iris-vector-graph 1.40.0

Transactional Graph + Vector retrieval system for InterSystems IRIS with hybrid search, openCypher, and GraphQL APIs

iris-vector-graph

Knowledge graph engine for InterSystems IRIS — vector search, openCypher, graph analytics, and PLAID multi-vector retrieval.

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Install

pip install iris-vector-graph              # Core: just intersystems-irispython
pip install iris-vector-graph[full]        # Full: + FastAPI, GraphQL, numpy, networkx
pip install iris-vector-graph[plaid]       # + sklearn for PLAID K-means build

ObjectScript Only (IPM)

zpm "install iris-vector-graph-core"

Pure ObjectScript — VecIndex, PLAIDSearch, PageRank, Subgraph, GraphIndex. No Python. Works on any IRIS 2024.1+, all license tiers.

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What It Does

Capability	Description
VecIndex	RP-tree ANN vector search — pure ObjectScript + $vectorop SIMD. Annoy-style two-means splitting.
PLAID	Multi-vector retrieval (ColBERT-style) — centroid scoring → candidate gen → exact MaxSim. Single server-side call.
HNSW	Native IRIS VECTOR index via kg_KNN_VEC. Sub-2ms search.
Cypher	openCypher parser/translator — MATCH, WHERE, RETURN, CREATE, DELETE, WITH, named paths, CALL subqueries.
Graph Analytics	PageRank, WCC, CDLP, PPR-guided subgraph — pure ObjectScript over ^KG globals.
FHIR Bridge	ICD-10→MeSH mapping via UMLS for clinical-to-KG integration.
GraphQL	Auto-generated schema from knowledge graph labels.

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Quick Start

Python

import iris
from iris_vector_graph.engine import IRISGraphEngine

conn = iris.connect(hostname='localhost', port=1972, namespace='USER', username='_SYSTEM', password='SYS')
engine = IRISGraphEngine(conn)
engine.initialize_schema()

Vector Search (VecIndex)

engine.vec_create_index("drugs", 384, "cosine")
engine.vec_insert("drugs", "metformin", embedding_vector)
engine.vec_build("drugs")

results = engine.vec_search("drugs", query_vector, k=5)
# [{"id": "metformin", "score": 0.95}, ...]

PLAID Multi-Vector Search

# Build: Python K-means + ObjectScript inverted index
engine.plaid_build("colbert_idx", docs)  # docs = [{"id": "x", "tokens": [[f1,...], ...]}, ...]

# Search: single server-side call, pure $vectorop
results = engine.plaid_search("colbert_idx", query_tokens, k=10)
# [{"id": "doc_3", "score": 0.94}, ...]

Cypher

-- Named paths
MATCH p = (a:Protein)-[r:INTERACTS_WITH]->(b:Protein)
WHERE a.id = 'TP53'
RETURN p, length(p), nodes(p), relationships(p)

-- Subqueries
MATCH (p:Protein)
CALL {
    WITH p
    MATCH (p)-[:INTERACTS_WITH]->(partner)
    RETURN count(partner) AS degree
}
RETURN p.id, degree

-- Vector search in Cypher
CALL ivg.vector.search('Gene', 'embedding', [0.1, 0.2, ...], 5) YIELD node, score
RETURN node, score

Graph Analytics

from iris_vector_graph.operators import IRISGraphOperators

ops = IRISGraphOperators(conn)

# Personalized PageRank
scores = ops.kg_PAGERANK(seed_entities=["MeSH:D011014"], damping=0.85)

# Subgraph extraction
subgraph = ops.kg_SUBGRAPH(seed_ids=["TP53", "MDM2"], k_hops=3)

# PPR-guided subgraph (prevents D^k blowup)
guided = ops.kg_PPR_GUIDED_SUBGRAPH(seed_ids=["TP53"], top_k=50, max_hops=5)

# Community detection
communities = ops.kg_CDLP()
components = ops.kg_WCC()

FHIR Bridge

# Load ICD-10→MeSH mappings from UMLS MRCONSO
# python scripts/ingest/load_umls_bridges.py --mrconso /path/to/MRCONSO.RRF

# Query: ICD codes → KG anchors
anchors = engine.get_kg_anchors(icd_codes=["J18.0", "E11.9"])
# → ["MeSH:D001996", "MeSH:D003924"]  (filtered to nodes in KG)

ObjectScript Direct (no Python)

// VecIndex
Do ##class(Graph.KG.VecIndex).Create("myidx", 384, "cosine", 4, 50)
Do ##class(Graph.KG.VecIndex).InsertJSON("myidx", "doc1", "[0.1, 0.2, ...]")
Do ##class(Graph.KG.VecIndex).Build("myidx")
Set results = ##class(Graph.KG.VecIndex).SearchJSON("myidx", "[0.3, ...]", 10, 8)

// PLAID
Set results = ##class(Graph.KG.PLAIDSearch).Search("myidx", queryTokensJSON, 10, 4)

// Graph analytics
Do ##class(Graph.KG.Traversal).BuildKG()
Set ppr = ##class(Graph.KG.PageRank).RunJson(seedsJSON, 0.85, 50)
Set sub = ##class(Graph.KG.Subgraph).SubgraphJson(seedsJSON, 3, "")

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Architecture

Global Structure

Global	Purpose
^KG	Knowledge graph — ("out",s,p,o), ("in",o,p,s), ("deg",s), ("label",label,s), ("prop",s,key)
^NKG	Integer-encoded ^KG for Arno acceleration — (-1,sIdx,-(pIdx+1),oIdx)
^VecIdx	VecIndex RP-tree ANN — centroids, tree nodes, leaf vectors
^PLAID	PLAID multi-vector — centroids, packed doc tokens, inverted index

Schema (Graph_KG)

Table	Purpose
nodes	Node registry (node_id PK)
rdf_edges	Edges (s, p, o_id)
rdf_labels	Node labels (s, label)
rdf_props	Node properties (s, key, val)
kg_NodeEmbeddings	HNSW vector index (id, emb VECTOR)
fhir_bridges	ICD-10→MeSH clinical code mappings

ObjectScript Classes (iris-vector-graph-core)

Class	Methods
Graph.KG.VecIndex	Create, Insert, InsertJSON, Build, Search, SearchJSON, SearchMultiJSON, SeededVectorExpand, Drop, Info
Graph.KG.PLAIDSearch	StoreCentroids, StoreDocTokens, BuildInvertedIndex, Search, Insert, Info, Drop
Graph.KG.PageRank	RunJson (PPR), PageRankGlobalJson (global)
Graph.KG.Algorithms	WCCJson, CDLPJson
Graph.KG.Subgraph	SubgraphJson, PPRGuidedJson
Graph.KG.Traversal	BuildKG, BuildNKG, BFSFastJson
Graph.KG.GraphIndex	InternNode, InternLabel, InsertIndex (dual ^KG+^NKG write)

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Performance

Operation	Latency	Details
VecIndex search (1K vecs, 128-dim)	4ms	RP-tree + $vectorop SIMD
HNSW search (143K vecs, 768-dim)	1.7ms	Native IRIS VECTOR index
PLAID search (500 docs, 4 query tokens)	~14ms	Centroid scoring + MaxSim
PPR (10K nodes)	62ms	Pure ObjectScript, early termination
1-hop neighbors	0.3ms	$Order on ^KG
k=2 subgraph (10K nodes)	1.8ms	BFS over ^KG

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Documentation

• Python SDK Reference
• Architecture
• Schema Reference
• Setup Guide
• Testing Policy
• Enhancement Specs

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Changelog

v1.40.0 (2026-04-02)

• iris_vector_graph.embedded.EmbeddedConnection — dbapi2 adapter for IRIS Language=python methods
• Zero-boilerplate usage: IRISGraphEngine(EmbeddedConnection()) works identically inside IRIS as with external iris.connect()
• commit()/rollback() are intentional no-ops (IRIS manages transactions in embedded context)
• START TRANSACTION/COMMIT/ROLLBACK via cursor.execute() are silent no-ops (avoids <COMMAND> in wgproto jobs)
• fetchmany(), rowcount, description fully implemented

v1.39.0 (2026-04-01)

• Pre-aggregated temporal analytics: ^KG("tagg", bucket, source, predicate, key) for O(1) COUNT/AVG/MIN/MAX
• New ObjectScript methods: GetAggregate, GetBucketGroups, GetDistinctCount (16-register HLL)
• New Python wrappers: get_temporal_aggregate(), get_bucket_groups(), get_distinct_count()
• HLL: 16-register HyperLogLog for COUNT DISTINCT using SHA1 hash, ~26% error (documented)
• MIN/MAX atomicity limitation documented (Phase 2: LOCK-based fix)
• Purge() extended to kill ^KG("tagg") subscripts
• 7 unit tests + 6 E2E tests; 300 total tests passing

v1.28.0 (2026-03-29)

• Lightweight default install — base requires only intersystems-irispython
• Optional extras: [full], [plaid], [dev], [ml], [visualization], [biodata]
• IPM packages: iris-vector-graph-core (ObjectScript only) + iris-vector-graph (full)

v1.27.0

• PLAID packed token storage — $ListBuild of $vector per document (53 $Order → 1 $Get)

v1.26.0

• PLAID multi-vector retrieval — PLAIDSearch.cls pure ObjectScript + $vectorop
• Python wrappers: plaid_build, plaid_search, plaid_insert, plaid_info, plaid_drop

v1.25.1

• VecIndex Annoy-style two-means tree splitting (fixes degenerate trees on clustered embeddings)

v1.24.0

• VecIndex nprobe recall fix (counts leaf visits, not branch points)
• SearchMultiJSON, InsertBatchJSON batch APIs

v1.22.0

• VecIndex SearchJSON/InsertJSON — eliminated xecute path (250ms → 4ms)

v1.21.0

• VecIndex RP-tree ANN — vec_create_index, vec_insert, vec_build, vec_search

v1.20.0

• Arno acceleration wrappers: khop(), ppr(), random_walk() with auto-detection

v1.19.0

• ^NKG integer index for Arno acceleration
• GraphIndex.cls, BenchSeeder.SeedRandom()

v1.18.0

• FHIR-to-KG bridge: fhir_bridges table, get_kg_anchors(), UMLS MRCONSO ingest

v1.17.0

• Cypher named path bindings (MATCH p = ... RETURN p, length(p), nodes(p), relationships(p))
• Cypher CALL subqueries (independent CTE + correlated scalar)
• kg_PPR_GUIDED_SUBGRAPH with ObjectScript fast path
• Repo cleanup: 80+ stale files removed

Earlier versions →

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License: MIT | Author: Thomas Dyar ([e-mail])