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Kurt @ RokiPark
Kurt @ RokiPark

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U2 UniData with Low-code AI: UOFast + VectorShift RAG Integration

Introduction

A new way to develop a pipeline that connects legacy UniData (U2) systems with modern AI using a Python library called UOFast, enabling seamless data extraction and feeding that into a Retrieval-Augmented Generation (RAG) app powered by VectorShift. If you're looking to modernize legacy MultiValue databases and make them AI-friendly with Low-code RAG, read on!

🔧The Problem

Many businesses still rely on Rocket's U2 (UniData/UniVerse) systems — battle-tested but difficult to integrate with modern AI workflows. The challenge? Bridging that gap with minimal disruption and enabling value extraction from data stored in a non-relational, MultiValue world.

💡 The Solution: UOFast + VectorShift

I created a project that connects U2 UniData to a RAG app using:

UOFast: A lightweight Python library that connects to UniData via Python uopy & extracts data into Python-native formats.

VectorShift: A low-code RAG platform that lets you build AI chatbots backed by your own data (PDFs, structured files, etc.).

🔗 Architecture Overview

Low Code RAG UI workflow

  • UOFast pulls data from UniData, flattens MultiValue structures, and prepares documents.
  • These documents are ingested into VectorShift, where they're indexed and used in a RAG pipeline.
  • The end result? A natural language interface to query legacy system data using GPT-powered search!

Steps

- Startup RokiPark/UOFast to connect to your local U2 Unidata.

Startup RokiPark/UOFast from command line
(Please see Github link) for more info.

- Connect to the UOFast API from VectorShift to extract information

Create VectorShift pipeline
VectorShift Documentation link

- Create a Chatbot interface in VectorShift

Tip - You can use a template from the existing Vectorshift templates to create this RAG workflow. I used a CSV template and replaced the CSV data extraction with UOFast API extraction.

Once completed, a chatbot link is created in Vectorshift which can be embedded in your web application. Here is a Demo which uses the Unidata/Demo CLIENT table to answer questions

Demo link

🤖 Why RAG?

Retrieval-Augmented Generation brings your private data into GPT-style answers without retraining. By plugging in UniData's structured output, I could enable:

Natural language search for records

Chatbot Q&A on sales history, order data, etc.

Summarization of legacy reports

🔜 What’s Next

Add real-time sync for live querying

Support Program generation and Auto-API conversion (auto-generating RESTful APIs from legacy logic)

Build a lightweight admin panel with FastAPI + NiceGUI for non-technical users

📢 Feedback Welcome!

This is a work in progress, and I’d love feedback, especially if you work with UniData or build enterprise RAG solutions. PRs, feature requests, and use-case suggestions are welcome on GitHub or email - tech@rokipark.ai

🔗 Links:

RokiPark UOFast

VectorShift Documentation

UniData by Rocket Software

Let’s bring legacy systems into the AI era — one table at a time. 🧠💾

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