Working with Collections

Collections are read-only snapshots of pod indexes. Use them to back up index data, duplicate an index, or restore a known-good state. Collections are only supported for pod-based indexes — serverless indexes use backups instead.

Create a collection

Pass the name of the pod index you want to snapshot:

python

from pinecone import Pinecone

pc = Pinecone(api_key="your-api-key")

collection = pc.collections.create(name="snap-2025-01", source="my-pod-index")
print(collection.status)   # "Initializing" immediately after creation

The collection transitions through Initializing → Ready when the snapshot is complete. create returns immediately without polling; check status with describe.

List collections

list returns a {class}~pinecone.models.collections.list.CollectionList you can iterate or call .names() on:

python

for col in pc.collections.list():
    print(col.name, col.status)

python

names = pc.collections.list().names()
print(names)   # e.g. ["snap-2025-01", "archive-q3"]

Describe a collection

describe returns a {class}~pinecone.models.collections.model.CollectionModel with detailed information:

python

col = pc.collections.describe("snap-2025-01")
print(col.name)          # "snap-2025-01"
print(col.status)        # "Ready"
print(col.dimension)     # vector dimension
print(col.vector_count)  # number of vectors stored
print(col.size)          # size in bytes
print(col.environment)   # cloud environment

Poll until ready after creation:

python

import time

while True:
    col = pc.collections.describe("snap-2025-01")
    if col.status == "Ready":
        break
    time.sleep(5)

Delete a collection

python

pc.collections.delete("snap-2025-01")

delete raises {exc}~pinecone.exceptions.NotFoundError if the collection does not exist.

Create an index from a collection

Pass source_collection inside a {class}~pinecone.PodSpec to restore collection data into a new pod index:

python

from pinecone import Pinecone
from pinecone.models.indexes.specs import PodSpec

pc = Pinecone(api_key="your-api-key")

pc.indexes.create(
    name="restored-index",
    dimension=1536,
    metric="cosine",
    spec=PodSpec(
        environment="us-east-1-aws",
        pod_type="p1.x1",
        source_collection="snap-2025-01",
    ),
)

The new index is pre-populated with all vectors from the collection snapshot.