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

TL;DR

This paper introduces a unified PostgreSQL-based data layer for RAG systems, significantly improving reliability, latency, and security in production environments.

Contribution

It proposes a novel unified data layer leveraging PostgreSQL with native vector search, addressing key deployment challenges in RAG systems.

Findings

92% latency reduction for date-filtered queries

74% latency reduction for tenant-scoped queries

Zero cross-tenant data leakage and 93% less synchronization code

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