Please use the form below to provide your feedback. Because your feedback is valuable to us, the information you submit in this form is recorded in our issue tracking system (JIRA), which is publicly available. You can track the status of your feedback using the ticket number displayed in the dialog once you submit the form.

You are viewing the documentation for a prerelease version.

View Latest

New in 3.2

    • Beta
      +
      Couchbase Lite 3.0 introduces some breaking changes.
      If you are upgrading from 2.x, please refer to the appropriate upgrade page — see: Upgrading Users should be able to upgrade to 3.1.x from 3.0.x without manual intervention.

      Release 3.2.0 - Beta 2 - .NET (May 2024)

      New Features

      Vector Search is available only for 64-bit architectures and Intel processors that support the Advanced Vector Extensions 2 (AVX2) instruction set. To verify whether your device supports the AVX2 instructions set, follow these instructions.

      A beta version of Vector Search is now available on Couchbase Lite for the .Net platform.

      Read more at:

      Release 3.2.0 - Beta 2 - Java Desktop (May 2024)

      New Features

      Vector Search

      Vector Search is available only for 64-bit architectures and Intel processors that support the Advanced Vector Extensions 2 (AVX2) instruction set. To verify whether your device supports the AVX2 instructions set, follow these instructions.

      A beta version of Vector Search is now available on Couchbase Lite for Java Desktop with updates to the iOS (Swift, Objective-C) and Android platforms.

      Read more at:

      Release 3.2.0 - Beta 1 - iOS and Android (March 2024)

      New Features

      Vector Search

      Vector Search is only available for 64-bit architectures.

      A beta version of Vector Search is now available on Couchbase Lite for iOS (Swift, Objective-C) and Android. 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. 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. You can use Vector Search to efficiently find similar items or content based on the similarity of their vector representations. This is useful for reducing the cost per query, performing semantic or similarity search, providing recommendations among others.

      Read more at:

      Extended Date/Time Functionality

      Six new DateTime functions have been added to Couchbase Lite N1Q1:

      • STR_TO_TZ()

      • MILLIS_TO_TZ()

      • DATE_DIFF_STR()

      • DATE_DIFF_MILLIS()

      • DATE_ADD_STR()

      • DATE_ADD_MILLIS()

      Read more at:

      See also

      What’s new in previous version 3.1

      Release Notes

      The Vector Search beta release is not available for the following platforms:

      • C

      Android | .NET | Java | Objective-C | Swift

      Upgrading

      Android | .NET | Java | Objective-C | Swift