Standard RAG pipelines fail when answering cross-document analytical questions. By combining semantic embeddings with entity-relationship knowledge graphs, agentic systems traverse contextual nodes deterministically.
1. The Limits of Flat Cosine Similarity
Vector embeddings group conceptually related paragraphs, but they cannot evaluate multi-hop questions like: Which customer contract has a penalty clause tied to vendor SLA defaults in region B?
2. Hybrid Entity-Graph Traversal
We link extracted entities to an operational Neo4j graph, enabling autonomous sub-agents to traverse relationships with explicit verification steps before passing context to generation models.
Architectural Takeaways
- → Use knowledge graphs to resolve multi-hop relational dependencies.
- → Enforce citation validation on every retrieved chunk before generating answers.
- → Maintain deterministic provenance links back to source PDF paragraphs.
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