> ## Documentation Index
> Fetch the complete documentation index at: https://upstash.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Resumable Query

The resumableQuery method allows you to perform queries that can be resumed to fetch additional results. This is particularly useful for large result sets or when implementing pagination.

<Note>
  The dimension of the query vector must match the dimension of your index.
</Note>

<Note>
  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.
</Note>

## Arguments

<ResponseField name="Payload" type="ResumableQueryPayload" required>
  <Expandable defaultOpen="true">
    <ResponseField name="vector | sparseVector | data" type="number[] | SparseVector | string" required>
      <Note>
        There are two ways to use the resumableQuery 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.
      </Note>

      The query data/vector that you want to search for in the index.
    </ResponseField>

    <ResponseField name="topK" type="number" required>
      The initial number of vectors to retrieve in the query result. The response will be sorted based on the distance metric score.
    </ResponseField>

    <ResponseField name="includeMetadata" type="boolean">
      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.
    </ResponseField>

    <ResponseField name="includeVectors" type="boolean">
      Whether to include the vector values in the response.
    </ResponseField>

    <ResponseField name="includeData" type="boolean">
      Whether to include [the data field](/vector/features/metadata#data) in the response.
    </ResponseField>

    <ResponseField name="filter" type="string">
      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 want to learn more about filtering check: [Metadata Filtering](/vector/features/filtering)
    </ResponseField>

    <ResponseField name="weightingStrategy" type="WeightingStrategy">
      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.
    </ResponseField>

    <ResponseField name="fusionAlgorithm" type="FusionAlgorithm">
      Fusion algorithm to use while fusing scores from dense and sparse components of a hybrid index. If not provided, defaults to `RRF`.
    </ResponseField>

    <ResponseField name="queryMode" type="QueryMode">
      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`.
    </ResponseField>

    <ResponseField name="maxIdle" type="number">
      Maximum idle time for the resumable query in seconds.
    </ResponseField>
  </Expandable>
</ResponseField>

## Response

<ResponseField name="ResumableQueryResponse" type="Object" required>
  <Expandable defaultOpen="true">
    <ResponseField name="result" type="QueryResult[]" required>
      The initial query results.
    </ResponseField>

    <ResponseField name="fetchNext" type="(additionalK: number) => Promise<Vector[]>" required>
      A function to fetch the next batch of results.
    </ResponseField>

    <ResponseField name="stop" type="() => Promise<string>" required>
      A function to stop the resumable query and release resources.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="QueryResult" type="Object" required>
  <Expandable defaultOpen="true">
    <ResponseField name="id" type="string | number" required>
      The ID of the resulting vector.
    </ResponseField>

    <ResponseField name="score" type="number" required>
      The score of the vector data, calculated based on the distance metric of your index.
    </ResponseField>

    <ResponseField name="vector" type="number[]">
      The resulting vector (if `includeVectors` is set to true)
    </ResponseField>

    <ResponseField name="sparseVector" type="SparseVector">
      The resulting sparseVector (if `includeVectors` is set to true)
    </ResponseField>

    <ResponseField name="metadata" type="Record<string, unknown>">
      The metadata of the vector (if `includeMetadata` is set to true)
    </ResponseField>

    <ResponseField name="data" type="string">
      The data of the vector (if `includeData` is set to true)
    </ResponseField>
  </Expandable>
</ResponseField>

<RequestExample>
  ```typescript with Vector theme={"system"}
  const { result, fetchNext, stop } = await index.resumableQuery({
    maxIdle: 3600,
    topK: 50,
    vector: [0, 1, 2, ..., 383], // 384-dimensional vector
    includeMetadata: true,
    includeVectors: true,
  });

  console.log(result);
  /*
  [
    {
      id: '6345',
      score: 1.00000012,
      vector: [0, 1, 2, ..., 383],
      metadata: {
        sentence: "Upstash is great."
      }
    },
    // ... more results
  ]
  */

  const nextBatch = await fetchNext(5); // Fetch next 5 results
  console.log(nextBatch);

  await stop(); // Stop the resumable query
  ```

  ```typescript with Data theme={"system"}
  const { result, fetchNext, stop } = await index.resumableQuery({
    maxIdle: 3600,
    topK: 50,
    data: "lord of the rings"
    includeMetadata: true,
    includeData: true,
  });

  console.log(result);
  /*
  [
    {
      id: '6345',
      score: 1.00000012,
      data: "hobbit",
      metadata: {
        sentence: "Upstash is great."
      }
    },
    // ... more results
  ]
  */

  const nextBatch = await fetchNext(5); // Fetch next 5 results
  console.log(nextBatch);

  await stop(); // Stop the resumable query
  ```

  ```typescript with Metadata Type theme={"system"}
  type Metadata = {
    title: string,
    genre: 'sci-fi' | 'fantasy' | 'horror' | 'action'
  }

  const { result, fetchNext, stop } = await index.resumableQuery<Metadata>({
    vector: [
      ... // query embedding
    ],
    includeMetadata: true,
    topK: 1,
    filter: "genre = 'fantasy' and title = 'Lord of the Rings'",
    maxIdle: 3600,
  })

  if (result[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 } = result[0].metadata;
    console.log(`The best match in fantasy was ${title}`)
  }

  await stop();
  ```
</RequestExample>