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qdrant_client.local.qdrant_local module

class QdrantLocal(location: str, force_disable_check_same_thread: bool = False)[source]

Bases: QdrantBase

Everything Qdrant server can do, but locally.

Use this implementation to run vector search without running a Qdrant server. Everything that works with local Qdrant will work with server Qdrant as well.

Use for small-scale data, demos, and tests. If you need more speed or size, use Qdrant server.

batch_update_points(collection_name: str, update_operations: Sequence[Union[UpsertOperation, DeleteOperation, SetPayloadOperation, OverwritePayloadOperation, DeletePayloadOperation, ClearPayloadOperation, UpdateVectorsOperation, DeleteVectorsOperation]], **kwargs: Any) List[UpdateResult][source]
clear_payload(collection_name: str, points_selector: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) UpdateResult[source]
close(**kwargs: Any) None[source]
closed() bool[source]
collection_exists(collection_name: str, **kwargs: Any) bool[source]
count(collection_name: str, count_filter: Optional[Union[Filter, Filter]] = None, exact: bool = True, **kwargs: Any) CountResult[source]
create_collection(collection_name: str, vectors_config: Union[VectorParams, Mapping[str, VectorParams]], init_from: Optional[Union[InitFrom, str]] = None, sparse_vectors_config: Optional[Mapping[str, SparseVectorParams]] = None, **kwargs: Any) bool[source]
create_full_snapshot(**kwargs: Any) SnapshotDescription[source]
create_payload_index(collection_name: str, field_name: str, field_schema: Optional[Union[PayloadSchemaType, TextIndexParams, IntegerIndexParams, int, PayloadIndexParams]] = None, field_type: Optional[Union[PayloadSchemaType, TextIndexParams, IntegerIndexParams, int, PayloadIndexParams]] = None, **kwargs: Any) UpdateResult[source]
create_shard_key(collection_name: str, shard_key: Union[str[str], int[int]], shards_number: Optional[int] = None, replication_factor: Optional[int] = None, placement: Optional[List[int]] = None, **kwargs: Any) bool[source]
create_shard_snapshot(collection_name: str, shard_id: int, **kwargs: Any) Optional[SnapshotDescription][source]
create_snapshot(collection_name: str, **kwargs: Any) Optional[SnapshotDescription][source]
delete(collection_name: str, points_selector: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) UpdateResult[source]
delete_collection(collection_name: str, **kwargs: Any) bool[source]
delete_full_snapshot(snapshot_name: str, **kwargs: Any) bool[source]
delete_payload(collection_name: str, keys: Sequence[str], points: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) UpdateResult[source]
delete_payload_index(collection_name: str, field_name: str, **kwargs: Any) UpdateResult[source]
delete_shard_key(collection_name: str, shard_key: Union[str[str], int[int]], **kwargs: Any) bool[source]
delete_shard_snapshot(collection_name: str, shard_id: int, snapshot_name: str, **kwargs: Any) bool[source]
delete_snapshot(collection_name: str, snapshot_name: str, **kwargs: Any) bool[source]
delete_vectors(collection_name: str, vectors: Sequence[str], points: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) UpdateResult[source]
discover(collection_name: str, target: Optional[Union[int[int], str[str], List[float[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[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, **kwargs: Any) List[ScoredPoint][source]
discover_batch(collection_name: str, requests: Sequence[Union[DiscoverRequest, DiscoverPoints]], **kwargs: Any) List[List[ScoredPoint]][source]
get_aliases(**kwargs: Any) CollectionsAliasesResponse[source]
get_collection(collection_name: str, **kwargs: Any) CollectionInfo[source]
get_collection_aliases(collection_name: str, **kwargs: Any) CollectionsAliasesResponse[source]
get_collections(**kwargs: Any) CollectionsResponse[source]
get_locks(**kwargs: Any) LocksOption[source]
list_full_snapshots(**kwargs: Any) List[SnapshotDescription][source]
list_shard_snapshots(collection_name: str, shard_id: int, **kwargs: Any) List[SnapshotDescription][source]
list_snapshots(collection_name: str, **kwargs: Any) List[SnapshotDescription][source]
lock_storage(reason: str, **kwargs: Any) LocksOption[source]
overwrite_payload(collection_name: str, payload: Dict[str, Any], points: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], **kwargs: Any) UpdateResult[source]
recommend(collection_name: str, positive: Optional[Sequence[Union[int[int], str[str], List[float[float]], SparseVector]]] = None, negative: Optional[Sequence[Union[int[int], str[str], List[float[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][source]
recommend_batch(collection_name: str, requests: Sequence[Union[RecommendRequest, RecommendPoints]], **kwargs: Any) List[List[ScoredPoint]][source]
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[str], WithLookup]] = None, strategy: Optional[RecommendStrategy] = None, **kwargs: Any) GroupsResult[source]
recover_shard_snapshot(collection_name: str, shard_id: int, location: str, **kwargs: Any) bool[source]
recover_snapshot(collection_name: str, location: str, **kwargs: Any) bool[source]
recreate_collection(collection_name: str, vectors_config: Union[VectorParams, Mapping[str, VectorParams]], init_from: Optional[Union[InitFrom, str]] = None, sparse_vectors_config: Optional[Mapping[str, SparseVectorParams]] = None, **kwargs: Any) bool[source]
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][source]
scroll(collection_name: str, scroll_filter: Optional[Union[Filter, Filter]] = None, limit: int = 10, order_by: Optional[Union[str[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], Optional[Union[int, str, PointId]]][source]
search(collection_name: str, query_vector: Union[ndarray[Any, dtype[Union[bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, float128]]], 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][source]
search_batch(collection_name: str, requests: Sequence[Union[SearchRequest, SearchPoints]], **kwargs: Any) List[List[ScoredPoint]][source]
search_groups(collection_name: str, query_vector: Union[ndarray[Any, dtype[Union[bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, float128]]], 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[str], WithLookup]] = None, **kwargs: Any) GroupsResult[source]
set_payload(collection_name: str, payload: Dict[str, Any], points: Union[List[Union[int, str, PointId]], Filter, Filter, PointIdsList, FilterSelector, PointsSelector], key: Optional[str] = None, **kwargs: Any) UpdateResult[source]
unlock_storage(**kwargs: Any) LocksOption[source]
update_collection(collection_name: str, **kwargs: Any) bool[source]
update_collection_aliases(change_aliases_operations: Sequence[Union[CreateAliasOperation, RenameAliasOperation, DeleteAliasOperation, AliasOperations]], **kwargs: Any) bool[source]
update_vectors(collection_name: str, points: Sequence[PointVectors], **kwargs: Any) UpdateResult[source]
upload_collection(collection_name: str, vectors: Union[Dict[str, ndarray[Any, dtype[Union[bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, float128]]]], ndarray[Any, dtype[Union[bool_, int8, int16, int32, int64, uint8, uint16, uint32, uint64, float16, float32, float64, float128]]], Iterable[Union[List[float[float]], Dict[str[str], Union[List[float[float]], SparseVector]]]]], payload: Optional[Iterable[Dict[Any, Any]]] = None, ids: Optional[Iterable[Union[int, str, PointId]]] = None, **kwargs: Any) None[source]
upload_points(collection_name: str, points: Iterable[PointStruct], **kwargs: Any) None[source]
upload_records(collection_name: str, records: Iterable[Record], **kwargs: Any) None[source]
upsert(collection_name: str, points: Union[Batch, List[Union[PointStruct, PointStruct]]], **kwargs: Any) UpdateResult[source]

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