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Latest KDB.AI release notes

This page details the latest updates to KDB.AI Cloud UI and Server releases. Includes brief, high-level descriptions of fixes, improvements and any new features added.

KDB.AI Cloud UI v1.4.0 release notes

Release date 2024.10.21

To use the latest version, log in or sign up for a free trial.

New

The KDB.AI Cloud UI 1.4.0 release contains the latest capabilities and improvements introduced in the KDB.AI Server 1.4.0 release.

Read previous KDB.AI Cloud UI release notes.

KDB.AI Server v1.4.0 release notes

Release date 2024.10.21

To use the latest version, upgrade your instance or sign up for a free trial.

Welcome to the KDB.AI Server 1.4.0 release, crafted to make your vector search experience faster, more consistent and powerful! Key highlights:

  1. macOS support
  2. Database layer
  3. Multiple indexes
  4. q API
  5. Version information
  6. Enhanced REST API
  7. Optimized kdb+ integration
  8. Enhanced symbol management

1. macOS support (New)

We're excited to announce that KDB.AI is now fully supported on macOS via Docker, bringing you the following benefits:

  • Enhanced Performance: Optimized for macOS to ensure smooth and efficient operation.
  • Seamless Integration: Easily integrate KDB.AI with your existing macOS workflows and applications.

Tip: macOS users can set up and leverage KDB.AI’s advanced features directly on their devices, boosting productivity and streamlining data management tasks.

2. Database layer (New)

We're introducing a new database layer above tables for better data management. Also, you can reference external database tables, such as kdb+ HDB tables.

  • Scalability: Reduces redundancy and simplifies handling large datasets, making it more scalable for enterprise use.
  • Automatic Setup: A default database is created automatically to ease the initial setup process.

Tip: Use the database layer to organize multiple tables and indexes efficiently, reducing complexity in large-scale environments.

3. Multiple indexes (New)

We're thrilled to support multiple indexes that can share the same embedding column. This means there’s no need to duplicate the embeddings. Get ready for:

  • Flexible Index Management: Create and manage multiple indexes on a single table for diverse querying needs.
  • Simultaneous Searches: Execute searches across different indexes at the same time, ideal for multimodal datasets.
  • Dimensional Experimentation: Support for indexes with different dimensions to refine search accuracy.

Tip: Leverage multiple indexes to perform hybrid searches, combining dense and sparse indexes for comprehensive results.

4. q API (New)

A fully documented public q API is here, allowing q developers to use KDB.AI’s features within their q environment, empowering you with:

  • Reduced Friction: Provides a consistent toolset for developing advanced applications.
  • Enhanced Capabilities: Leverage KDB.AI’s power directly in q programming.

Tip: Use the q API to integrate KDB.AI’s advanced search functionalities into your existing q-based applications effortlessly.

5. Version information (New)

Quickly access KDB.AI server version information for compatibility checks in q API, Python API, and REST API.

  • Deployment Management: Helps ensure consistency across different environments.
  • Simplified Troubleshooting: Facilitates easier management of deployments.

Tip: Regularly check version information to maintain compatibility and streamline troubleshooting.

6. Enhanced REST API (Improvement)

1.4.0 brings improved adherence to RESTful conventions for a more user-friendly experience:

  • Consistent Error Handling: Better error management and troubleshooting tips for reliable application development.
  • Developer-Friendly: Enhances the overall developer experience with more intuitive API interactions.

Tip: Use the enhanced REST API to build robust applications with consistent error handling and improved debuggability.

7. Optimized kdb+ integration (Improvement)

  • Direct Access: Query kdb+ tables directly from KDB.AI, maintaining data integrity while utilizing advanced search features.
  • Time Series Analysis: Run Time Series Similarity (TSS) searches on kdb+ tables for insightful decision-making.
  • Optimized Indexing: Create indexes on kdb+ data within KDB.AI without altering original tables.

Tip: Use direct table access to seamlessly integrate kdb+ data with KDB.AI’s search capabilities, streamlining your data analysis workflows.

8. Enhanced symbol management (Improvement)

Symbol atoms are stored in symfiles, which are used to enumerate symbols in splayed or partitioned databases. Using symbols instead of strings can cause inefficiencies, especially when symfiles become large due to numerous non-distinct values. Using Python bytes objects to avoid too many symbols helps, providing:

  • Optimized Storage: Helps in reducing symfile size and improving performance.
  • Performance Management: Prevents degradation from large, non-distinct symbol values.
  • Effective Data Handling: Ensures efficient data processing and retrieval.

Tip: Use strings instead of symbols for large numbers of distinct values to keep symfile sizes manageable. Symbols are effective when you have many repeated values. If each value occurs only once, use strings instead. For more details, refer to the Kx documentation.

Upgrade procedures

To use the latest version, upgrade your instance or sign up for a free trial.

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Summary

We hope you enjoy the v1.4.0 enhancements and new capabilities. Feel free to explore these updates. If you need assistance, reach out to our Support Team at support@kdb.ai or join our Slack channel.

For a complete list of past features and improvements, visit the previous KDB.AI Server release notes.

Thanks for reading. Stay tuned for our next release!

The KDB.AI Team