7 posts tagged with "generative ai"

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.

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.