qdrant_client.local.async_qdrant_local module

class AsyncQdrantLocal(location: str, force_disable_check_same_thread: bool = False)

基类: AsyncQdrantBase

Qdrant 服务器能做的所有事情，都在本地完成。

使用此实现可以在不运行 Qdrant 服务器的情况下进行向量搜索。本地 Qdrant 可用的所有功能，服务器版 Qdrant 也都可用。

适用于小规模数据、演示和测试。如果您需要更高的速度或容量，请使用 Qdrant 服务器。

async batch_update_points(collection_name: str, update_operations: Sequence[Union[UpsertOperation, DeleteOperation, SetPayloadOperation, OverwritePayloadOperation, DeletePayloadOperation, ClearPayloadOperation, UpdateVectorsOperation, DeleteVectorsOperation]], **kwargs: Any) → list[UpdateResult]

async clear_payload(collection_name: str, points_selector: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) → UpdateResult

async close(**kwargs: Any) → None

async collection_exists(collection_name: str, **kwargs: Any) → bool

async count(collection_name: str, count_filter: Optional[Union[Filter, Filter]] = None, exact: bool = True, **kwargs: Any) → CountResult

async create_collection(collection_name: str, vectors_config: Optional[Union[VectorParams, Mapping[str, VectorParams]]] = None, init_from: Optional[Union[InitFrom, str]] = None, sparse_vectors_config: Optional[Mapping[str, SparseVectorParams]] = None, **kwargs: Any) → bool

async create_full_snapshot(**kwargs: Any) → SnapshotDescription

async create_payload_index(collection_name: str, field_name: str, field_schema: Optional[Union[PayloadSchemaType, KeywordIndexParams, IntegerIndexParams, FloatIndexParams, GeoIndexParams, TextIndexParams, BoolIndexParams, DatetimeIndexParams, UuidIndexParams, int, PayloadIndexParams]] = None, field_type: Optional[Union[PayloadSchemaType, KeywordIndexParams, IntegerIndexParams, FloatIndexParams, GeoIndexParams, TextIndexParams, BoolIndexParams, DatetimeIndexParams, UuidIndexParams, int, PayloadIndexParams]] = None, **kwargs: Any) → UpdateResult

async create_shard_key(collection_name: str, shard_key: Union[int, str], shards_number: Optional[int] = None, replication_factor: Optional[int] = None, placement: Optional[list[int]] = None, **kwargs: Any) → bool

async create_shard_snapshot(collection_name: str, shard_id: int, **kwargs: Any) → Optional[SnapshotDescription]

async create_snapshot(collection_name: str, **kwargs: Any) → Optional[SnapshotDescription]

async delete(collection_name: str, points_selector: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) → UpdateResult

async delete_collection(collection_name: str, **kwargs: Any) → bool

async delete_full_snapshot(snapshot_name: str, **kwargs: Any) → bool

async delete_payload(collection_name: str, keys: Sequence[str], points: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) → UpdateResult

async delete_payload_index(collection_name: str, field_name: str, **kwargs: Any) → UpdateResult

async delete_shard_key(collection_name: str, shard_key: Union[int, str], **kwargs: Any) → bool

async delete_shard_snapshot(collection_name: str, shard_id: int, snapshot_name: str, **kwargs: Any) → bool

async delete_snapshot(collection_name: str, snapshot_name: str, **kwargs: Any) → bool

async delete_vectors(collection_name: str, vectors: Sequence[str], points: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) → UpdateResult

async discover(collection_name: str, target: Optional[Union[int, str, List[float], SparseVector, TargetVector]] = None, context: Optional[Sequence[Union[ContextExamplePair, ContextExamplePair]]] = None, query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, offset: int = 0, with_payload: Union[bool, list[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector] = True, with_vectors: Union[bool, list[str]] = False, using: Optional[str] = None, lookup_from: Optional[Union[LookupLocation, LookupLocation]] = None, consistency: Optional[Union[int, ReadConsistencyType]] = None, timeout: Optional[int] = None, **kwargs: Any) → list[ScoredPoint]

async discover_batch(collection_name: str, requests: Sequence[Union[DiscoverRequest, DiscoverPoints]], **kwargs: Any) → list[list[ScoredPoint]]

async facet(collection_name: str, key: str, facet_filter: Optional[Union[Filter, Filter]] = None, limit: int = 10, exact: bool = False, **kwargs: Any) → FacetResponse

async get_aliases(**kwargs: Any) → CollectionsAliasesResponse

async get_collection(collection_name: str, **kwargs: Any) → CollectionInfo

async get_collection_aliases(collection_name: str, **kwargs: Any) → CollectionsAliasesResponse

async get_collections(**kwargs: Any) → CollectionsResponse

async get_locks(**kwargs: Any) → LocksOption

async info() → VersionInfo

async list_full_snapshots(**kwargs: Any) → list[SnapshotDescription]

async list_shard_snapshots(collection_name: str, shard_id: int, **kwargs: Any) → list[SnapshotDescription]

async list_snapshots(collection_name: str, **kwargs: Any) → list[SnapshotDescription]

async lock_storage(reason: str, **kwargs: Any) → LocksOption

async overwrite_payload(collection_name: str, payload: Dict[str, Any], points: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) → UpdateResult

async query_batch_points(collection_name: str, requests: Sequence[Union[QueryRequest, QueryPoints]], **kwargs: Any) → list[QueryResponse]

async query_points(collection_name: str, query: Optional[Union[NearestQuery, RecommendQuery, DiscoverQuery, ContextQuery,OrderByQuery, FusionQuery, FormulaQuery, SampleQuery]] = None, using: Optional[str] = None, prefetch: Optional[Union[Prefetch, list[Prefetch]]] = None, query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, offset: Optional[int] = None, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector] = True, with_vectors: Union[bool, Sequence[str]] = False, score_threshold: Optional[float] = None, lookup_from: Optional[Union[LookupLocation, LookupLocation]] = None, **kwargs: Any) → QueryResponse

async query_points_groups(collection_name: str, group_by: str, query: Optional[Union[int, str, PointId, list[float], list[list[float]], SparseVector, NearestQuery, RecommendQuery, DiscoverQuery, ContextQuery,OrderByQuery, FusionQuery, FormulaQuery, SampleQuery, ndarray[tuple[int, ...], dtype[Union[bool, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, longdouble]]], Document, Image, InferenceObject]] = None, using: Optional[str] = None, prefetch: Optional[Union[Prefetch, list[Prefetch]]] = None, query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, group_size: int = 3, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector] = True, with_vectors: Union[bool, Sequence[str]] = False, score_threshold: Optional[float] = None, with_lookup: Optional[Union[str, WithLookup]] = None, lookup_from: Optional[Union[LookupLocation, LookupLocation]] = None, **kwargs: Any) → GroupsResult

async recommend(collection_name: str, positive: Optional[Sequence[Union[int, str, List[float], SparseVector]]] = None, negative: Optional[Sequence[Union[int, str, List[float], SparseVector]]] = None, query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, offset: int = 0, with_payload: Union[bool, list[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector]] = True, with_vectors: Union[bool, list[str]] = False, score_threshold: Optional[float] = None, using: Optional[str] = None, lookup_from: Optional[Union[LookupLocation, LookupLocation]] = None, strategy: Optional[RecommendStrategy]] = None, **kwargs: Any) → list[ScoredPoint]

async recommend_batch(collection_name: str, requests: Sequence[Union[RecommendRequest, RecommendPoints]], **kwargs: Any) → list[list[ScoredPoint]]

async recommend_groups(collection_name: str, group_by: str, positive: Optional[Sequence[Union[int, str, PointId, list[float]]]] = None, negative: Optional[Sequence[Union[int, str, PointId, list[float]]]] = None, query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, group_size: int = 1, score_threshold: Optional[float] = None, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector]] = True, with_vectors: Union[bool, Sequence[str]] = False, using: Optional[str] = None, lookup_from: Optional[Union[LookupLocation, LookupLocation]] = None, with_lookup: Optional[Union[str, WithLookup]] = None, strategy: Optional[RecommendStrategy]] = None, **kwargs: Any) → GroupsResult

async recover_shard_snapshot(collection_name: str, shard_id: int, location: str, **kwargs: Any) → bool

async recover_snapshot(collection_name: str, location: str, **kwargs: Any) → bool

async recreate_collection(collection_name: str, vectors_config: Union[VectorParams, Mapping[str, VectorParams]] = None, init_from: Optional[Union[InitFrom, str]] = None, sparse_vectors_config: Optional[Mapping[str, SparseVectorParams]] = None, **kwargs: Any) → bool

async retrieve(collection_name: str, ids: Sequence[Union[int, str, PointId]], with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector]] = True, with_vectors: Union[bool, Sequence[str]] = False, **kwargs: Any) → list[Record]

async scroll(collection_name: str, scroll_filter: Optional[Union[Filter, Filter]] = None, limit: int = 10, order_by: Optional[Union[str, OrderBy, OrderBy]] = None, offset: Optional[Union[int, str, PointId]] = None, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector]] = True, with_vectors: Union[bool, Sequence[str]] = False, **kwargs: Any) → tuple[list[Record], Union[int, str, points_pb2.PointId, NoneType]]

async search(collection_name: str, query_vector: Union[ndarray[tuple[int, ...], dtype[Union[bool, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, longdouble]]], Sequence[float], tuple[str, list[float]], NamedVector, NamedSparseVector], query_filter: Optional[Union[Filter, Filter]] = None, search_params: Optional[Union[SearchParams, SearchParams]] = None, limit: int = 10, offset: Optional[int] = None, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude, WithPayloadSelector]] = True, with_vectors: Union[bool, Sequence[str]] = False, score_threshold: Optional[float] = None, **kwargs: Any) → list[ScoredPoint]

async search_batch(collection_name: str, requests: Sequence[Union[SearchRequest, SearchPoints]], **kwargs: Any) → list[list[ScoredPoint]]

async search_groups(collection_name: str, query_vector: Union[ndarray[tuple[int, ...], dtype[Union[bool, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, longdouble]]], Sequence[float], tuple[str, list[float]], NamedVector], group_by: str, query_filter: Optional[Filter]] = None, search_params: Optional[SearchParams]] = None, limit: int = 10, group_size: int = 1, with_payload: Union[bool, Sequence[str], PayloadSelectorInclude, PayloadSelectorExclude]] = True, with_vectors: Union[bool, Sequence[str]] = False, score_threshold: Optional[float] = None, with_lookup: Optional[Union[str, WithLookup]] = None, **kwargs: Any) → GroupsResult

async search_matrix_offsets(collection_name: str, query_filter: Optional[Union[Filter, Filter]] = None, limit: int = 3, sample: int = 10, using: Optional[str] = None, **kwargs: Any) → SearchMatrixOffsetsResponse

async search_matrix_pairs(collection_name: str, query_filter: Optional[Union[Filter, Filter]] = None, limit: int = 3, sample: int = 10, using: Optional[str] = None, **kwargs: Any) → SearchMatrixPairsResponse

async set_payload(collection_name: str, payload: Dict[str, Any], points: Union[list[Union[int, str, points_pb2.PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], key: Optional[str] = None, **kwargs: Any) → UpdateResult

async unlock_storage(**kwargs: Any) → LocksOption

async update_collection(collection_name: str, sparse_vectors_config: Optional[Mapping[str, SparseVectorParams]] = None, **kwargs: Any) → bool

async update_collection_aliases(change_aliases_operations: Sequence[Union[CreateAliasOperation, RenameAliasOperation, DeleteAliasOperation, AliasOperations]], **kwargs: Any) → bool

async update_vectors(collection_name: str, points: Sequence[PointVectors], **kwargs: Any) → UpdateResult

upload_collection(collection_name: str, vectors: Union[dict[str, numpy.ndarray[tuple[int, ...], numpy.dtype[Union[numpy.bool, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.uint8, numpy.uint16, numpy.uint32, numpy.uint64, numpy.float16, numpy.float32, numpy.float64, numpy.longdouble]]]], ndarray[tuple[int, ...], dtype[Union[bool, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, longdouble]]], Iterable[Union[List[float], List[List[float]], Dict[str, Union[List[float], SparseVector, List[List[float]], Document, Image, InferenceObject]], Document, Image, InferenceObject]]], payload: Optional[Iterable[dict[Any, Any]]] = None, ids: Optional[Iterable[Union[int, str, PointId]]] = None, **kwargs: Any) → None

upload_points(collection_name: str, points: Iterable[PointStruct], **kwargs: Any) → None

upload_records(collection_name: str, records: Iterable[Record], **kwargs: Any) → None

async upsert(collection_name: str, points: Union[Batch, Sequence[Union[PointStruct, PointStruct]]], **kwargs: Any) → UpdateResult

LARGE_DATA_THRESHOLD = 20000

property closed: bool