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arXiv · 2605.03275

Beyond Similarity Search: A Unified Data Layer for Production RAG Systems

Venkata Krishna Prasanth Budigi·Siri Chandana Sirigiri
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Abstract

Retrieval-Augmented Generation (RAG) systems have become the standard architecture for grounding large language models in organizational knowledge. Yet production deployments consistently expose a gap between clean prototype performance and real-world reliability. This paper identifies three root causes of that gap: data staleness, tenant data leakage, and query composition explosion. All three trace back to the conventional split-system data layer. We propose and evaluate a unified data layer built on PostgreSQL with native vector search (pgvector) and HNSW indexing. Controlled benchmarks on 50,000 documents show 92% latency reduction for date-filtered queries, 74% for tenant-scoped queries, zero synchronization inconsistency, and complete elimination of cross-tenant data leakage with 93% less synchronization code. We additionally discuss a recommended hybrid tier architecture

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Venkata Krishna Prasanth Budigi, Siri Chandana Sirigiri. 2026-05-05. Beyond Similarity Search: A Unified Data Layer for Production RAG Systems. https://arxiv.org/abs/2605.03275

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