Shortcuts

qdrant_client.http.api.points_api module

class AsyncPointsApi(api_client: Union[ApiClient, AsyncApiClient])[source]

Bases: _PointsApi

async batch_update(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, update_operations: Optional[UpdateOperations] = None) InlineResponse20014[source]

Apply a series of update operations for points, vectors and payloads

async clear_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, points_selector: Optional[Union[PointIdsList, FilterSelector]] = None) InlineResponse2006[source]

Remove all payload for specified points

async count_points(collection_name: str, timeout: Optional[int] = None, count_request: Optional[CountRequest] = None) InlineResponse20019[source]

Count points which matches given filtering condition

async delete_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, delete_payload: Optional[DeletePayload] = None) InlineResponse2006[source]

Delete specified key payload for points

async delete_points(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, points_selector: Optional[Union[PointIdsList, FilterSelector]] = None) InlineResponse2006[source]

Delete points

async delete_vectors(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, delete_vectors: Optional[DeleteVectors] = None) InlineResponse2006[source]

Delete named vectors from the given points.

async discover_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, discover_request_batch: Optional[DiscoverRequestBatch] = None) InlineResponse20017[source]

Look for points based on target and/or positive and negative example pairs, in batch.

async discover_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, discover_request: Optional[DiscoverRequest] = None) InlineResponse20016[source]

Use context and a target to find the most similar points to the target, constrained by the context. When using only the context (without a target), a special search - called context search - is performed where pairs of points are used to generate a loss that guides the search towards the zone where most positive examples overlap. This means that the score minimizes the scenario of finding a point closer to a negative than to a positive part of a pair. Since the score of a context relates to loss, the maximum score a point can get is 0.0, and it becomes normal that many points can have a score of 0.0. When using target (with or without context), the score behaves a little different: The integer part of the score represents the rank with respect to the context, while the decimal part of the score relates to the distance to the target. The context part of the score for each pair is calculated +1 if the point is closer to a positive than to a negative part of a pair, and -1 otherwise.

async facet(collection_name: str, timeout: Optional[int] = None, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, facet_request: Optional[FacetRequest] = None) InlineResponse20020[source]

Count points that satisfy the given filter for each unique value of a payload key.

async get_point(collection_name: str, id: Union[int[int], str[str]], consistency: Optional[Union[int[int], ReadConsistencyType]] = None) InlineResponse20012[source]

Retrieve full information of single point by id

async get_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, point_request: Optional[PointRequest] = None) InlineResponse20013[source]

Retrieve multiple points by specified IDs

async overwrite_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, set_payload: Optional[SetPayload] = None) InlineResponse2006[source]

Replace full payload of points with new one

async query_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_request_batch: Optional[QueryRequestBatch] = None) InlineResponse20022[source]

Universally query points in batch. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.

async query_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_request: Optional[QueryRequest] = None) InlineResponse20021[source]

Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.

async query_points_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_groups_request: Optional[QueryGroupsRequest] = None) InlineResponse20018[source]

Universally query points, grouped by a given payload field

async recommend_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_request_batch: Optional[RecommendRequestBatch] = None) InlineResponse20017[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples.

async recommend_point_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_groups_request: Optional[RecommendGroupsRequest] = None) InlineResponse20018[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples, grouped by a given payload field.

async recommend_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_request: Optional[RecommendRequest] = None) InlineResponse20016[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples.

async scroll_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, scroll_request: Optional[ScrollRequest] = None) InlineResponse20015[source]

Scroll request - paginate over all points which matches given filtering condition

async search_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_request_batch: Optional[SearchRequestBatch] = None) InlineResponse20017[source]

Retrieve by batch the closest points based on vector similarity and given filtering conditions

async search_matrix_offsets(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_matrix_request: Optional[SearchMatrixRequest] = None) InlineResponse20024[source]

Compute distance matrix for sampled points with an offset based output format

async search_matrix_pairs(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_matrix_request: Optional[SearchMatrixRequest] = None) InlineResponse20023[source]

Compute distance matrix for sampled points with a pair based output format

async search_point_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_groups_request: Optional[SearchGroupsRequest] = None) InlineResponse20018[source]

Retrieve closest points based on vector similarity and given filtering conditions, grouped by a given payload field

async search_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_request: Optional[SearchRequest] = None) InlineResponse20016[source]

Retrieve closest points based on vector similarity and given filtering conditions

async set_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, set_payload: Optional[SetPayload] = None) InlineResponse2006[source]

Set payload values for points

async update_vectors(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, update_vectors: Optional[UpdateVectors] = None) InlineResponse2006[source]

Update specified named vectors on points, keep unspecified vectors intact.

async upsert_points(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, point_insert_operations: Optional[Union[PointsBatch, PointsList]] = None) InlineResponse2006[source]

Perform insert + updates on points. If point with given ID already exists - it will be overwritten.

class SyncPointsApi(api_client: Union[ApiClient, AsyncApiClient])[source]

Bases: _PointsApi

batch_update(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, update_operations: Optional[UpdateOperations] = None) InlineResponse20014[source]

Apply a series of update operations for points, vectors and payloads

clear_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, points_selector: Optional[Union[PointIdsList, FilterSelector]] = None) InlineResponse2006[source]

Remove all payload for specified points

count_points(collection_name: str, timeout: Optional[int] = None, count_request: Optional[CountRequest] = None) InlineResponse20019[source]

Count points which matches given filtering condition

delete_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, delete_payload: Optional[DeletePayload] = None) InlineResponse2006[source]

Delete specified key payload for points

delete_points(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, points_selector: Optional[Union[PointIdsList, FilterSelector]] = None) InlineResponse2006[source]

Delete points

delete_vectors(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, delete_vectors: Optional[DeleteVectors] = None) InlineResponse2006[source]

Delete named vectors from the given points.

discover_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, discover_request_batch: Optional[DiscoverRequestBatch] = None) InlineResponse20017[source]

Look for points based on target and/or positive and negative example pairs, in batch.

discover_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, discover_request: Optional[DiscoverRequest] = None) InlineResponse20016[source]

Use context and a target to find the most similar points to the target, constrained by the context. When using only the context (without a target), a special search - called context search - is performed where pairs of points are used to generate a loss that guides the search towards the zone where most positive examples overlap. This means that the score minimizes the scenario of finding a point closer to a negative than to a positive part of a pair. Since the score of a context relates to loss, the maximum score a point can get is 0.0, and it becomes normal that many points can have a score of 0.0. When using target (with or without context), the score behaves a little different: The integer part of the score represents the rank with respect to the context, while the decimal part of the score relates to the distance to the target. The context part of the score for each pair is calculated +1 if the point is closer to a positive than to a negative part of a pair, and -1 otherwise.

facet(collection_name: str, timeout: Optional[int] = None, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, facet_request: Optional[FacetRequest] = None) InlineResponse20020[source]

Count points that satisfy the given filter for each unique value of a payload key.

get_point(collection_name: str, id: Union[int[int], str[str]], consistency: Optional[Union[int[int], ReadConsistencyType]] = None) InlineResponse20012[source]

Retrieve full information of single point by id

get_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, point_request: Optional[PointRequest] = None) InlineResponse20013[source]

Retrieve multiple points by specified IDs

overwrite_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, set_payload: Optional[SetPayload] = None) InlineResponse2006[source]

Replace full payload of points with new one

query_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_request_batch: Optional[QueryRequestBatch] = None) InlineResponse20022[source]

Universally query points in batch. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.

query_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_request: Optional[QueryRequest] = None) InlineResponse20021[source]

Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries.

query_points_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, query_groups_request: Optional[QueryGroupsRequest] = None) InlineResponse20018[source]

Universally query points, grouped by a given payload field

recommend_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_request_batch: Optional[RecommendRequestBatch] = None) InlineResponse20017[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples.

recommend_point_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_groups_request: Optional[RecommendGroupsRequest] = None) InlineResponse20018[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples, grouped by a given payload field.

recommend_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, recommend_request: Optional[RecommendRequest] = None) InlineResponse20016[source]

Look for the points which are closer to stored positive examples and at the same time further to negative examples.

scroll_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, scroll_request: Optional[ScrollRequest] = None) InlineResponse20015[source]

Scroll request - paginate over all points which matches given filtering condition

search_batch_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_request_batch: Optional[SearchRequestBatch] = None) InlineResponse20017[source]

Retrieve by batch the closest points based on vector similarity and given filtering conditions

search_matrix_offsets(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_matrix_request: Optional[SearchMatrixRequest] = None) InlineResponse20024[source]

Compute distance matrix for sampled points with an offset based output format

search_matrix_pairs(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_matrix_request: Optional[SearchMatrixRequest] = None) InlineResponse20023[source]

Compute distance matrix for sampled points with a pair based output format

search_point_groups(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_groups_request: Optional[SearchGroupsRequest] = None) InlineResponse20018[source]

Retrieve closest points based on vector similarity and given filtering conditions, grouped by a given payload field

search_points(collection_name: str, consistency: Optional[Union[int[int], ReadConsistencyType]] = None, timeout: Optional[int] = None, search_request: Optional[SearchRequest] = None) InlineResponse20016[source]

Retrieve closest points based on vector similarity and given filtering conditions

set_payload(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, set_payload: Optional[SetPayload] = None) InlineResponse2006[source]

Set payload values for points

update_vectors(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, update_vectors: Optional[UpdateVectors] = None) InlineResponse2006[source]

Update specified named vectors on points, keep unspecified vectors intact.

upsert_points(collection_name: str, wait: Optional[bool] = None, ordering: Optional[WriteOrdering] = None, point_insert_operations: Optional[Union[PointsBatch, PointsList]] = None) InlineResponse2006[source]

Perform insert + updates on points. If point with given ID already exists - it will be overwritten.

jsonable_encoder(obj: Any, include: Optional[Union[Set[Union[int, str]], Dict[Union[int, str], Any]]] = None, exclude=None, by_alias: bool = True, skip_defaults: Optional[bool] = None, exclude_unset: bool = True, exclude_none: bool = True)[source]
to_json(model: BaseModel, *args: Any, **kwargs: Any) str[source]

Qdrant

Learn more about Qdrant vector search project and ecosystem

Discover Qdrant

Similarity Learning

Explore practical problem solving with Similarity Learning

Learn Similarity Learning

Community

Find people dealing with similar problems and get answers to your questions

Join Community