With NiFi version 2.x, the ability to interface with OpenSearch using pre-built Python components (processors) was introduced.

These processors address various needs related to AI solutions, specifically vector embedding using tools like ChatGPT or HuggingFace. The selection and implementation of these components were partly driven by the increasing interest in these topics in recent times.

In the presentation, we will showcase an ETL flow for populating an OpenSearch index with vector fields. The flow is built using NiFi for both data acquisition and vector embedding generation, as well as querying via the dedicated OpenSearch components.

A growing trend in recent times is the demand for AI systems that use local engines, independent of external access.

As an example of possible application, we will present a solution based on customizing PutOpenSearchVector QueryOpenSearchVector processors to perform vector embedding and search using local LLMs based on Ollama.