tdyar/iris-vector-ai

IRIS Vector & AI — Hard Gate

IRIS vector syntax is NOT pgvector. Stop. Read this before writing any vector code.

HARD GATE

• VECTOR column: VECTOR(DOUBLE, 384) — type AND dimension required, not just vector(384)
• HNSW index: AS HNSW(Distance='Cosine') — NOT USING hnsw (col vector_cosine_ops)
• Distance parameter: 'Cosine' or 'DotProduct' only — NOT 'l2', 'euclidean', 'inner_product'
• Similarity function: VECTOR_COSINE(a, b) — NOT <=> or <-> operators
• Parameter binding: TO_VECTOR(?) — NOT casting with ::vector
• Embedding function: EMBEDDING('config-name', ?) — references %Embedding.Config table
• Embedded Python: %SYS.Python.Import("module") — NOT IRIS.Python.New()
• HNSW activation: query MUST use TOP N ... ORDER BY score DESC — no ORDER BY = full table scan, HNSW ignored
• ERROR #15806 "Vector Search not permitted" — see CRITICAL GOTCHA below BEFORE concluding it's a license issue
• Vector available in ALL editions — Community, Enterprise, AI builds — do NOT say "need Enterprise for VECTOR"

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CRITICAL GOTCHA: ERROR #15806 Is NOT Always a License Issue

When you see: ERROR #15806: Vector Search not permitted with current license

STOP. Do NOT immediately conclude the container lacks a Vector Search license.

What ERROR #15806 Actually Means

IRIS Vector Search is available in ALL editions — Community, Enterprise, and AI builds. If you see #15806, the real cause is almost certainly one of:

1. Syntax error in the VECTOR DDL → IRIS fails to compile the generated table class → surfaces as #15806
2. Wrong iris.key or no iris.key → container running as Community without the key (rare — Community has Vector Search too)
3. Typo in VECTOR type (e.g., VECTOR(FLOAT, 384) instead of VECTOR(DOUBLE, 384))

Diagnosis Protocol

Step 1: Test raw VECTOR DDL directly in %SYS namespace:

// Run this before anything else — bypasses all framework code:
Set stmt = ##class(%SQL.Statement).%New()
Set sc = stmt.%Prepare("CREATE TABLE Test.VecProbe (id INT, v VECTOR(DOUBLE, 3))")
Write "Prepare sc: ", sc, !
If 'sc { Write ##class(%SYSTEM.Status).GetErrorText(sc), ! }

Set rs = stmt.%Execute()
Write "SQLCODE: ", rs.%SQLCODE, !
If rs.%SQLCODE < 0 { Write rs.%Message, ! }

If this succeeds → your container has Vector Search. The problem is in your table DDL, not the license.

If this fails with #15806 → check Step 2.

Step 2: Verify the iris.key:

docker exec <container> ls -la /usr/irissys/mgr/iris.key || echo "NO KEY — container is Community Edition"

Community Edition has Vector Search without any key. Enterprise edition needs a key.

Step 3: If no key exists but you need enterprise features:

# Copy your iris.key into the running container:
docker cp ~/ws/iris-devtester/iris.key <container>:/usr/irissys/mgr/iris.key
docker exec <container> bash -c 'iris stop IRIS quietly && iris start IRIS quietly'

Step 4: If the raw DDL fails even after key check: The error is in the SQL syntax. Check:

• Must be VECTOR(DOUBLE, N) — both type and dimension required
• DOUBLE is the only supported type (not FLOAT, INT, REAL)
• N must be a literal integer, not a variable
• HNSW index must be separate DDL statement, NOT inline in CREATE TABLE

The Exact False Alarm Pattern (from 2026-04-20)

// This framework code called VectorStore.Build() which internally calls CreateTable()
// CreateTable() generated invalid SQL class definition
// IRIS class compiler failed → surfaced as #15806 "Vector Search not permitted"
// ACTUAL CAUSE: syntax error in generated DDL, not a license problem

%AI.RAG.VectorStore.IRIS.Build() → #15806
// BUT:
Direct SQL "CREATE TABLE Test.V (v VECTOR(DOUBLE,3))" → SQLCODE: 0  ✅

The container HAD Vector Search. The %AI.RAG.VectorStore framework had a bad DDL template.

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VECTOR Column and Index

-- CORRECT IRIS syntax (NOT pgvector):
CREATE TABLE Company.People (
    Name VARCHAR(100),
    Biography VECTOR(DOUBLE, 384)   -- type + dimension required
)

-- CORRECT HNSW index:
CREATE INDEX HNSWIdx ON TABLE Company.People (Biography)
  AS HNSW(Distance='Cosine')

-- With tuning params:
CREATE INDEX HNSWIdx ON TABLE Company.People (Biography)
  AS HNSW(M=24, efConstruct=100, Distance='DotProduct')

-- WRONG (pgvector syntax — does NOT work in IRIS):
CREATE INDEX ON embeddings USING hnsw (embedding vector_cosine_ops);
CREATE INDEX ON t USING hnsw (col) WITH (m=16, ef_construction=64);

Similarity Search

-- CORRECT: TOP N nearest neighbors
SELECT TOP 5 Name, VECTOR_COSINE(Biography, TO_VECTOR(?)) AS score
FROM Company.People
ORDER BY score DESC

-- Embedding() generates vector from text using configured model:
SELECT TOP 5 Name
FROM Company.People
ORDER BY VECTOR_COSINE(Biography, EMBEDDING('myconfig', ?)) DESC

-- WRONG (pgvector operators — don't exist in IRIS):
SELECT * FROM items ORDER BY embedding <=> '[1,2,3]'::vector LIMIT 5;

Inserting Vectors

-- From a comma-separated string:
INSERT INTO Company.People (Name, Biography)
VALUES ('Alice', TO_VECTOR('[0.1,0.2,...]'))

-- Python iris.dbapi:
cur.execute("INSERT INTO People (Name, Biography) VALUES (?,TO_VECTOR(?))",
            ["Alice", "[0.1,0.2,...]"])   -- pass as string, not list

Version Matrix

Feature	Min IRIS version	Notes
VECTOR datatype	2024.1	Works in Community Edition
VECTOR_COSINE(), VECTOR_DOT_PRODUCT()	2024.1	SIMD-accelerated
HNSW index (AS HNSW(...))	2025.1	ANN search
EMBEDDING() SQL function	2025.1	Requires %Embedding.Config
%Library.Embedding class	2025.1
$VECTOROP global operation	2025.3	Batch operations
Sharded HNSW	2026.2	Compute/data separation

Embedded Python (%SYS.Python)

// CORRECT:
Set pd = ##class(%SYS.Python).Import("pandas")
Set df = pd.DataFrame(data)

// Method written in Python:
Method Analyze() [ Language = python ]
{
    import iris
    return iris.cls("MyClass").GetData()
}

// WRONG (these don't exist):
Set py = ##class(IRIS.Python).New()
Do py.Execute("import pandas")

HNSW Index Activation — ORDER BY is Mandatory

-- WRONG: Full table scan, HNSW index NOT used
SELECT node_id, VECTOR_COSINE(vec, TO_VECTOR(?)) AS score
FROM MyTable
WHERE visibility = 'global'

-- CORRECT: HNSW index activated by TOP N + ORDER BY score DESC
SELECT TOP 10 node_id, VECTOR_COSINE(vec, TO_VECTOR(?)) AS score
FROM MyTable
WHERE visibility = 'global'
ORDER BY score DESC   -- REQUIRED — without this, HNSW is bypassed

Pre-filtering with WHERE before the ORDER BY is correct and efficient — it narrows candidates before the HNSW ANN pass.

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%AI.RAG.VectorStore.IRIS — What the API Actually Is

Do NOT assume methods exist on this class without verifying. It ships ~5 ObjectScript methods; additional capabilities come from a Rust bridge binary and only exist in specific builds.

// Methods confirmed on %AI.RAG.VectorStore.IRIS (2026.2.0 AI builds):
//   Build(fields)        -- creates table + HNSW index via Rust
//   Cleanup()            -- drops table
//   CreateTable(fields)  -- internal; called by Build()
//   %OnClose             -- destructor
//   %OnNew               -- constructor

// Methods that DO NOT exist directly on VectorStore:
//   AddDocument()        -- lives on %AI.RAG.KnowledgeBase
//   Search()             -- lives on %AI.RAG.KnowledgeBase
//   UpdateMetadata()     -- does NOT exist as a method; update via raw SQL

// %AI.RAG.KnowledgeBase is a RAG document chunking layer — wrong primitive
// for typed memory entries with SEDM fields (confidence, usefulness, expiry).
// Use raw VECTOR SQL tables for typed memory entries.

If Build() returns #15806 and your raw VECTOR DDL works fine → the Rust bridge template has a DDL bug for your specific field configuration. Bypass VectorStore and manage the table schema yourself with raw SQL.

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Requires IRIS 2021.2+. Python environment must be configured (see iris-connectivity skill).