The dimension of the query vector must match the dimension of your index.
The score returned from query requests is a normalized value between 0 and 1,
where 1 indicates the highest similarity and 0 the lowest regardless of the
similarity function used.
Arguments
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There are two ways to use the query method. You can either create the vectors on your own and pass directly the
vector or sparseVector field, depending on your index type. Or you can pass the data field and create the embeddings using Upstash Embedding.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
topK vectors will be returned.Whether to include the metadata of the vectors in the response. Setting
this
true would be the best practice, since it will make it easier to
identify the vectors.Whether to include the vector themselves in the response.
Whether to include the data field in the response.
The metadata filtering of the vector. This is used to query your data based on the filters and narrow down the query results.
If you wanna learn more about filtering check: Metadata Filtering
For sparse vectors, 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.
Fusion algorithm to use while fusing scores from dense and sparse components of a hybrid index. If not provided, defaults to
RRF.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.Hide child attributes
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Namespace to query. If not set, default namespace is used.
Response
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The ID of the resulting vector.
The score of the vector data, calculated based on the distance metric of your index.
The resulting vector (if
includeVectors is set to true)The resulting sparseVector (if
includeVectors is set to true)The metadata of the vector (if
includeMetadata is set to true)The data of the vector (if
includeData is set to true)await index.query({
topK: 2,
vector: [ ... ],
includeMetadata: true,
includeVectors: true
}, { namespace: "my-namespace" })
/*
[
{
id: '6345',
score: 0.85,
vector: [],
metadata: {
sentence: "Upstash is great."
}
},
{
id: '1233',
score: 0.75,
vector: [],
metadata: undefined
},
]
*/
const results = await index.query({
data: "Movie about an adventure of a hobbit in a fantasy world.",
includeVectors: true,
includeMetadata: true,
topK: 1,
filter: "genre = 'fantasy' and title = 'Lord of the Rings'",
});
/*
[
{
id: "1234",
vector: [0.1, 0.2, 0.3, 0.4, 0.5],
score: 0.9999999,
metadata: {
title: "Lord of The Rings",
genre: "fantasy",
category: "classic",
},
}
]
*/
type Metadata = {
title: string,
genre: 'sci-fi' | 'fantasy' | 'horror' | 'action'
}
const results = await index.query<Metadata>({
vector: [
... // query embedding
],
includeVectors: true,
topK: 1,
filter: "genre = 'fantasy' and title = 'Lord of the Rings'"
})
if (results[0].metadata) {
// Since we passed the Metadata type parameter above,
// we can interact with metadata fields without having to
// do any typecasting.
const { title, genre } = results[0].metadata;
console.log(`The best match in fantasy was ${title}`)
}