7 Integrating GenAI in your data ecosystem
In this chapter
- Get started with the KNIME platform for running data transformation and advanced analytics.
- Explore the integration of GenAI flows with external data sources and systems.
- Learn how to build hybrid KNIME and Langflow applications that systematically pull large amounts of data, run LLM calls, and store the outcome for future use.
So far, we’ve built GenAI applications using LLMs to build conversational experiences. In all these cases, there was a human user interacting through chat with an LLM-driven entity. Conversational AI is definitely one of the most popular applications of AI these days, but—clearly—is just one of many.
Quite often, in fact, the input data doesn’t come from a human user at all. It may arrive from structured sources such as spreadsheets, enterprise applications like SAP or Salesforce, relational databases, or other digital systems. This means that your AI applications must be integrated with the larger data ecosystem you use in your activities. Whether it’s an Excel file or a cloud-based CRM system, you can leverage a data source of any kind to fuel inputs for the LLM to do its magic. As illustrated in figure 1, this means going beyond the conversational paradigm we’ve seen so far, paving the way for a multitude of business applications.