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661 Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

VentureBeat
VentureBeat 2 place · 05/17/2026 14:00 EDT

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search.However, for enterprise domains characterized by highly interconnected data (supply chain, financial compliance, fraud detection), vector-only RAG often fails. It captures similarity but misses st

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