Index Info - Upstash Documentation

curl $UPSTASH_VECTOR_REST_URL/info \
  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN"

{
  "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
      }
    }
  }
}

GET

info

curl $UPSTASH_VECTOR_REST_URL/info \
  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN"

{
  "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
      }
    }
  }
}

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

number

required

The number of vectors in the index, that are ready to use. This is the total number of vectors across all namespaces.

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.

number

required

The total size of the index, in bytes.

number

required

Dimension of the vectors.

string

required

Name of the similarity function used in indexing and queries.

string

required

Type of the index. Possible values: "DENSE", "SPARSE", "HYBRID"

object

Information about the dense vector index configuration.

Show child attributes

number

required

Dimension of the dense vectors.

string

required

Similarity function used for dense vector comparisons. Possible values: "COSINE", "EUCLIDEAN", "DOT_PRODUCT"

string

required

Name of the embedding model used for dense vectors.

object

Information about the sparse vector index configuration.

Show child attributes

string

required

Name of the embedding model used for sparse vectors.

object

required

Map of namespace names to namespace .

Every index has at least one namespace called default namespace, whose name is the empty string "".

Hide child attributes

number

required

The number of vectors in the namespace, that are ready to use.

number

required

The number of vectors in the namespace, that are still processing and not ready to use.

curl $UPSTASH_VECTOR_REST_URL/info \
  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN"

{
  "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
      }
    }
  }
}

​Request

​Response

Request

Response