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

​
vectorCount
number
required
The number of vectors in the index, that are ready to use. This is the total number of vectors across all namespaces.
​
pendingVectorCount
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.
​
indexSize
number
required
The total size of the index, in bytes.
​
dimension
number
required
Dimension of the vectors.
​
similarityFunction
string
required
Name of the similarity function used in indexing and queries.
​
indexType
string
required
Type of the index. Possible values: "DENSE", "SPARSE", "HYBRID"
​
denseIndex
object
Information about the dense vector index configuration.
​
sparseIndex
object
Information about the sparse vector index configuration.
​
namespaces
object
required
Map of namespace names to namespace .
Every index has at least one namespace called default namespace, whose name is the empty string "".
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
      }
    }
  }
}