Release notes
This document contains details about Pinecone releases. For information about using specific features, see our API reference.
September 16, 2022
Public collections
You can now create indexes from public collections, which are collections containing public data from real-world data sources. Currently, public collections include the Glue - SSTB collection, the TREC Question classification collection, and the SQuAD collection.
August 16, 2022
Collections (Public Preview)("Beta")
You can now make static copies of your index using collections. After you create a collection from an index, you can create a new index from that collection. The new index can use any pod type and any number of pods. Collections only consume storage.
This is a public preview feature and is not appropriate for production workloads.
Vertical scaling
You can now change the size of the pods
for a live index to accommodate more vectors or queries without interrupting
reads or writes. The p1 and s1 pod types are now available in 4 different
sizes: 1x, 2x, 4x, and 8x.
Capacity and compute per pod double with each size increment.
p2 pod type (Public Preview)("Beta")
The new p2 pod type provides search speeds of around 5ms and throughput of 200 queries per second per replica, or approximately 10x faster speeds and higher throughput than the p1 pod type, depending on your data and network conditions.
This is a public preview feature and is not appropriate for production workloads.
Improved p1 and s1 performance
The s1 and p1 pod types now offer approximately 50% higher query throughput and 50% lower latency, depending on your workload.
July 26, 2022
You can now specify a metadata
filter to get results for a
subset of the vectors in your index by calling
describe_index_stats
with a
filter
object.
The describe_index_stats operation now uses the POST HTTP request type. The
filter parameter is only accepted by describe_index_stats calls using the
POST request type. Calls to describe_index_stats using the GET request
type are
now deprecated.
July 12, 2022
Pinecone Console Guided Tour
You can now choose to follow a guided tour in the Pinecone Console. This interactive tutorial walks you through creating your first index, upserting vectors, and querying your data. The purpose of the tour is to show you all the steps you need to start your first project in Pinecone.
June 24, 2022
Updated response codes
The
create_index,
delete_index,
and scale_index
operations now use more specific HTTP response codes that describe the type
of operation that succeeded.
June 7, 2022
Selective metadata indexing
You can now store more metadata and more unique metadata values! Select which metadata fields you want to index for filtering and which fields you only wish to store and retrieve. When you index metadata fields, you can filter vector search queries using those fields. When you store metadata fields without indexing them, you keep memory utilization low, especially when you have many unique metadata values, and therefore can fit more vectors per pod.
Single-vector queries
You can now specify a single query vector using the vector
input.
We now encourage all users to query using a single vector rather than a
batch of vectors, because batching queries can lead to long response messages
and query times, and single queries execute just as fast on the server side.
Query by ID
You can now query your Pinecone index using only the ID for another vector. This is useful when you want to search for the nearest neighbors of a vector that is already stored in Pinecone.
Improved index fullness accuracy
The index fullness metric in
describe_index_stats()
results is now more accurate.
April 25, 2022
Partial updates (Public Preview)
You can now perform a partial update by ID and individual value pairs. This allows you to update individual metadata fields without having to upsert a matching vector or update all metadata fields at once.
New metrics
Users on all plans can now see metrics for the past one (1) week in the Pinecone console. Users on the Enterprise and Enterprise Dedicated plan now have access to the following metrics via the Prometheus metrics endpoint:
pinecone_vector_countpinecone_request_count_totalpinecone_request_error_count_totalpinecone_request_latency_secondspinecone_index_fullness(Public Preview)
Note: The accuracy of the pinecone_index_fullness metric is improved. This may result in changes from historic reported values. This metric is in public preview.
Spark Connector
Spark users who want to manage parallel upserts into Pinecone can now use the official Spark connector for Pinecone to upsert their data from a Spark dataframe.
Support for Boolean and float metadata in Pinecone indexes
You can now add Boolean and float64 values to metadata JSON objects associated with a Pinecone index.
New state field in describe_index results
The describe_index operation results now contain a value for state, which describes the state of the index. The possible values for state are Initializing, ScalingUp, ScalingDown, Terminating, and Ready.
Delete by metadata filter
The Delete operation now supports filtering my metadata.