in vectorization, REQUIRED.

• method (Literal['mean']) – The method to use. Defaults to mean.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

Return type:

_NamedVectorConfigCreate

static text2vec_azure_openai(name, resource_name, deployment_id, *, base_url=None, dimensions=None, model=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_azure_openai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• resource_name (str) – The resource name to use, REQUIRED.

• deployment_id (str) – The deployment ID to use, REQUIRED.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The dimensionality of the vectors. Defaults to None, which uses the server-defined default.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static text2vec_gpt4all(name, *, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_gpt4all model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_NamedVectorConfigCreate

static text2vec_huggingface(name, *, model=None, passage_model=None, query_model=None, endpoint_url=None, wait_for_model=None, use_gpu=None, use_cache=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_huggingface model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• passage_model (str | None) – The passage model to use. Defaults to None, which uses the server-defined default.

• query_model (str | None) – The query model to use. Defaults to None, which uses the server-defined default.

• endpoint_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The endpoint URL to use. Defaults to None, which uses the server-defined default.

• wait_for_model (bool | None) – Whether to wait for the model to be loaded. Defaults to None, which uses the server-defined default.

• use_gpu (bool | None) – Whether to use the GPU. Defaults to None, which uses the server-defined default.

• use_cache (bool | None) – Whether to use the cache. Defaults to None, which uses the server-defined default.

Raises:

pydantic.ValidationError –

If the arguments passed to the function are invalid. It is important to note that some of these variables are mutually exclusive. See the documentation for more details.

Return type:

_NamedVectorConfigCreate

static text2vec_palm(name, project_id, *, source_properties=None, vector_index_config=None, vectorize_collection_name=True, api_endpoint=None, model_id=None, title_property=None)

Create a named vector using the text2vec_palm model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• project_id (str) – The project ID to use, REQUIRED.

• source_properties – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_NamedVectorConfigCreate

Deprecated since version 4.9.0.

This method is deprecated and will be removed in Q2 ‘25. Please use text2vec_google() instead.

static text2vec_google(name, project_id, *, api_endpoint=None, model_id=None, title_property=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_palm model.

See the [documentation]https://weaviate.io/developers/weaviate/model-providers/google/embeddings) for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• project_id (str) – The project ID to use, REQUIRED.

• source_properties – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_NamedVectorConfigCreate

static text2vec_google_aistudio(name, *, model_id=None, title_property=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_palm model.

See the [documentation]https://weaviate.io/developers/weaviate/model-providers/google/embeddings) for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• source_properties – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_NamedVectorConfigCreate

static text2vec_transformers(name, *, pooling_strategy='masked_mean', inference_url=None, passage_inference_url=None, query_inference_url=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec_transformers model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• pooling_strategy (Literal['masked_mean', 'cls']) – The pooling strategy to use. Defaults to masked_mean.

• inference_url (str | None) – The inferenceUrl to use where API requests should go. You can use either this OR passage/query_inference_url. Defaults to None, which uses the server-defined default.

• passage_inference_url (str | None) – The inferenceUrl to use where passage API requests should go. You can use either this and query_inference_url OR inference_url. Defaults to None, which uses the server-defined default.

• query_inference_url (str | None) – The inferenceUrl to use where query API requests should go. You can use either this and passage_inference_url OR inference_url. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static text2vec_jinaai(name, *, base_url=None, dimensions=None, model=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-jinaai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (str | None) – The base URL to send the vectorization requests to. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The number of dimensions for the generated embeddings. Defaults to None, which uses the server-defined default.

• model (Literal['jina-embeddings-v2-base-en', 'jina-embeddings-v2-small-en', 'jina-embeddings-v2-base-zh', 'jina-embeddings-v2-base-es', 'jina-embeddings-v2-base-code', 'jina-embeddings-v3'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static multi2vec_jinaai(name, *, base_url=None, model=None, dimensions=None, image_fields=None, text_fields=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the multi2vec_jinaai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['jina-clip-v1', 'jina-clip-v2'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The number of dimensions for the generated embeddings (only available for some models). Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

Raises:

pydantic.ValidationError – If model is not a valid value from the JinaMultimodalModel type.

Return type:

_NamedVectorConfigCreate

static text2vec_voyageai(name, *, model=None, base_url=None, truncate=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-jinaai model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (Literal['voyage-3.5', 'voyage-3.5-lite', 'voyage-3', 'voyage-3-lite', 'voyage-large-2', 'voyage-code-2', 'voyage-2', 'voyage-law-2', 'voyage-large-2-instruct', 'voyage-finance-2', 'voyage-multilingual-2'] | str | None) –

  The model to use. Defaults to None, which uses the server-defined default. See the documentation for more details.

• base_url (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• truncate (bool | None) – Whether to truncate the input texts to fit within the context length. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

static text2vec_weaviate(name, *, model=None, base_url=None, dimensions=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Parameters:

• name (str)

• model (Literal['Snowflake/snowflake-arctic-embed-l-v2.0', 'Snowflake/snowflake-arctic-embed-m-v1.5'] | str | None)

• base_url (str | None)

• dimensions (int | None)

• source_properties (List[str] | None)

• vector_index_config (_VectorIndexConfigCreate | None)

• vectorize_collection_name (bool)

Return type:

_NamedVectorConfigCreate

static text2vec_nvidia(name, *, model=None, base_url=None, truncate=None, source_properties=None, vector_index_config=None, vectorize_collection_name=True)

Create a named vector using the text2vec-nvidia model.

See the documentation for detailed usage.

Parameters:

• name (str) – The name of the named vector.

• source_properties (List[str] | None) – Which properties should be included when vectorizing. By default all text properties are included.

• vector_index_config (_VectorIndexConfigCreate | None) – The configuration for Weaviate’s vector index. Use wvc.config.Configure.VectorIndex to create a vector index configuration. None by default

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• model (str | None) –

  The model to use. Defaults to None, which uses the server-defined default. See the documentation for more details.

• base_url (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• truncate (bool | None) – Whether to truncate the input texts to fit within the context length. Defaults to None, which uses the server-defined default.

Return type:

_NamedVectorConfigCreate

class weaviate.collections.classes.config_named_vectors._NamedVectorsUpdate

Bases: object

static update(name, *, vector_index_config)

Update the vector index configuration of a named vector.

This is the only update operation allowed currently. If you wish to change the vectorization configuration itself, you will have to recreate the collection with the new configuration.

Parameters:

• name (str) – The name of the named vector.

• vector_index_config (_VectorIndexConfigHNSWUpdate | _VectorIndexConfigFlatUpdate | _VectorIndexConfigDynamicUpdate) – The configuration for Weaviate’s vector index. Use wvc.config.Reconfigure.VectorIndex to create a vector index configuration. None by default

Return type:

_NamedVectorConfigUpdate

weaviate.collections.classes.config_vector_index

class weaviate.collections.classes.config_vector_index.VectorFilterStrategy(*values)

Bases: str, Enum

Set the strategy when doing a filtered HNSW search.

SWEEPING

Do normal ANN search and skip nodes.

ACORN

Multi-hop search to find new candidates matching the filter.

SWEEPING = 'sweeping'

ACORN = 'acorn'

class weaviate.collections.classes.config_vector_index.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'

pydantic model weaviate.collections.classes.config_vector_index._MultiVectorConfigCreateBase

Bases: _ConfigCreateModel

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 enabled: bool = True

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._EncodingConfigCreate

Bases: _MultiVectorConfigCreateBase

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 enabled: bool = True

abstractmethod static encoding_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._MuveraConfigCreate

Bases: _EncodingConfigCreate

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 dprojections: int | None [Required]

field ksim: int | None [Required]

field repetitions: int | None [Required]

static encoding_name()

Return type:

str

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._MultiVectorConfigCreate

Bases: _MultiVectorConfigCreateBase

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 aggregation: str | None [Required]

field encoding: _EncodingConfigCreate | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigCreate

Bases: _ConfigCreateModel

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 distance: VectorDistances | None [Required]

field multivector: _MultiVectorConfigCreate | None [Required]

field quantizer: _QuantizerConfigCreate | None [Required]

abstractmethod static vector_index_type()

Return type:

VectorIndexType

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigUpdate

Bases: _ConfigUpdateModel

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 quantizer: _QuantizerConfigUpdate | None [Required]

abstractmethod static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigSkipCreate

Bases: _VectorIndexConfigCreate

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 skip: bool = True

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigHNSWCreate

Bases: _VectorIndexConfigCreate

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 cleanupIntervalSeconds: int | None [Required]

field dynamicEfFactor: int | None [Required]

field dynamicEfMax: int | None [Required]

field dynamicEfMin: int | None [Required]

field ef: int | None [Required]

field efConstruction: int | None [Required]

field filterStrategy: VectorFilterStrategy | None [Required]

field flatSearchCutoff: int | None [Required]

field maxConnections: int | None [Required]

field vectorCacheMaxObjects: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigFlatCreate

Bases: _VectorIndexConfigCreate

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 vectorCacheMaxObjects: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigHNSWUpdate

Bases: _VectorIndexConfigUpdate

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 dynamicEfFactor: int | None [Required]

field dynamicEfMax: int | None [Required]

field dynamicEfMin: int | None [Required]

field ef: int | None [Required]

field filterStrategy: VectorFilterStrategy | None [Required]

field flatSearchCutoff: int | None [Required]

field vectorCacheMaxObjects: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigFlatUpdate

Bases: _VectorIndexConfigUpdate

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 vectorCacheMaxObjects: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigDynamicCreate

Bases: _VectorIndexConfigCreate

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 flat: _VectorIndexConfigFlatCreate | None [Required]

field hnsw: _VectorIndexConfigHNSWCreate | None [Required]

field threshold: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_to_dict()

Return type:

dict

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vector_index._VectorIndexConfigDynamicUpdate

Bases: _VectorIndexConfigUpdate

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 flat: _VectorIndexConfigFlatUpdate | None [Required]

field hnsw: _VectorIndexConfigHNSWUpdate | None [Required]

field threshold: int | None [Required]

static vector_index_type()

Return type:

VectorIndexType

_abc_impl = <_abc._abc_data object>

weaviate.collections.classes.config_vectorizers

class weaviate.collections.classes.config_vectorizers.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'

class weaviate.collections.classes.config_vectorizers.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'

pydantic model weaviate.collections.classes.config_vectorizers._VectorizerConfigCreate

Bases: _ConfigCreateModel

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 vectorizer: Vectorizers | _EnumLikeStr [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2ColbertJinaAIConfig

Bases: _VectorizerConfigCreate

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 dimensions: int | None [Required]

field model: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2COLBERT_JINAAI

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecContextionaryConfig

Bases: _VectorizerConfigCreate

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 vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_CONTEXTIONARY

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._VectorizerCustomConfig

Bases: _VectorizerConfigCreate

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 module_config: Dict[str, Any] | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecAWSConfig

Bases: _VectorizerConfigCreate

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 endpoint: str | None [Required]

field model: str | None [Required]

field region: str [Required]

Validated by:

• _check_name

field service: str [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_AWS

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecAzureOpenAIConfig

Bases: _VectorizerConfigCreate

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field deploymentId: str [Required]

field dimensions: int | None [Required]

field model: str | None [Required]

field resourceName: str [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_OPENAI

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecHuggingFaceConfig

Bases: _VectorizerConfigCreate

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 endpointURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field passageModel: str | None [Required]

field queryModel: str | None [Required]

field useCache: bool | None [Required]

field useGPU: bool | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_HUGGINGFACE

field waitForModel: bool | None [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecMistralConfig

Bases: _VectorizerConfigCreate

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_MISTRAL

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecDatabricksConfig

Bases: _VectorizerConfigCreate

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 endpoint: str [Required]

field instruction: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_DATABRICKS

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecOpenAIConfig

Bases: _VectorizerConfigCreate

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field dimensions: int | None [Required]

field model: str | None [Required]

field modelVersion: str | None [Required]

field type_: Literal['text', 'code'] | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_OPENAI

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecCohereConfig

Bases: _VectorizerConfigCreate

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field truncate: Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_COHERE

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecGoogleConfig

Bases: _VectorizerConfigCreate

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 apiEndpoint: str | None [Required]

field modelId: str | None [Required]

field projectId: str | None [Required]

field titleProperty: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_PALM

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecTransformersConfig

Bases: _VectorizerConfigCreate

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 inferenceUrl: str | None [Required]

field passageInferenceUrl: str | None [Required]

field poolingStrategy: Literal['masked_mean', 'cls'] [Required]

field queryInferenceUrl: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_TRANSFORMERS

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecGPT4AllConfig

Bases: _VectorizerConfigCreate

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 vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_GPT4ALL

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecJinaConfig

Bases: _VectorizerConfigCreate

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 baseURL: str | None [Required]

field dimensions: int | None [Required]

field model: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_JINAAI

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecVoyageConfig

Bases: _VectorizerConfigCreate

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 baseURL: str | None [Required]

field model: str | None [Required]

field truncate: bool | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_VOYAGEAI

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecNvidiaConfig

Bases: _VectorizerConfigCreate

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 baseURL: str | None [Required]

field model: str | None [Required]

field truncate: bool | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_NVIDIA

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecWeaviateConfig

Bases: _VectorizerConfigCreate

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 baseURL: str | None [Required]

field dimensions: int | None [Required]

field model: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_WEAVIATE

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Text2VecOllamaConfig

Bases: _VectorizerConfigCreate

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 apiEndpoint: str | None [Required]

field model: str | None [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.TEXT2VEC_OLLAMA

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Img2VecNeuralConfig

Bases: _VectorizerConfigCreate

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 imageFields: List[str] [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.IMG2VEC_NEURAL

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers.Multi2VecField

Bases: BaseModel

Use this class when defining the fields to use in the Multi2VecClip and Multi2VecBind vectorizers.

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 name: str [Required]

field weight: float | None = None

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecBase

Bases: _VectorizerConfigCreate

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 imageFields: List[Multi2VecField] | None [Required]

field textFields: List[Multi2VecField] | None [Required]

field vectorizeClassName: bool [Required]

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecCohereConfig

Bases: _Multi2VecBase

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field truncate: Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_COHERE

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecJinaConfig

Bases: _Multi2VecBase

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field dimensions: int | None [Required]

field model: str | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_JINAAI

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecClipConfig

Bases: _Multi2VecBase

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 inferenceUrl: str | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_CLIP

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecGoogleConfig

Bases: _Multi2VecBase, _VectorizerConfigCreate

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 dimensions: int | None [Required]

field location: str | None [Required]

field modelId: str | None [Required]

field projectId: str [Required]

field vectorizeClassName: bool [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_PALM

field videoFields: List[Multi2VecField] | None [Required]

field videoIntervalSeconds: int | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecBindConfig

Bases: _Multi2VecBase

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 IMUFields: List[Multi2VecField] | None [Required]

field audioFields: List[Multi2VecField] | None [Required]

field depthFields: List[Multi2VecField] | None [Required]

field thermalFields: List[Multi2VecField] | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_BIND

field videoFields: List[Multi2VecField] | None [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecVoyageaiConfig

Bases: _Multi2VecBase

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field output_encoding: str | None [Required]

field truncation: bool | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_VOYAGEAI

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Multi2VecNvidiaConfig

Bases: _Multi2VecBase

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 baseURL: Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None [Required]

field model: str | None [Required]

field output_encoding: str | None [Required]

field truncation: bool | None [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.MULTI2VEC_NVIDIA

_to_dict()

Return type:

Dict[str, Any]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.config_vectorizers._Ref2VecCentroidConfig

Bases: _VectorizerConfigCreate

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 method: Literal['mean'] [Required]

field referenceProperties: List[str] [Required]

field vectorizer: Vectorizers | _EnumLikeStr = Vectorizers.REF2VEC_CENTROID

_abc_impl = <_abc._abc_data object>

weaviate.collections.classes.config_vectorizers._map_multi2vec_fields(fields)

Parameters:

fields (List[str] | List[Multi2VecField] | None)

Return type:

List[Multi2VecField] | None

class weaviate.collections.classes.config_vectorizers._Vectorizer

Bases: object

Use this factory class to create the correct object for the vectorizer_config argument in the collections.create() method.

Each staticmethod provides options specific to the named vectorizer in the function’s name. Under-the-hood data validation steps will ensure that any mis-specifications will be caught before the request is sent to Weaviate.

static none()

Create a _VectorizerConfigCreate object with the vectorizer set to Vectorizer.NONE.

Return type:

_VectorizerConfigCreate

static img2vec_neural(image_fields)

Create a _Img2VecNeuralConfigCreate object for use when vectorizing using the img2vec-neural model.

See the documentation for detailed usage.

Parameters:

image_fields (List[str]) – The image fields to use. This is a required field and must match the property fields of the collection that are defined as DataType.BLOB.

Raises:

pydantic.ValidationError – If image_fields is not a list.

Return type:

_VectorizerConfigCreate

static multi2vec_clip(image_fields=None, text_fields=None, interference_url=None, inference_url=None, vectorize_collection_name=True)

Create a _Multi2VecClipConfigCreate object for use when vectorizing using the multi2vec-clip model.

See the documentation for detailed usage.

Parameters:

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• inference_url (str | None) – The inference url to use where API requests should go. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• interference_url (str | None)

Raises:

pydantic.ValidationError – If image_fields or text_fields are not None or a list.

Return type:

_VectorizerConfigCreate

static multi2vec_bind(audio_fields=None, depth_fields=None, image_fields=None, imu_fields=None, text_fields=None, thermal_fields=None, video_fields=None, vectorize_collection_name=True)

Create a _Multi2VecBindConfigCreate object for use when vectorizing using the multi2vec-clip model.

See the documentation for detailed usage.

Parameters:

• audio_fields (List[str] | List[Multi2VecField] | None) – The audio fields to use in vectorization.

• depth_fields (List[str] | List[Multi2VecField] | None) – The depth fields to use in vectorization.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• imu_fields (List[str] | List[Multi2VecField] | None) – The IMU fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• thermal_fields (List[str] | List[Multi2VecField] | None) – The thermal fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If any of the *_fields are not None or a list.

Return type:

_VectorizerConfigCreate

static ref2vec_centroid(reference_properties, method='mean')

Create a _Ref2VecCentroidConfigCreate object for use when vectorizing using the ref2vec-centroid model.

See the documentation for detailed usage.

Parameters:

• reference_properties (List[str]) – The reference properties to use in vectorization, REQUIRED.

• method (Literal['mean']) – The method to use in vectorization. Defaults to mean.

Raises:

pydantic.ValidationError – If reference_properties is not a list.

Return type:

_VectorizerConfigCreate

static text2vec_aws(model=None, region='', endpoint=None, service='bedrock', vectorize_collection_name=True)

Create a _Text2VecAWSConfigCreate object for use when vectorizing using the text2vec-aws model.

See the documentation for detailed usage.

Parameters:

• model (Literal['amazon.titan-embed-text-v1', 'cohere.embed-english-v3', 'cohere.embed-multilingual-v3'] | str | None) – The model to use, REQUIRED for service “bedrock”.

• region (str) – The AWS region to run the model from, REQUIRED.

• endpoint (str | None) – The model to use, REQUIRED for service “sagemaker”.

• service (Literal['bedrock', 'sagemaker'] | str) – The AWS service to use, options are “bedrock” and “sagemaker”.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_VectorizerConfigCreate

static text2vec_azure_openai(resource_name, deployment_id, vectorize_collection_name=True, base_url=None, dimensions=None, model=None)

Create a _Text2VecAzureOpenAIConfigCreate object for use when vectorizing using the text2vec-azure-openai model.

See the documentation for detailed usage.

Parameters:

• resource_name (str) – The resource name to use, REQUIRED.

• deployment_id (str) – The deployment ID to use, REQUIRED.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The dimensionality of the vectors. Defaults to None, which uses the server-defined default.

• model (str | None)

Raises:

pydantic.ValidationError – If resource_name or deployment_id are not str.

Return type:

_VectorizerConfigCreate

static text2vec_contextionary(vectorize_collection_name=True)

Create a _Text2VecContextionaryConfigCreate object for use when vectorizing using the text2vec-contextionary model.

See the documentation for detailed usage.

Parameters:

vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If vectorize_collection_name is not a bool.

Return type:

_VectorizerConfigCreate

static custom(module_name, module_config=None)

Create a _VectorizerCustomConfig object for use when vectorizing using a custom specification.

Parameters:

• module_name (str) – The name of the module to use, REQUIRED.

• module_config (Dict[str, Any] | None) – The configuration to use for the module. Defaults to None, which uses the server-defined default.

Return type:

_VectorizerConfigCreate

static text2vec_cohere(model=None, truncate=None, vectorize_collection_name=True, base_url=None)

Create a _Text2VecCohereConfigCreate object for use when vectorizing using the text2vec-cohere model.

See the documentation for detailed usage.

Parameters:

• model (Literal['embed-v4.0', 'embed-multilingual-v2.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'small', 'medium', 'large', 'multilingual-22-12', 'embed-english-v2.0', 'embed-english-light-v2.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate (Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

Raises:

pydantic.ValidationError – If model is not a valid value from the CohereModel type or if truncate is not a valid value from the CohereTruncation type.

Return type:

_VectorizerConfigCreate

static multi2vec_cohere(*, model=None, truncate=None, vectorize_collection_name=True, base_url=None, image_fields=None, text_fields=None)

Create a _Multi2VecCohereConfig object for use when vectorizing using the multi2vec-cohere model.

See the documentation for detailed usage.

Parameters:

• model (Literal['embed-v4.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate (Literal['NONE', 'START', 'END', 'LEFT', 'RIGHT'] | None) – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

Raises:

pydantic.ValidationError – If model is not a valid value from the CohereMultimodalModel type or if truncate is not a valid value from the CohereTruncation type.

Return type:

_VectorizerConfigCreate

static multi2vec_voyageai(*, model=None, truncation=None, output_encoding, vectorize_collection_name=True, base_url=None, image_fields=None, text_fields=None)

Create a _Multi2VecCohereConfig object for use when vectorizing using the multi2vec-cohere model.

See the documentation for detailed usage.

Parameters:

• model (Literal['embed-v4.0', 'embed-multilingual-v3.0', 'embed-multilingual-light-v3.0', 'embed-english-v3.0', 'embed-english-light-v3.0'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• output_encoding (str | None) – Format in which the embeddings are encoded. Defaults to None, so the embeddings are represented as a list of floating-point numbers.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• truncation (bool | None)

Raises:

pydantic.ValidationError – If model is not a valid value from the CohereMultimodalModel type or if truncate is not a valid value from the CohereTruncation type.

Return type:

_VectorizerConfigCreate

static multi2vec_nvidia(*, model=None, truncation=None, output_encoding, vectorize_collection_name=True, base_url=None, image_fields=None, text_fields=None)

Create a _Multi2VecNvidiaConfig object for use when vectorizing using the multi2vec-nvidia model.

See the documentation for detailed usage.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• truncate – The truncation strategy to use. Defaults to None, which uses the server-defined default.

• output_encoding (str | None) – Format in which the embeddings are encoded. Defaults to None, so the embeddings are represented as a list of floating-point numbers.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• truncation (bool | None)

Raises:

pydantic.ValidationError – If model is not a valid value from the NvidiaMultimodalModel type or if truncate is not a valid value from the NvidiaTruncation type.

Return type:

_VectorizerConfigCreate

static text2vec_databricks(*, endpoint, instruction=None, vectorize_collection_name=True)

Create a _Text2VecDatabricksConfig object for use when vectorizing using the text2vec-databricks model.

See the documentation for detailed usage.

Parameters:

• endpoint (str) – The endpoint to use.

• instruction (str | None) – The instruction strategy to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If truncate is not a valid value from the CohereModel type.

Return type:

_VectorizerConfigCreate

static text2vec_gpt4all(vectorize_collection_name=True)

Create a _Text2VecGPT4AllConfigCreate object for use when vectorizing using the text2vec-gpt4all model.

See the documentation for detailed usage.

Parameters:

vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If vectorize_collection_name is not a bool.

Return type:

_VectorizerConfigCreate

static text2vec_huggingface(model=None, passage_model=None, query_model=None, endpoint_url=None, wait_for_model=None, use_gpu=None, use_cache=None, vectorize_collection_name=True)

Create a _Text2VecHuggingFaceConfigCreate object for use when vectorizing using the text2vec-huggingface model.

See the documentation for detailed usage.

Parameters:

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• passage_model (str | None) – The passage model to use. Defaults to None, which uses the server-defined default.

• query_model (str | None) – The query model to use. Defaults to None, which uses the server-defined default.

• endpoint_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The endpoint URL to use. Defaults to None, which uses the server-defined default.

• wait_for_model (bool | None) – Whether to wait for the model to be loaded. Defaults to None, which uses the server-defined default.

• use_gpu (bool | None) – Whether to use the GPU. Defaults to None, which uses the server-defined default.

• use_cache (bool | None) – Whether to use the cache. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError –

If the arguments passed to the function are invalid. It is important to note that some of these variables are mutually exclusive. See the documentation for more details.

Return type:

_VectorizerConfigCreate

static text2vec_mistral(*, base_url=None, model=None, vectorize_collection_name=True)

Create a _Text2VecMistralConfig object for use when vectorizing using the text2vec-mistral model.

See the documentation for detailed usage.

Parameters:

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_VectorizerConfigCreate

static text2vec_ollama(*, api_endpoint=None, model=None, vectorize_collection_name=True)

Create a _Text2VecOllamaConfig object for use when vectorizing using the text2vec-ollama model.

See the documentation for detailed usage.

Parameters:

• api_endpoint (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default. Docker users may need to specify an alias, such as http://host.docker.internal:11434 so that the container can access the host machine.

• model (str | None) – The model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_VectorizerConfigCreate

static text2vec_openai(model=None, model_version=None, type_=None, vectorize_collection_name=True, base_url=None, dimensions=None)

Create a _Text2VecOpenAIConfigCreate object for use when vectorizing using the text2vec-openai model.

See the documentation for detailed usage.

Parameters:

• model (Literal['text-embedding-3-small', 'text-embedding-3-large', 'text-embedding-ada-002'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• model_version (str | None) – The model version to use. Defaults to None, which uses the server-defined default.

• type – The type of model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – Number of dimensions. Applicable to v3 OpenAI models only. Defaults to None, which uses the server-defined default.

• type_ (Literal['text', 'code'] | None)

Raises:

pydantic.ValidationError – If type_ is not a valid value from the OpenAIType type.

Return type:

_VectorizerConfigCreate

static text2vec_palm(project_id, api_endpoint=None, model_id=None, title_property=None, vectorize_collection_name=True)

Create a _Text2VecGoogleConfig object for use when vectorizing using the text2vec-palm model.

See the documentation for detailed usage.

Parameters:

• project_id (str) – The project ID to use, REQUIRED.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_VectorizerConfigCreate

Deprecated since version 4.9.0.

This method is deprecated and will be removed in Q2 ‘25. Please use text2vec_google() instead.

static text2vec_google_aistudio(model_id=None, title_property=None, vectorize_collection_name=True)

Create a _Text2VecGoogleConfig object for use when vectorizing using the text2vec-google model.

See the documentation for detailed usage.

Parameters:

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_VectorizerConfigCreate

static text2vec_google(project_id, api_endpoint=None, model_id=None, title_property=None, vectorize_collection_name=True)

Create a _Text2VecGoogleConfig object for use when vectorizing using the text2vec-google model.

See the documentation for detailed usage.

Parameters:

• project_id (str) – The project ID to use, REQUIRED.

• api_endpoint (str | None) – The API endpoint to use without a leading scheme such as http://. Defaults to None, which uses the server-defined default

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• title_property (str | None) – The Weaviate property name for the gecko-002 or gecko-003 model to use as the title.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_VectorizerConfigCreate

static multi2vec_palm(*, location, project_id, image_fields=None, text_fields=None, video_fields=None, dimensions=None, model_id=None, video_interval_seconds=None, vectorize_collection_name=True)

Create a _Multi2VecPalmConfig object for use when vectorizing using the text2vec-palm model.

See the documentation for detailed usage.

Parameters:

• location (str) – Where the model runs. REQUIRED.

• project_id (str) – The project ID to use, REQUIRED.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• dimensions (int | None) – The number of dimensions to use. Defaults to None, which uses the server-defined default.

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• video_interval_seconds (int | None) – Length of a video interval. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_VectorizerConfigCreate

Deprecated since version 4.9.0.

This method is deprecated and will be removed in Q2 ‘25. Please use multi2vec_google() instead.

static multi2vec_google(*, location, project_id, image_fields=None, text_fields=None, video_fields=None, dimensions=None, model_id=None, video_interval_seconds=None, vectorize_collection_name=True)

Create a _Multi2VecGoogleConfig object for use when vectorizing using the text2vec-google model.

See the documentation for detailed usage.

Parameters:

• location (str) – Where the model runs. REQUIRED.

• project_id (str) – The project ID to use, REQUIRED.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

• video_fields (List[str] | List[Multi2VecField] | None) – The video fields to use in vectorization.

• dimensions (int | None) – The number of dimensions to use. Defaults to None, which uses the server-defined default.

• model_id (str | None) – The model ID to use. Defaults to None, which uses the server-defined default.

• video_interval_seconds (int | None) – Length of a video interval. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Raises:

pydantic.ValidationError – If api_endpoint is not a valid URL.

Return type:

_VectorizerConfigCreate

static text2vec_transformers(pooling_strategy='masked_mean', vectorize_collection_name=True, inference_url=None, passage_inference_url=None, query_inference_url=None)

Create a _Text2VecTransformersConfigCreate object for use when vectorizing using the text2vec-transformers model.

See the documentation for detailed usage.

Parameters:

• pooling_strategy (Literal['masked_mean', 'cls']) – The pooling strategy to use. Defaults to masked_mean.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• inference_url (str | None) – The inference url to use where API requests should go. You can use either this OR passage/query_inference_url. Defaults to None, which uses the server-defined default.

• passage_inference_url (str | None) – The inference url to use where passage API requests should go. You can use either this and query_inference_url OR inference_url. Defaults to None, which uses the server-defined default.

• query_inference_url (str | None) – The inference url to use where query API requests should go. You can use either this and passage_inference_url OR inference_url. Defaults to None, which uses the server-defined default.

Raises:

pydantic.ValidationError – If pooling_strategy is not a valid value from the PoolingStrategy type.

Return type:

_VectorizerConfigCreate

static text2vec_jinaai(model=None, vectorize_collection_name=True, base_url=None, dimensions=None)

Create a _Text2VecJinaConfigCreate object for use when vectorizing using the text2vec-jinaai model.

See the documentation for detailed usage.

Parameters:

• model (Literal['jina-embeddings-v2-base-en', 'jina-embeddings-v2-small-en', 'jina-embeddings-v2-base-zh', 'jina-embeddings-v2-base-es', 'jina-embeddings-v2-base-code', 'jina-embeddings-v3'] | str | None) –

  The model to use. Defaults to None, which uses the server-defined default. See the documentation for more details.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (str | None) – The base URL to send the vectorization requests to. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The number of dimensions for the generated embeddings. Defaults to None, which uses the server-defined default.

Return type:

_VectorizerConfigCreate

static multi2vec_jinaai(*, model=None, vectorize_collection_name=True, base_url=None, dimensions=None, image_fields=None, text_fields=None)

Create a _Multi2VecJinaConfig object for use when vectorizing using the multi2vec-jinaai model.

See the documentation for detailed usage.

Parameters:

• model (Literal['jina-clip-v1', 'jina-clip-v2'] | str | None) – The model to use. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

• base_url (Annotated[Url, UrlConstraints(max_length=None, allowed_schemes=['http', 'https'], host_required=None, default_host=None, default_port=None, default_path=None)] | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• dimensions (int | None) – The number of dimensions for the generated embeddings (only available for some models). Defaults to None, which uses the server-defined default.

• image_fields (List[str] | List[Multi2VecField] | None) – The image fields to use in vectorization.

• text_fields (List[str] | List[Multi2VecField] | None) – The text fields to use in vectorization.

Raises:

pydantic.ValidationError – If model is not a valid value from the JinaMultimodalModel type.

Return type:

_VectorizerConfigCreate

static text2vec_voyageai(*, model=None, base_url=None, truncate=None, vectorize_collection_name=True)

Create a _Text2VecVoyageConfigCreate object for use when vectorizing using the text2vec-voyageai model.

See the documentation for detailed usage.

Parameters:

• model (Literal['voyage-3.5', 'voyage-3.5-lite', 'voyage-3', 'voyage-3-lite', 'voyage-large-2', 'voyage-code-2', 'voyage-2', 'voyage-law-2', 'voyage-large-2-instruct', 'voyage-finance-2', 'voyage-multilingual-2'] | str | None) –

  The model to use. Defaults to None, which uses the server-defined default. See the documentation for more details.

• base_url (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• truncate (bool | None) – Whether to truncate the input texts to fit within the context length. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_VectorizerConfigCreate

static text2vec_weaviate(*, model=None, base_url=None, vectorize_collection_name=True, dimensions=None)

TODO: add docstrings when the documentation is available.

Parameters:

• model (Literal['Snowflake/snowflake-arctic-embed-l-v2.0', 'Snowflake/snowflake-arctic-embed-m-v1.5'] | str | None)

• base_url (str | None)

• vectorize_collection_name (bool)

• dimensions (int | None)

Return type:

_VectorizerConfigCreate

static text2vec_nvidia(*, model=None, base_url=None, truncate=None, vectorize_collection_name=True)

Create a _Text2VecNvidiaConfigCreate object for use when vectorizing using the text2vec-nvidia model.

See the documentation for detailed usage.

Parameters:

• model (str | None) –

  The model to use. Defaults to None, which uses the server-defined default. See the documentation for more details.

• base_url (str | None) – The base URL to use where API requests should go. Defaults to None, which uses the server-defined default.

• truncate (bool | None) – Whether to truncate the input texts to fit within the context length. Defaults to None, which uses the server-defined default.

• vectorize_collection_name (bool) – Whether to vectorize the collection name. Defaults to True.

Return type:

_VectorizerConfigCreate

weaviate.collections.classes.data

class weaviate.collections.classes.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)

message: str

code: int | None = None

original_uuid: str | UUID | None = None

class weaviate.collections.classes.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

class weaviate.collections.classes.data.DataObject(properties=None, uuid=None, vector=None, references=None)

Bases: Generic[P, R]

This class represents an entire object within a collection to be used when batching.

Parameters:

• properties (P)

• uuid (str | UUID | None)

• vector (Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | None)

• references (R)

properties: P = None

uuid: str | UUID | None = None

vector: Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]]] | Sequence[int | float] | None = None

references: R = None

class weaviate.collections.classes.data._DataReference(from_property: str, from_uuid: str | uuid.UUID, to_uuid: str | uuid.UUID | List[str | uuid.UUID])

Bases: object

Parameters:

• from_property (str)

• from_uuid (str | UUID)

• to_uuid (str | UUID | List[str | UUID])

from_property: str

from_uuid: str | UUID

to_uuid: str | UUID | List[str | UUID]

_to_uuids()

Return type:

List[str | UUID]

class weaviate.collections.classes.data.DataReferenceMulti(from_property, from_uuid, to_uuid, target_collection)

Bases: _DataReference

This class represents a reference between objects within a collection to be used when batching.

Parameters:

• from_property (str)

• from_uuid (str | UUID)

• to_uuid (str | UUID | List[str | UUID])

• target_collection (str)

target_collection: str

_to_beacons()

Return type:

List[str]

class weaviate.collections.classes.data.DataReference(from_property, from_uuid, to_uuid)

Bases: _DataReference

This class represents a reference between objects within a collection to be used when batching.

Parameters:

• from_property (str)

• from_uuid (str | UUID)

• to_uuid (str | UUID | List[str | UUID])

MultiTarget

alias of DataReferenceMulti

_to_beacons()

Return type:

List[str]

weaviate.collections.classes.filters

class weaviate.collections.classes.filters._Operator(*values)

Bases: str, Enum

EQUAL = 'Equal'

NOT_EQUAL = 'NotEqual'

LESS_THAN = 'LessThan'

LESS_THAN_EQUAL = 'LessThanEqual'

GREATER_THAN = 'GreaterThan'

GREATER_THAN_EQUAL = 'GreaterThanEqual'

LIKE = 'Like'

IS_NULL = 'IsNull'

CONTAINS_ANY = 'ContainsAny'

CONTAINS_ALL = 'ContainsAll'

WITHIN_GEO_RANGE = 'WithinGeoRange'

AND = 'And'

OR = 'Or'

_to_grpc()

Return type:

<google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper object at 0x714151c4a720>

class weaviate.collections.classes.filters._Filters

Bases: object

class weaviate.collections.classes.filters._FilterAnd(filters)

Bases: _Filters

Parameters:

filters (List[_Filters])

property operator: _Operator

class weaviate.collections.classes.filters._FilterOr(filters)

Bases: _Filters

Parameters:

filters (List[_Filters])

property operator: _Operator

pydantic model weaviate.collections.classes.filters._GeoCoordinateFilter

Bases: GeoCoordinate

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 distance: float [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.filters._SingleTargetRef

Bases: _WeaviateInput

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 link_on: str [Required]

field target: _FilterTargets | None = None

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.filters._MultiTargetRef

Bases: _WeaviateInput

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 link_on: str [Required]

field target: _FilterTargets | None = None

field target_collection: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.filters._CountRef

Bases: _WeaviateInput

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 link_on: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.filters._FilterValue

Bases: _Filters, _WeaviateInput

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 operator: _Operator [Required]

field target: _SingleTargetRef | _MultiTargetRef | _CountRef | str [Required]

field value: int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID] [Required]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.filters._FilterBase

Bases: object

_target: _SingleTargetRef | _MultiTargetRef | None = None

_property: str | _CountRef

_target_path()

Return type:

_SingleTargetRef | _MultiTargetRef | _CountRef | str

class weaviate.collections.classes.filters._FilterByProperty(prop, length, target=None)

Bases: _FilterBase

Parameters:

• prop (str)

• length (bool)

• target (_SingleTargetRef | _MultiTargetRef | None)

is_none(val)

Filter on whether the property is None.

Parameters:

val (bool)

Return type:

_Filters

contains_any(val)

Filter on whether the property contains any of the given values.

Parameters:

val (Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

contains_all(val)

Filter on whether the property contains all of the given values.

Parameters:

val (Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

equal(val)

Filter on whether the property is equal to the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

not_equal(val)

Filter on whether the property is not equal to the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

less_than(val)

Filter on whether the property is less than the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

less_or_equal(val)

Filter on whether the property is less than or equal to the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

greater_than(val)

Filter on whether the property is greater than the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

greater_or_equal(val)

Filter on whether the property is greater than or equal to the given value.

Parameters:

val (int | float | str | bool | datetime | UUID | _GeoCoordinateFilter | None | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[str | UUID])

Return type:

_Filters

like(val)

Filter on whether the property is like the given value.

This filter can make use of * and ? as wildcards. See the docs for more details.

Parameters:

val (str)

Return type:

_Filters

within_geo_range(coordinate, distance)

Filter on whether the property is within a given range of a geo-coordinate.

See the docs for more details.

Parameters:

• coordinate (GeoCoordinate)

• distance (float)

Return type:

_Filters

class weaviate.collections.classes.filters._FilterByTime

Bases: _FilterBase

contains_any(dates)

Filter for objects with the given time.

Parameters:

• dates (List[datetime]) – List of dates to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

equal(date)

Filter on whether the creation time is equal to the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

not_equal(date)

Filter on whether the creation time is not equal to the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

less_than(date)

Filter on whether the creation time is less than the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

less_or_equal(date)

Filter on whether the creation time is less than or equal to the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

greater_than(date)

Filter on whether the creation time is greater than the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

greater_or_equal(date)

Filter on whether the creation time is greater than or equal to the given time.

Parameters:

• date (datetime) – date to filter on.

• on_reference_path – If the filter is on a cross-ref property, the path to the property to be filtered on, example: on_reference_path=[“ref_property”, “target_collection”].

Return type:

_Filters

class weaviate.collections.classes.filters._FilterByUpdateTime(target=None)

Bases: _FilterByTime

Parameters:

target (_SingleTargetRef | _MultiTargetRef | None)

class weaviate.collections.classes.filters._FilterByCreationTime(target=None)

Bases: _FilterByTime

Parameters:

target (_SingleTargetRef | _MultiTargetRef | None)

class weaviate.collections.classes.filters._FilterById(target=None)

Bases: _FilterBase

Parameters:

target (_SingleTargetRef | _MultiTargetRef | None)

contains_any(uuids)

Filter for objects that has one of the given ID.

Parameters:

uuids (Sequence[str | UUID])

Return type:

_Filters

equal(uuid)

Filter for object that has the given ID.

Parameters:

uuid (str | UUID)

Return type:

_Filters

not_equal(uuid)

Filter our object that has the given ID.

Parameters:

uuid (str | UUID)

Return type:

_Filters

class weaviate.collections.classes.filters._FilterByCount(link_on, target=None)

Bases: _FilterBase

Parameters:

• link_on (str)

• target (_SingleTargetRef | _MultiTargetRef | None)

equal(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

not_equal(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

less_than(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

less_or_equal(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

greater_than(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

greater_or_equal(count)

Filter on whether the number of references is equal to the given integer.

Parameters:

count (int) – count to filter on.

Return type:

_Filters

class weaviate.collections.classes.filters._FilterByRef(target)

Bases: object

Parameters:

target (_SingleTargetRef | _MultiTargetRef)

by_ref(link_on)

Filter on the given reference.

Parameters:

link_on (str)

Return type:

_FilterByRef

by_ref_multi_target(reference, target_collection)

Filter on the given multi-target reference.

Parameters:

• reference (str)

• target_collection (str)

Return type:

_FilterByRef

by_ref_count(link_on)

Filter on the given reference.

Parameters:

link_on (str)

Return type:

_FilterByCount

by_id()

Define a filter based on the uuid to be used when querying and deleting from a collection.

Return type:

_FilterById

by_creation_time()

Define a filter based on the creation time to be used when querying and deleting from a collection.

Return type:

_FilterByCreationTime

by_update_time()

Define a filter based on the update time to be used when querying and deleting from a collection.

Return type:

_FilterByUpdateTime

by_property(name, length=False)

Define a filter based on a property to be used when querying and deleting from a collection.

Parameters:

• name (str)

• length (bool)

Return type:

_FilterByProperty

class weaviate.collections.classes.filters.Filter

Bases: object

This class is used to define filters to be used when querying and deleting from a collection.

It forms the root of a method chaining hierarchy that allows you to iteratively define filters that can hop between objects through references in a formulaic way.

See the docs for more information.

static by_ref(link_on)

Define a filter based on a reference to be used when querying and deleting from a collection.

Parameters:

link_on (str)

Return type:

_FilterByRef

static by_ref_multi_target(link_on, target_collection)

Define a filter based on a reference to be used when querying and deleting from a collection.

Parameters:

• link_on (str)

• target_collection (str)

Return type:

_FilterByRef

static by_ref_count(link_on)

Define a filter based on the number of references to be used when querying and deleting from a collection.

Parameters:

link_on (str)

Return type:

_FilterByCount

static by_id()

Define a filter based on the uuid to be used when querying and deleting from a collection.

Return type:

_FilterById

static by_creation_time()

Define a filter based on the creation time to be used when querying and deleting from a collection.

Return type:

_FilterByCreationTime

static by_update_time()

Define a filter based on the update time to be used when querying and deleting from a collection.

Return type:

_FilterByUpdateTime

static by_property(name, length=False)

Define a filter based on a property to be used when querying and deleting from a collection.

Parameters:

• name (str)

• length (bool)

Return type:

_FilterByProperty

static all_of(filters)

Combine all filters in the input list with an AND operator.

Parameters:

filters (List[_Filters])

Return type:

_Filters

static any_of(filters)

Combine all filters in the input list with an OR operator.

Parameters:

filters (List[_Filters])

Return type:

_Filters

weaviate.collections.classes.filters.FilterByProperty

alias of _FilterByProperty

weaviate.collections.classes.filters.FilterById

alias of _FilterById

weaviate.collections.classes.filters.FilterByCreationTime

alias of _FilterByCreationTime

weaviate.collections.classes.filters.FilterByUpdateTime

alias of _FilterByUpdateTime

weaviate.collections.classes.filters.FilterByRef

alias of _FilterByRef

weaviate.collections.classes.filters.FilterReturn

alias of _Filters

weaviate.collections.classes.grpc

class weaviate.collections.classes.grpc.HybridFusion(*values)

Bases: str, BaseEnum

Define how the query’s hybrid fusion operation should be performed.

RANKED = 'FUSION_TYPE_RANKED'

RELATIVE_SCORE = 'FUSION_TYPE_RELATIVE_SCORE'

class weaviate.collections.classes.grpc.Move(force, objects=None, concepts=None)

Bases: object

Define how the query’s move operation should be performed.

Parameters:

• force (float)

• objects (List[str | UUID] | str | UUID | None)

• concepts (List[str] | str | None)

property _objects_list: List[str] | None

property _concepts_list: List[str] | None

_to_gql_payload()

Return type:

dict

pydantic model weaviate.collections.classes.grpc.MetadataQuery

Bases: _WeaviateInput

Define which metadata should be returned in the query’s results.

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 certainty: bool = False

field creation_time: bool = False

field distance: bool = False

field explain_score: bool = False

field is_consistent: bool = False

field last_update_time: bool = False

field score: bool = False

classmethod full()

Return a MetadataQuery with all fields set to True.

Return type:

MetadataQuery

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.grpc._MetadataQuery(vector: bool, uuid: bool = True, creation_time_unix: bool = False, last_update_time_unix: bool = False, distance: bool = False, certainty: bool = False, score: bool = False, explain_score: bool = False, is_consistent: bool = False, vectors: List[str] | None = None)

Bases: object

Parameters:

• vector (bool)

• uuid (bool)

• creation_time_unix (bool)

• last_update_time_unix (bool)

• distance (bool)

• certainty (bool)

• score (bool)

• explain_score (bool)

• is_consistent (bool)

• vectors (List[str] | None)

vector: bool

uuid: bool = True

creation_time_unix: bool = False

last_update_time_unix: bool = False

distance: bool = False

certainty: bool = False

score: bool = False

explain_score: bool = False

is_consistent: bool = False

vectors: List[str] | None = None

classmethod from_public(public, include_vector)

Parameters:

• public (MetadataQuery | None)

• include_vector (bool | str | List[str])

Return type:

_MetadataQuery

pydantic model weaviate.collections.classes.grpc.Generate

Bases: _WeaviateInput

Define how the query’s RAG capabilities should be performed.

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 grouped_properties: List[str] | None = None

field grouped_task: str | None = None

field single_prompt: str | None = None

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc.GroupBy

Bases: _WeaviateInput

Define how the query’s group-by operation should be performed.

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 number_of_groups: int [Required]

field objects_per_group: int [Required]

field prop: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc._Sort

Bases: _WeaviateInput

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 ascending: bool = True

field prop: str [Required]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.grpc._Sorting

Bases: object

by_property(name, ascending=True)

Sort by an object property in the collection.

Parameters:

• name (str)

• ascending (bool)

Return type:

_Sorting

by_id(ascending=True)

Sort by an object’s ID in the collection.

Parameters:

ascending (bool)

Return type:

_Sorting

by_creation_time(ascending=True)

Sort by an object’s creation time.

Parameters:

ascending (bool)

Return type:

_Sorting

by_update_time(ascending=True)

Sort by an object’s last update time.

Parameters:

ascending (bool)

Return type:

_Sorting

weaviate.collections.classes.grpc.Sorting

The type returned by the Sort class to be used when defining programmatic sort chains.

class weaviate.collections.classes.grpc.Sort

Bases: object

Define how the query’s sort operation should be performed using the available static methods.

static by_property(name, ascending=True)

Sort by an object property in the collection.

Parameters:

• name (str)

• ascending (bool)

Return type:

_Sorting

static by_id(ascending=True)

Sort by an object’s ID in the collection.

Parameters:

ascending (bool)

Return type:

_Sorting

static by_creation_time(ascending=True)

Sort by an object’s creation time.

Parameters:

ascending (bool)

Return type:

_Sorting

static by_update_time(ascending=True)

Sort by an object’s last update time.

Parameters:

ascending (bool)

Return type:

_Sorting

pydantic model weaviate.collections.classes.grpc.Rerank

Bases: _WeaviateInput

Define how the query’s rerank operation should be performed.

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 prop: str [Required]

field query: str | None = None

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.grpc.BM25OperatorOptions

Bases: object

operator: ClassVar[Any]

class weaviate.collections.classes.grpc.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

class weaviate.collections.classes.grpc.BM25OperatorAnd

Bases: BM25OperatorOptions

Define the ‘And’ operator for keyword queries.

operator: ClassVar[Any] = 2

class weaviate.collections.classes.grpc.BM25OperatorFactory

Bases: object

Define how the BM25 query’s token matching should be performed.

static or_(minimum_match)

Use the ‘Or’ operator for keyword queries, where at least a minimum number of tokens must match.

Note that the query is tokenized using the respective tokenization method of each property.

Parameters:

minimum_match (int) – The minimum number of keyword tokens (excluding stopwords) that must match for an object to be considered a match.

Return type:

BM25OperatorOptions

static and_()

Use the ‘And’ operator for keyword queries, where all query tokens must match.

Note that the query is tokenized using the respective tokenization method of each property.

Return type:

BM25OperatorOptions

weaviate.collections.classes.grpc.OneDimensionalVectorType

Represents a one-dimensional vector, e.g. one produced by text2vec-jinaai

alias of Sequence[int | float]

weaviate.collections.classes.grpc.TwoDimensionalVectorType

Represents a two-dimensional vector, e.g. one produced by `text2colbert-jinaai

alias of Sequence[Sequence[int | float]]

pydantic model weaviate.collections.classes.grpc._ListOfVectorsQuery

Bases: _WeaviateInput, Generic[V]

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 dimensionality: Literal['1D', '2D'] [Required]

field vectors: Sequence[V] [Required]

static is_one_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Union[int, float]]]]

static is_two_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]]

_abc_impl = <_abc._abc_data object>

weaviate.collections.classes.grpc.ListOfVectorsQuery

Define a many-vectors query to be used within a near vector search, i.e. multiple vectors over a single-vector space.

weaviate.collections.classes.grpc.NearVectorInputType

Define the input types that can be used in a near vector search

alias of Sequence[int | float] | Sequence[Sequence[int | float]] | Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]] | _ListOfVectorsQuery[Sequence[Union[int, float]]] | _ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]]

class weaviate.collections.classes.grpc.NearVector

Bases: object

Factory class to use when defining near vector queries with multiple vectors in near_vector() and hybrid() methods.

static list_of_vectors(*vectors)

Define a many-vectors query to be used within a near vector search, i.e. multiple vectors over a single-vector space.

Parameters:

vectors (V)

Return type:

_ListOfVectorsQuery

pydantic model weaviate.collections.classes.grpc._HybridNearBase

Bases: _WeaviateInput

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 certainty: float | None = None

field distance: float | None = None

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc._HybridNearText

Bases: _HybridNearBase

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 move_away: Move | None = None

field move_to: Move | None = None

field text: str | List[str] [Required]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.grpc._HybridNearVector(*, vector, distance=None, certainty=None)

Bases: object

Parameters:

• vector (Sequence[int | float] | Sequence[Sequence[int | float]] | Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]] | _ListOfVectorsQuery[Sequence[Union[int, float]]] | _ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]])

• distance (float | None)

• certainty (float | None)

vector: Sequence[int | float] | Sequence[Sequence[int | float]] | Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]] | _ListOfVectorsQuery[Sequence[int | float]] | _ListOfVectorsQuery[Sequence[Sequence[int | float]]]]

distance: float | None

certainty: float | None

class weaviate.collections.classes.grpc._MultiTargetVectorJoinEnum(*values)

Bases: BaseEnum

Define how multi target vector searches should be combined.

SUM = 1

AVERAGE = 2

MINIMUM = 3

RELATIVE_SCORE = 4

MANUAL_WEIGHTS = 5

class weaviate.collections.classes.grpc._MultiTargetVectorJoin(combination: weaviate.collections.classes.grpc._MultiTargetVectorJoinEnum, target_vectors: List[str], weights: Dict[str, float | List[float]] | None = None)

Bases: object

Parameters:

• combination (_MultiTargetVectorJoinEnum)

• target_vectors (List[str])

• weights (Dict[str, float | List[float]] | None)

combination: _MultiTargetVectorJoinEnum

target_vectors: List[str]

weights: Dict[str, float | List[float]] | None = None

to_grpc_target_vector(version)

Parameters:

version (_ServerVersion)

Return type:

Targets

class weaviate.collections.classes.grpc.TargetVectors

Bases: object

Define how the distances from different target vectors should be combined using the available methods.

static sum(target_vectors)

Combine the distance from different target vectors by summing them.

Parameters:

target_vectors (List[str])

Return type:

_MultiTargetVectorJoin

static average(target_vectors)

Combine the distance from different target vectors by averaging them.

Parameters:

target_vectors (List[str])

Return type:

_MultiTargetVectorJoin

static minimum(target_vectors)

Combine the distance from different target vectors by using the minimum distance.

Parameters:

target_vectors (List[str])

Return type:

_MultiTargetVectorJoin

static manual_weights(weights)

Combine the distance from different target vectors by summing them using manual weights.

Parameters:

weights (Dict[str, float | List[float]])

Return type:

_MultiTargetVectorJoin

static relative_score(weights)

Combine the distance from different target vectors using score fusion.

Parameters:

weights (Dict[str, float | List[float]])

Return type:

_MultiTargetVectorJoin

class weaviate.collections.classes.grpc.HybridVector

Bases: object

Use this factory class to define the appropriate classes needed when defining near text and near vector sub-searches in hybrid queries.

static near_text(query, *, certainty=None, distance=None, move_to=None, move_away=None)

Define a near text search to be used within a hybrid query.

Parameters:

• query (str | List[str]) – The text to search for as a string or a list of strings.

• certainty (float | None) – The minimum similarity score to return. If not specified, the default certainty specified by the server is used.

• distance (float | None) – The maximum distance to search. If not specified, the default distance specified by the server is used.

• move_to (Move | None) – Define the concepts that should be moved towards in the vector space during the search.

• move_away (Move | None) – Define the concepts that should be moved away from in the vector space during the search.

Returns:

A _HybridNearText object to be used in the vector parameter of the query.hybrid and generate.hybrid search methods.

Return type:

_HybridNearText

static near_vector(vector, *, certainty=None, distance=None)

Define a near vector search to be used within a hybrid query.

Parameters:

• certainty (float | None) – The minimum similarity score to return. If not specified, the default certainty specified by the server is used.

• distance (float | None) – The maximum distance to search. If not specified, the default distance specified by the server is used.

• vector (Sequence[int | float] | Sequence[Sequence[int | float]] | Mapping[str, Sequence[int | float] | Sequence[Sequence[int | float]] | _ListOfVectorsQuery[Sequence[Union[int, float]]] | _ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]])

Returns:

A _HybridNearVector object to be used in the vector parameter of the query.hybrid and generate.hybrid search methods.

Return type:

_HybridNearVector

pydantic model weaviate.collections.classes.grpc._QueryReference

Bases: _WeaviateInput

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 include_vector: bool | str | List[str] = False

field link_on: str [Required]

field return_metadata: MetadataQuery | None = None

field return_properties: PROPERTIES | bool | None = None

field return_references: REFERENCES | None = None

_abc_impl = <_abc._abc_data object>

property _return_metadata: _MetadataQuery

pydantic model weaviate.collections.classes.grpc._QueryReferenceMultiTarget

Bases: _QueryReference

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 target_collection: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc.QueryReference

Bases: _QueryReference

Define a query-time reference to a single-target property when querying through cross-references.

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 include_vector: INCLUDE_VECTOR = False

field link_on: str [Required]

field return_metadata: MetadataQuery | None = None

field return_properties: 'PROPERTIES' | bool | None = None

field return_references: 'REFERENCES' | None = None

MultiTarget

Define a query-time reference to a multi-target property when querying through cross-references.

alias of _QueryReferenceMultiTarget

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc.QueryNested

Bases: _WeaviateInput

Define the query-time return properties of a nested property.

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 name: str [Required]

field properties: PROPERTIES [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc._ListOfVectorsQuery

Bases: _WeaviateInput, Generic[V]

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 dimensionality: Literal['1D', '2D'] [Required]

field vectors: Sequence[V] [Required]

static is_one_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Union[int, float]]]]

static is_two_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.grpc._ListOfVectorsQuery

Bases: _WeaviateInput, Generic[V]

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 dimensionality: Literal['1D', '2D'] [Required]

field vectors: Sequence[V] [Required]

static is_one_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Union[int, float]]]]

static is_two_dimensional(self_)

Parameters:

self_ (_ListOfVectorsQuery)

Return type:

TypeGuard[_ListOfVectorsQuery[Sequence[Sequence[Union[int, float]]]]]

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.grpc.NearMediaType(*values)

Bases: str, Enum

The different types of media that can be used in a near_media query to leverage the multi2vec-* modules.

All are available when using multi2vec-bind but only IMAGE is available when using multi2vec-clip.

AUDIO = 'audio'

DEPTH = 'depth'

IMAGE = 'image'

IMU = 'imu'

THERMAL = 'thermal'

VIDEO = 'video'

weaviate.collections.classes.internal

class weaviate.collections.classes.internal.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)

creation_time: datetime | None = None

last_update_time: datetime | None = None

distance: float | None = None

certainty: float | None = None

score: float | None = None

explain_score: str | None = None

is_consistent: bool | None = None

rerank_score: float | None = None

_is_empty()

Return type:

bool

class weaviate.collections.classes.internal.GroupByMetadataReturn(distance=None)

Bases: object

Metadata of an object returned by a group by query.

Parameters:

distance (float | None)

distance: float | None = None

class weaviate.collections.classes.internal._Object(uuid: uuid.UUID, metadata: M, properties: P, references: R, vector: Dict[str, List[float] | List[List[float]]], collection: str)

Bases: Generic[P, R, M]

Parameters:

• uuid (UUID)

• metadata (M)

• properties (P)

• references (R)

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

• collection (str)

uuid: UUID

metadata: M

properties: P

references: R

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

collection: str

class weaviate.collections.classes.internal.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.collections.classes.internal.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.collections.classes.internal.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.collections.classes.internal.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

class weaviate.collections.classes.internal.GenerativeSingle(debug, metadata, text)

Bases: object

The generative data returned relevant to a single prompt generative query.

Parameters:

• debug (GenerativeDebug | None)

• metadata (GenerativeAnthropicMetadata | GenerativeAnyscaleMetadata | GenerativeAWSMetadata | GenerativeCohereMetadata | GenerativeDatabricksMetadata | GenerativeDummyMetadata | GenerativeFriendliAIMetadata | GenerativeGoogleMetadata | GenerativeMistralMetadata | GenerativeNvidiaMetadata | GenerativeOllamaMetadata | GenerativeOpenAIMetadata | None)

• text (str | None)

debug: GenerativeDebug | None

metadata: GenerativeAnthropicMetadata | GenerativeAnyscaleMetadata | GenerativeAWSMetadata | GenerativeCohereMetadata | GenerativeDatabricksMetadata | GenerativeDummyMetadata | GenerativeFriendliAIMetadata | GenerativeGoogleMetadata | GenerativeMistralMetadata | GenerativeNvidiaMetadata | GenerativeOllamaMetadata | GenerativeOpenAIMetadata | None

text: str | None

class weaviate.collections.classes.internal.GenerativeGrouped(metadata, text)

Bases: object

The generative data returned relevant to a grouped prompt generative query.

Parameters:

• metadata (GenerativeAnthropicMetadata | GenerativeAnyscaleMetadata | GenerativeAWSMetadata | GenerativeCohereMetadata | GenerativeDatabricksMetadata | GenerativeDummyMetadata | GenerativeFriendliAIMetadata | GenerativeGoogleMetadata | GenerativeMistralMetadata | GenerativeNvidiaMetadata | GenerativeOllamaMetadata | GenerativeOpenAIMetadata | None)

• text (str | None)

metadata: GenerativeAnthropicMetadata | GenerativeAnyscaleMetadata | GenerativeAWSMetadata | GenerativeCohereMetadata | GenerativeDatabricksMetadata | GenerativeDummyMetadata | GenerativeFriendliAIMetadata | GenerativeGoogleMetadata | GenerativeMistralMetadata | GenerativeNvidiaMetadata | GenerativeOllamaMetadata | GenerativeOpenAIMetadata | None

text: str | None

class weaviate.collections.classes.internal.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)

__generated: str | None

generative: GenerativeSingle | None

property generated: str | None

The single generated text of the object.

class weaviate.collections.classes.internal.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)

__generated: str | None

objects: List[GenerativeObject[P, R]]

generative: GenerativeGrouped | None

property generated: str | None

The grouped generated text of the objects.

class weaviate.collections.classes.internal.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.collections.classes.internal.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

class weaviate.collections.classes.internal.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.collections.classes.internal.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.collections.classes.internal.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]]

class weaviate.collections.classes.internal._RawGQLReturn(aggregate: Dict[str, List[Dict[str, Any]]], explore: Dict[str, List[Dict[str, Any]]], get: Dict[str, List[Dict[str, Any]]], errors: Dict[str, Any] | None)

Bases: object

Parameters:

• aggregate (Dict[str, List[Dict[str, Any]]])

• explore (Dict[str, List[Dict[str, Any]]])

• get (Dict[str, List[Dict[str, Any]]])

• errors (Dict[str, Any] | None)

aggregate: Dict[str, List[Dict[str, Any]]]

explore: Dict[str, List[Dict[str, Any]]]

get: Dict[str, List[Dict[str, Any]]]

errors: Dict[str, Any] | None

class weaviate.collections.classes.internal._Generative(single, grouped, grouped_properties, generative_provider=None)

Bases: object

Parameters:

• single (str | _SinglePrompt | None)

• grouped (str | _GroupedTask | None)

• grouped_properties (List[str] | None)

• generative_provider (_GenerativeConfigRuntime | None)

single: str | _SinglePrompt | None

grouped: str | _GroupedTask | None

grouped_properties: List[str] | None

generative_provider: _GenerativeConfigRuntime | None

to_grpc(server_version)

Parameters:

server_version (_ServerVersion)

Return type:

GenerativeSearch

class weaviate.collections.classes.internal._GroupBy(prop, number_of_groups, objects_per_group)

Bases: object

Parameters:

• prop (str)

• number_of_groups (int)

• objects_per_group (int)

prop: str

number_of_groups: int

objects_per_group: int

to_grpc()

Return type:

GroupBy

classmethod from_input(group_by)

Parameters:

group_by (GroupBy | None)

Return type:

_GroupBy | None

weaviate.collections.classes.internal.__is_nested(value)

Parameters:

value (Any)

Return type:

bool

weaviate.collections.classes.internal.__create_nested_property_from_nested(name, value)

Parameters:

• name (str)

• value (Any)

Return type:

QueryNested

class weaviate.collections.classes.internal._Reference(target_collection, uuids)

Bases: object

You should not initialise this class directly. Use the .to_multi() class methods instead.

Parameters:

• target_collection (str | None)

• uuids (Sequence[str | UUID] | str | UUID)

_to_beacons()

Return type:

List[Dict[str, str]]

property is_one_to_many: bool

Returns True if the reference is to a one-to-many references, i.e. points to more than one object.

pydantic model weaviate.collections.classes.internal.ReferenceToMulti

Bases: _WeaviateInput

Use this class when you want to insert a multi-target reference property.

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 target_collection: str [Required]

field uuids: Sequence[str | UUID] | str | UUID [Required]

_to_beacons()

Return type:

List[Dict[str, str]]

_abc_impl = <_abc._abc_data object>

property uuids_str: List[str]

Returns the UUIDs as strings.

class weaviate.collections.classes.internal._CrossReference(objects)

Bases: Generic[Properties, IReferences]

Parameters:

objects (List[Object[Properties, IReferences]] | None)

classmethod _from(objects)

Parameters:

objects (List[Object[Properties, IReferences]])

Return type:

_CrossReference[Properties, IReferences]

property objects: List[Object[Properties, IReferences]]

Returns the objects of the cross reference.

weaviate.collections.classes.internal.CrossReference

Use this TypeAlias when you want to type hint a cross reference within a generic data model.

If you want to define a reference property when creating your collection, use ReferenceProperty or ReferencePropertyMultiTarget instead.

If you want to create a reference when inserting an object, supply the UUIDs directly or use Reference.to_multi() instead.

Example

>>> import typing
>>> import weaviate.classes as wvc
>>>
>>> class One(typing.TypedDict):
...     prop: str
>>>
>>> class Two(typing.TypedDict):
...     one: wvc.CrossReference[One]

alias of _CrossReference[Properties, IReferences]

weaviate.collections.classes.internal.ReferenceInput

This type alias is used when providing references as inputs within the .data namespace of a collection.

alias of str | UUID | Sequence[str | UUID] | ReferenceToMulti

weaviate.collections.classes.internal.ReferenceInputs

This type alias is used when providing references as inputs within the .data namespace of a collection.

alias of Mapping[str, str | UUID | Sequence[str | UUID] | ReferenceToMulti]

class weaviate.collections.classes.internal.CrossReferenceAnnotation(include_vector=False, metadata=None, target_collection=None)

Bases: object

Dataclass to be used when annotating a generic cross reference property with options for retrieving data from the cross referenced object when querying.

Example

>>> import typing
>>> import weaviate.classes as wvc
>>>
>>> class One(typing.TypedDict):
...     prop: str
>>>
>>> class Two(typing.TypedDict):
...     one: typing.Annotated[
...         wvc.CrossReference[One],
...         wvc.CrossReferenceAnnotation(include_vector=True)
...     ]

Parameters:

• include_vector (bool)

• metadata (MetadataQuery | None)

• target_collection (str | None)

include_vector: bool = False

metadata: MetadataQuery | None = None

target_collection: str | None = None

weaviate.collections.classes.internal._extract_types_from_reference(type_, field)

Extract first inner type from CrossReference[Properties, References].

Parameters:

• type_ (_CrossReference[Properties, References])

• field (str)

Return type:

Tuple[Type[Properties], Type[References]]

weaviate.collections.classes.internal._extract_types_from_annotated_reference(type_, field)

Extract inner type from Annotated[CrossReference[Properties, References]].

Parameters:

• type_ (Annotated[_CrossReference[Properties, References], CrossReferenceAnnotation])

• field (str)

Return type:

Tuple[Type[Properties], Type[References]]

weaviate.collections.classes.internal.__is_annotated_reference(value)

Parameters:

value (Any)

Return type:

bool

weaviate.collections.classes.internal.__create_link_to_from_annotated_reference(link_on, value)

Create FromReference or FromReferenceMultiTarget from Annotated[CrossReference[Properties], ReferenceAnnotation].

Parameters:

• link_on (str)

• value (Annotated[_CrossReference[Properties, References], CrossReferenceAnnotation])

Return type:

_QueryReference | _QueryReferenceMultiTarget

weaviate.collections.classes.internal.__create_link_to_from_reference(link_on, value)

Create FromReference from CrossReference[Properties].

Parameters:

• link_on (str)

• value (_CrossReference[Properties, References])

Return type:

_QueryReference

weaviate.collections.classes.internal._extract_properties_from_data_model(type_)

Extract properties of Properties recursively from Properties.

Checks to see if there is a _Reference[Properties], Annotated[_Reference[Properties]], or _Nested[Properties] in the data model and lists out the properties as classes readily consumable by the underlying API.

Parameters:

type_ (Type[Properties])

Return type:

Sequence[str | QueryNested] | str | QueryNested

weaviate.collections.classes.internal._extract_references_from_data_model(type_)

Extract references of References recursively from References.

Checks to see if there is a _Reference[References], Annotated[_Reference[References]], or _Nested[References] in the data model and lists out the references as classes readily consumable by the underlying API.

Parameters:

type_ (Type[References])

Return type:

Sequence[_QueryReference | _QueryReferenceMultiTarget] | _QueryReference | _QueryReferenceMultiTarget | None

class weaviate.collections.classes.internal._QueryOptions(include_metadata: bool, include_properties: bool, include_references: bool, include_vector: bool, is_group_by: bool)

Bases: object

Parameters:

• include_metadata (bool)

• include_properties (bool)

• include_references (bool)

• include_vector (bool)

• is_group_by (bool)

include_metadata: bool

include_properties: bool

include_references: bool

include_vector: bool

is_group_by: bool

classmethod from_input(return_metadata, return_properties, include_vector, collection_references, query_references, rerank=None, group_by=None)

Parameters:

• return_metadata (List[Literal['creation_time', 'last_update_time', 'distance', 'certainty', 'score', 'explain_score', 'is_consistent']] | ~weaviate.collections.classes.grpc.MetadataQuery | None)

• return_properties (Sequence[str | QueryNested] | str | QueryNested | bool | Type[Any] | None)

• include_vector (bool | str | List[str])

• collection_references (Type[Any] | None)

• query_references (_QueryReference | Sequence[_QueryReference] | Type[Any] | None)

• rerank (Rerank | None)

• group_by (GroupBy | None)

Return type:

_QueryOptions

weaviate.collections.classes.internal.GenerativeNearMediaReturnType

Use GenerativeSearchReturnType instead.

Type:

@Deprecated

alias of GenerativeReturn[Properties, References] | GenerativeReturn[TProperties, TReferences] | GenerativeReturn[Properties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | GenerativeReturn[Properties, TReferences] | GenerativeReturn[TProperties, References] | GenerativeReturn[TProperties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | GenerativeGroupByReturn[Properties, References] | GenerativeGroupByReturn[TProperties, TReferences] | GenerativeGroupByReturn[Properties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | GenerativeGroupByReturn[Properties, TReferences] | GenerativeGroupByReturn[TProperties, References] | GenerativeGroupByReturn[TProperties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]]

weaviate.collections.classes.internal.QueryNearMediaReturnType

Use QuerySearchReturnType instead.

Type:

@Deprecated

alias of QueryReturn[Properties, References] | QueryReturn[TProperties, TReferences] | QueryReturn[Properties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | QueryReturn[Properties, TReferences] | QueryReturn[TProperties, References] | QueryReturn[TProperties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | GroupByReturn[Properties, References] | GroupByReturn[TProperties, TReferences] | GroupByReturn[Properties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]] | GroupByReturn[Properties, TReferences] | GroupByReturn[TProperties, References] | GroupByReturn[TProperties, Mapping[str, _CrossReference[Mapping[str, None | str | bool | int | float | datetime | UUID | GeoCoordinate | PhoneNumber | _PhoneNumber | Mapping[str, WeaviateField] | Sequence[str] | Sequence[bool] | Sequence[int] | Sequence[float] | Sequence[datetime] | Sequence[UUID] | Sequence[Mapping[str, WeaviateField]]], CrossReferences]]]

weaviate.collections.classes.orm

weaviate.collections.classes.tenants

class weaviate.collections.classes.tenants._TenantActivistatusServerValues(*values)

Bases: str, Enum

Values to be used when sending tenants to weaviate. Needed for BC.

HOT = 'HOT'

COLD = 'COLD'

FROZEN = 'FROZEN'

OTHER = 'OTHER'

static from_string(value)

Parameters:

value (str)

Return type:

_TenantActivistatusServerValues

class weaviate.collections.classes.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'

pydantic model weaviate.collections.classes.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.

pydantic model weaviate.collections.classes.tenants.TenantOutput

Bases: Tenant

Wrapper around Tenant for output purposes.

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.

model_post_init(_TenantOutput__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:

_TenantOutput__context (Any)

Return type:

None

_abc_impl = <_abc._abc_data object>

class weaviate.collections.classes.tenants.TenantCreateActivityStatus(*values)

Bases: str, Enum

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

ACTIVE

The tenant is fully active and can be used.

INACTIVE

The tenant is not active, files stored locally.

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.

ACTIVE = 'ACTIVE'

INACTIVE = 'INACTIVE'

HOT = 'HOT'

COLD = 'COLD'

class weaviate.collections.classes.tenants.TenantUpdateActivityStatus(*values)

Bases: str, Enum

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

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'

HOT = 'HOT'

COLD = 'COLD'

FROZEN = 'FROZEN'

pydantic model weaviate.collections.classes.tenants.TenantCreate

Bases: BaseModel

Tenant class used to describe a tenant to create in Weaviate.

name

the name of the tenant.

activity_status

TenantCreateActivityStatus, 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: TenantCreateActivityStatus = TenantCreateActivityStatus.ACTIVE (alias 'activity_status')

field name: str [Required]

model_post_init(_TenantCreate__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:

_TenantCreate__context (Any)

Return type:

None

_abc_impl = <_abc._abc_data object>

property activity_status: TenantCreateActivityStatus

Getter for the activity status of the tenant.

pydantic model weaviate.collections.classes.tenants.TenantUpdate

Bases: BaseModel

Tenant class used to describe a tenant to create in Weaviate.

name

The name of the tenant.

activity_status

TenantUpdateActivityStatus, 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: TenantUpdateActivityStatus = TenantUpdateActivityStatus.ACTIVE (alias 'activity_status')

field name: str [Required]

model_post_init(_TenantUpdate__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:

_TenantUpdate__context (Any)

Return type:

None

_abc_impl = <_abc._abc_data object>

property activity_status: TenantUpdateActivityStatus

Getter for the activity status of the tenant.

weaviate.collections.classes.types

pydantic model weaviate.collections.classes.types._WeaviateInput

Bases: BaseModel

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.collections.classes.types.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>

pydantic model weaviate.collections.classes.types._PhoneNumberBase

Bases: _WeaviateInput

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 number: str [Required]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.types.PhoneNumber

Bases: _PhoneNumberBase

Input for the phone number datatype.

default_country should correspond to the ISO 3166-1 alpha-2 country code. This is used to figure out the correct countryCode and international format if only a national number (e.g. 0123 4567) is provided.

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 default_country: str | None = None

field number: str [Required]

_to_dict()

Return type:

Mapping[str, str]

_abc_impl = <_abc._abc_data object>

pydantic model weaviate.collections.classes.types._PhoneNumber

Bases: _PhoneNumberBase

Output for the phone number 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 country_code: int [Required]

field default_country: str [Required]

field international_formatted: str [Required]

field national: int [Required]

field national_formatted: str [Required]

field valid: bool [Required]

_abc_impl = <_abc._abc_data object>

weaviate.collections.classes.types.PhoneNumberType

alias of _PhoneNumber

class weaviate.collections.classes.types.Properties

Properties is used wherever a single generic type is needed for properties

alias of TypeVar(‘Properties’, bound=Mapping[str, Any])

class weaviate.collections.classes.types.TProperties

TProperties is used alongside Properties wherever there are two generic types needed

E.g., in _DataCollection, Properties is used when defining the generic of the class while TProperties is used when defining the generic to be supplied in .with_data_model to create a new instance of _DataCollection with a different Properties type.

To be clear: _DataCollection[Properties]().with_data_model(TProperties) -> _DataCollection[TProperties]()

alias of TypeVar(‘TProperties’, bound=Mapping[str, Any])

class weaviate.collections.classes.types.M

M is a completely general type that is used wherever generic metadata objects are defined that can be used

alias of TypeVar(‘M’)

class weaviate.collections.classes.types.P

P is a completely general type that is used wherever generic properties objects are defined that can be used within the non-ORM and ORM APIs interchangeably

alias of TypeVar(‘P’)

class weaviate.collections.classes.types.QP

QP is a completely general type that is used wherever generic properties objects are defined that can be used within the non-ORM and ORM APIs interchangeably

alias of TypeVar(‘QP’)

class weaviate.collections.classes.types.R

R is a completely general type that is used wherever generic reference objects are defined that can be used within the non-ORM and ORM APIs interchangeably

alias of TypeVar(‘R’)

class weaviate.collections.classes.types.QR

QR is a completely general type that is used wherever generic reference objects are defined that can be used within the non-ORM and ORM APIs interchangeably

alias of TypeVar(‘QR’)

class weaviate.collections.classes.types.T

T is a completely general type that is used in any kind of generic

alias of TypeVar(‘T’)

class weaviate.collections.classes.types.References

References is used wherever a single generic type is needed for references

alias of TypeVar(‘References’, bound=Mapping[str, Any] | None)

class weaviate.collections.classes.types.TReferences

TReferences is used alongside References wherever there are two generic types needed

alias of TypeVar(‘TReferences’, bound=Mapping[str, Any] | None)

weaviate.collections.classes.types._check_properties_generic(properties)

Parameters:

properties (Type[Properties] | None)

Return type:

None

weaviate.collections.classes.types._check_references_generic(references)

Parameters:

references (Type[References] | None)

Return type:

None