Beyond Similarity Search: A Unified Data Layer for Production RAG Systems
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866909013490270208 |
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| author | Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana |
| author_facet | Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03275 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Similarity Search: A Unified Data Layer for Production RAG Systems Budigi, Venkata Krishna Prasanth Sirigiri, Siri Chandana Information Retrieval Databases H.2.4; H.3.3 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 |
| title | Beyond Similarity Search: A Unified Data Layer for Production RAG Systems |
| topic | Information Retrieval Databases H.2.4; H.3.3 |
| url | https://arxiv.org/abs/2605.03275 |