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Latest KDB.AI release notes

This section 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 v1.2.1 release notes

Release Date 2024.07.02

Fixes and improvements

KDB.AI Cloud UI 1.2.1 includes a number of bug fixes and internal improvements.

Read previous KDB.AI Cloud UI release notes.

KDB.AI Server v1.2.0 release notes

Release Date 2024.06.27

Welcome to the KDB.AI Server 1.2.0 release! Let's look at the highlights:

  1. Higher performance – launching a series of significant performance enhancements.
  2. On disk indexes: qFlat – a Flat index that's not memory bound, leading to major infrastructure savings.

Now check out what's in store for each:

1. Higher performance (Improvement)

KDB.AI Server 1.2.0 brings considerable speed-ups, demonstrating KDB.AI’s ability to help you build fast, efficient, accurate, cost-predictable, and scalable solutions for your organization’s projects.

2. On-disk indexes: qFlat (New)

Managing indexes on-disk, as opposed to in-memory, provides a low-memory solution for working with large datasets of vector embeddings. That's why the 1.2.0 release introduces an on-disk alternative to the in-memory Flat index.

Labelled qFlat, this index supports all the API create and search capabilities. The only difference is that you need to specify qFlat instead of flat if you prefer the on-disk index to the in-memory one.

Tip

Pick qFlat over Flat if you prioritize capacity over speed. Keep in mind that on-disk is slower but cost-effective, while in-memory is faster but more expensive.

Read more about the differences between in-memory vs. on-disk indexes, then learn how to use the qFlat index.

Note

Please ensure that your data explicitly matches the data type required by your table schema. Not doing this may result in a type error. The example below ensures the correct data type for the StockPrice column.

import numpy as np

table_schema = {
    "columns": [
        {"name": "StockPrice", "pytype": "float32"},
        # rest of schema
    ]}

df = pd.DataFrame()
df = ReadStockData('stock_price_data.csv')
# Ensure correct stock price type
df['StockPrice'] = [np.float32(x) for x in df.StockPrice]

Summary

We hope you'll make the best of the v1.2.0 performance improvements and the new qFlat index. Now feel free to try them out. If you need any help, email our Support Team at support@kdb.ai.

For a full list of features and improvements we rolled out in the past, check out the previous KDB.AI Server release notes.

Download the latest KDB.AI Server version!

Thanks for reading. Stay tuned for the next release!

The KDB.AI team