This page describes known limitations and feature restrictions in Pinecone.
Pinecone is eventually consistent, so there can be a slight delay before upserted records are available to query.
After upserting records, use the describe_index_stats
operation to check if the current vector count matches the number of records you expect, although this method may not work for pod-based indexes with multiple replicas.
Only indexes using the dotproduct distance metric support querying sparse-dense vectors.
Upserting, updating, and fetching sparse-dense vectors in indexes with a different distance metric will succeed, but querying will return an error.
Indexes created before February 22, 2023 do not support sparse vectors.
Serverless indexes do not support the following features:
s1
and p1
pod-based indexes using the dotproduct distance metric support sparse-dense vectors.This page describes known limitations and feature restrictions in Pinecone.
Pinecone is eventually consistent, so there can be a slight delay before upserted records are available to query.
After upserting records, use the describe_index_stats
operation to check if the current vector count matches the number of records you expect, although this method may not work for pod-based indexes with multiple replicas.
Only indexes using the dotproduct distance metric support querying sparse-dense vectors.
Upserting, updating, and fetching sparse-dense vectors in indexes with a different distance metric will succeed, but querying will return an error.
Indexes created before February 22, 2023 do not support sparse vectors.
Serverless indexes do not support the following features:
s1
and p1
pod-based indexes using the dotproduct distance metric support sparse-dense vectors.