| 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 TABLE documents (
id INT IDENTITY,
content VARCHAR(2000),
embedding VECTOR(FLOAT, 1536)
)
INSERT INTO documents (content, embedding)
VALUES ('InterSystems IRIS overview', TO_VECTOR('[0.1, 0.2, ...]', FLOAT, 1536))
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
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"