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GET
/
collections
/
{collection_name}
# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key='API_KEY')
pc.describe_collection(name="tiny-collection")

{
    "name": "example-collection",
    "status": "Ready",
    "environment": "us-east-1-aws",
    "size": 3075398,
    "vector_count": 99,
    "dimension": 1536
}

Documentation Index

Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt

Use this file to discover all available pages before exploring further.

# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key='API_KEY')
pc.describe_collection(name="tiny-collection")

{
    "name": "example-collection",
    "status": "Ready",
    "environment": "us-east-1-aws",
    "size": 3075398,
    "vector_count": 99,
    "dimension": 1536
}

Authorizations

​
Api-Key
string
header
required

An API Key is required to call Pinecone APIs. Get yours from the console.

Path Parameters

​
collection_name
string
required

The name of the collection to be described.

Response

Configuration information and status of the collection.

The CollectionModel describes the configuration and status of a Pinecone collection.

​
name
string
required

The name of the collection.

Example:

"example-collection"

​
status
enum<string>
required

The status of the collection.

Available options:
Initializing,
Ready,
Terminating
Example:

"Initializing"

​
environment
string
required

The environment where the collection is hosted.

Example:

"us-east1-gcp"

​
size
integer<int64>

The size of the collection in bytes.

Example:

10000000

​
dimension
integer<int32>

The dimension of the vectors stored in each record held in the collection.

Required range: 1 <= x <= 20000
Example:

1536

​
vector_count
integer<int32>

The number of records stored in the collection.

Example:

120000