4 posts tagged with "embeddings"

There a few reasons why we would want to perform "generative AI" tasks, such as generating embeddings, summary, and text, using local resources, rather than relying on external, third-party providers. Perhaps the most important ones are data privacy, costs, and the convenience of choosing what large language models (LLMs) to use. About four months ago, I wrote that with the Oracle Database 23ai 23.5.0.24.06 release on the Oracle Autonomous Database (ADB), it was possible to create the credentials to access these third-party LLMs through their REST APIs. Fast-forward, my ADB now has the upgraded version 23.6.0.24.10 that has support for "local" third-party REST providers, specifically, Ollama. In this article, I will demonstrate how you can utilise other Oracle Cloud Infrastructure (OCI) managed resources to quickly get started.

In my previous blog post on Get Started with Oracle Database 23ai AI Vector Search, I mentioned that it was not yet possible to use third-party REST APIs to perform tasks like generating embeddings and text.

This is only the tip of the iceberg. Next up is text generation but there is currently an issue that prohibits the adding of credentials required to work with third-party REST APIs using the DBMS_VECTOR and DBMS_VECTOR_CHAIN packages on the ADB. Hopefully, this will be fixed very soon.

I am happy to post that with the latest 23.5.0.24.06 database release update, this is no longer an issue!

While preparing for my upcoming Kscope24 presentations, I thought I'd introduce a section on Oracle's recently announced AI Vector Search that is now generally available with the Oracle Database 23ai rebranding and release. At the time of writing, you can either provision the new version of the database on the Oracle Cloud Infrastructure (OCI), download the Oracle Autonomous Database (ADB) Free container image, or the Oracle Database 23ai Free. On the OCI, you can either provision an Always Free ADB, or an Oracle Base Database service instance.

The documentation provided by Oracle is comprehensive and should get you started. Here, I condensed everything that I have learned so far into a step-by-step guide to get you started quickly.

In the last one year since OpenAI popularised generative AI, a lot of the excitement and intrigue have been centred on the generative power of large language models (LLMs) or foundational models. Understandably so. However, the generative power of this models only cover a segment of business needs. The underlying promise of these frontier models is in its ability to "understand" natural language, and that to me, is where we will find more utility.

For the last few years at Insum, we've had an almost biannual tradition of reviewing what's been going on in the AI/ML space, in particular, around what Oracle has been doing to provide innovative solutions and tools. If you missed those, I have provided the links below. In the absence of an updated review, I am hoping that this final 2023 blog post would serve somewhat as a stopgap.