Skip to content

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 v1.5.0 release notes

Release date 2024.11.28

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

We’re excited to bring you several updates focused on enhancing your user experience with KDB.AI Cloud UI:

UX Updates (Improvements)

  • YouTube integration: Added a View all button that links directly to our YouTube channel for easy access to all videos.
  • Enhanced navigation: Renamed and reordered tabs for easier access to key features.
  • Streamlined setup: Reorganized and moved Connection panels to the Overview tab for better navigation. Quick-to-watch Video tutorials include duration for added clarity.

The KDB.AI Cloud UI 1.5.0 release also includes the latest capabilities and improvements introduced in the KDB.AI Server 1.5.0 release.

For a complete list of past features and improvements, refer to previous KDB.AI Cloud UI release notes.

KDB.AI Server v1.5.0 release notes

Release date 2024.11.28

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

Welcome to the KDB.AI Server 1.5.0 release, designed to elevate your data management and search capabilities to new heights! Key highlights include:

  1. Partitioning in KDB.AI (New)
  2. Enhanced non-transformed TSS (Improvement)
  3. Range-based similarity search for qFlat index (New)
  4. Optimized search relevance with reranking capability (New)
  5. Multithreading in KDB.AI (New)
  6. Memory-mapping parameter for qHNSW (New)

1. Partitioning in KDB.AI (New)

Manage large data volumes more effectively by partitioning tables on metadata columns!

  • Improved efficiency: Partitioning distributes the workload across multiple shards on disk, enhancing query performance and resource utilization.
  • Enhanced scalability: By partitioning data, vector databases can handle larger datasets more effectively, allowing for horizontal scaling.
  • Similarity-based partitioning: Group similar vectors together to reduce cross-shards-on-disk search times and improve query efficiency.

Tip: Learn about partitioning and how to partition data in KDB.AI. Use cases include time-series data management, geographic data segmentation, and optimizing data retrieval based on specific criteria.

2. Enhanced non-transformed TSS (Improvement)

Unlock the power of KDB.AI’s enhanced non-transformed Temporal Similarity Search:

  • Expanded numeric search: We've broadened our search capabilities to include all numerical formats (for example, reals, longs, ints, and shorts), not just floats, allowing more flexibility in your data searches.
  • Optional matching vector inclusion: Retrieve the matching vector from your search results for more detailed insights. You’re no longer limited to just the row containing the first element of the matching vector!
  • Search by unique column values: Perform searches on each distinct value of a specific column (for example, each ticker symbol), or multiple columns. This eliminates the need for multiple searches and filtering on each unique value.

Tip: Include the matching vector in search results to help your analysts make better decisions. Search by unique column values to streamline data filtering, save time and reduce financial analysis errors. Perform comprehensive queries and enhance the robustness of research analysis with the expanded numeric column search.

3. Range-based similarity search for qFlat index (New)

Discover the benefits of setting a distance threshold to improve classification accuracy.

  • Customizable queries: Use the keyword range to find all vectors within a distance you define.
  • Efficient search: Perform similarity searches for all vectors within a specified distance threshold, enhancing search efficiency across large datasets.
  • Improved relevance: Range search helps you define relevance more precisely and optimize it for a specific dataset, ensuring more accurate and meaningful results.

Tip: Range-based similarity search for qFlat indexing is particularly beneficial for classification tasks, as it helps provide more precise results compared to traditional k-nearest neighbors searches.

4. Optimized search relevance with reranking capability (New)

Unlock the power of KDB.AI’s integration with top rerankers like Cohere, Jina AI, and Voyage, designed to let you reorder query results to match your preferences!

  • Enhanced relevance: Rerankers use advanced natural language processing to grasp the intent behind queries, ensuring the most relevant search outcomes.
  • Handling complex queries: Rerankers excel at capturing nuances and context, delivering accurate results for even the most complex or ambiguous queries.
  • Optimization for specific metrics: Customize search results to meet your unique business goals by optimizing for specific metrics beyond simple relevance.

Tip: Explore reranking in KDB.AI. Enhance search relevance and personalization by learning how to integrate rerankers like Cohere, Jina AI, and Voyage AI within your KDB.AI databases.

5. Multithreading in KDB.AI (New)

Boost your application's performance with multithreading! Configure the number of THREADS each worker uses to optimize parallel execution of tasks.

  • Parallel task execution: Setting the THREADS environment variable allows tasks to be executed simultaneously across multiple threads, significantly speeding up processing times.
  • Adaptability: Easily adjust the number of threads to match your server's resources and workload requirements, ensuring optimal performance.
  • Optimized resource use: Multithreading distributes the workload more effectively, improving resource utilization and query performance.

Tip: Utilize multithreading for tasks such as qHNSW insert, qFlat/qHNSW searches across partitions, and TSS search across partitions to see significant performance improvements. For a comprehensive understanding of how multithreading works and how to optimize the THREADS variable, refer to the dedicated Multithreading page.

6. Memory-mapping parameter for qHNSW (New)

Take your data processing to the next level with the new mmapLevel parameter for qHNSW! This parameter allows you to control how vectors and node connections are accessed during computation.

  • Flexible memory access: The mmapLevel parameter offers three memory mapping options: 0 for fully in memory, 1 for vectors memory mapped and nodes in memory, and 2 for both vectors and node connections memory mapped.
  • Default setting: If not specified, mmapLevel defaults to 1, balancing memory usage and performance.
  • Enhanced control: Adjust the mmapLevel to suit your specific use case and optimize resource utilization. For example, 0 grants the fastest performance, while 2 is the slowest but conserves memory.

Tip: Use mmapLevel to fine-tune your qHNSW index settings for tasks requiring different memory and performance characteristics. For more details on configuring mmapLevel, refer to the How to use indexes - qHNSW documentation.

Documentation

Upgrade procedures

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

Upgrade Sign Up & Download

Summary

We hope you appreciate the enhancements and new features in v1.5.0. Enjoy exploring all the updates! If you need help, reach out to our Support Team at support@kdb.ai or join our Slack channel.

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

Thanks for reading. Stay tuned for the next release!

The KDB.AI Team