Pinecone · Schema

IndexDescription

The response for the `describe_index_stats` operation.

Vector DatabasesAIEmbeddingsRAG

Properties

Name	Type	Description
namespaces	object	A mapping for each namespace in the index from the namespace name to a summary of its contents. If a metadata filter expression is present, the summary will reflect only vectors matching that expressi
dimension	integer	The dimension of the indexed vectors. Not specified if `sparse` index.
indexFullness	number	The fullness of the index, regardless of whether a metadata filter expression was passed. The granularity of this metric is 10%. Serverless indexes scale automatically as needed, so index fullness is
totalVectorCount	integer	The total number of vectors in the index, regardless of whether a metadata filter expression was passed
metric	string	The metric used to measure similarity.
vectorType	string	The type of vectors stored in the index.
memory_fullness	number	The amount of memory used by a dedicated index
storage_fullness	number	The amount of storage used by a dedicated index

View JSON Schema on GitHub

JSON Schema

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "#/components/schemas/IndexDescription",
  "title": "IndexDescription",
  "example": {
    "dimension": 1024,
    "index_fullness": 0.4,
    "namespaces": {
      "": {
        "vectorCount": 50000
      },
      "example-namespace-2": {
        "vectorCount": 30000
      }
    },
    "totalVectorCount": 80000
  },
  "description": "The response for the `describe_index_stats` operation.",
  "type": "object",
  "properties": {
    "namespaces": {
      "description": "A mapping for each namespace in the index from the namespace name to a summary of its contents. If a metadata filter expression is present, the summary will reflect only vectors matching that expression.",
      "type": "object",
      "additionalProperties": {
        "$ref": "#/components/schemas/NamespaceSummary"
      }
    },
    "dimension": {
      "example": 1024,
      "description": "The dimension of the indexed vectors. Not specified if `sparse` index.",
      "type": "integer",
      "format": "int64"
    },
    "indexFullness": {
      "example": 0.4,
      "description": "The fullness of the index, regardless of whether a metadata filter expression was passed. The granularity of this metric is 10%.\n\nServerless indexes scale automatically as needed, so index fullness  is relevant only for pod-based indexes.\n\nThe index fullness result may be inaccurate during pod resizing; to get the status of a pod resizing process, use [`describe_index`](https://docs.pinecone.io/reference/api/2024-10/control-plane/describe_index).",
      "type": "number",
      "format": "float"
    },
    "totalVectorCount": {
      "example": 80000,
      "description": "The total number of vectors in the index, regardless of whether a metadata filter expression was passed",
      "type": "integer",
      "format": "int64"
    },
    "metric": {
      "example": "cosine",
      "description": "The metric used to measure similarity.",
      "type": "string"
    },
    "vectorType": {
      "example": "dense",
      "description": "The type of vectors stored in the index.",
      "type": "string"
    },
    "memory_fullness": {
      "description": "The amount of memory used by a dedicated index",
      "type": "number",
      "format": "float"
    },
    "storage_fullness": {
      "description": "The amount of storage used by a dedicated index",
      "type": "number",
      "format": "float"
    }
  }
}