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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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.

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 and bitemporal audit trails.

To run the demos:

# Start the demo server
export PYTHONPATH=$PYTHONPATH:$(pwd)/src
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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Author: Thomas Dyar (thomas.dyar@intersystems.com)