Index Info

Returns some information about the index.

GET /info

curl

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

200 OK

{
  "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.

Show child attributes

dimension · number · required

Dimension of the dense vectors.

similarityFunction · string · required

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

embeddingModel · string · required

Name of the embedding model used for dense vectors.

sparseIndex · object

Information about the sparse vector index configuration.

Show child attributes

embeddingModel · string · required

Name of the embedding model used for sparse vectors.

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 "".

Hide child attributes

vectorCount · number · required

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

pendingVectorCount · number · required

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

curl

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

200 OK

{
  "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
      }
    }
  }
}