qdrant_client.http.api.points_api module

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

Bases: _PointsApi

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

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

Remove all payload for specified points

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

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

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

Delete points

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Bases: _PointsApi

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

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

Remove all payload for specified points

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

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

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

Delete points

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Set payload values for points

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

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

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)

to_json(model: BaseModel, *args: Any, **kwargs: Any) → str