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Change list:
-- Remove 3.x release notes for CBL
-- Update 1.0.0 vector search relnotes to point to 4.0 doc
-- Add placeholder 1.0.0 CBL release notes
-- Update gs-downloads to point to 4.0 packages
-- Update whatsnew page to 4.0 content
-- Update global antora.yml to reflect version 4.0.0
NOTE: Couchbase Lite 3.0 introduces some breaking changes. +
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If you're upgrading from 2.x, refer to the appropriate upgrade page -- see: <<lbl-upgrade>>
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Users should be able to upgrade to 3.1.x from 3.0.x without manual intervention.
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NOTE: Couchbase Lite 4.0 introduces some breaking changes. +
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If you're upgrading from 3.x, refer to the appropriate upgrade page -- see: <<lbl-upgrade>>
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You cannot downgrade from 4.0 to earlier versions of Couchbase Lite.
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== Release 3.2.1 (November 2024)
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== Release 4.0.0 (Q1 2025)
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=== New Features
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==== Array UNNEST and the Array Index
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==== Version Vectors
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You can use UNNEST in queries to unpack arrays within a document into individual rows. This capability makes it possible to join them with their parent object in the query.
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NOTE: TODO
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You can use UNNEST within the FROM clause.
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You can chain UNNEST to perform multi-level UNNEST.
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==== Mobile XDCR Coexistence
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You can also use a new type of index, the Array Index, to allow querying with UNNEST more efficiently.
IMPORTANT: Databases upgraded from 3.1.x to 3.2.x cannot be downgraded.
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=== New Features
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==== Vector Search
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[IMPORTANT]
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--
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Vector Search is available only for 64-bit architectures and
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Intel processors that support the Advanced Vector Extensions 2 (AVX2) instruction set.
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To verify whether your device supports the AVX2 instructions set, https://www.intel.com/content/www/us/en/support/articles/000090473/processors/intel-core-processors.html[follow these instructions.]
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--
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Vector Search is now available on Couchbase Lite for all platforms.
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Vector Search is a sophisticated data retrieval technique that focuses on matching the contextual meanings of search queries and data entries, rather than simple text matching.
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Vectors are represented by arrays of numbers known as embeddings, which are generated by Large Language Models (LLMs) to represent objects such as text, images, and audio.
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You can use Vector Search to efficiently find similar items or content based on the similarity of their vector representations.
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This is useful for reducing the cost per query, performing semantic or similarity search, providing recommendations among others.
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