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#Vector Search

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Vector search is a method used in information retrieval and machine learning to find similar items based on their mathematical representations as vectors. In this approach, each item is represented as a high-dimensional vector, with each dimension corresponding to a feature or characteristic of the item. Vector search algorithms then compare these vectors to find similar items, such as having similar features or being close together in the vector space. Read more here.

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10:47 AM — Jose Garcia's creatinine test results arrive at the hospital FHIR server.
2.1 mg/dL — a 35% increase from last month.

What happens next?

  • Most systems: ❌ The result sits in a queue until a clinician reviews it manually — hours or days later.
  • This system: 👍 An AI agent evaluates the trend, consults clinical guidelines, and generates evidence-based recommendations — in seconds, automatically.

No chatbot. No manual prompts. No black-box reasoning.

This is event-driven clinical decision support with full explainability:

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✅ Triggered automatically by FHIR events
✅ Multi-agent reasoning (context, guidelines, recommendations)
✅ Complete audit trail in SQL (every decision, every evidence source)
✅ FHIR-native outputs (DiagnosticReport published to server)

Built with:
- InterSystems IRIS for Health — Orchestration, FHIR, persistence, vector search
- CrewAI — Multi-agent framework for structured reasoning

You'll learn: 🖋️ How to orchestrate agentic AI workflows within production-grade interoperability systems — and why explainability matters more than accuracy alone.

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TL;DR: This article demonstrates how to run GraphRAG-style hybrid retrieval—combining vector similarity, graph traversal, and full-text search—entirely within InterSystems IRIS using the iris-vector-graph package. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.

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Vector search is a retrieval method that converts text, images, audio, and other data into numeric vectors using an AI model, and then searches for items that are semantically close. It enables “semantic similarity search” from free text, which is difficult with keyword search alone.

However, in real use, I encountered cases where results that are “close in meaning” but logically the opposite appeared near the top of the search results.

This is a serious issue in situations where affirmation vs. negation matters. If the system returns the wrong answer, the impact can be significant, so we cannot ignore this problem.

This article does not propose a new algorithm. I wrote it to share a practical way I found useful when semantic search fails due to negation.

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