Enterprise AI

Retrieval Augmented Generation (RAG) allows for adding knowledge from external sources into a Large Language Model (LLM) prompt. In this blog post I will be using this approach for using an LLM to retrieve data from an SAP S4/HANA system and visualizing the data using Streamlit. You will learn how to build a dashboard converting natural language questions into data visualizations using only a minimal amount of code!

GPT-based models have several inherent problems such as correctly aggregating figures in tabular structures and getting access to corporate data in database system. I will show an approach for connecting GPT to your custom database schema using the Langchain Python package. This allows text-based queries to be executed on the custom database schema, showing impressive results in GPTs ability to formulate complex SQL queries to the database and retrieving the correct information.

One of the major drawbacks of GPT-based models is their lack of knowledge about custom documents living in knowledge bases in large organizations. In this blog post I will demonstate a way to set up adding custom documents into the body of knowledge of GPT-based models.

Large language models are emerging as a tool for solving many information and knowledge-related matters. Back in 2020 GPT-3 made huge improvements in the way that humans are able to interact with an AI, where the model showcased its ability to engage in compelling conversations with a human counterpart. In this article I will showcase how these large language models can be used for answering questions from data stored in tabular form, just as the majority of the world’s business data.

In December 2022 SAP held a machine learning competition called “I quit!” attended by 50 participants to showcase the machine learning capabilities of SAP HANA Cloud. The idea is to predict from a database of employees who is the most probable to be leaving his or her job in the short term, based on historical information of employees which have quit recently.

Background Master data maintenance is a time-consuming activity for many businesses. Companies like retailers selling large amounts of different articles or manufacturing companies processing raw materials into finished goods can easily collect databases containing hundreds of thousands of master data items, which in turn may possess many hundreds of attributes.

This is part 2 in a two-part series of blogs on large-scale and explainable forecasting using APL. In part 1 I have outlined a way to utilize the APL library for in-database training of a regression model in HANA in order to be used together with an external Node.

Background Last year I became involved in a project for a retailer based in The Netherlands who had finished construction of a new distribution center. This new innovative DC is fully mechanized and is operated with a minimal amount of personnel, which is different from conventional distribution centers where sudden large order spikes are fulfilled by having more staff pick these orders in parallel.