qdrant_client.http.models.models module
- class AbortReshardingOperation(*, abort_resharding: Any)[source]
Bases:
BaseModel
- class AbortShardTransfer(*, shard_id: int, to_peer_id: int, from_peer_id: int)[source]
Bases:
BaseModel
- class AbortTransferOperation(*, abort_transfer: AbortShardTransfer)[source]
Bases:
BaseModel- abort_transfer: AbortShardTransfer
- class AbsExpression(*, abs: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class AliasDescription(*, alias_name: str, collection_name: str)[source]
Bases:
BaseModel
- class AppBuildTelemetry(*, name: str, version: str, features: Optional[AppFeaturesTelemetry] = None, system: Optional[RunningEnvironmentTelemetry] = None, jwt_rbac: Optional[bool] = None, hide_jwt_dashboard: Optional[bool] = None, startup: Union[datetime, date])[source]
Bases:
BaseModel- features: Optional[AppFeaturesTelemetry]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- system: Optional[RunningEnvironmentTelemetry]
- class AppFeaturesTelemetry(*, debug: bool, web_feature: bool, service_debug_feature: bool, recovery_mode: bool, gpu: bool)[source]
Bases:
BaseModel
- class Batch(*, ids: List[Union[int, str]], vectors: Union[List[List[float]], List[List[List[float]]], Dict[str, List[Union[List[float], SparseVector, List[List[float]], Document, Image, InferenceObject]]], List[Document], List[Image], List[InferenceObject]], payloads: Optional[List[Dict[str, Any]]] = None)[source]
Bases:
BaseModel
- class BinaryQuantization(*, binary: BinaryQuantizationConfig)[source]
Bases:
BaseModel- binary: BinaryQuantizationConfig
- class BinaryQuantizationConfig(*, always_ram: Optional[bool] = None)[source]
Bases:
BaseModel
- class BoolIndexParams(*, type: BoolIndexType, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: BoolIndexType
- class BoolIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class ChangeAliasesOperation(*, actions: List[Union[CreateAliasOperation, DeleteAliasOperation, RenameAliasOperation]])[source]
Bases:
BaseModelOperation for performing changes of collection aliases. Alias changes are atomic, meaning that no collection modifications can happen between alias operations.
- class ClearPayloadOperation(*, clear_payload: Union[PointIdsList, FilterSelector])[source]
Bases:
BaseModel
- class ClusterConfigTelemetry(*, grpc_timeout_ms: int, p2p: P2pConfigTelemetry, consensus: ConsensusConfigTelemetry)[source]
Bases:
BaseModel- consensus: ConsensusConfigTelemetry
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- p2p: P2pConfigTelemetry
- class ClusterStatusOneOf(*, status: Literal['disabled'])[source]
Bases:
BaseModel
- class ClusterStatusOneOf1(*, status: Literal['enabled'], peer_id: int, peers: Dict[str, PeerInfo], raft_info: RaftInfo, consensus_thread_status: Union[ConsensusThreadStatusOneOf, ConsensusThreadStatusOneOf1, ConsensusThreadStatusOneOf2], message_send_failures: Dict[str, MessageSendErrors])[source]
Bases:
BaseModelDescription of enabled cluster
- message_send_failures: Dict[str, MessageSendErrors]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- peers: Dict[str, PeerInfo]
- raft_info: RaftInfo
- class ClusterStatusTelemetry(*, number_of_peers: int, term: int, commit: int, pending_operations: int, role: Optional[StateRole] = None, is_voter: bool, peer_id: Optional[int] = None, consensus_thread_status: Union[ConsensusThreadStatusOneOf, ConsensusThreadStatusOneOf1, ConsensusThreadStatusOneOf2])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- role: Optional[StateRole]
- class ClusterTelemetry(*, enabled: bool, status: Optional[ClusterStatusTelemetry] = None, config: Optional[ClusterConfigTelemetry] = None, peers: Optional[Dict[str, PeerInfo]] = None, metadata: Optional[Dict[str, Any]] = None)[source]
Bases:
BaseModel- config: Optional[ClusterConfigTelemetry]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- peers: Optional[Dict[str, PeerInfo]]
- status: Optional[ClusterStatusTelemetry]
- class CollectionClusterInfo(*, peer_id: int, shard_count: int, local_shards: List[LocalShardInfo], remote_shards: List[RemoteShardInfo], shard_transfers: List[ShardTransferInfo], resharding_operations: Optional[List[ReshardingInfo]] = None)[source]
Bases:
BaseModelCurrent clustering distribution for the collection
- local_shards: List[LocalShardInfo]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- remote_shards: List[RemoteShardInfo]
- resharding_operations: Optional[List[ReshardingInfo]]
- shard_transfers: List[ShardTransferInfo]
- class CollectionConfig(*, params: CollectionParams, hnsw_config: HnswConfig, optimizer_config: OptimizersConfig, wal_config: Optional[WalConfig] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, strict_mode_config: Optional[StrictModeConfigOutput] = None)[source]
Bases:
BaseModelInformation about the collection configuration
- hnsw_config: HnswConfig
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizer_config: OptimizersConfig
- params: CollectionParams
- strict_mode_config: Optional[StrictModeConfigOutput]
- wal_config: Optional[WalConfig]
- class CollectionConfigTelemetry(*, params: CollectionParams, hnsw_config: HnswConfig, optimizer_config: OptimizersConfig, wal_config: WalConfig, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, strict_mode_config: Optional[StrictModeConfigOutput] = None, uuid: Optional[UUID] = None)[source]
Bases:
BaseModel- hnsw_config: HnswConfig
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizer_config: OptimizersConfig
- params: CollectionParams
- strict_mode_config: Optional[StrictModeConfigOutput]
- wal_config: WalConfig
- class CollectionDescription(*, name: str)[source]
Bases:
BaseModel
- class CollectionExistence(*, exists: bool)[source]
Bases:
BaseModelState of existence of a collection, true = exists, false = does not exist
- class CollectionInfo(*, status: CollectionStatus, optimizer_status: Union[OptimizersStatusOneOf, OptimizersStatusOneOf1], vectors_count: Optional[int] = None, indexed_vectors_count: Optional[int] = None, points_count: Optional[int] = None, segments_count: int, config: CollectionConfig, payload_schema: Dict[str, PayloadIndexInfo])[source]
Bases:
BaseModelCurrent statistics and configuration of the collection
- config: CollectionConfig
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- payload_schema: Dict[str, PayloadIndexInfo]
- status: CollectionStatus
- class CollectionParams(*, vectors: Optional[Union[VectorParams, Dict[str, VectorParams]]] = None, shard_number: Optional[int] = 1, sharding_method: Optional[ShardingMethod] = None, replication_factor: Optional[int] = 1, write_consistency_factor: Optional[int] = 1, read_fan_out_factor: Optional[int] = None, on_disk_payload: Optional[bool] = True, sparse_vectors: Optional[Dict[str, SparseVectorParams]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- sharding_method: Optional[ShardingMethod]
- sparse_vectors: Optional[Dict[str, SparseVectorParams]]
- class CollectionParamsDiff(*, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, read_fan_out_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None)[source]
Bases:
BaseModel
- class CollectionStatus(value)[source]
Bases:
str,EnumCurrent state of the collection. Green - all good. Yellow - optimization is running, 'Grey' - optimizations are possible but not triggered, Red - some operations failed and was not recovered
- class CollectionTelemetry(*, id: str, init_time_ms: int, config: CollectionConfigTelemetry, shards: Optional[List[ReplicaSetTelemetry]] = None, transfers: Optional[List[ShardTransferInfo]] = None, resharding: Optional[List[ReshardingInfo]] = None, shard_clean_tasks: Optional[Dict[str, Union[ShardCleanStatusTelemetryOneOf, ShardCleanStatusTelemetryOneOf1, ShardCleanStatusTelemetryOneOf2]]] = None)[source]
Bases:
BaseModel- config: CollectionConfigTelemetry
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- resharding: Optional[List[ReshardingInfo]]
- shards: Optional[List[ReplicaSetTelemetry]]
- transfers: Optional[List[ShardTransferInfo]]
- class CollectionsAggregatedTelemetry(*, vectors: int, optimizers_status: Union[OptimizersStatusOneOf, OptimizersStatusOneOf1], params: CollectionParams)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: CollectionParams
- class CollectionsAliasesResponse(*, aliases: List[AliasDescription])[source]
Bases:
BaseModel- aliases: List[AliasDescription]
- class CollectionsResponse(*, collections: List[CollectionDescription])[source]
Bases:
BaseModel- collections: List[CollectionDescription]
- class CollectionsTelemetry(*, number_of_collections: int, collections: Optional[List[Union[CollectionTelemetry, CollectionsAggregatedTelemetry]]] = None)[source]
Bases:
BaseModel
- class CompressionRatio(value)[source]
Bases:
str,EnumAn enumeration.
- class ConsensusConfigTelemetry(*, max_message_queue_size: int, tick_period_ms: int, bootstrap_timeout_sec: int)[source]
Bases:
BaseModel
- class ConsensusThreadStatusOneOf(*, consensus_thread_status: Literal['working'], last_update: Union[datetime, date])[source]
Bases:
BaseModel
- class ConsensusThreadStatusOneOf1(*, consensus_thread_status: Literal['stopped'])[source]
Bases:
BaseModel
- class ConsensusThreadStatusOneOf2(*, consensus_thread_status: Literal['stopped_with_err'], err: str)[source]
Bases:
BaseModel
- class ContextExamplePair(*, positive: Union[int, str, List[float], SparseVector], negative: Union[int, str, List[float], SparseVector])[source]
Bases:
BaseModel
- class ContextPair(*, positive: Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject], negative: Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject])[source]
Bases:
BaseModel
- class ContextQuery(*, context: Union[ContextPair, List[ContextPair]])[source]
Bases:
BaseModel
- class CountRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, filter: Optional[Filter] = None, exact: Optional[bool] = True)[source]
Bases:
BaseModelCount Request Counts the number of points which satisfy the given filter. If filter is not provided, the count of all points in the collection will be returned.
- filter: Optional[Filter]
- class CountResult(*, count: int)[source]
Bases:
BaseModel
- class CpuEndian(value)[source]
Bases:
str,EnumAn enumeration.
- class CreateAlias(*, collection_name: str, alias_name: str)[source]
Bases:
BaseModelCreate alternative name for a collection. Collection will be available under both names for search, retrieve,
- class CreateAliasOperation(*, create_alias: CreateAlias)[source]
Bases:
BaseModel- create_alias: CreateAlias
- class CreateCollection(*, vectors: Optional[Union[VectorParams, Dict[str, VectorParams]]] = None, shard_number: Optional[int] = None, sharding_method: Optional[ShardingMethod] = None, replication_factor: Optional[int] = None, write_consistency_factor: Optional[int] = None, on_disk_payload: Optional[bool] = None, hnsw_config: Optional[HnswConfigDiff] = None, wal_config: Optional[WalConfigDiff] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, init_from: Optional[InitFrom] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, sparse_vectors: Optional[Dict[str, SparseVectorParams]] = None, strict_mode_config: Optional[StrictModeConfig] = None)[source]
Bases:
BaseModelOperation for creating new collection and (optionally) specify index params
- hnsw_config: Optional[HnswConfigDiff]
- init_from: Optional[InitFrom]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizers_config: Optional[OptimizersConfigDiff]
- sharding_method: Optional[ShardingMethod]
- sparse_vectors: Optional[Dict[str, SparseVectorParams]]
- strict_mode_config: Optional[StrictModeConfig]
- wal_config: Optional[WalConfigDiff]
- class CreateFieldIndex(*, field_name: str, field_schema: Optional[Union[PayloadSchemaType, KeywordIndexParams, IntegerIndexParams, FloatIndexParams, GeoIndexParams, TextIndexParams, BoolIndexParams, DatetimeIndexParams, UuidIndexParams]] = None)[source]
Bases:
BaseModel
- class CreateShardingKey(*, shard_key: Union[int, str], shards_number: Optional[int] = None, replication_factor: Optional[int] = None, placement: Optional[List[int]] = None)[source]
Bases:
BaseModel
- class CreateShardingKeyOperation(*, create_sharding_key: CreateShardingKey)[source]
Bases:
BaseModel- create_sharding_key: CreateShardingKey
- class Datatype(value)[source]
Bases:
str,EnumAn enumeration.
- class DatetimeExpression(*, datetime: str)[source]
Bases:
BaseModel
- class DatetimeIndexParams(*, type: DatetimeIndexType, is_principal: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: DatetimeIndexType
- class DatetimeIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class DatetimeKeyExpression(*, datetime_key: str)[source]
Bases:
BaseModel
- class DatetimeRange(*, lt: Optional[Union[datetime, date]] = None, gt: Optional[Union[datetime, date]] = None, gte: Optional[Union[datetime, date]] = None, lte: Optional[Union[datetime, date]] = None)[source]
Bases:
BaseModelRange filter request
- class DecayParamsExpression(*, x: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression], target: Optional[Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression]] = None, scale: Optional[float] = None, midpoint: Optional[float] = None)[source]
Bases:
BaseModel
- class DeleteAlias(*, alias_name: str)[source]
Bases:
BaseModelDelete alias if exists
- class DeleteAliasOperation(*, delete_alias: DeleteAlias)[source]
Bases:
BaseModelDelete alias if exists
- delete_alias: DeleteAlias
- class DeleteOperation(*, delete: Union[PointIdsList, FilterSelector])[source]
Bases:
BaseModel
- class DeletePayload(*, keys: List[str], points: Optional[List[Union[int, str]]] = None, filter: Optional[Filter] = None, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModelThis data structure is used in API interface and applied across multiple shards
- filter: Optional[Filter]
- class DeletePayloadOperation(*, delete_payload: DeletePayload)[source]
Bases:
BaseModel- delete_payload: DeletePayload
- class DeleteVectors(*, points: Optional[List[Union[int, str]]] = None, filter: Optional[Filter] = None, vector: List[str], shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- class DeleteVectorsOperation(*, delete_vectors: DeleteVectors)[source]
Bases:
BaseModel- delete_vectors: DeleteVectors
- class Direction(value)[source]
Bases:
str,EnumAn enumeration.
- class Disabled(value)[source]
Bases:
str,EnumAn enumeration.
- class DiscoverInput(*, target: Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject], context: Union[List[ContextPair], ContextPair])[source]
Bases:
BaseModel- context: Union[List[ContextPair], ContextPair]
- class DiscoverQuery(*, discover: DiscoverInput)[source]
Bases:
BaseModel- discover: DiscoverInput
- class DiscoverRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, target: Optional[Union[int, str, List[float], SparseVector]] = None, context: Optional[List[ContextExamplePair]] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, limit: int, offset: Optional[int] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, using: Optional[str] = None, lookup_from: Optional[LookupLocation] = None)[source]
Bases:
BaseModelUse context and a target to find the most similar points, constrained by the context.
- context: Optional[List[ContextExamplePair]]
- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class DiscoverRequestBatch(*, searches: List[DiscoverRequest])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- searches: List[DiscoverRequest]
- class Distance(value)[source]
Bases:
str,EnumType of internal tags, build from payload Distance function types used to compare vectors
- class DivParams(*, left: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression], right: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression], by_zero_default: Optional[float] = None)[source]
Bases:
BaseModel
- class Document(*, text: str, model: str, options: Optional[Dict[str, Any]] = None)[source]
Bases:
BaseModelWARN: Work-in-progress, unimplemented Text document for embedding. Requires inference infrastructure, unimplemented.
- class DropShardingKey(*, shard_key: Union[int, str])[source]
Bases:
BaseModel
- class DropShardingKeyOperation(*, drop_sharding_key: DropShardingKey)[source]
Bases:
BaseModel- drop_sharding_key: DropShardingKey
- class ErrorResponse(*, time: Optional[float] = None, status: Optional[ErrorResponseStatus] = None, result: Optional[Any] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- status: Optional[ErrorResponseStatus]
- class ErrorResponseStatus(*, error: Optional[str] = None)[source]
Bases:
BaseModel
- class ExpDecayExpression(*, exp_decay: DecayParamsExpression)[source]
Bases:
BaseModel- exp_decay: DecayParamsExpression
- class ExpExpression(*, exp: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class FacetRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, key: str, limit: Optional[int] = None, filter: Optional[Filter] = None, exact: Optional[bool] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- class FacetResponse(*, hits: List[FacetValueHit])[source]
Bases:
BaseModel- hits: List[FacetValueHit]
- class FacetValueHit(*, value: Union[bool, int, str], count: int)[source]
Bases:
BaseModel
- class FieldCondition(*, key: str, match: Optional[Union[MatchValue, MatchText, MatchAny, MatchExcept]] = None, range: Optional[Union[Range, DatetimeRange]] = None, geo_bounding_box: Optional[GeoBoundingBox] = None, geo_radius: Optional[GeoRadius] = None, geo_polygon: Optional[GeoPolygon] = None, values_count: Optional[ValuesCount] = None, is_empty: Optional[bool] = None, is_null: Optional[bool] = None)[source]
Bases:
BaseModelAll possible payload filtering conditions
- geo_bounding_box: Optional[GeoBoundingBox]
- geo_polygon: Optional[GeoPolygon]
- geo_radius: Optional[GeoRadius]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- values_count: Optional[ValuesCount]
- class Filter(*, should: Optional[Union[List[Union[FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]], FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]] = None, min_should: Optional[MinShould] = None, must: Optional[Union[List[Union[FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]], FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]] = None, must_not: Optional[Union[List[Union[FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]], FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]] = None)[source]
Bases:
BaseModel- min_should: Optional[MinShould]
- class FilterSelector(*, filter: Filter, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel- filter: Filter
- class FloatIndexParams(*, type: FloatIndexType, is_principal: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: FloatIndexType
- class FloatIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class FormulaQuery(*, formula: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression], defaults: Optional[Dict[str, Any]] = {})[source]
Bases:
BaseModel
- class Fusion(value)[source]
Bases:
str,EnumFusion algorithm allows to combine results of multiple prefetches. Available fusion algorithms: * rrf - Reciprocal Rank Fusion * dbsf - Distribution-Based Score Fusion
- class GaussDecayExpression(*, gauss_decay: DecayParamsExpression)[source]
Bases:
BaseModel- gauss_decay: DecayParamsExpression
- class GeoBoundingBox(*, top_left: GeoPoint, bottom_right: GeoPoint)[source]
Bases:
BaseModelGeo filter request Matches coordinates inside the rectangle, described by coordinates of lop-left and bottom-right edges
- bottom_right: GeoPoint
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- top_left: GeoPoint
- class GeoDistance(*, geo_distance: GeoDistanceParams)[source]
Bases:
BaseModel- geo_distance: GeoDistanceParams
- class GeoDistanceParams(*, origin: GeoPoint, to: str)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- origin: GeoPoint
- class GeoIndexParams(*, type: GeoIndexType, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: GeoIndexType
- class GeoIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class GeoLineString(*, points: List[GeoPoint])[source]
Bases:
BaseModelOrdered sequence of GeoPoints representing the line
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- points: List[GeoPoint]
- class GeoPoint(*, lon: float, lat: float)[source]
Bases:
BaseModelGeo point payload schema
- class GeoPolygon(*, exterior: GeoLineString, interiors: Optional[List[GeoLineString]] = None)[source]
Bases:
BaseModelGeo filter request Matches coordinates inside the polygon, defined by exterior and interiors
- exterior: GeoLineString
- interiors: Optional[List[GeoLineString]]
- class GeoRadius(*, center: GeoPoint, radius: float)[source]
Bases:
BaseModelGeo filter request Matches coordinates inside the circle of radius and center with coordinates center
- center: GeoPoint
- class GpuDeviceTelemetry(*, name: str)[source]
Bases:
BaseModel
- class GroupsResult(*, groups: List[PointGroup])[source]
Bases:
BaseModel- groups: List[PointGroup]
- class GrpcTelemetry(*, responses: Dict[str, OperationDurationStatistics])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- responses: Dict[str, OperationDurationStatistics]
- class HardwareTelemetry(*, collection_data: Dict[str, HardwareUsage])[source]
Bases:
BaseModel- collection_data: Dict[str, HardwareUsage]
- class HardwareUsage(*, cpu: int, payload_io_read: int, payload_io_write: int, payload_index_io_read: int, payload_index_io_write: int, vector_io_read: int, vector_io_write: int)[source]
Bases:
BaseModelUsage of the hardware resources, spent to process the request
- class HasIdCondition(*, has_id: Sequence[Union[int, str]])[source]
Bases:
BaseModelID-based filtering condition
- class HasVectorCondition(*, has_vector: str)[source]
Bases:
BaseModelFilter points which have specific vector assigned
- class HnswConfig(*, m: int, ef_construct: int, full_scan_threshold: int, max_indexing_threads: Optional[int] = 0, on_disk: Optional[bool] = None, payload_m: Optional[int] = None)[source]
Bases:
BaseModelConfig of HNSW index
- class HnswConfigDiff(*, m: Optional[int] = None, ef_construct: Optional[int] = None, full_scan_threshold: Optional[int] = None, max_indexing_threads: Optional[int] = None, on_disk: Optional[bool] = None, payload_m: Optional[int] = None)[source]
Bases:
BaseModel
- class Image(*, image: Any, model: str, options: Optional[Dict[str, Any]] = None)[source]
Bases:
BaseModelWARN: Work-in-progress, unimplemented Image object for embedding. Requires inference infrastructure, unimplemented.
- class IndexesOneOf(*, type: Literal['plain'], options: Any)[source]
Bases:
BaseModelDo not use any index, scan whole vector collection during search. Guarantee 100% precision, but may be time consuming on large collections.
- class IndexesOneOf1(*, type: Literal['hnsw'], options: HnswConfig)[source]
Bases:
BaseModelUse filterable HNSW index for approximate search. Is very fast even on a very huge collections, but require additional space to store index and additional time to build it.
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- options: HnswConfig
- class InferenceObject(*, object: Any, model: str, options: Optional[Dict[str, Any]] = None)[source]
Bases:
BaseModelWARN: Work-in-progress, unimplemented Custom object for embedding. Requires inference infrastructure, unimplemented.
- class InitFrom(*, collection: str)[source]
Bases:
BaseModelOperation for creating new collection and (optionally) specify index params
- class InlineResponse200(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- usage: Optional[HardwareUsage]
- class InlineResponse2001(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[TelemetryData] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[TelemetryData]
- usage: Optional[HardwareUsage]
- class InlineResponse20010(*, time: Optional[float] = None, status: Optional[str] = None, result: Optional[bool] = None)[source]
Bases:
BaseModel
- class InlineResponse20011(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[SnapshotDescription]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[SnapshotDescription]]
- usage: Optional[HardwareUsage]
- class InlineResponse20012(*, time: Optional[float] = None, status: Optional[str] = None, result: Optional[SnapshotDescription] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[SnapshotDescription]
- class InlineResponse20013(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[Record] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[Record]
- usage: Optional[HardwareUsage]
- class InlineResponse20014(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[Record]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[Record]]
- usage: Optional[HardwareUsage]
- class InlineResponse20015(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[UpdateResult]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[UpdateResult]]
- usage: Optional[HardwareUsage]
- class InlineResponse20016(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[ScrollResult] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[ScrollResult]
- usage: Optional[HardwareUsage]
- class InlineResponse20017(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[ScoredPoint]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[ScoredPoint]]
- usage: Optional[HardwareUsage]
- class InlineResponse20018(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[List[ScoredPoint]]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[List[ScoredPoint]]]
- usage: Optional[HardwareUsage]
- class InlineResponse20019(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[GroupsResult] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[GroupsResult]
- usage: Optional[HardwareUsage]
- class InlineResponse2002(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[LocksOption] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[LocksOption]
- usage: Optional[HardwareUsage]
- class InlineResponse20020(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CountResult] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CountResult]
- usage: Optional[HardwareUsage]
- class InlineResponse20021(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[FacetResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[FacetResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse20022(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[QueryResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[QueryResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse20023(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[List[QueryResponse]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[List[QueryResponse]]
- usage: Optional[HardwareUsage]
- class InlineResponse20024(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[SearchMatrixPairsResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[SearchMatrixPairsResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse20025(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[SearchMatrixOffsetsResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[SearchMatrixOffsetsResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse2003(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[Union[ClusterStatusOneOf, ClusterStatusOneOf1]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- usage: Optional[HardwareUsage]
- class InlineResponse2004(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CollectionsResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CollectionsResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse2005(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CollectionInfo] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CollectionInfo]
- usage: Optional[HardwareUsage]
- class InlineResponse2006(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[UpdateResult] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[UpdateResult]
- usage: Optional[HardwareUsage]
- class InlineResponse2007(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CollectionExistence] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CollectionExistence]
- usage: Optional[HardwareUsage]
- class InlineResponse2008(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CollectionClusterInfo] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CollectionClusterInfo]
- usage: Optional[HardwareUsage]
- class InlineResponse2009(*, usage: Optional[HardwareUsage] = None, time: Optional[float] = None, status: Optional[str] = None, result: Optional[CollectionsAliasesResponse] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- result: Optional[CollectionsAliasesResponse]
- usage: Optional[HardwareUsage]
- class InlineResponse202(*, time: Optional[float] = None, status: Optional[str] = None)[source]
Bases:
BaseModel
- class IntegerIndexParams(*, type: IntegerIndexType, lookup: Optional[bool] = None, range: Optional[bool] = None, is_principal: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: IntegerIndexType
- class IntegerIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class IsEmptyCondition(*, is_empty: PayloadField)[source]
Bases:
BaseModelSelect points with empty payload for a specified field
- is_empty: PayloadField
- class IsNullCondition(*, is_null: PayloadField)[source]
Bases:
BaseModelSelect points with null payload for a specified field
- is_null: PayloadField
- class KeywordIndexParams(*, type: KeywordIndexType, is_tenant: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: KeywordIndexType
- class KeywordIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class LinDecayExpression(*, lin_decay: DecayParamsExpression)[source]
Bases:
BaseModel- lin_decay: DecayParamsExpression
- class LnExpression(*, ln: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class LocalShardInfo(*, shard_id: int, shard_key: Optional[Union[int, str]] = None, points_count: int, state: ReplicaState)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- state: ReplicaState
- class LocalShardTelemetry(*, variant_name: Optional[str] = None, status: Optional[ShardStatus] = None, total_optimized_points: int, vectors_size_bytes: Optional[int] = None, payloads_size_bytes: Optional[int] = None, num_points: Optional[int] = None, num_vectors: Optional[int] = None, segments: Optional[List[SegmentTelemetry]] = None, optimizations: OptimizerTelemetry, async_scorer: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizations: OptimizerTelemetry
- segments: Optional[List[SegmentTelemetry]]
- status: Optional[ShardStatus]
- class LocksOption(*, error_message: Optional[str] = None, write: bool)[source]
Bases:
BaseModel
- class Log10Expression(*, log10: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class LookupLocation(*, collection: str, vector: Optional[str] = None, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModelDefines a location to use for looking up the vector. Specifies collection and vector field name.
- class MatchAny(*, any: Union[List[str], List[int]])[source]
Bases:
BaseModelExact match on any of the given values
- class MatchExcept(*, except_: Union[List[str], List[int]])[source]
Bases:
BaseModelShould have at least one value not matching the any given values
- class MatchText(*, text: str)[source]
Bases:
BaseModelFull-text match of the strings.
- class MatchValue(*, value: Union[bool, int, str])[source]
Bases:
BaseModelExact match of the given value
- class MaxOptimizationThreadsSetting(value)[source]
Bases:
str,EnumAn enumeration.
- class MemoryTelemetry(*, active_bytes: int, allocated_bytes: int, metadata_bytes: int, resident_bytes: int, retained_bytes: int)[source]
Bases:
BaseModel
- class MessageSendErrors(*, count: int, latest_error: Optional[str] = None, latest_error_timestamp: Optional[Union[datetime, date]] = None)[source]
Bases:
BaseModelMessage send failures for a particular peer
- class MinShould(*, conditions: List[Union[FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter]], min_count: int)[source]
Bases:
BaseModel
- class Modifier(value)[source]
Bases:
str,EnumIf used, include weight modification, which will be applied to sparse vectors at query time: None - no modification (default) Idf - inverse document frequency, based on statistics of the collection
- class MoveShard(*, shard_id: int, to_peer_id: int, from_peer_id: int, method: Optional[Union[ShardTransferMethodOneOf, ShardTransferMethodOneOf1, ShardTransferMethodOneOf2, ShardTransferMethodOneOf3]] = None)[source]
Bases:
BaseModel
- class MoveShardOperation(*, move_shard: MoveShard)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- move_shard: MoveShard
- class MultExpression(*, mult: List[Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression]])[source]
Bases:
BaseModel
- class MultiVectorComparator(value)[source]
Bases:
str,EnumAn enumeration.
- class MultiVectorConfig(*, comparator: MultiVectorComparator)[source]
Bases:
BaseModel- comparator: MultiVectorComparator
- class NamedSparseVector(*, name: str, vector: SparseVector)[source]
Bases:
BaseModelSparse vector data with name
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- vector: SparseVector
- class NamedVector(*, name: str, vector: List[float])[source]
Bases:
BaseModelDense vector data with name
- class NearestQuery(*, nearest: Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject])[source]
Bases:
BaseModel
- class NegExpression(*, neg: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class Nested(*, key: str, filter: Filter)[source]
Bases:
BaseModelSelect points with payload for a specified nested field
- filter: Filter
- class NestedCondition(*, nested: Nested)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- nested: Nested
- class OperationDurationStatistics(*, count: int, fail_count: Optional[int] = None, avg_duration_micros: Optional[float] = None, min_duration_micros: Optional[float] = None, max_duration_micros: Optional[float] = None, total_duration_micros: Optional[int] = None, last_responded: Optional[Union[datetime, date]] = None)[source]
Bases:
BaseModel
- class OptimizerTelemetry(*, status: Union[OptimizersStatusOneOf, OptimizersStatusOneOf1], optimizations: OperationDurationStatistics, log: Optional[List[TrackerTelemetry]] = None)[source]
Bases:
BaseModel- log: Optional[List[TrackerTelemetry]]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizations: OperationDurationStatistics
- class OptimizersConfig(*, deleted_threshold: float, vacuum_min_vector_number: int, default_segment_number: int, max_segment_size: Optional[int] = None, memmap_threshold: Optional[int] = None, indexing_threshold: Optional[int] = None, flush_interval_sec: int, max_optimization_threads: Optional[int] = None)[source]
Bases:
BaseModel
- class OptimizersConfigDiff(*, deleted_threshold: Optional[float] = None, vacuum_min_vector_number: Optional[int] = None, default_segment_number: Optional[int] = None, max_segment_size: Optional[int] = None, memmap_threshold: Optional[int] = None, indexing_threshold: Optional[int] = None, flush_interval_sec: Optional[int] = None, max_optimization_threads: Optional[Union[int, MaxOptimizationThreadsSetting]] = None)[source]
Bases:
BaseModel
- class OptimizersStatusOneOf(value)[source]
Bases:
str,EnumOptimizers are reporting as expected
- class OptimizersStatusOneOf1(*, error: str)[source]
Bases:
BaseModelSomething wrong happened with optimizers
- class OrderBy(*, key: str, direction: Optional[Direction] = None, start_from: Optional[Union[int, float, datetime, date]] = None)[source]
Bases:
BaseModel- direction: Optional[Direction]
- class OrderByQuery(*, order_by: Union[str, OrderBy])[source]
Bases:
BaseModel
- class OverwritePayloadOperation(*, overwrite_payload: SetPayload)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- overwrite_payload: SetPayload
- class P2pConfigTelemetry(*, connection_pool_size: int)[source]
Bases:
BaseModel
- class PayloadField(*, key: str)[source]
Bases:
BaseModelPayload field
- class PayloadIndexInfo(*, data_type: PayloadSchemaType, params: Optional[Union[KeywordIndexParams, IntegerIndexParams, FloatIndexParams, GeoIndexParams, TextIndexParams, BoolIndexParams, DatetimeIndexParams, UuidIndexParams]] = None, points: int)[source]
Bases:
BaseModelDisplay payload field type & index information
- data_type: PayloadSchemaType
- class PayloadIndexTelemetry(*, field_name: Optional[str] = None, index_type: str, points_values_count: int, points_count: int, histogram_bucket_size: Optional[int] = None)[source]
Bases:
BaseModel
- class PayloadSchemaType(value)[source]
Bases:
str,EnumAll possible names of payload types
- class PayloadSelectorExclude(*, exclude: List[str])[source]
Bases:
BaseModel
- class PayloadSelectorInclude(*, include: List[str])[source]
Bases:
BaseModel
- class PayloadStorageTypeOneOf(*, type: Literal['in_memory'])[source]
Bases:
BaseModel
- class PayloadStorageTypeOneOf1(*, type: Literal['on_disk'])[source]
Bases:
BaseModel
- class PayloadStorageTypeOneOf2(*, type: Literal['mmap'])[source]
Bases:
BaseModel
- class PeerInfo(*, uri: str)[source]
Bases:
BaseModelInformation of a peer in the cluster
- class PointGroup(*, hits: List[ScoredPoint], id: Union[int, str], lookup: Optional[Record] = None)[source]
Bases:
BaseModel- hits: List[ScoredPoint]
- lookup: Optional[Record]
- class PointIdsList(*, points: List[Union[int, str]], shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel
- class PointRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, ids: List[Union[int, str]], with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None)[source]
Bases:
BaseModel
- class PointStruct(*, id: Union[int, str], vector: Union[List[float], List[List[float]], Dict[str, Union[List[float], SparseVector, List[List[float]], Document, Image, InferenceObject]], Document, Image, InferenceObject], payload: Optional[Dict[str, Any]] = None)[source]
Bases:
BaseModel
- class PointVectors(*, id: Union[int, str], vector: Union[List[float], List[List[float]], Dict[str, Union[List[float], SparseVector, List[List[float]], Document, Image, InferenceObject]], Document, Image, InferenceObject])[source]
Bases:
BaseModel
- class PointsBatch(*, batch: Batch, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel- batch: Batch
- class PointsList(*, points: List[PointStruct], shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- points: List[PointStruct]
- class PowExpression(*, pow: PowParams)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- pow: PowParams
- class PowParams(*, base: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression], exponent: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class Prefetch(*, prefetch: Optional[Union[List[Prefetch], Prefetch]] = None, query: Optional[Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject, NearestQuery, RecommendQuery, DiscoverQuery, ContextQuery, OrderByQuery, FusionQuery, FormulaQuery, SampleQuery]] = None, using: Optional[str] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, score_threshold: Optional[float] = None, limit: Optional[int] = None, lookup_from: Optional[LookupLocation] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class ProductQuantization(*, product: ProductQuantizationConfig)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- product: ProductQuantizationConfig
- class ProductQuantizationConfig(*, compression: CompressionRatio, always_ram: Optional[bool] = None)[source]
Bases:
BaseModel- compression: CompressionRatio
- class QuantizationSearchParams(*, ignore: Optional[bool] = False, rescore: Optional[bool] = None, oversampling: Optional[float] = None)[source]
Bases:
BaseModelAdditional parameters of the search
- class QueryGroupsRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, prefetch: Optional[Union[List[Prefetch], Prefetch]] = None, query: Optional[Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject, NearestQuery, RecommendQuery, DiscoverQuery, ContextQuery, OrderByQuery, FusionQuery, FormulaQuery, SampleQuery]] = None, using: Optional[str] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, score_threshold: Optional[float] = None, with_vector: Optional[Union[bool, List[str]]] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, lookup_from: Optional[LookupLocation] = None, group_by: str, group_size: Optional[int] = None, limit: Optional[int] = None, with_lookup: Optional[Union[str, WithLookup]] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class QueryRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, prefetch: Optional[Union[List[Prefetch], Prefetch]] = None, query: Optional[Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject, NearestQuery, RecommendQuery, DiscoverQuery, ContextQuery, OrderByQuery, FusionQuery, FormulaQuery, SampleQuery]] = None, using: Optional[str] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, score_threshold: Optional[float] = None, limit: Optional[int] = None, offset: Optional[int] = None, with_vector: Optional[Union[bool, List[str]]] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, lookup_from: Optional[LookupLocation] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class QueryRequestBatch(*, searches: List[QueryRequest])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- searches: List[QueryRequest]
- class QueryResponse(*, points: List[ScoredPoint])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- points: List[ScoredPoint]
- class RaftInfo(*, term: int, commit: int, pending_operations: int, leader: Optional[int] = None, role: Optional[StateRole] = None, is_voter: bool)[source]
Bases:
BaseModelSummary information about the current raft state
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- role: Optional[StateRole]
- class Range(*, lt: Optional[float] = None, gt: Optional[float] = None, gte: Optional[float] = None, lte: Optional[float] = None)[source]
Bases:
BaseModelRange filter request
- class ReadConsistencyType(value)[source]
Bases:
str,Enummajority - send N/2+1 random request and return points, which present on all of them * quorum - send requests to all nodes and return points which present on majority of nodes * all - send requests to all nodes and return points which present on all nodes
- class RecommendGroupsRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, positive: Optional[List[Union[int, str, List[float], SparseVector]]] = [], negative: Optional[List[Union[int, str, List[float], SparseVector]]] = [], strategy: Optional[RecommendStrategy] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, score_threshold: Optional[float] = None, using: Optional[str] = None, lookup_from: Optional[LookupLocation] = None, group_by: str, group_size: int, limit: int, with_lookup: Optional[Union[str, WithLookup]] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- strategy: Optional[RecommendStrategy]
- class RecommendInput(*, positive: Optional[List[Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject]]] = None, negative: Optional[List[Union[List[float], SparseVector, List[List[float]], int, str, Document, Image, InferenceObject]]] = None, strategy: Optional[RecommendStrategy] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- strategy: Optional[RecommendStrategy]
- class RecommendQuery(*, recommend: RecommendInput)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- recommend: RecommendInput
- class RecommendRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, positive: Optional[List[Union[int, str, List[float], SparseVector]]] = [], negative: Optional[List[Union[int, str, List[float], SparseVector]]] = [], strategy: Optional[RecommendStrategy] = None, filter: Optional[Filter] = None, params: Optional[SearchParams] = None, limit: int, offset: Optional[int] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, score_threshold: Optional[float] = None, using: Optional[str] = None, lookup_from: Optional[LookupLocation] = None)[source]
Bases:
BaseModelRecommendation request. Provides positive and negative examples of the vectors, which can be ids of points that are already stored in the collection, raw vectors, or even ids and vectors combined. Service should look for the points which are closer to positive examples and at the same time further to negative examples. The concrete way of how to compare negative and positive distances is up to the strategy chosen.
- filter: Optional[Filter]
- lookup_from: Optional[LookupLocation]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- strategy: Optional[RecommendStrategy]
- class RecommendRequestBatch(*, searches: List[RecommendRequest])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- searches: List[RecommendRequest]
- class RecommendStrategy(value)[source]
Bases:
str,EnumHow to use positive and negative examples to find the results, default is average_vector: * average_vector - Average positive and negative vectors and create a single query with the formula query = avg_pos + avg_pos - avg_neg. Then performs normal search. * best_score - Uses custom search objective. Each candidate is compared against all examples, its score is then chosen from the max(max_pos_score, max_neg_score). If the max_neg_score is chosen then it is squared and negated, otherwise it is just the max_pos_score. * sum_scores - Uses custom search objective. Compares against all inputs, sums all the scores. Scores against positive vectors are added, against negatives are subtracted.
- class Record(*, id: Union[int, str], payload: Optional[Dict[str, Any]] = None, vector: Optional[Union[List[float], List[List[float]], Dict[str, Union[List[float], SparseVector, List[List[float]]]]]] = None, shard_key: Optional[Union[int, str]] = None, order_value: Optional[Union[int, float]] = None)[source]
Bases:
BaseModelPoint data
- class RemoteShardInfo(*, shard_id: int, shard_key: Optional[Union[int, str]] = None, peer_id: int, state: ReplicaState)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- state: ReplicaState
- class RemoteShardTelemetry(*, shard_id: int, peer_id: Optional[int] = None, searches: OperationDurationStatistics, updates: OperationDurationStatistics)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- searches: OperationDurationStatistics
- updates: OperationDurationStatistics
- class RenameAlias(*, old_alias_name: str, new_alias_name: str)[source]
Bases:
BaseModelChange alias to a new one
- class RenameAliasOperation(*, rename_alias: RenameAlias)[source]
Bases:
BaseModelChange alias to a new one
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- rename_alias: RenameAlias
- class Replica(*, shard_id: int, peer_id: int)[source]
Bases:
BaseModel
- class ReplicaSetTelemetry(*, id: int, key: Optional[Union[int, str]] = None, local: Optional[LocalShardTelemetry] = None, remote: List[RemoteShardTelemetry], replicate_states: Dict[str, ReplicaState])[source]
Bases:
BaseModel- local: Optional[LocalShardTelemetry]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- remote: List[RemoteShardTelemetry]
- replicate_states: Dict[str, ReplicaState]
- class ReplicaState(value)[source]
Bases:
str,EnumState of the single shard within a replica set.
- class ReplicateShard(*, shard_id: int, to_peer_id: int, from_peer_id: int, method: Optional[Union[ShardTransferMethodOneOf, ShardTransferMethodOneOf1, ShardTransferMethodOneOf2, ShardTransferMethodOneOf3]] = None)[source]
Bases:
BaseModel
- class ReplicateShardOperation(*, replicate_shard: ReplicateShard)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- replicate_shard: ReplicateShard
- class RequestsTelemetry(*, rest: WebApiTelemetry, grpc: GrpcTelemetry)[source]
Bases:
BaseModel- grpc: GrpcTelemetry
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- rest: WebApiTelemetry
- class ReshardingDirectionOneOf(value)[source]
Bases:
str,EnumScale up, add a new shard
- class ReshardingDirectionOneOf1(value)[source]
Bases:
str,EnumScale down, remove a shard
- class ReshardingInfo(*, direction: Union[ReshardingDirectionOneOf, ReshardingDirectionOneOf1], shard_id: int, peer_id: int, shard_key: Optional[Union[int, str]] = None)[source]
Bases:
BaseModel
- class RestartTransfer(*, shard_id: int, from_peer_id: int, to_peer_id: int, method: Union[ShardTransferMethodOneOf, ShardTransferMethodOneOf1, ShardTransferMethodOneOf2, ShardTransferMethodOneOf3])[source]
Bases:
BaseModel
- class RestartTransferOperation(*, restart_transfer: RestartTransfer)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- restart_transfer: RestartTransfer
- class RunningEnvironmentTelemetry(*, distribution: Optional[str] = None, distribution_version: Optional[str] = None, is_docker: bool, cores: Optional[int] = None, ram_size: Optional[int] = None, disk_size: Optional[int] = None, cpu_flags: str, cpu_endian: Optional[CpuEndian] = None, gpu_devices: Optional[List[GpuDeviceTelemetry]] = None)[source]
Bases:
BaseModel- cpu_endian: Optional[CpuEndian]
- gpu_devices: Optional[List[GpuDeviceTelemetry]]
- class Sample(value)[source]
Bases:
str,EnumAn enumeration.
- class SampleQuery(*, sample: Sample)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- sample: Sample
- class ScalarQuantization(*, scalar: ScalarQuantizationConfig)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- scalar: ScalarQuantizationConfig
- class ScalarQuantizationConfig(*, type: ScalarType, quantile: Optional[float] = None, always_ram: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: ScalarType
- class ScalarType(value)[source]
Bases:
str,EnumAn enumeration.
- class ScoredPoint(*, id: Union[int, str], version: int, score: float, payload: Optional[Dict[str, Any]] = None, vector: Optional[Union[List[float], List[List[float]], Dict[str, Union[List[float], SparseVector, List[List[float]]]]]] = None, shard_key: Optional[Union[int, str]] = None, order_value: Optional[Union[int, float]] = None)[source]
Bases:
BaseModelSearch result
- class ScrollRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, offset: Optional[Union[int, str]] = None, limit: Optional[int] = None, filter: Optional[Filter] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, order_by: Optional[Union[str, OrderBy]] = None)[source]
Bases:
BaseModelScroll request - paginate over all points which matches given condition
- filter: Optional[Filter]
- class ScrollResult(*, points: List[Record], next_page_offset: Optional[Union[int, str]] = None)[source]
Bases:
BaseModelResult of the points read request
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- points: List[Record]
- class SearchGroupsRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, vector: Union[List[float], NamedVector, NamedSparseVector], filter: Optional[Filter] = None, params: Optional[SearchParams] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, score_threshold: Optional[float] = None, group_by: str, group_size: int, limit: int, with_lookup: Optional[Union[str, WithLookup]] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class SearchMatrixOffsetsResponse(*, offsets_row: List[int], offsets_col: List[int], scores: List[float], ids: List[Union[int, str]])[source]
Bases:
BaseModel
- class SearchMatrixPair(*, a: Union[int, str], b: Union[int, str], score: float)[source]
Bases:
BaseModelPair of points (a, b) with score
- class SearchMatrixPairsResponse(*, pairs: List[SearchMatrixPair])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- pairs: List[SearchMatrixPair]
- class SearchMatrixRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, filter: Optional[Filter] = None, sample: Optional[int] = None, limit: Optional[int] = None, using: Optional[str] = None)[source]
Bases:
BaseModel- filter: Optional[Filter]
- class SearchParams(*, hnsw_ef: Optional[int] = None, exact: Optional[bool] = False, quantization: Optional[QuantizationSearchParams] = None, indexed_only: Optional[bool] = False)[source]
Bases:
BaseModelAdditional parameters of the search
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- quantization: Optional[QuantizationSearchParams]
- class SearchRequest(*, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, vector: Union[List[float], NamedVector, NamedSparseVector], filter: Optional[Filter] = None, params: Optional[SearchParams] = None, limit: int, offset: Optional[int] = None, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vector: Optional[Union[bool, List[str]]] = None, score_threshold: Optional[float] = None)[source]
Bases:
BaseModelSearch request. Holds all conditions and parameters for the search of most similar points by vector similarity given the filtering restrictions.
- filter: Optional[Filter]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- params: Optional[SearchParams]
- class SearchRequestBatch(*, searches: List[SearchRequest])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- searches: List[SearchRequest]
- class SegmentConfig(*, vector_data: Optional[Dict[str, VectorDataConfig]] = {}, sparse_vector_data: Optional[Dict[str, SparseVectorDataConfig]] = None, payload_storage_type: Union[PayloadStorageTypeOneOf, PayloadStorageTypeOneOf1, PayloadStorageTypeOneOf2])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- sparse_vector_data: Optional[Dict[str, SparseVectorDataConfig]]
- vector_data: Optional[Dict[str, VectorDataConfig]]
- class SegmentInfo(*, segment_type: SegmentType, num_vectors: int, num_points: int, num_indexed_vectors: int, num_deleted_vectors: int, vectors_size_bytes: int, payloads_size_bytes: int, ram_usage_bytes: int, disk_usage_bytes: int, is_appendable: bool, index_schema: Dict[str, PayloadIndexInfo], vector_data: Dict[str, VectorDataInfo])[source]
Bases:
BaseModelAggregated information about segment
- index_schema: Dict[str, PayloadIndexInfo]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- segment_type: SegmentType
- vector_data: Dict[str, VectorDataInfo]
- class SegmentTelemetry(*, info: SegmentInfo, config: SegmentConfig, vector_index_searches: List[VectorIndexSearchesTelemetry], payload_field_indices: List[PayloadIndexTelemetry])[source]
Bases:
BaseModel- config: SegmentConfig
- info: SegmentInfo
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- payload_field_indices: List[PayloadIndexTelemetry]
- vector_index_searches: List[VectorIndexSearchesTelemetry]
- class SegmentType(value)[source]
Bases:
str,EnumType of segment
- class SetPayload(*, payload: Dict[str, Any], points: Optional[List[Union[int, str]]] = None, filter: Optional[Filter] = None, shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None, key: Optional[str] = None)[source]
Bases:
BaseModelThis data structure is used in API interface and applied across multiple shards
- filter: Optional[Filter]
- class SetPayloadOperation(*, set_payload: SetPayload)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- set_payload: SetPayload
- class ShardCleanStatusFailedTelemetry(*, reason: str)[source]
Bases:
BaseModel
- class ShardCleanStatusProgressTelemetry(*, deleted_points: int)[source]
Bases:
BaseModel
- class ShardCleanStatusTelemetryOneOf(value)[source]
Bases:
str,EnumAn enumeration.
- class ShardCleanStatusTelemetryOneOf1(*, progress: ShardCleanStatusProgressTelemetry)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- progress: ShardCleanStatusProgressTelemetry
- class ShardCleanStatusTelemetryOneOf2(*, failed: ShardCleanStatusFailedTelemetry)[source]
Bases:
BaseModel- failed: ShardCleanStatusFailedTelemetry
- class ShardSnapshotRecover(*, location: str, priority: Optional[SnapshotPriority] = None, checksum: Optional[str] = None, api_key: Optional[str] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- priority: Optional[SnapshotPriority]
- class ShardStatus(value)[source]
Bases:
str,EnumCurrent state of the shard (supports same states as the collection) Green - all good. Yellow - optimization is running, 'Grey' - optimizations are possible but not triggered, Red - some operations failed and was not recovered
- class ShardTransferInfo(*, shard_id: int, to_shard_id: Optional[int] = None, from_: int, to: int, sync: bool, method: Optional[Union[ShardTransferMethodOneOf, ShardTransferMethodOneOf1, ShardTransferMethodOneOf2, ShardTransferMethodOneOf3]] = None, comment: Optional[str] = None)[source]
Bases:
BaseModel
- class ShardTransferMethodOneOf(value)[source]
Bases:
str,EnumStream all shard records in batches until the whole shard is transferred.
- class ShardTransferMethodOneOf1(value)[source]
Bases:
str,EnumSnapshot the shard, transfer and restore it on the receiver.
- class ShardTransferMethodOneOf2(value)[source]
Bases:
str,EnumAttempt to transfer shard difference by WAL delta.
- class ShardTransferMethodOneOf3(value)[source]
Bases:
str,EnumShard transfer for resharding: stream all records in batches until all points are transferred.
- class ShardingMethod(value)[source]
Bases:
str,EnumAn enumeration.
- class SnapshotDescription(*, name: str, creation_time: Optional[str] = None, size: int, checksum: Optional[str] = None)[source]
Bases:
BaseModel
- class SnapshotPriority(value)[source]
Bases:
str,EnumDefines source of truth for snapshot recovery: NoSync means - restore snapshot without any additional synchronization. Snapshot means - prefer snapshot data over the current state. Replica means - prefer existing data over the snapshot.
- class SnapshotRecover(*, location: str, priority: Optional[SnapshotPriority] = None, checksum: Optional[str] = None, api_key: Optional[str] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- priority: Optional[SnapshotPriority]
- class SparseIndexConfig(*, full_scan_threshold: Optional[int] = None, index_type: Union[SparseIndexTypeOneOf, SparseIndexTypeOneOf1, SparseIndexTypeOneOf2], datatype: Optional[VectorStorageDatatype] = None)[source]
Bases:
BaseModelConfiguration for sparse inverted index.
- datatype: Optional[VectorStorageDatatype]
- class SparseIndexParams(*, full_scan_threshold: Optional[int] = None, on_disk: Optional[bool] = None, datatype: Optional[Datatype] = None)[source]
Bases:
BaseModelConfiguration for sparse inverted index.
- datatype: Optional[Datatype]
- class SparseIndexTypeOneOf(value)[source]
Bases:
str,EnumMutable RAM sparse index
- class SparseIndexTypeOneOf1(value)[source]
Bases:
str,EnumImmutable RAM sparse index
- class SparseIndexTypeOneOf2(value)[source]
Bases:
str,EnumMmap sparse index
- class SparseVector(*, indices: List[int], values: List[float])[source]
Bases:
BaseModelSparse vector structure
- class SparseVectorDataConfig(*, index: SparseIndexConfig, storage_type: Optional[Union[SparseVectorStorageTypeOneOf, SparseVectorStorageTypeOneOf1]] = None)[source]
Bases:
BaseModelConfig of single sparse vector data storage
- index: SparseIndexConfig
- class SparseVectorParams(*, index: Optional[SparseIndexParams] = None, modifier: Optional[Modifier] = None)[source]
Bases:
BaseModelParams of single sparse vector data storage
- index: Optional[SparseIndexParams]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- modifier: Optional[Modifier]
- class SparseVectorStorageTypeOneOf(value)[source]
Bases:
str,EnumStorage on disk
- class SparseVectorStorageTypeOneOf1(value)[source]
Bases:
str,EnumStorage in memory maps
- class SqrtExpression(*, sqrt: Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression])[source]
Bases:
BaseModel
- class StartResharding(*, direction: Union[ReshardingDirectionOneOf, ReshardingDirectionOneOf1], peer_id: Optional[int] = None, shard_key: Optional[Union[int, str]] = None)[source]
Bases:
BaseModel
- class StartReshardingOperation(*, start_resharding: StartResharding)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- start_resharding: StartResharding
- class StateRole(value)[source]
Bases:
str,EnumRole of the peer in the consensus
- class StrictModeConfig(*, enabled: Optional[bool] = None, max_query_limit: Optional[int] = None, max_timeout: Optional[int] = None, unindexed_filtering_retrieve: Optional[bool] = None, unindexed_filtering_update: Optional[bool] = None, search_max_hnsw_ef: Optional[int] = None, search_allow_exact: Optional[bool] = None, search_max_oversampling: Optional[float] = None, upsert_max_batchsize: Optional[int] = None, max_collection_vector_size_bytes: Optional[int] = None, read_rate_limit: Optional[int] = None, write_rate_limit: Optional[int] = None, max_collection_payload_size_bytes: Optional[int] = None, max_points_count: Optional[int] = None, filter_max_conditions: Optional[int] = None, condition_max_size: Optional[int] = None, multivector_config: Optional[Dict[str, StrictModeMultivector]] = None, sparse_config: Optional[Dict[str, StrictModeSparse]] = None)[source]
Bases:
BaseModel
- class StrictModeConfigOutput(*, enabled: Optional[bool] = None, max_query_limit: Optional[int] = None, max_timeout: Optional[int] = None, unindexed_filtering_retrieve: Optional[bool] = None, unindexed_filtering_update: Optional[bool] = None, search_max_hnsw_ef: Optional[int] = None, search_allow_exact: Optional[bool] = None, search_max_oversampling: Optional[float] = None, upsert_max_batchsize: Optional[int] = None, max_collection_vector_size_bytes: Optional[int] = None, read_rate_limit: Optional[int] = None, write_rate_limit: Optional[int] = None, max_collection_payload_size_bytes: Optional[int] = None, max_points_count: Optional[int] = None, filter_max_conditions: Optional[int] = None, condition_max_size: Optional[int] = None, multivector_config: Optional[Dict[str, StrictModeMultivectorOutput]] = None, sparse_config: Optional[Dict[str, StrictModeSparseOutput]] = None)[source]
Bases:
BaseModel
- class StrictModeMultivector(*, max_vectors: Optional[int] = None)[source]
Bases:
BaseModel
- class StrictModeMultivectorOutput(*, max_vectors: Optional[int] = None)[source]
Bases:
BaseModel
- class StrictModeSparse(*, max_length: Optional[int] = None)[source]
Bases:
BaseModel
- class StrictModeSparseOutput(*, max_length: Optional[int] = None)[source]
Bases:
BaseModel
- class SumExpression(*, sum: List[Union[float, str, FieldCondition, IsEmptyCondition, IsNullCondition, HasIdCondition, HasVectorCondition, NestedCondition, Filter, GeoDistance, DatetimeExpression, DatetimeKeyExpression, MultExpression, SumExpression, NegExpression, AbsExpression, DivExpression, SqrtExpression, PowExpression, ExpExpression, Log10Expression, LnExpression, LinDecayExpression, ExpDecayExpression, GaussDecayExpression]])[source]
Bases:
BaseModel
- class TelemetryData(*, id: str, app: AppBuildTelemetry, collections: CollectionsTelemetry, cluster: Optional[ClusterTelemetry] = None, requests: Optional[RequestsTelemetry] = None, memory: Optional[MemoryTelemetry] = None, hardware: Optional[HardwareTelemetry] = None)[source]
Bases:
BaseModel- app: AppBuildTelemetry
- cluster: Optional[ClusterTelemetry]
- collections: CollectionsTelemetry
- hardware: Optional[HardwareTelemetry]
- memory: Optional[MemoryTelemetry]
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- requests: Optional[RequestsTelemetry]
- class TextIndexParams(*, type: TextIndexType, tokenizer: Optional[TokenizerType] = None, min_token_len: Optional[int] = None, max_token_len: Optional[int] = None, lowercase: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- tokenizer: Optional[TokenizerType]
- type: TextIndexType
- class TextIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class TokenizerType(value)[source]
Bases:
str,EnumAn enumeration.
- class TrackerStatusOneOf(value)[source]
Bases:
str,EnumAn enumeration.
- class TrackerStatusOneOf1(*, cancelled: str)[source]
Bases:
BaseModel
- class TrackerStatusOneOf2(*, error: str)[source]
Bases:
BaseModel
- class TrackerTelemetry(*, name: str, segment_ids: List[int], status: Union[TrackerStatusOneOf, TrackerStatusOneOf1, TrackerStatusOneOf2], start_at: Union[datetime, date], end_at: Optional[Union[datetime, date]] = None)[source]
Bases:
BaseModelTracker object used in telemetry
- class UpdateCollection(*, vectors: Optional[Dict[str, VectorParamsDiff]] = None, optimizers_config: Optional[OptimizersConfigDiff] = None, params: Optional[CollectionParamsDiff] = None, hnsw_config: Optional[HnswConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization, Disabled]] = None, sparse_vectors: Optional[Dict[str, SparseVectorParams]] = None, strict_mode_config: Optional[StrictModeConfig] = None)[source]
Bases:
BaseModelOperation for updating parameters of the existing collection
- hnsw_config: Optional[HnswConfigDiff]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- optimizers_config: Optional[OptimizersConfigDiff]
- params: Optional[CollectionParamsDiff]
- strict_mode_config: Optional[StrictModeConfig]
- class UpdateOperations(*, operations: List[Union[UpsertOperation, DeleteOperation, SetPayloadOperation, OverwritePayloadOperation, DeletePayloadOperation, ClearPayloadOperation, UpdateVectorsOperation, DeleteVectorsOperation]])[source]
Bases:
BaseModel
- class UpdateResult(*, operation_id: Optional[int] = None, status: UpdateStatus)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- status: UpdateStatus
- class UpdateStatus(value)[source]
Bases:
str,EnumAcknowledged - Request is saved to WAL and will be process in a queue. Completed - Request is completed, changes are actual.
- class UpdateVectors(*, points: List[PointVectors], shard_key: Optional[Union[int, str, List[Union[int, str]]]] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- points: List[PointVectors]
- class UpdateVectorsOperation(*, update_vectors: UpdateVectors)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- update_vectors: UpdateVectors
- class UpsertOperation(*, upsert: Union[PointsBatch, PointsList])[source]
Bases:
BaseModel
- class UuidIndexParams(*, type: UuidIndexType, is_tenant: Optional[bool] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- type: UuidIndexType
- class UuidIndexType(value)[source]
Bases:
str,EnumAn enumeration.
- class ValuesCount(*, lt: Optional[int] = None, gt: Optional[int] = None, gte: Optional[int] = None, lte: Optional[int] = None)[source]
Bases:
BaseModelValues count filter request
- class VectorDataConfig(*, size: int, distance: Distance, storage_type: Union[VectorStorageTypeOneOf, VectorStorageTypeOneOf1, VectorStorageTypeOneOf2, VectorStorageTypeOneOf3], index: Union[IndexesOneOf, IndexesOneOf1], quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, multivector_config: Optional[MultiVectorConfig] = None, datatype: Optional[VectorStorageDatatype] = None)[source]
Bases:
BaseModelConfig of single vector data storage
- datatype: Optional[VectorStorageDatatype]
- distance: Distance
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- multivector_config: Optional[MultiVectorConfig]
- class VectorDataInfo(*, num_vectors: int, num_indexed_vectors: int, num_deleted_vectors: int)[source]
Bases:
BaseModel
- class VectorIndexSearchesTelemetry(*, index_name: Optional[str] = None, unfiltered_plain: OperationDurationStatistics, unfiltered_hnsw: OperationDurationStatistics, unfiltered_sparse: OperationDurationStatistics, filtered_plain: OperationDurationStatistics, filtered_small_cardinality: OperationDurationStatistics, filtered_large_cardinality: OperationDurationStatistics, filtered_exact: OperationDurationStatistics, filtered_sparse: OperationDurationStatistics, unfiltered_exact: OperationDurationStatistics)[source]
Bases:
BaseModel- filtered_exact: OperationDurationStatistics
- filtered_large_cardinality: OperationDurationStatistics
- filtered_plain: OperationDurationStatistics
- filtered_small_cardinality: OperationDurationStatistics
- filtered_sparse: OperationDurationStatistics
- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- unfiltered_exact: OperationDurationStatistics
- unfiltered_hnsw: OperationDurationStatistics
- unfiltered_plain: OperationDurationStatistics
- unfiltered_sparse: OperationDurationStatistics
- class VectorParams(*, size: int, distance: Distance, hnsw_config: Optional[HnswConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization]] = None, on_disk: Optional[bool] = None, datatype: Optional[Datatype] = None, multivector_config: Optional[MultiVectorConfig] = None)[source]
Bases:
BaseModelParams of single vector data storage
- datatype: Optional[Datatype]
- distance: Distance
- hnsw_config: Optional[HnswConfigDiff]
- model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- multivector_config: Optional[MultiVectorConfig]
- class VectorParamsDiff(*, hnsw_config: Optional[HnswConfigDiff] = None, quantization_config: Optional[Union[ScalarQuantization, ProductQuantization, BinaryQuantization, Disabled]] = None, on_disk: Optional[bool] = None)[source]
Bases:
BaseModel- hnsw_config: Optional[HnswConfigDiff]
- class VectorStorageDatatype(value)[source]
Bases:
str,EnumStorage types for vectors
- class VectorStorageTypeOneOf(value)[source]
Bases:
str,EnumStorage in memory (RAM) Will be very fast at the cost of consuming a lot of memory.
- class VectorStorageTypeOneOf1(value)[source]
Bases:
str,EnumStorage in mmap file, not appendable Search performance is defined by disk speed and the fraction of vectors that fit in memory.
- class VectorStorageTypeOneOf2(value)[source]
Bases:
str,EnumStorage in chunked mmap files, appendable Search performance is defined by disk speed and the fraction of vectors that fit in memory.
- class VectorStorageTypeOneOf3(value)[source]
Bases:
str,EnumSame as ChunkedMmap, but vectors are forced to be locked in RAM In this way we avoid cold requests to disk, but risk to run out of memory Designed as a replacement for Memory, which doesn't depend on RocksDB
- class VersionInfo(*, title: str, version: str, commit: Optional[str] = None)[source]
Bases:
BaseModel
- class WalConfig(*, wal_capacity_mb: int, wal_segments_ahead: int)[source]
Bases:
BaseModel
- class WalConfigDiff(*, wal_capacity_mb: Optional[int] = None, wal_segments_ahead: Optional[int] = None)[source]
Bases:
BaseModel
- class WebApiTelemetry(*, responses: Dict[str, Dict[str, OperationDurationStatistics]])[source]
Bases:
BaseModel- model_config: ClassVar[ConfigDict] = {}
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- responses: Dict[str, Dict[str, OperationDurationStatistics]]
- class WithLookup(*, collection: str, with_payload: Optional[Union[bool, List[str], PayloadSelectorInclude, PayloadSelectorExclude]] = None, with_vectors: Optional[Union[bool, List[str]]] = None)[source]
Bases:
BaseModel
- class WriteOrdering(value)[source]
Bases:
str,EnumDefines write ordering guarantees for collection operations * weak - write operations may be reordered, works faster, default * medium - write operations go through dynamically selected leader, may be inconsistent for a short period of time in case of leader change * strong - Write operations go through the permanent leader, consistent, but may be unavailable if leader is down
