12 posts tagged with "oracle apex"

In previous blog posts, I described how you can create a question and answer application using LangChain and the Oracle Cloud Infrastructure (OCI) Generative AI service. With the Oracle Database 23ai release, we looked at the power of AI Vector Search. This no-cost, built-in database feature provides another alternative to create Retrieval Augmented Generation (RAG) applications using just PL/SQL. However, both solutions require a good amount of code. With the recent availability of the OCI Generative AI Agents service, you can rapidly create a RAG application using low code!

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.

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!

One of the key highlights at Oracle Cloudworld 2023 is general availability of Select AI feature that allows anyone to query the Oracle Database using natural language. This is likely made possible with a template wrapping the database metadata with the prompt, and then calling either the OpenAI or Cohere APIs to generate the intended text.

Doug Gault had suggested a while ago that I could bring more awareness to the Oracle APEX Challenge Coin project if I had an Oracle APEX application that plots the location of each sponsored coin on a world map. I agree. However, aside from a lack of time, I had also wanted to find interesting ways for awardees to submit their claim of ownership. Over the last week, I had worked on making this dream of a "claims" map a reality with the use of the Oracle Cloud Infrastructure (OCI) Vision AI service, more specifically, its custom model training feature.

Here's an early preview of the claims process in the Claim My Oracle APEX Challenge Coin application.

I'm finally finding more time, post-conference, to catch up on my writing. In this latest post, I'd like to talk about a presentation that I did at Kscope23, "Solving Everyday Problems with the Oracle Cloud". During the session, I had described and demonstrated how we can use the Oracle Cloud Infrastructure (OCI) AI services and Oracle APEX to build compelling solutions, even for personal life challenges.

In case you missed it, Oracle recently announced the availability of a new feature for the Autonomous Database (ADB) that allows developers and database administrators to send messages using either emails, Slack, or Microsoft Teams (Teams). At Insum, we use teams for internal communications and collaboration, and thus, you can appreciate my excitement when I heard about this feature release. Here's an early guide to getting started with DBMS_CLOUD_NOTIFICATION for those using Teams at their organisation.

When I was first introduced to generative adversarial networks (GANs), I was impressed, but yet not impressed. The concept, performance, and outcomes of these advanced machine learning (ML) algorithms were truly jaw-dropping, but easily abused. And so, when everyone was excitedly chatting about ChatGPT, I was cynical and tried to tune out until I came across a comment by an old acquaintance, following a news article on concerns about ChatGPT and potential plagiarism in school.

It's like someone using a calculator in an arithmetic exam, ChatGPT is just another tool.

-- Sau Sheong Chang

That statement got me rethinking my stance on the technology, and curious about its utility in my day-to-day job.