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KDB.AI release notes

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

KDB.AI Cloud v1.1.2 release notes

Release Date 2024.05.23

Fixes and improvements

  • KDB.AI sample notebooks can be run directly in Google Colab. No need to install the required libraries on local laptop/environment anymore.

KDB.AI Server v1.1.0 release notes

Release date 2024.03.13

The KDB.AI Server 1.1.0 release is here! This time we focused on enhancing your KDB.AI toolset with three new searches and an integration. Here are the highlights:

1. Hybrid search – combines a sparse vector search with a dense vector search.

2. Transformed TSS – temporal similarity search for transformed data.

3. Non-transformed TSS – temporal similarity search for non-transformed data.

4. LlamaIndex integration – you can now find KDB.AI in the LlamaIndex library.

5. Azure ML integration - includes the latest OpenAI release 1.17.0 and more notebooks to run tests on.

Now let's see what's in store for each new addition:

1. Hybrid search (NEW)

KDB.AI Server 1.1.0 brings a specialized vector search that increases the result relevancy across your applications. It's called a hybrid search and it’s a mix of two powerful search methods:

  • The precision of keyword-based sparse vector search.

  • The contextual understanding of semantic dense vector search.

This means that with KDB.AI, you can now combine and re-rank the results based on a user-set alpha value that weights the importance of each search type.

Tip

Hybrid search is particularly useful in content recommendation systems, customer support automation, legal document search or human resources and recruitment.

Discover more uses cases on our Learning hub, head to our docs to understand the concepts behind this search method, or even better, learn how to conduct a hybrid search.

2. Transformed temporal similarity search (NEW)

Looking for a compression model that dimensionally reduces time series windows by over 99%, while faithfully preserving the original data’s shape? Using the Transformed TSS method, KDB.AI can store and perform lightning-fast vector searches on time-series data.

This breakthrough enables rapid and profound analytical insights, based on the following:

  • 1% of embedding size, 10x increase in search performance.

  • Memory and disk storage can be 1/100th of the original size.

  • Embeddings are auto generated at ingestion; no need for an external model/LLM.

  • When low latency results are more important than low latency data availability.

Leveraging windowed time series data compressed with Transformed TSS on KDB.AI enables the deciphering of emerging trends.

Tip

By providing fast comparison between current data and vast historical datasets, Transformed TSS is a game changer for financial and market predictions; online advertisement optimization, or retail customer understanding.

Head to the KDB.AI Learning hub for detailed use cases, read the docs to understand the main concepts behind this search method, then learn how to perform a Transformed TSS search.

3. Non-transformed temporal similarity search (NEW)

KDB.AI Non-Transformed TSS is a groundbreaking algorithm for near real-time similarity searches across fast-moving time series data. This new feature provides:

  • A precise and efficient method to analyse patterns and trends in time series data.

  • No need to embed, extract, or store time series vectors in the database.

  • The ability to re-configure search without requiring the overhead of re-indexing.

Tip

By enabling pattern matching, outlier detection, and near real-time analysis of datasets, you can count on Non-Transformed TSS for financial market analysis, sensor monitoring, cybersecurity threat detection and prevention, and healthcare monitoring.

Explore more use cases on the KDB.AI Learning hub, read our docs to learn the key terms, and learn how to execute a Non-Transformed TSS search.

4. LlamaIndex integration (NEW)

We're thrilled to introduce the integration of KDB.AI on LlamaIndex, a platform that simplifies the storage and retrieval of public and private datasets for RAG-enabled applications. With LlamaIndex and KDB.AI, developers can easily build popular RAG-enabled applications quickly and at scale, without worrying about the technical details and complexities.

The KDB.AI and LlamaIndex integration:

  • Enables LLMs to return dynamic and timely information.

  • Helps reduce memory requirements.

  • Enhances accessibility when contrasted with processing searches via raw embeddings.

  • Augments the LLM’s native output, resulting in a more precise and contextually relevant response to the user’s question.

Tip

You can combine LlamaIndex and KDB.AI to improve variety of applications, such as: document Q&A, data-augmented chatbots, knowledge base, FAQs, workflows, procedures, structured analytics, and content generation (blogs, articles, books, etc.).

Read more details on our LlamaIndex integration page and try our Advanced RAG with temporal filters using LlamaIndex and KDB.AI vector store notebook.

5. Azure ML integration

The Azure Marketplace package allows you to deploy an Azure ML studio, with an integrated KDB.AI Server 1.1.0. This includes the following third party dependencies:

  • KDB.AI client 1.1.0

  • LangChain OpenAI 0.1.1

  • OpenAI 1.17.0

Discover 6 new example notebooks, together with a much richer and extended set of data you can leverage to run your notebook tests on.

Read more about the Azure setup. If you have an existing Azure KDB.AI Server instance, learn how to upgrade it.

Summary

We hope you’re as excited as we are about the new KDB.AI Server searches and the LlamaIndex integration. Try them out and email our Support Team at support@kdb.ai if you need any help.

Download the latest KDB.AI Server version!

Thanks for reading. Stay tuned for the next release!

The KDB.AI team