| name | iris-vector-search |
| description | Implement IRIS native vector similarity search. Use when adding semantic search, embedding storage, or nearest-neighbor queries to an IRIS application. |
| license | MIT |
| metadata | {"version":"1.0.0","author":"InterSystems Developer Community","compatibility":"iris, objectscript, python"} |
Purpose
Use IRIS SQL VECTOR type and HNSW index for fast semantic similarity search — no external VectorDB.
When to Use
- Storing and searching embeddings in IRIS globals/tables
- Adding
VECTOR_COSINE or VECTOR_DOT_PRODUCT search to existing ObjectScript classes
- Replacing an external VectorDB with native IRIS capability
Core SQL Patterns
Store embeddings
INSERT INTO MyEmbeddings (content, embedding)
VALUES (?, TO_VECTOR(?, FLOAT, 1536))
Search by cosine similarity
SELECT TOP 10 content,
VECTOR_COSINE(embedding, TO_VECTOR(?, FLOAT, 1536)) AS score
FROM MyEmbeddings
ORDER BY score DESC
Create HNSW index
CREATE INDEX emb_hnsw ON MyEmbeddings (embedding)
WITH (TYPE='HNSW', M=16, EF_CONSTRUCTION=200, DISTANCE='COSINE')
ObjectScript Access
Set stmt = ##class(%SQL.Statement).%New()
Do stmt.%Prepare("SELECT TOP ? content, VECTOR_COSINE(embedding, TO_VECTOR(?, FLOAT, 1536)) score FROM MyEmbeddings ORDER BY score DESC")
Set rs = stmt.%Execute(topK, embeddingVector)
While rs.%Next() { Write rs.content, " (", rs.score, ")", ! }
Dimension Guide
| Model | Dimensions |
|---|
| text-embedding-3-small | 1536 |
| text-embedding-3-large | 3072 |
| nomic-embed-text | 768 |
| fastembed BAAI/bge-small | 384 |
