iris-vector-graph 1.42.0

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

iris-vector-graph

Knowledge graph engine for InterSystems IRIS — temporal property graph, vector search, openCypher, graph analytics, and pre-aggregated analytics.

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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, TemporalIndex. No Python. Works on any IRIS 2024.1+, all license tiers.

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What It Does

Capability	Description
Temporal Graph	Bidirectional time-indexed edges — ^KG("tout"/"tin"/"bucket"). O(results) window queries via B-tree traversal. 134K+ edges/sec ingest (RE2-TT benchmark).
Pre-aggregated Analytics	^KG("tagg") per-bucket COUNT/SUM/AVG/MIN/MAX and HLL COUNT DISTINCT. O(1) aggregation queries — 0.085ms for 1-bucket, 0.24ms for 24-hour window.
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, UNION, CASE WHEN, variable-length 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.
Embedded Python	EmbeddedConnection — zero-boilerplate dbapi2 adapter for IRIS Language=python methods.

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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()

Inside IRIS (Language=python, no connection needed)

from iris_vector_graph.embedded import EmbeddedConnection
from iris_vector_graph.engine import IRISGraphEngine

engine = IRISGraphEngine(EmbeddedConnection())
engine.initialize_schema()
# Same API as external callers — use from any ObjectScript Language=python method

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Temporal Property Graph

Store and query time-stamped edges — service calls, events, metrics, log entries — with sub-millisecond window queries and O(1) aggregation.

Ingest

import time

# Single edge
engine.create_edge_temporal(
    source="service:auth",
    predicate="CALLS_AT",
    target="service:payment",
    timestamp=int(time.time()),
    weight=42.7,            # latency_ms, metric value, or 1.0
)

# Bulk ingest — 134K+ edges/sec (RE2-TT benchmark, 535M edges validated)
edges = [
    {"s": "service:auth",    "p": "CALLS_AT",       "o": "service:payment", "ts": 1712000000, "w": 42.7},
    {"s": "service:payment", "p": "CALLS_AT",       "o": "db:postgres",     "ts": 1712000001, "w": 8.1},
    {"s": "service:auth",    "p": "EMITS_METRIC_AT","o": "metric:cpu",      "ts": 1712000000, "w": 73.2},
]
engine.bulk_create_edges_temporal(edges)

Window Queries

now = int(time.time())

# All calls from auth in the last 5 minutes
edges = engine.get_edges_in_window(
    source="service:auth",
    predicate="CALLS_AT",
    start=now - 300,
    end=now,
)
# [{"s": "service:auth", "p": "CALLS_AT", "o": "service:payment", "ts": 1712000042, "w": 38.2}, ...]

# Edge velocity — call count in last N seconds (reads pre-aggregated bucket, O(1))
velocity = engine.get_edge_velocity("service:auth", window_seconds=300)
# 847

# Burst detection — which nodes exceeded threshold in last N seconds
bursts = engine.find_burst_nodes(predicate="CALLS_AT", window_seconds=60, threshold=500)
# [{"id": "service:auth", "velocity": 1243}, {"id": "service:checkout", "velocity": 731}]

Pre-aggregated Analytics (O(1) per bucket)

now = int(time.time())

# Average latency for auth→payment calls in the last 5 minutes
avg_latency = engine.get_temporal_aggregate(
    source="service:auth",
    predicate="CALLS_AT",
    metric="avg",           # "count" | "sum" | "avg" | "min" | "max"
    ts_start=now - 300,
    ts_end=now,
)
# 41.3  (float, milliseconds)

# All metrics for count, and extremes
count = engine.get_temporal_aggregate("service:auth", "CALLS_AT", "count", now-300, now)
p_min = engine.get_temporal_aggregate("service:auth", "CALLS_AT", "min", now-300, now)
p_max = engine.get_temporal_aggregate("service:auth", "CALLS_AT", "max", now-300, now)

# GROUP BY source — all services, CALLS_AT, last 5 minutes
groups = engine.get_bucket_groups(predicate="CALLS_AT", ts_start=now-300, ts_end=now)
# [
#   {"source": "service:auth",     "predicate": "CALLS_AT", "count": 847, "avg": 41.3, "min": 2.1, "max": 312.0},
#   {"source": "service:checkout", "predicate": "CALLS_AT", "count": 312, "avg": 28.7, "min": 1.4, "max": 189.0},
#   ...
# ]

# COUNT DISTINCT targets — fanout detection (16-register HLL, ~26% error, good for threshold detection)
distinct_targets = engine.get_distinct_count("service:auth", "CALLS_AT", now-3600, now)
# 14   (distinct services called by auth in last hour)

Rich Edge Properties

# Attach arbitrary attributes to any temporal edge
engine.create_edge_temporal(
    source="service:auth",
    predicate="CALLS_AT",
    target="service:payment",
    timestamp=1712000000,
    weight=42.7,
    attrs={"trace_id": "abc123", "status": 200, "region": "us-east-1"},
)

# Retrieve attributes
attrs = engine.get_edge_attrs(
    ts=1712000000,
    source="service:auth",
    predicate="CALLS_AT",
    target="service:payment",
)
# {"trace_id": "abc123", "status": 200, "region": "us-east-1"}

NDJSON Import / Export

# Export temporal edges for a time window
engine.export_temporal_edges_ndjson(
    path="traces_2026-04-01.ndjson",
    start=1743465600,
    end=1743552000,
)

# Import — resume an ingest from a file
engine.import_graph_ndjson("traces_2026-04-01.ndjson")

ObjectScript Direct

// Ingest
Do ##class(Graph.KG.TemporalIndex).InsertEdge("svc:auth","CALLS_AT","svc:pay",ts,42.7,"")

// Bulk ingest (JSON array)
Set n = ##class(Graph.KG.TemporalIndex).BulkInsert(edgesJSON)

// Query window — returns JSON array
Set result = ##class(Graph.KG.TemporalIndex).QueryWindow("svc:auth","CALLS_AT",tsStart,tsEnd)

// Pre-aggregated average latency
Set avg = ##class(Graph.KG.TemporalIndex).GetAggregate("svc:auth","CALLS_AT","avg",tsStart,tsEnd)

// GROUP BY source
Set groups = ##class(Graph.KG.TemporalIndex).GetBucketGroups("CALLS_AT",tsStart,tsEnd)

// COUNT DISTINCT targets (HLL)
Set n = ##class(Graph.KG.TemporalIndex).GetDistinctCount("svc:auth","CALLS_AT",tsStart,tsEnd)

---

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

Temporal edge filtering (v1.42.0+)

-- Filter edges by timestamp — routes to ^KG("tout") B-tree, O(results)
MATCH (a)-[r:CALLS_AT]->(b)
WHERE r.ts >= $start AND r.ts <= $end
RETURN r.ts, r.weight
ORDER BY r.ts DESC

-- Temporal + property filter
MATCH (a:Service)-[r:CALLS_AT]->(b)
WHERE r.ts >= $start AND r.ts <= $end
  AND r.weight > 1000
RETURN a.id, b.id, r.ts, r.weight
ORDER BY r.weight DESC

-- Inbound direction — routes to ^KG("tin")
MATCH (b:Service)<-[r:CALLS_AT]-(a)
WHERE r.ts >= $start AND r.ts <= $end
RETURN a.id, b.id, r.ts

Sweet spot: Temporal Cypher is designed for trajectory-style queries (≤~50 edges, ordered output). For aggregation over large windows, use get_temporal_aggregate() / get_bucket_groups() — these are O(1) pre-aggregated and 400× faster.

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

-- Variable-length paths
MATCH (a:Service)-[:CALLS*1..3]->(b:Service)
WHERE a.id = 'auth'
RETURN b.id

-- CASE WHEN
MATCH (n:Service)
RETURN n.id,
       CASE WHEN n.calls > 1000 THEN 'high' WHEN n.calls > 100 THEN 'medium' ELSE 'low' END AS load

-- UNION
MATCH (n:ServiceA) RETURN n.id
UNION
MATCH (n:ServiceB) RETURN n.id

-- Vector search in Cypher
CALL ivg.vector.search('Service', '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=["service:auth"], damping=0.85)

# K-hop subgraph
subgraph = ops.kg_SUBGRAPH(seed_ids=["service:auth"], k_hops=3)

# PPR-guided subgraph (prevents k^n blowup)
guided = ops.kg_PPR_GUIDED_SUBGRAPH(seed_ids=["service:auth"], 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

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

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Architecture

Global Structure

Global	Purpose
^KG("out", s, p, o)	Knowledge graph — outbound edges
^KG("in", o, p, s)	Knowledge graph — inbound edges
^KG("tout", ts, s, p, o)	Temporal index — outbound, ordered by timestamp
^KG("tin", ts, o, p, s)	Temporal index — inbound, ordered by timestamp
^KG("bucket", bucket, s)	Pre-aggregated edge count per 5-minute bucket
^KG("tagg", bucket, s, p, key)	Pre-aggregated COUNT/SUM/MIN/MAX/HLL per bucket
^KG("edgeprop", ts, s, p, o, key)	Rich edge attributes
^NKG	Integer-encoded ^KG for Arno acceleration
^VecIdx	VecIndex RP-tree ANN
^PLAID	PLAID multi-vector

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

Class	Key Methods
Graph.KG.TemporalIndex	InsertEdge, BulkInsert, QueryWindow, GetVelocity, FindBursts, GetAggregate, GetBucketGroups, GetDistinctCount, Purge
Graph.KG.VecIndex	Create, InsertJSON, Build, SearchJSON, SearchMultiJSON, InsertBatchJSON
Graph.KG.PLAIDSearch	StoreCentroids, BuildInvertedIndex, Search
Graph.KG.PageRank	RunJson, PageRankGlobalJson
Graph.KG.Algorithms	WCCJson, CDLPJson
Graph.KG.Subgraph	SubgraphJson, PPRGuidedJson
Graph.KG.Traversal	BuildKG, BuildNKG, BFSFastJson
Graph.KG.BulkLoader	BulkLoad (INSERT %NOINDEX %NOCHECK + %BuildIndices)

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Performance

Operation	Latency	Dataset
Temporal edge ingest	134K edges/sec	RE2-TT 535M edges, Enterprise IRIS
Window query (selective)	0.1ms	O(results), B-tree traversal
GetAggregate (1 bucket, 5min)	0.085ms	50K-edge dataset
GetAggregate (288 buckets, 24hr)	0.160ms	O(buckets), not O(edges)
GetBucketGroups (3 sources, 1hr)	0.193ms
GetDistinctCount (1 bucket)	0.101ms	16-register HLL
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 tokens)	~14ms	Centroid scoring + MaxSim
PPR (10K nodes)	62ms	Pure ObjectScript
1-hop neighbors	0.3ms	$Order on ^KG

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Documentation

• Python SDK Reference
• Architecture
• Schema Reference
• Temporal Graph Full Spec
• Setup Guide
• Testing Policy

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Changelog

v1.42.0 (2026-04-03)

• Cypher temporal edge filtering: WHERE r.ts >= $start AND r.ts <= $end routes MATCH patterns to ^KG("tout") B-tree — O(results), not O(total edges)
• r.ts and r.weight accessible in RETURN and ORDER BY on temporal edges
• Inbound direction (b)<-[r:P]-(a) WHERE r.ts >= $start routes to ^KG("tin")
• r.ts without WHERE filter → NULL + query-level warning (prevents accidental full scans)
• r.weight > expr in WHERE applies as post-filter on temporal result set
• Uses IRIS-compatible derived table subquery (not WITH CTE) — works on protocol 65 xDBC
• w → weight canonical field name in temporal CTE (consistent with v1.41.0 API aliases)
• Sweet spot: trajectory queries ≤50 edges. For aggregation, use get_temporal_aggregate().

v1.41.0 (2026-04-03)

• get_edges_in_window() now returns source/target/predicate/timestamp/weight aliases alongside s/o/p/ts/w — backward compatible
• get_edges_in_window(direction="in") — query inbound edges by target node (uses ^KG("tin"))
• create_edge_temporal(..., upsert=True) and bulk_create_edges_temporal(..., upsert=True) — skip write if edge already exists at that timestamp
• purge_before(ts) — delete all temporal edges older than ts, with ^KG("tagg") and ^KG("bucket") cleanup
• Graph.KG.TemporalIndex.PurgeBefore(ts) and QueryWindowInbound(target, predicate, ts_start, ts_end) ObjectScript methods

v1.40.0 (2026-04-02)

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

v1.39.0 (2026-04-01)

• Pre-aggregated temporal analytics: ^KG("tagg") COUNT/SUM/AVG/MIN/MAX at O(1)
• GetAggregate, GetBucketGroups, GetDistinctCount ObjectScript methods
• get_temporal_aggregate(), get_bucket_groups(), get_distinct_count() Python wrappers
• 16-register HyperLogLog COUNT DISTINCT (SHA1, ~26% error — suitable for fanout threshold detection)
• Benchmark: 134K–157K edges/sec sustained across RE2-TT/RE2-OB/RE1-TT (535M edges total)

v1.38.0

• Rich edge properties: ^KG("edgeprop", ts, s, p, o, key) — arbitrary typed attributes per temporal edge
• get_edge_attrs(), create_edge_temporal(attrs={...})
• NDJSON import/export: import_graph_ndjson(), export_graph_ndjson(), export_temporal_edges_ndjson()

v1.37.0

• Temporal property graph: create_edge_temporal(), bulk_create_edges_temporal()
• get_edges_in_window(), get_edge_velocity(), find_burst_nodes()
• ^KG("tout"/"tin"/"bucket") globals — bidirectional time-indexed edge store
• Graph.KG.TemporalIndex ObjectScript class

v1.35.0

• UNION / UNION ALL in Cypher
• EXISTS {} subquery predicates

v1.34.0

• Variable-length paths: MATCH (a)-[:REL*1..5]->(b) via BFSFastJson bridge

v1.33.0

• CASE WHEN / THEN / ELSE / END in Cypher RETURN and WHERE

v1.32.0

• CAST functions: toInteger(), toFloat(), toString(), toBoolean()

v1.31.0

• RDF 1.2 reification API: reify_edge(), get_reifications(), delete_reification()

v1.30.0

• BulkLoader: INSERT %NOINDEX %NOCHECK + %BuildIndices — 46K rows/sec SQL ingest
• RDF 1.2 reification schema DDL

v1.29.0

• OBO ontology ingest: load_obo(), load_networkx()

v1.28.0

• Lightweight install — base requires only intersystems-irispython
• Optional extras: [full], [plaid], [dev], [ml], [visualization], [biodata]

v1.26.0–v1.27.0

• PLAID multi-vector retrieval — PLAIDSearch.cls pure ObjectScript + $vectorop
• PLAID packed token storage: 53 $Order → 1 $Get

v1.24.0–v1.25.1

• VecIndex nprobe recall fix (counts leaf visits, not branch points)
• Annoy-style two-means tree splitting (fixes degenerate trees)
• Batch APIs: SearchMultiJSON, InsertBatchJSON

v1.21.0–v1.22.1

• VecIndex RP-tree ANN
• SearchJSON/InsertJSON — eliminated xecute path (250ms → 4ms)

v1.20.0

• Arno acceleration wrappers: khop(), ppr(), random_walk()

v1.19.0

• ^NKG integer index for Arno acceleration

v1.18.0

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

v1.17.0

• Cypher named path bindings, CALL subqueries, PPR-guided subgraph

Earlier versions →

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