Knowledge GraphsInteractive Sandbox Active

Ontology Modeler

High-Throughput Semantic Graph Construction & pgvector Knowledge Engine

System Simulator Sandbox

Node Ready

Sample Interaction:

Generate domain ontology graph and query nearest concepts for 'KRAS G12D inhibitor resistance pathways'.

Execution Output:

Ontology Modeler Graph Generated (24 nodes, 58 edges): • Root Concept: KRAS G12D Mutation (Oncogenic Signaling) • Downstream Edges: MAPK/ERK pathway activation (weight: 0.94), SHP2/SOS1 feedback loops (0.91) • Vector Proximity: Secondary switch II pocket mutations (Cosine Sim: 0.942)

The Problem

Unstructured enterprise knowledge is trapped in disconnected document silos without ontological relationships or fast semantic search.

The Architecture Solution

Combines dense pgvector embeddings with automated entity-relationship ontology builders and HNSW vector clustering for multi-agent reasoning.

Key Innovations

Automated Concept Ontology & Dynamic Graph Topology Synthesis
Sub-15ms HNSW Approximate Nearest Neighbor Vector Search in PostgreSQL
Hybrid Dense-Sparse (pgvector + BM25) Cross-Encoder Reranking
Multi-Tenant Domain Hierarchy Partitioning

Verified Telemetry

Vector Latency

3.8ms

Graph Index Capacity

5M+ nodes

Recall@10

97.6%

QPS Throughput

1,400 QPS

Tech Stack

PythonFastAPIPostgreSQLpgvectorHNSWlibSentenceTransformersReactTypeScript

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