POST
/
resumable-query
/
{namespace}

Resumable queries allow you to fetch results in batches, which is useful for large result sets or when you want to implement pagination.

Request

vector
number[]
required

The query vector

The query vector should have the same dimensions as your index.

topK
number
default: "10"

The total number of the vectors that you want to receive as a query result. The response will be sorted based on the distance metric score, and at most topK many vectors will be returned.

includeMetadata
boolean
default: "false"

Whether to include the metadata of the vectors in the response, if any. It is recommended to set this to true to easily identify vectors.

includeVectors
boolean
default: "false"

Whether to include the vector values in the response. It is recommended to set this to false as the vector values can be quite big, and not needed most of the time.

includeData
boolean
default: "false"

Whether to include the data of the vectors in the response, if any.

filter
string
default: ""

Metadata filter to apply.

maxIdle
number

Maximum idle time for the resumable query in seconds.

weightingStrategy
string

For sparse vectors of sparse and hybrid indexes, specifies what kind of weighting strategy should be used while querying the matching non-zero dimension values of the query vector with the documents.

If not provided, no weighting will be used.

Only possible value is IDF (inverse document frequency).

fusionAlgorithm
string

Fusion algorithm to use while fusing scores from dense and sparse components of a hybrid index.

If not provided, defaults to RRF (Reciprocal Rank Fusion).

Other possible value is DBSF (Distribution-Based Score Fusion).

Path

namespace
string
default: ""

The namespace to use. When no namespace is specified, the default namespace will be used.

Response

uuid
string
required

A unique identifier for the resumable query.

scores
Object[]
id
string
required

The id of the vector.

score
number
required

The similarity score of the vector, calculated based on the distance metric of your index.

vector
number[]

The dense vector value for dense and hybrid indexes.

sparseVector
Object[]

The sparse vector value for sparse and hybrid indexes.

indices
number[]

Indices of the non-zero valued dimensions.

values
number[]

Values of the non-zero valued dimensions.

metadata
Object

The metadata of the vector, if any.

data
string

The unstructured data of the vector, if any.