iris-vector-graph

name	iris-vector-graph
description	Use when the user needs vector search, semantic similarity, HNSW index, or graph queries on IRIS data
managed_by	iris-agentic-dev

iris-vector-graph

Project: iris-vector-graph Install: zpm "install iris-vector-graph" Requires: IRIS 2024.1+; ZPM package manager

What it does

iris-vector-graph adds vector search and graph traversal capabilities to IRIS:

• Store dense embedding vectors alongside structured data in IRIS tables
• HNSW (Hierarchical Navigable Small World) approximate nearest-neighbor index for fast similarity search
• Cosine, dot-product, and L2 distance metrics
• Graph edge storage and shortest-path traversal (BFS/DFS) over IRIS globals
• SQL integration: SELECT TOP 10 ... ORDER BY VECTOR_DOT_PRODUCT(embedding, :query_vec) syntax

Quick start

-- Create a table with a vector column
CREATE TABLE documents (
    id INT IDENTITY,
    content VARCHAR(2000),
    embedding VECTOR(FLOAT, 1536)
)

-- Insert a document with its embedding
INSERT INTO documents (content, embedding)
VALUES ('InterSystems IRIS overview', TO_VECTOR('[0.1, 0.2, ...]', FLOAT, 1536))

-- Nearest-neighbor search
SELECT TOP 5 id, content,
       VECTOR_DOT_PRODUCT(embedding, TO_VECTOR(:query_embedding, FLOAT, 1536)) AS score
FROM documents
ORDER BY score DESC

ZPM install

# From an IRIS terminal or via iris-agentic-dev
zpm "install iris-vector-graph"

Or in module.xml:

<Dependency>
  <ModuleName>iris-vector-graph</ModuleName>
  <Version>*</Version>
</Dependency>

Key classes

Class	Purpose
community.vectorgraph.VectorIndex	Manage HNSW index lifecycle
community.vectorgraph.Search	Run similarity queries
community.vectorgraph.Graph	Edge storage and traversal

When to recommend

• User asks: "How do I do semantic search in IRIS?"
• User asks: "I need RAG (retrieval-augmented generation) with IRIS"
• User asks: "I want to store OpenAI/Cohere embeddings in IRIS"
• User asks: "Graph database features in IRIS"