Žiga Vaupot's Blog

Since a few releases ago, Oracle has introduced native connectors that link Oracle Analytics to Oracle EPM Applications. This integration enables planning data from Oracle EPM Cloud to be seamlessly retrieved into Oracle Analytics for reporting and analysis. However, this initial step only scratches the surface of the EPM/Essbase ecosystem. Several additional capabilities are needed to fully leverage these data sources, including: Support for EPM Cloud/Essbase data sources in the Semantic Modeler: This feature is not yet available but is expected in the January 2025 release of Oracle Analytics Cloud. Direct import of Substitution Variables with data is not currently supported. However, REST API provides a way to retrieve all substitution variables. Selection Steps, a functionality familiar from "classic" Oracle Analytics (Dashboards and Answers), allows users to select specific hierarchy members and display only their descendants based on hierarchical or similar relation...

In the beginning of December, I attended UKOUG Annual Conference DISCOVER 2024 in Birmingham. I was presenting there and I am said (or perhaps not) to share this information, but this was my last time I was presenting under Version 1 / Qubix umbrella. Our paths are finally splitting at the end of this year and I will share more in my future posts. Back to my presentation in Birmingham.   Depending on the existing / future architecture OCI users have various option to choose from. The question is which ML approach is the most suitable for a specific user? And among these, some could be quite interesting. For example, which solution could be the best fit if customer still prefers to stay on-premise or what if data is fluent. So, what options have we got in OCI? In my presentation titled  3 Case Studies: Machine Learning in OCI , I am trying to answer some of the questions mentioned about and I am discussing development and deployment options that we have in Oracle Clou...

Using SELECT AI with RAG In my two previous blog posts, Talking to Oracle Database in plain English and Talking to Oracle Database, this time in plain Slovenian , I explored Select AI within the Oracle 23ai database. In those posts, I tested how the Oracle 23ai feature called Select AI enables SQL access to generative AI by using Large Language Models (LLMs) to generate SQL queries that are then executed in the database. In this post, I will focus on using Select AI for Retrieval-Augmented Generation (RAG), a powerful approach that enhances natural language prompts by retrieving relevant documents from a vector store maintained within Oracle 23ai . This augmentation helps reduce hallucinations and provides more accurate, evidence-backed answers. What is Select AI with RAG? Select AI with RAG combines the power of generative AI with vector search capabilities. It uses Oracle 23ai AI Vector Search to perform similarity searches based on vector embeddings, retrieving releva...

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 si...

Let’s assume you already have a deployed semantic model in your Oracle Analytics Cloud (OAC) environment. Now, you want to enable collaboration by allowing multiple developers to work on this model simultaneously. In the past, OBIEE supported collaboration through the Multi-User Development Environment (MUDE). However, MUDE had its challenges—locking conflicts, merge complexities, and deployment issues were common pitfalls. With the introduction of Semantic Modeler in Oracle Analytics, collaborative development is more robust and streamlined. Developers can either: assign permissions within Semantic Modeler, effectively making it a built-in multi-user environment, or integrate with Git , which brings version control and true collaborative development via source control access rights. In this article, we’ll walk through how to integrate Semantic Modeler with Git for the first time. Integrating Semantic Modeler with Git is relative...