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Using Oracle 23ai Vector Store and Select AI with Retrieval-Augmented Generation (RAG)

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In my two previous blog posts Talking to Oracle Database in plain English and Talking to Oracle Database, this time in plain Slovenian I have been playing with Select AI in Oracle 23ai database. In these two blog posts I tested how Oracle 23ai feature called Select AI provides SQL access to generative AI using Large Language Models to generate SQL query which is then executed in database. In this blog post I am testing an option to use Select AI for Retrieval-Augmented Generation (RAG). Select AI with RAG augments natural language prompt by retrieving data (documents) from vector store (stored in Oracle 23ai ). With this additional content, hallucinations can be reduced and much more accurate answers could be retrieved. Setting it up Select AI is using Oracle 23ai AI Vector Search for similarity search using vector embeddings. To set the environment for Select AI with RAG two main tasks needs to be performed: set up vector store in Object Storage and create vector
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My Presentations @ HROUG 2024

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HROUG  hosted its 2024 's conference between16th and 19th October, as usual, in Rovinj, Croatia. This year, I was invited to present the two of my presentations: Extending Oracle Analytics Machine Learning Capabilities with OCI Services and Talking to Oracle Database in plain English … i Hrvatski! Talking to Oracle Database in plain English … i Hrvatski! My first session was about one of the coolest additions to Oracle Database 23ai, SELECT AI . SELECT AI allows users to ask the database (questions in natural language. By enabling connections with generative Large-Language Models such as ChatGPT, OpenAI Azure, Cohere and others, users can simply ask the question and can expect results, based on data stored in the database, returned narrated or simply by datasets which can be visualised and nicely presented. In this presentation, I am describing and demonstrating how to setup Oracle Database to use LLMs and  explore how well Oracle Database speaks and understand English. And since E
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Semantic Modeler Series: Setting up Git repository

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I will assume that I have already created a semantic model which has been deployed in my OAC environment. My intention is to share this semantic model with other developers who are working on this same model in the same time. In OBIEE this was only possible by setting up Multi-User Development Environment (MUDE). My experience with MUDE is that it wasn't perfect, often, if not careful, it was leading to problems with locking, merging, deployments. With Oracle Analytics and in particular with Semantic Modeler introduction, this has changed. When working with Semantic Modeler, Oracle Analytics gives developers two options how to setup (much stronger) collaborative environment by: giving permissions to make updates to the model to other users - Semantic Modeler is, in a way, by default multi-user development environment, and using Git , which provides proper multi-user development environment. In this case, access rights are given through Git directly. Integrating Semantic
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Semantic Modeler Series: Introduction

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Oracle BI Administration Tool Metadata management used to be one of the key strengths of Oracle Business Intelligence. Using BI Administration Tool developers were able to design and implement enterprise data models that were sitting on top of data warehouse data schemas. OBIEE developer designs and develops a metadata model, a repository, using BI Administration Tool, which results in a RPD file. This binary file is then deployed on BI Server. BI Server is a component of Oracle Business Intelligence architecture and is responsible for processing user requests and data queries against underlying data sources. BI Server uses metadata information from repository to perform the following two tasks: use logical SQL query and tranform it into corresponding query (ie. SQL, MDX), depending on the underlying data source (ie. Oracle Database, Microsoft SQL Server, Oracle Essbase, ...) transform and combine physical datasets retrieved and perform required calculations. Typical metadat
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Embedding Oracle Analytics Data Visualization in APEX application

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In one of my recent projects, the customer had a requirement to integrate visualizations from Oracle Analytics into Oracle APEX application that is running within Oracle Cloud Infrastructure. It is not my prime focus, the integrations, so I checked Oracle's documentation first. There is no "cook book" in there, so I looked it up for some good resources on the internet. Actually, you can find quite a lot of content out there, from blog posts, articles, even videos, however most of these are a bit old (not exactly obsolete) as both products have developed rapidly over the last couple of years. But still, I found some really good ones that I was able to help myself with. For example Mike Durran's blog is usually the ultimate go-to source for these kind of things, and you can find quite a lot of content on this subject there. For example, you definitely want to check his article A Guide to Embedding Oracle Analytics into Oracle APEX . In this blog post, I will try t
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