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Spring AI uses Pgvector for multi-tenant RAG security

By PulseAugur Editorial · [2 sources] ·

This article proposes a multi-tenant solution for Spring AI applications using Pgvector, a PostgreSQL extension for vector embeddings. It advocates for logical tenant isolation through metadata filtering within a shared Pgvector store, rather than provisioning separate databases per tenant. The approach leverages Spring Security to inject tenant context into Spring AI's filter expressions, ensuring secure data segregation and improved performance by indexing metadata fields. AI

IMPACT Provides a practical solution for securely scaling RAG applications by enabling multi-tenancy with existing database infrastructure.

RANK_REASON The article describes a technical implementation detail for using existing tools (Spring AI, Pgvector) to solve a specific problem (multi-tenancy in RAG applications).

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. dev.to — LLM tag TIER_1 English(EN) · Lav Kumar Dixit ·

    Semantic Caching with Spring AI and PgVector: Reduce LLM Costs and Improve Response Time by 90%

    <p>Large Language Models are powerful, but they're also expensive and slow when handling repetitive queries. If your AI application receives thousands of similar questions every day, repeatedly calling an LLM for nearly identical requests is inefficient.</p> <p>What if you could …

  2. dev.to — LLM tag TIER_1 English(EN) · Machine coding Master ·

    Stop Spinning Up Separate Vector DBs: Multi-Tenant Spring AI with Pgvector Metadata Filtering

    <h2> Stop Spinning Up Separate Vector DBs: Multi-Tenant Spring AI with Pgvector Metadata Filtering </h2> <p>Shipping RAG to production in 2026 means solving the multi-tenancy problem without blowing up your cloud budget on isolated vector database instances. If you aren't enforci…

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