Known limitations

This page describes known limitations and feature restrictions in Pinecone.

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General

• Upserts

  • 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.

  • Dense vectors must contain at least one non-zero value.

  • Max length for a record ID is 512 characters.

  • Max dimensionality for dense vectors is 20,000.

  • Sparse vectors can contain no more than 1000 non-zero values.

  • Max dimensionality for sparse vectors is 4.2 billion.

  • Only indexes using the dotproduct distance metric support sparse-dense vectors.

    Upserting sparse-dense vectors into 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.

• Metadata
  • Max metadata size per vector is 40 KB.
  • Null metadata values are not supported. Instead of setting a key to hold a null value, we recommend you remove that key from the metadata payload.
• Queries
  • Max value for top_k, the number of results to return, is 10,000.
• Fetches and deletes
  • Max vectors per fetch or delete request is 1,000.

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Serverless indexes

Serverless indexes do not support the following features:

• Collections
• Deleting records by metadata
  Instead, you can delete records by ID prefix.
• Selective metadata indexing
• Configuring indexes (configure_index())
  Because serverless indexes scale automantically based on usage, this operation is not relevant.
• Describing indexes with metadata filtering
• Metrics

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Pod-based indexes

• Pod storage capacity
  • Each p1 pod has enough capacity for 1M vectors with 768 dimensions.
  • Each s1 pod has enough capacity for 5M vectors with 768 dimensions.
• Metadata
  • Metadata with high cardinality, such as a unique value for every vector in a large index, uses more memory than expected and can cause the pods to become full.
• Collections
  • You cannot query or write to a collection after its creation. For this reason, a collection only incurs storage costs.
  • You can only perform operations on collections in the current Pinecone project.
• Sparse-dense vectors
  • Only s1 and p1 pod-based indexes using the dotproduct distance metric support sparse-dense vectors.