Query - Upstash Documentation

The query method is designed to retrieve the most similar vectors from the index, using the specific distance metric defined for your index. This method supports a variety of options to configure the query to your needs.

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

PayloadQueryCommandPayloadrequired

vector | sparseVector | datanumber[] | SparseVector | stringrequired

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 query data/vector that you want to search for in the index.

topKnumberrequired

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.

includeMetadataboolean

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.

includeVectorsboolean

Whether to include the vector themselves in the response.

includeDataboolean

Whether to include the data field in the response.

filterstring

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

weightingStrategyWeightingStrategy

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.

fusionAlgorithmFusionAlgorithm

Fusion algorithm to use while fusing scores from dense and sparse components of a hybrid index. If not provided, defaults to RRF.

queryModeQueryMode

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.

OptionsQueryCommandOptions

Namespacestring

Namespace to query. If not set, default namespace is used.

Response

ResponseQueryResultrequired

idstring | numberrequired

The ID of the resulting vector.

scorenumberrequired

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

vectornumber[]

The resulting vector (if includeVectors is set to true)

sparseVectorSparseVector

The resulting sparseVector (if includeVectors is set to true)

metadataRecord<string, unknown>

The metadata of the vector (if includeMetadata is set to true)

datastring

The data of the vector (if includeData is set to true)

Using Vector

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  },]*/

Using Data

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",    },  }]*/

Improved Typechecking

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}`)}