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Enterprise data architects often face a critical dilemma when implementing semantic search<\/strong>: the tendency to conflate the transactional system of record with the AI retrieval layer. This architectural ambiguity leads to fragile integrations, security risks regarding data sovereignty, and uncertain Total Cost of Ownership (TCO). To evaluate database candidates fairly, you must first establish a clear separation of concerns.<\/p>\n A robust enterprise architecture for semantic search<\/strong> and Retrieval-Augmented Generation (RAG) consists of distinct layers, each with specific responsibilities. Treating semantic search<\/strong> as a single product feature rather than a capability pattern invites vendor lock-in and integration failure.<\/p>\n The following roles define the boundaries of a resilient system:<\/p>\n When evaluating a database for this workload, you must determine which of these roles the candidate database is intended to fill. Some vendors position their product as a unified platform that replaces all four layers. Others provide specialized components for each. The evaluation framework must assess whether the database supports the specific role you assign to it, particularly regarding hybrid search<\/strong> (combining vector similarity with keyword or metadata filtering) and metadata filtering<\/strong> performance.<\/p>\n Traditional keyword matching fails to capture the intent behind user queries. Semantic search<\/strong> addresses this by converting text into vectors and measuring cosine similarity or other distance metrics. However, enterprise requirements rarely stop at pure vector search. You need hybrid search<\/strong> to combine dense vector retrieval with sparse keyword or metadata filtering.<\/p>\n When evaluating database vendors, use this checklist to determine technical fit.<\/p>\n For vendors offering native vector capabilities, verify the performance of these operations under load. For relational databases that do not yet offer native vector support, treat them as candidates for the transactional layer or as platforms that integrate with dedicated vector stores. Do not assume a relational database can handle high-volume vector workloads without explicit evidence of vector indexing throughput.<\/p>\n Storing embeddings and unstructured data introduces unique security and compliance challenges. In Malaysia, enterprises must navigate strict data sovereignty requirements and security standards. The evaluation of a database vendor must include a rigorous assessment of their ability to meet these obligations.<\/p>\n Data sovereignty is not a blanket mandate but a set of conditions based on the type of data and the regulatory framework. You must verify that the vendor\u2019s architecture supports your specific residency and governance needs.<\/p>\n When assessing overseas vendors, pay close attention to their data handling policies. Do not assume that a vendor\u2019s global presence automatically guarantees compliance with local Malaysian standards. You must request specific evidence of their compliance certifications and data handling practices relevant to your industry.<\/p>\n Procurement teams must evaluate semantic search<\/strong> implementations through a TCO lens. The cost extends beyond software licensing to include infrastructure, operational overhead, and migration risks.<\/p>\n A key decision point is whether to use a specialized vector database or a unified relational database with vector capabilities. Each approach has distinct cost implications.<\/p>\n\n
Hybrid Search & Indexing: The Core Selection Criteria<\/h2>\n
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VECTOR<\/code>, ARRAY<\/code> of floats) without requiring proprietary binary formats? Standard types ensure interoperability with external embedding models and reduce lock-in risk.<\/li>\ndepartment_id<\/code>, date_range<\/code>) before<\/em> or during<\/em> the vector search? Post-filtering vector results is computationally expensive and often leads to poor recall. Pre-filtering or hybrid indexing is essential for low-latency enterprise queries.<\/li>\nSecurity, Sovereignty & Compliance: The Malaysian Context<\/h2>\n
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Total Cost of Ownership (TCO): Infrastructure Consolidation vs. Specialization<\/h2>\n