5 posts tagged with "large language model"

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.

Staring from langchain_community version 0.0.31, Oracle introduced a new LangChain document loader that allows you to load the results of an SQL query, against an Oracle Autonomous Database (ADB), as documents that your retrieval-augmented generation (RAG) application can use for text generation tasks. In a previous article, I described how the select ai feature available in ADBs combines the use of database metadata and large-language models (LLMs) to generate an SQL statement that answers a question asked in natural language. In this article, we will take a quick look at how we can load data from the ADB, generate embeddings, then use an LLM to respond to human questions.

A new year, a new product! Yesterday, Oracle announced the general availability of the OCI Generative AI (GenAI) service. In my final blog post last year, I briefly looked at the beta information that was available to the public, and suggested how you can quite easily add powerful GenAI capability in Oracle APEX applications by calling the REST APIs using APEX_WEB_SERVICE. With the service in GA, I am now finally able to take a deeper dive and hopefully, help you get started.