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
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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).