Request
The text data to be embedded and used for querying.
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.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.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.Whether to include the data of the vectors in the response, if any.
Metadata filter to apply.
Maximum idle time for the resumable query in seconds.
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).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).Query mode for hybrid indexes with Upstash-hosted
embedding models.Specifies whether to run the query in only the
dense index, only the sparse index, or in both.If not provided, defaults to
HYBRID
.Possible values are HYBRID
, DENSE
, and SPARSE
.Path
The namespace to use. When no namespace is specified, the default namespace
will be used.