Observed Signal · Jun 2, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
2asy.ai Builds Cross-Domain Ontology Graph for Tariff News
2asy.ai updated its tariff briefing pipeline, moving from plain vector RAG to per-article graph extraction and now to a cross-domain ontology Graph RAG. The system extracts entities, events, and relations against a shared ontology, resolves entities across documents, and exposes a causal graph on the latest briefing (June 2 story). The author notes the current graph is sparse but will densify as more documents are ingested. The pipeline runs on local hardware (RTX 4090 and an AMD W6800) using open models and no cloud inference. The new approach enables connections and causal chains that plain vector similarity retrieval could not represent across a corpus.
Demonstrates a practical progression from vector RAG to cross-document ontology graphs enabling causal chaining and entity resolution; relevant as a technical case study but limited industry impact because it is a single project running on local hardware rather than a major platform release.
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Key Takeaways & Evidence Grounding
- 2asy.ai migrated its retrieval pipeline from vector RAG to per-article graph extraction and then to a cross-domain ontology Graph RAG.
- The cross-domain system extracts entities, events, and relations against a shared ontology and resolves entities across documents into single canonical nodes.
- The June 2 briefing ('US Trade Remedies Expand Amid Global Investigations') has an extraction of about 41 nodes and 60 edges with a Sunset Review node at the root.
- The pipeline runs on local hardware (RTX 4090 and an AMD W6800) using open models with no cloud inference costs.
- The visible causal graph is published on 2asy.ai and is expected to grow denser as more documents flow through the system.
Connected Companies & Entities
1 Entity mappedOntology Mapping & Concepts
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