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
- 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.
(Please see Github link) for more info.
- Connect to the UOFast API from VectorShift to extract information
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
🤖 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:
Let’s bring legacy systems into the AI era — one table at a time. 🧠💾
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