> ## 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.

# Index Info

> Returns some information about the index.

Info will be updated eventually, so it might take some time to see the effect of changes in this endpoint.

## Request

This request doesn't require any additional data.

## Response

<ResponseField name="vectorCount" type="number" required>
  The number of vectors in the index, that are ready to use. This is the total
  number of vectors across all namespaces.
</ResponseField>

<ResponseField name="pendingVectorCount" type="number" required>
  The number of vectors in the index, that are still processing and not ready to
  use. This is the total number of pending vectors across all namespaces.
</ResponseField>

<ResponseField name="indexSize" type="number" required>
  The total size of the index, in **bytes**.
</ResponseField>

<ResponseField name="dimension" type="number" required>
  Dimension of the vectors.
</ResponseField>

<ResponseField name="similarityFunction" type="string" required>
  Name of the similarity function used in indexing and queries.
</ResponseField>

<ResponseField name="indexType" type="string" required>
  Type of the index. Possible values: `"DENSE"`, `"SPARSE"`, `"HYBRID"`
</ResponseField>

<ResponseField name="denseIndex" type="object">
  Information about the dense vector index configuration.

  <Expandable>
    <ResponseField name="dimension" type="number" required>
      Dimension of the dense vectors.
    </ResponseField>

    <ResponseField name="similarityFunction" type="string" required>
      Similarity function used for dense vector comparisons.
      Possible values: `"COSINE"`, `"EUCLIDEAN"`, `"DOT_PRODUCT"`
    </ResponseField>

    <ResponseField name="embeddingModel" type="string" required>
      Name of the embedding model used for dense vectors.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="sparseIndex" type="object">
  Information about the sparse vector index configuration.

  <Expandable>
    <ResponseField name="embeddingModel" type="string" required>
      Name of the embedding model used for sparse vectors.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="namespaces" type="object" required>
  Map of namespace names to namespace .

  <Note>Every index has at least one namespace called default namespace, whose name is the empty string `""`.</Note>

  <Expandable defaultOpen="true">
    <ResponseField name="vectorCount" type="number" required>
      The number of vectors in the namespace, that are ready to use.
    </ResponseField>

    <ResponseField name="pendingVectorCount" type="number" required>
      The number of vectors in the namespace, that are still processing
      and not ready to use.
    </ResponseField>
  </Expandable>
</ResponseField>

<RequestExample>
  ```sh curl theme={"system"}
  curl $UPSTASH_VECTOR_REST_URL/info \
    -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN"
  ```
</RequestExample>

<ResponseExample>
  ```json 200 OK theme={"system"}
  {
    "result": {
      "vectorCount": 7,
      "pendingVectorCount": 0,
      "indexSize": 43501,
      "dimension": 1024,
      "similarityFunction": "COSINE",
      "indexType": "HYBRID",
      "denseIndex": {
        "dimension": 1024,
        "similarityFunction": "COSINE",
        "embeddingModel": "BGE_M3"
      },
      "sparseIndex": {
        "embeddingModel": "BM25"
      },
      "namespaces": {
        "": {
          "vectorCount": 6,
          "pendingVectorCount": 0
        },
        "ns": {
          "vectorCount": 1,
          "pendingVectorCount": 0
        }
      }
    }
  }
  ```
</ResponseExample>