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

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 structure. It struggles with multi-hop reasoning questions like, "How will the delay in Component X impact our Q3 deliverable for Client Y?" because the vector store doesn't "know" that Component X is part of Client Y's deliverable. This article explores the graph-enhanced RAG pattern.

Drawing on my experience building high-throughput logging systems at Meta and private data infrastructure at Cognee, we will walk through a reference architecture that combines the semantic flexibility of vector search with the structural determinism of graph databases. The problem: When vector search loses context Vector databases excel at capturing meaning but discard topology. When a document is chunked and embedded, explicit relationships (hierarchy, dependency, ownership) are often flattened or lost entirely.

Consider a supply chain risk scenario. While this is a hypothetical example, it represents the exact class of structural problems we see constantly in enterprise data architectures: Structured data: A SQL database defining that Supplier A provides Component X to Factory Y. Unstructured data: A news report stating, "Flooding in Thailand has halted production at Supplier A's facility." A standard vector search for "production risks" will retrieve the news report.

However, it likely lacks the context to link that report to Factory Y's output. The LLM receives the news but cannot answer the critical business question: "Which downstream factories are at risk?" In production, this manifests as hallucination. The LLM attempts to bridge the gap between the news report and the factory but lacks the explicit link, leading it to either guess relationships or return an "I don't know" response despite the data being present in the system.

The pattern: Hybrid retrieval To solve this, we move from a "Flat RAG" to a "Graph RAG" architecture. This involves a three-layer stack: Ingestion (The "Meta" Lesson): At Meta, working on the Shops logging infrastructure, we learned that structure must be enforced at ingestion. You cannot guarantee reliable analytics if you try to reconstruct structure from messy logs later.

Similarly, in RAG, we must extract entities (nodes) and relationships (edges) during ingestion. We can use an LLM or named entity recognition (NER) model to extract entities from text chunks and link them to existing records in the graph. Storage: We use a graph database (like Neo4j) to store the structural graph.