25 posts tagged with "oci"

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.

Now that I caught your attention, I hope the image isn't too triggering. I grew up learning to fear these little black monsters that potentially carry a load of Dengue virus that could make you really sick. As recently reported, the disease continues to be an issue in Singapore. During my undergraduate years, I was deeply passionate about viral diseases, and spent a few months in the laboratory learning to characterize the structural components of the virus using cyrogenic electron microscopy. Hence, when Luc Demanche challenged me again to demonstrate retrieval-augmented generation (RAG), I decided to collect and use a few recent articles (PMIDs 38140548, 37478848, 37114191), and use them to create a quick proof-of-concept Oracle APEX application that allows the user to ask questions about the disease. These articles cover topics on the Dengue virus, its genomics and ultrastructure, and progress in vaccine development.

In case you missed it, for the last few blogs posts, I had been using Stability AI's Stable Diffusion model to generate the banner images, and I have been mostly successful. Did I really need to? Perhaps not. For this post, I spent almost 30 minutes trying to figure out the "right" prompt to generate the image I wanted: "A car stopped in front a stop sign."

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.

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.

In my previous post, you read about how I performed fine-tuning and deployed a large language model (LLM) on the Oracle Cloud Infrastructure (OCI) Data Science service. Here, I was hoping to do the same with the Stable Diffusion XL, a Latent Diffusion Model (LDM), but unfortunately, the platform currently does not support a Hugging Face pipeline using this model. I found an alternative, and you will read about it later in this article.

To demonstrate its utility, I create a simple Oracle APEX application that reads NFC tags using the experimental Web NFC API, and then displays an image generated by the deployed machine learning (ML) model.

In my previous post, I wrote about how a scikit-learn machine learning (ML) could be trained and deployed on the Oracle Cloud Infrastructure (OCI) Data Science service. The model is deployed on the service's managed infrastructure, allowing developers to simply call a HTTP endpoint to perform ML model inference on the submitted data. In my latest adventure, I built an Oracle APEX application that takes product reviews and then automatically ranks the review using a fine-tuned large language model (LLM) available through Hugging Face.

This has been one of those weeks where I couldn't wait to wake up and get to work. Just last week, Luc Demanche threw me a challenge and asked that I help develop an Oracle APEX application that could be used to predict the winner of the Rugby World Cup 2023 (RWC2023). I couldn't resist the challenge!

It's been too long since I last posted anything on my blog. Thanks to Tim Hall, I am writing again, after a long pause, to celebrate Joel Kallman Day 2022. My contribution today is about my deep appreciation for the team behind the Oracle Cloud Infrastructure (OCI) AI Services. Below is a quick scoop on how I have used these services to improve on my OCI-powered e-flashcard for learning new words in a different language. I will be doing a quick demonstration (session LRN3673) of this work at the upcoming Oracle CloudWorld 2022.

In September 2020, Oracle first announced that it was working with Ampere Computing to provide ARM-based chips for its Compute instance offering on the Oracle Cloud Infrastructure. They have delivered!

ARM chips are found in many low-powered devices like smartphones, single-board computers, and IoT devices. So, if you are an Oracle Application Express (APEX) developer, you might be wondering, "What's in it for me?" Well, remember we talked about running a customer-managed Oracle REST Data Services (ORDS) instance for an Oracle Autonomous Database?

The Oracle Cloud Infrastructure (OCI) is a great platform in many ways. However, as much as I love it, I have long griped about the lack of out-of-the-box support for AI services that many of Oracle's competitors have. Well, as of May 6, 2021, I am glad to see that things are starting to turn around with the launch of the OCI Language service.

A reader left an interesting comment/question in my previous post on Making the Move to the Autonomous Database:

"Would You explain how can i check my on-premise DB to satisfy ADB size limitation 20G? What is the size 20G of?"

This is an excellent question to ask before embarking on any Autonomous Database (ADB) migration plans and so I dug a bit deeper.

My #ThanksOGB will be short and sweet!

Most in the community probably don't know, but I am a sucker for military stories and even more crazy about hard-to-find memorabilia. Earlier this year, I took a gamble and started a little social experiment and minted a small batch of APEX Challenge Coins.

The coins demonstrated our deep appreciation for APEX developers who have impacted our lives. Through the process of distributing them worldwide, I was blessed to have basked in the warmth of givers and the happiness of receivers. Words cannot describe the emotions and joy I felt each time someone opens his or her little mail packet to find a gift of gratitude.

So... for my #ThanksOGB appreciation post this year, I wanted to express again, my heartfelt gratitude to all APEX Challenge Coin sponsors for making this project a success. Thank you for supporting APEX developers!

In my previous post, I mentioned that I have created my Oracle Cloud Free Tier account and provisioned an Autonomous Database (ADB). Now, I'm ready to move my existing Oracle Application Express (APEX) to this new environment. Next, I'll describe the steps I took to move (1) workspace, (2) application and (3) data, from the old to new database.

As mentioned in a previous post, the Free Tier for Oracle Cloud (OCI) is every Oracle Developer's dream. For a while now, I have hosted a personal Oracle Application Express (APEX) instance on Linode. It currently runs an older Oracle Database 11gR2 Express Edition (XE), and I would like to upgrade it to version 18c. The key issue I had though, was that the existing virtual machine hosted on Linode, only has one gigabyte of RAM and that does not meet the 18c XE minimum requirement of two. That left me procrastinating for a long time, but perhaps a blessing in disguise. With this new offering, the next steps are pretty obvious.