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
The number of vectors in the index, that are ready to use. This is the total
number of vectors across all namespaces.
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.
The total size of the index, in bytes.
Dimension of the vectors.
Name of the similarity function used in indexing and queries.
Type of the index. Possible values: "DENSE", "SPARSE", "HYBRID"
Information about the dense vector index configuration.
Dimension of the dense vectors.
Similarity function used for dense vector comparisons.
Possible values: "COSINE", "EUCLIDEAN", "DOT_PRODUCT"
Name of the embedding model used for dense vectors.
Information about the sparse vector index configuration.
Name of the embedding model used for sparse vectors.
Map of namespace names to namespace .
Every index has at least one namespace called default namespace, whose name is the empty string "".
The number of vectors in the namespace, that are ready to use.
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
}
}
}
}
