iris-vector-graph 1.5.2

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

IRIS Vector Graph

The ultimate Graph + Vector + Text Retrieval Engine for InterSystems IRIS.

IRIS Vector Graph is a general-purpose graph utility built on InterSystems IRIS that supports and demonstrates knowledge graph construction and query techniques. It combines graph traversal, HNSW vector similarity, and lexical search in a single, unified database.

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Why IRIS Vector Graph?

• Multi-Query Power: Query your graph via SQL, openCypher (v1.3 with DML), or GraphQL — all on the same data.
• Transactional Engine: Beyond retrieval — support for CREATE, DELETE, and MERGE operations.
• Blazing Fast Vectors: Native HNSW indexing delivering ~1.7ms search latency (vs 5.8s standard).
• Zero-Dependency Integration: Built with IRIS Embedded Python — no external vector DBs or graph engines required.
• Production-Ready: The engine behind iris-vector-rag for advanced RAG pipelines.

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Installation

pip install iris-vector-graph

Note: Requires InterSystems IRIS 2025.1+ with the irispython runtime enabled.

Quick Start

# 1. Clone & Sync
git clone https://github.com/intersystems-community/iris-vector-graph.git && cd iris-vector-graph
uv sync

# 2. Spin up IRIS
docker-compose up -d

# 3. Start API
uvicorn api.main:app --reload

Visit:

• GraphQL Playground: http://localhost:8000/graphql
• API Docs: http://localhost:8000/docs

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Unified Query Engines

openCypher (Advanced RD Parser)

IRIS Vector Graph features a custom recursive-descent Cypher parser supporting multi-stage queries and transactional updates:

// Complex fraud analysis with WITH and Aggregations
MATCH (a:Account)-[r]->(t:Transaction)
WITH a, count(t) AS txn_count
WHERE txn_count > 5
MATCH (a)-[:OWNED_BY]->(p:Person)
RETURN p.name, txn_count

Supported Clauses: MATCH, OPTIONAL MATCH, WITH, WHERE, RETURN, UNWIND, CREATE, DELETE, DETACH DELETE, MERGE, SET, REMOVE.

GraphQL

query {
  protein(id: "PROTEIN:TP53") {
    name
    interactsWith(first: 5) { id name }
    similar(limit: 3) { protein { name } similarity }
  }
}

SQL (Hybrid Search)

SELECT TOP 10 id, 
       kg_RRF_FUSE(id, vector, 'cancer suppressor') as score
FROM nodes
ORDER BY score DESC

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Scaling & Performance

The integration of a native HNSW (Hierarchical Navigable Small World) functional index directly into InterSystems IRIS provides massive scaling benefits for hybrid graph-vector workloads.

By keeping the vector index in-process with the graph data, we achieve subsecond multi-modal queries that would otherwise require complex application-side joins across multiple databases.

Performance Benchmarks (2026 Refactor)

• High-Speed Traversal: ~1.84M TEPS (Traversed Edges Per Second).
• Sub-millisecond Latency: 2-hop BFS on 10k nodes in <40ms.
• RDF 1.2 Support: Native support for Quoted Triples (Metadata on edges) via subject-referenced properties.
• Query Signatures: O(1) hop-rejection using ASQ-inspired Master Label Sets.

Why fast vector search matters for graphs

Consider a "Find-and-Follow" query common in fraud detection:

1. Find the top 10 accounts most semantically similar to a known fraudulent pattern (Vector Search).
2. Follow all outbound transactions from those 10 accounts to identify the next layer of the money laundering ring (Graph Hop).

In a standard database without HNSW, the first step (vector search) can take several seconds as the dataset grows, blocking the subsequent graph traversals. With iris-vector-graph, the vector lookup is reduced to ~1.7ms, enabling the entire hybrid traversal to complete in a fraction of a second.

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Interactive Demos

Experience the power of IRIS Vector Graph through our interactive demo applications.

Biomedical Research Demo

Explore protein-protein interaction networks with vector similarity and D3.js visualization.

Fraud Detection Demo

Real-time fraud scoring with transaction networks, Cypher-based pattern matching, and bitemporal audit trails.

To run the CLI demos:

export PYTHONPATH=$PYTHONPATH:.
# Cypher-powered fraud detection
python3 examples/demo_fraud_detection.py

# SQL-powered "drop down" example
python3 examples/demo_fraud_detection_sql.py

To run the Web Visualization demos:

# Start the demo server
uv run uvicorn src.iris_demo_server.app:app --port 8200 --host 0.0.0.0

Visit http://localhost:8200 to begin.

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iris-vector-rag Integration

IRIS Vector Graph is the core engine powering iris-vector-rag. You can use it in your RAG pipelines like this:

from iris_vector_rag import create_pipeline

# Create a GraphRAG pipeline powered by this engine
pipeline = create_pipeline('graphrag')

# Combined vector + text + graph retrieval
result = pipeline.query(
    "What are the latest cancer treatment approaches?",
    top_k=5
)

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Documentation

• Detailed Architecture
• Biomedical Domain Examples
• Full Test Suite
• iris-vector-rag Integration
• Verbose README (Legacy)

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Changelog

v1.5.2 (2025-01-31)

• Engine Acceleration: Ported high-performance SQL paths for get_node() and count_nodes()
• Bulk Loading: New bulk_create_nodes() and bulk_create_edges() methods with %NOINDEX support
• Performance: Verified 80x speedup for single-node reads and 450x for counts vs standard Cypher

v1.5.1 (2025-01-31)

• Extreme Performance: Verified 38ms latency for 5,000-node property queries (at 10k entity scale)
• Subquery Stability: Optimized REPLACE string aggregation to avoid IRIS %QPAR optimizer bugs
• Scale Verified: Robust E2E stress tests confirm industrial-grade performance for 10,000+ nodes

v1.4.9 (2025-01-31)

• Exact Collation: Added %EXACT to VARCHAR columns for case-sensitive matching
• Performance: Prevents default UPPER collation behavior in IRIS 2024.2+
• Case Sensitivity: Ensures node IDs, labels, and property keys are case-sensitive

v1.4.8 (2025-01-31)

• Fix SUBSCRIPT error: Removed idx_props_key_val which caused errors with large values
• Improved Performance: Maintained composite indexes that don't include large VARCHAR columns

v1.4.7 (2025-01-31)

• Revert to VARCHAR(64000): LONGVARCHAR broke REPLACE; VARCHAR(64000) keeps compatibility
• Large Values: 64KB property values, REPLACE works, no CAST needed

v1.4.5/1.4.6 (deprecated - use 1.4.7)

• v1.4.5 used LONGVARCHAR which broke REPLACE function
• v1.4.6 used CAST which broke on old schemas

v1.4.4 (2025-01-31)

• Bulk Loading Support: %NOINDEX INSERTs, disable_indexes(), rebuild_indexes()
• Fast Ingest: Skip index maintenance during bulk loads, rebuild after

v1.4.3 (2025-01-31)

• Composite Indexes: Added (s,key), (s,p), (p,o_id), (s,label) based on TrustGraph patterns
• 12 indexes total: Optimized for label filtering, property lookups, edge traversal

v1.4.2 (2025-01-31)

• Performance Indexes: Added indexes on rdf_labels, rdf_props, rdf_edges for fast graph traversal
• ensure_indexes(): New method to add indexes to existing databases
• Composite Index: Added (key, val) index on rdf_props for property value lookups

v1.4.1 (2025-01-31)

• Embedding API: Added get_embedding(), get_embeddings(), delete_embedding() methods
• Schema Prefix in Engine: All engine SQL now uses configurable schema prefix

v1.4.0 (2025-01-31)

• Schema Prefix Support: set_schema_prefix('Graph_KG') for qualified table names
• Pattern Operators Fixed: CONTAINS, STARTS WITH, ENDS WITH now work correctly
• IRIS Compatibility: Removed recursive CTEs and NULLS LAST (unsupported by IRIS)
• ORDER BY Fix: Properties in ORDER BY now properly join rdf_props table
• type(r) Verified: Relationship type function works in RETURN/WHERE clauses

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Author: Thomas Dyar ([e-mail])