How to set up RAG for OpenAI agents using IRIS Vector DB in Python

In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.

One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.

In today’s healthcare data landscape, FHIR has become the standard for structured clinical data exchange. However, while FHIR excels at interoperability, its JSON format makes analytics challenging—including FHIR QuestionnaireResponse.

Introduction

In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.

This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.

You can find them at the Open Exchange:

• IRIStool and Data Manager
• FHIR Data Explorer

In this article we'll cover:

• Connecting to InterSystems IRIS database through Python
• Creating a FHIR-ready database schema
• Importing FHIR data with vector embeddings for semantic search

With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?

Hi Community,

We’re excited to share a brand-new Instruqt tutorial:

🧑‍🏫 RAG using InterSystems IRIS Vector Search

This hands-on lab walks you through building a Retrieval Augmented Generation (RAG) AI chatbot powered by InterSystems IRIS Vector Search. You’ll see how vector search can be leveraged to deliver up-to-date and accurate responses, combining the strengths of IRIS with generative AI.

✨ Why try it?

Hey Community,

We're excited to invite you to the next InterSystems UKI Tech Talk webinar:

👉AI Vector Search Technology in InterSystems IRIS

⏱ Date & Time: Thursday, September 25, 2025 10:30-11:30 UK

Speakers:
👨‍🏫 @Saurav Gupta, Data Platform Team Leader, InterSystems
👨‍🏫 @Ruby Howard, Sales Engineer, InterSystems

Customer support questions span structured data (orders, products 🗃️), unstructured knowledge (docs/FAQs 📚), and live systems (shipping updates 🚚). In this post we’ll ship a compact AI agent that handles all three—using:

• 🧠 Python + smolagents to orchestrate the agent’s “brain”
• 🧰 InterSystems IRIS for SQL, Vector Search (RAG), and Interoperability (a mock shipping status API)

#InterSystems Demo Games entry

⏯️ Care Compass – InterSystems IRIS powered RAG AI assistant for Care Managers

Care Compass is a prototype AI assistant that helps caseworkers prioritize clients by analyzing clinical and social data. Using Retrieval Augmented Generation (RAG) and large language models, it generates narrative risk summaries, calculates dynamic risk scores, and recommends next steps. The goal is to reduce preventable ER visits and support early, informed interventions.

Presenters:
🗣 @Brad Nissenbaum, Sales Engineer, InterSystems
🗣 @Andrew Wardly, Sales Engineer, InterSystems
🗣 @Fan Ji, Solution Developer, InterSystems
🗣 @Lynn Wu, Sales Engineer, InterSystems

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InterSystems IRIS 2024 recently introduced the vector types.
This addition empowers developers to work with vector search, enabling efficient similarity searches, clustering, and a range of other applications.
In this article, we will delve into the intricacies of vector types, explore their applications, and provide practical examples to guide your implementation.

Learn how to design scalable, autonomous AI agents that combine reasoning, vector search, and tool integration using LangGraph.

I am testing vectorsearch, while doing so I am trying to paginate my resultset for a "next page" function to give me the first, second, third 15 entries within a table.

For this I have two embedding classes. One with a HNSW Index (vectornomicembedtextlatest) , and one without (vectornomicembedtexttest).

Calling SELECT ID,PRIMKEY FROM SQLUser.vectornomicembedtexttest LIMIT 5 OFFSET 1 works fine with the first entry having the rowID of 486448. (I deleted old entries in the beginning and reused the table)