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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 v1.3.0 release notes

Release date 2024.09.19

New

The KDB.AI Cloud UI 1.2.4 release contains all the latest capabilities and enhancements introduced in the KDB.AI Server 1.3.0 release.

Read previous KDB.AI Cloud UI release notes.

KDB.AI Server v1.3.0 release notes

Release date 2024.09.19

Requires Python client >= 1.3.0

Welcome to the KDB.AI Server 1.3.0 release! Key highlights:

  1. Fuzzy filtering on metadata
  2. On-disk index: qHNSW
  3. Early Access program
  4. Data migration
  5. Integration with Unstructured.io

1. Fuzzy filtering on metadata (New)

We’ve added support for fuzzy filtering on metadata, allowing for more flexible and approximate matching in queries. This enhancement significantly improves search relevance and result accuracy, making it easier to find what you’re looking for even with imperfect data.

Fuzzy filters are the ultimate game changers when dealing with data that might include errors, typos, or variations. For instance, applying fuzzy filters to searches allows you to locate documents with terms similar to your query, even if they aren’t an exact match.

Tip

Fuzzy filters have a wide range of applications across various domains. Key use cases include spell checking, data cleaning, autocomplete and suggestions, information retrieval, product matching in e-commerce, name matching in databases, geographic search, and code search.

Discover the concepts behind fuzzy filtering and head to our How to use fuzzy filters section to learn how to apply this method to your searches.

2. On-disk index: qHNSW (New)

Introducing the qHNSW index, a high-performance Hierarchical Navigable Small Worlds (HNSW) index that you can store and access directly on disk. This means you can use large indexes without the need for equivalent amounts of RAM, optimizing performance and resource utilization.

When stored on disk, the qHNSW index maintains its hierarchical graph structure, where vertices are connected based on their distances. This setup is crucial for enabling quick and efficient traversal through the graph during searches.

Tip

qHNSW can be extremely handy in several use cases such as web searches, e-commerce, Content-Based Image Retrieval (CBIR), genomic data search, protein structure search, geospatial applications, and financial fraud detection.

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

3. Early Acesss for KDB.AI integration with kdb+ (New)

We're thrilled to unveil an Early Access program for KDB.AI integration with kdb+. Participants will be provided with code samples to facilitate an understanding of how to set up this integration. If you're keen on joining, please send us an e-mail to obtain early insights and contribute to the development journey.

More about kdb+/q

kdb+ is a powerful column-based relational time series database (TSDB) with in-memory (IMDB) abilities. Built on top of the q programming language, kdb+ allows to operate directly on data. kdb+ is widely used in High-Frequency Trading (HFT) for storing, analyzing, processing, and retrieving large datasets at high speed.

4. Data migration (Improvement)

For users of previous versions such as 1.1.0 or 1.2.0 who wish to retain their existing data, please reach out to KX for detailed migration instructions. Our team is ready to assist you in ensuring a smooth transition to the latest version. Feel free to reach out if you have any questions or need further assistance!

5. Integration with Unstructured.io (New)

KDB.AI is now a destination connector on Unstructured.io, a popular ETL (Extract, Transform, Load) platform for unstructured data. This makes it much easier to ingest various complex documents into your KDB.AI vector database.

The benefits of this integration are:

  • Supports formats like plain text (TXT, XML), documents (CSV, HTML, PDF, and PPTX) and images (PNG, JPG).
  • Simplifies and speeds up the efficient transformation of unstructured data into structured formats.
  • More accurate LLM-generated RAG responses based on the ingestion of semantically relevant data into your vector database.

Tip

Rely on this integration to process and analyze unstructured data effectively and achieve effortless document extraction. Get ready to improve recommendation systems and chatbots by seamlessly handling both structured and unstructured data.

To see the integration at work, go to the Unstructured integration page and follow the steps.

Summary

We hope you enjoy the v1.3.0 enhancements, new index and integrations. 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.

Download the latest KDB.AI Server version!

Thanks for reading. Stay tuned for our next release!

The KDB.AI Team