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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
      }
    }
  }
}

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.

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
      }
    }
  }
}