This page shows you how to use the [fetch]/reference/api/data-plane/fetch) operation to fetch records by ID from an index namespace. The returned records are complete, including all relevant dense vector, metadata, and sparse vector values.

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Fetch records from the default namespace

To fetch records from the default namespace ("") in an index, specify the record IDs but no namespace:

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("pinecone-index")

index.fetch(["id-1", "id-2"])

# Returns:
# {'namespace': '',
#  'usage': {'readUnits': 1},
#  'vectors': {'id-1': {'id': 'id-1',
#                       'values': [0.568879, 0.632687092, 0.856837332, ...]},
#              'id-2': {'id': 'id-2',
#                       'values': [0.00891787093, 0.581895, 0.315718859, ...]}}}

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Fetch records from a non-default namespace

To fetch records from a non-default namespace in an index, specify the record IDs and the namespace:

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("pinecone-index")

index.fetch(ids=["id-1", "id-2"], namespace="ns1")

# Returns:
# {'namespace': 'ns1',
#  'usage': {'readUnits': 1},
#  'vectors': {'id-1': {'id': 'id-1',
#                       'values': [0.568879, 0.632687092, 0.856837332, ...]},
#              'id-2': {'id': 'id-2',
#                       'values': [0.00891787093, 0.581895, 0.315718859, ...]}}}

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Data freshness

Pinecone is eventually consistent, so there can be a slight delay before new or changed records are visible to queries. You can use the describe_index_stats operation to check data freshness.

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