{"id":1984,"date":"2026-10-29T16:11:37","date_gmt":"2026-10-29T16:11:37","guid":{"rendered":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/enterprise-database-with-ai-capabilities-evaluation\/"},"modified":"2026-10-29T16:11:37","modified_gmt":"2026-10-29T16:11:37","slug":"enterprise-database-with-ai-capabilities-evaluation","status":"publish","type":"post","link":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/enterprise-database-with-ai-capabilities-evaluation\/","title":{"rendered":"Enterprise Database with AI Capabilities_ Evaluation"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/kingbase-bbs.oss-cn-beijing.aliyuncs.com\/qywx\/blogImage\/ff2f1964-219d-4727-b909-7c558eb3f544.webp\" alt=\"Selection guide metaphor showing sorted samples in a compartmentalized tray for enterprise database evaluation\" \/><\/p>\n<h2>Enterprise Database with AI Capabilities: Evaluation Framework<\/h2>\n<p>Enterprise data architects in Malaysia are moving past the prototype phase of artificial intelligence. The current challenge is not merely storing embeddings but maintaining strict transactional integrity while querying high-dimensional vector data alongside structured business metadata. A true <strong>enterprise database with ai capabilities<\/strong> must co-locate these data types to eliminate the latency and governance gaps inherent in fragmented stacks.<\/p>\n<p>This selection guide provides a transparent evaluation framework for Malaysian enterprises. It defines weighted criteria, evidence requirements, and disqualifiers to help buyers narrow candidates. The framework treats KingbaseES as a commercial enterprise-grade general-purpose converged database product. It does not predetermine KingbaseES as the winner but provides the criteria to evaluate it against other commercial options based on workload fit.<\/p>\n<h3>Workload and Stakeholder Requirements<\/h3>\n<p>Before evaluating specific vendors, define the workload and stakeholder needs.<\/p>\n<ul>\n<li><strong>Workload:<\/strong> Hybrid search workloads combining vector similarity with structured filters (e.g., date range, document type, jurisdiction).<\/li>\n<li><strong>Stakeholders:<\/strong> Data Architects (governance, architecture fit), Security Officers (RBAC, access control), Operations (TCO, reliability), and Procurement (licensing, support).<\/li>\n<li><strong>Malaysian Context:<\/strong> Compliance with the Personal Data Protection Act (PDPA). While PDPA does not create a blanket data-residency mandate, enterprises must verify data handling practices. Local support availability and compliance verification are critical procurement factors.<\/li>\n<\/ul>\n<h3>Evaluation Criteria and Weights<\/h3>\n<p>The following criteria are weighted to reflect enterprise priorities. Scores must be derived from verified evidence, not vendor claims.<\/p>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Criterion<\/th>\n<th style=\"text-align:left\">Weight<\/th>\n<th style=\"text-align:left\">Key Requirements<\/th>\n<th style=\"text-align:left\">Evidence Needed<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\"><strong>Risk Control &amp; Governance<\/strong><\/td>\n<td style=\"text-align:left\">30%<\/td>\n<td style=\"text-align:left\">Unified RBAC for vector and relational data; Audit trails; Data residency compliance verification.<\/td>\n<td style=\"text-align:left\">Security architecture diagrams; RBAC implementation docs; PDPA compliance statements.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Performance<\/strong><\/td>\n<td style=\"text-align:left\">25%<\/td>\n<td style=\"text-align:left\">Hybrid search latency; Index freshness; Concurrency handling.<\/td>\n<td style=\"text-align:left\">PoC test results for 500 QPS; Index update latency metrics; Dimensional scalability tests.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Security<\/strong><\/td>\n<td style=\"text-align:left\">20%<\/td>\n<td style=\"text-align:left\">Access control for vector embeddings; Integration with LLM orchestration frameworks securely.<\/td>\n<td style=\"text-align:left\">MCP Server integration docs; RBAC policy examples; Integration test results.<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>TCO &amp; Operations<\/strong><\/td>\n<td style=\"text-align:left\">25%<\/td>\n<td style=\"text-align:left\">Consolidation benefits; Migration ease; Local support availability; Licensing model.<\/td>\n<td style=\"text-align:left\">TCO breakdown; Migration tool capabilities (KDMS); Support SLA verification.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Risk Control and Security: Unified Governance<\/h3>\n<p>Security remains the primary bottleneck for enterprise AI adoption. Many specialized vector databases lack the granular access control mechanisms required for sensitive corporate data. When vector embeddings are stored in siloed systems, enforcing Role-Based Access Control (RBAC) becomes inconsistent. An architect must ensure that the same user who has read access to a structured customer record also has restricted access to the corresponding vector embeddings that might reveal proprietary insights.<\/p>\n<p>The evaluation framework must prioritize unified security policies. You need a platform that applies the same RBAC rules to both relational tables and vector columns. This prevents data leakage where an application might retrieve vector data without proper authorization checks. The risk of inconsistent access control is high in best-of-breed stacks where the vector store and the relational database are managed by different teams or tools.<\/p>\n<p>To mitigate this, verify that the database platform supports centralized security management for all data types. Look for evidence of integration with standard enterprise identity providers. The platform should allow you to define permissions at the table or column level, applying them uniformly whether the data is a string, a timestamp, or a vector. This unified governance reduces the attack surface and simplifies compliance audits.<\/p>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES supports RBAC and access control for vector data.<\/li>\n<li><strong>Evidence:<\/strong> Product Manual (V9) states &quot;KingbaseES MCP Server \u8ba9 AI \u5b89\u5168\u8c03\u7528 KingbaseES \u6570\u636e\u5e93\u80fd\u529b&quot; (KingbaseES MCP Server allows AI to securely call KingbaseES database capabilities).<\/li>\n<li><strong>Quality:<\/strong> Medium. Requires verification of specific RBAC implementation details for vector types.<\/li>\n<\/ul>\n<p><strong>Malaysian Regulatory Context (PDPA):<\/strong><br \/>\nKingbaseES does not have documented local Malaysian offices or data centers. Enterprises must verify local support and compliance independently.<\/p>\n<ul>\n<li><strong>Action:<\/strong> Request a Data Processing Agreement (DPA) that aligns with Malaysia&#8217;s PDPA requirements.<\/li>\n<li><strong>Verification:<\/strong> Confirm if the vendor offers local support channels or partners with local MSPs for incident response.<\/li>\n<li><strong>Data Residency:<\/strong> Ensure the deployment architecture allows data to reside within Malaysia if required by internal policy or specific sector regulations, even if not mandated by PDPA for all data.<\/li>\n<\/ul>\n<h3>Performance Under Load: Measuring Latency, Freshness, and Indexing<\/h3>\n<p>Marketing claims often cite &quot;millisecond&quot; latency without defining the workload context. To evaluate an <strong>enterprise database with ai capabilities<\/strong>, you must define specific performance benchmarks. The critical metric is not just raw query speed but sustained throughput under concurrent load while maintaining index freshness. Index freshness refers to the time delay between a data update and its availability for vector search. In a RAG pipeline, stale vectors lead to hallucinated or irrelevant AI responses.<\/p>\n<p>You need to validate performance using production-scale data. Evidence of hybrid search performance should include metrics for both vector similarity and structured filtering. For example, a validated case involves processing 47 million chunks across 2.3 million documents. The system must handle 500 queries per second (QPS) with hybrid search requirements. This test proves the engine can manage high-concurrency AI application responses without degrading transactional performance.<\/p>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES provides millisecond-level response for vector retrieval.<\/li>\n<li><strong>Evidence:<\/strong> Product Manual (V9) states &quot;\u6027\u80fd\u5f3a\u52b2\uff1a\u6beb\u79d2\u7ea7\u54cd\u5e94\uff0c\u6570\u636e\u7cbe\u51c6\u53ec\u56de&quot; (Strong performance: millisecond-level response, accurate data recall).<\/li>\n<li><strong>Context:<\/strong> High-concurrency AI applications.<\/li>\n<li><strong>Quality:<\/strong> Medium. Lacks specific workload context (e.g., 47M chunks).<\/li>\n<\/ul>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES handles 47 million chunks across 2.3 million documents.<\/li>\n<li><strong>Evidence:<\/strong> Case Study (Early 2026) for a US Client Legal RAG System.<\/li>\n<li><strong>Context:<\/strong> Legal document RAG system using hybrid search.<\/li>\n<li><strong>Quality:<\/strong> High. Specific to a case study, not a general capability guarantee.<\/li>\n<\/ul>\n<p>When designing your Proof of Concept (PoC), include the following test cases:<\/p>\n<ol>\n<li><strong>Hybrid Search Latency:<\/strong> Measure the time to retrieve top-K results combining vector similarity and structured metadata filters (e.g., date range).<\/li>\n<li><strong>Index Update Latency:<\/strong> Inject new data and measure the time until the new vector is searchable. This validates index freshness.<\/li>\n<li><strong>Concurrency Stress:<\/strong> Run multiple concurrent queries to ensure response times do not degrade under load.<\/li>\n<li><strong>Dimensional Scalability:<\/strong> Test with high-dimensional embeddings (e.g., 3072 dimensions) to ensure the indexing algorithm performs efficiently at scale.<\/li>\n<\/ol>\n<p>These tests provide verifiable evidence of performance. They move the evaluation beyond vendor brochures to actual operational metrics. You must also verify the indexing algorithms supported. Different algorithms (e.g., HNSW, IVF) have different trade-offs between build time, memory usage, and query speed. Choose the algorithm that aligns with your specific latency and throughput requirements.<\/p>\n<h3>TCO and Operations: Consolidation vs. Specialized Vector Stores<\/h3>\n<p>The Total Cost of Ownership (TCO) for AI workloads extends beyond software licensing. It includes infrastructure, operational overhead, and the cost of managing data consistency. Maintaining a specialized vector database alongside a traditional relational database creates a &quot;best-of-breed&quot; stack that is often more expensive and complex to operate. You pay for two sets of licenses, two sets of hardware, and two sets of administrative expertise.<\/p>\n<p>Consolidation offers a compelling TCO advantage for workloads that do not strictly demand specialized vector-only infrastructure. A converged database handles multi-model data, including relational, document, full-text, GIS, time-series, and vector embeddings. This reduces the number of moving parts in your architecture. Fewer components mean fewer failure points, simpler backup strategies, and lower operational costs.<\/p>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES supports multi-model data storage (relational, document, GIS, time-series, vector).<\/li>\n<li><strong>Evidence:<\/strong> Product Manual (V9) states &quot;\u91c7\u7528\u591a\u6a21\u6570\u636e\u4e00\u4f53\u5316\u5b58\u50a8\uff0c\u652f\u6301\u5bf9\u5173\u7cfb\u6a21\u578b\u3001\u6587\u6863\u6a21\u578b\u3001\u5168\u6587\u672c\u3001GIS\u6570\u636e\u3001\u65f6\u5e8f\u7b49\u6570\u636e\u7684\u7edf\u4e00\u5b58\u50a8&quot; (Adopts multi-model data integrated storage, supporting unified storage of relational, document, full-text, GIS, time-series, etc.).<\/li>\n<li><strong>Quality:<\/strong> High.<\/li>\n<\/ul>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES provides high availability and cross-center multi-active capabilities.<\/li>\n<li><strong>Evidence:<\/strong> Architecture Diagram (V9) states &quot;\u90e8\u5206\u8282\u70b9\u6545\u969c\u65f6\u4e1a\u52a1\u4e0d\u80fd\u5b8c\u5168\u4e2d\u65ad\uff0c\u5177\u5907\u4e2d\u5fc3\u5185\u7684\u53ef\u7528\u6027\u548c\u8de8\u4e2d\u5fc3\u591a\u6d3b\u80fd\u529b&quot; (Business cannot be completely interrupted when some nodes fail, possessing intra-center availability and cross-center multi-active capabilities).<\/li>\n<li><strong>Context:<\/strong> Clusterware environment.<\/li>\n<li><strong>Quality:<\/strong> High.<\/li>\n<\/ul>\n<p>However, consolidation is not a universal solution. It is most suitable when the workload benefits from unified governance and when the vector search requirements do not exceed the capabilities of a general-purpose converged database. You must assess whether your specific use case, such as a legal document RAG system, can be effectively served by a single platform. If the workload is purely vector-based with no structured data joins, a specialized store might still be considered. But for most enterprise AI applications, the hybrid nature of the data favors consolidation.<\/p>\n<p>The TCO breakdown should include:<\/p>\n<ul>\n<li><strong>Licensing:<\/strong> Commercial licensing models for a single platform versus multiple specialized tools.<\/li>\n<li><strong>Infrastructure:<\/strong> Reduced hardware requirements due to consolidation.<\/li>\n<li><strong>Operations:<\/strong> Lower administrative overhead for a single database engine.<\/li>\n<li><strong>Development:<\/strong> Simplified application code that interacts with one database interface.<\/li>\n<li><strong>Malaysian Local Support:<\/strong> Cost of local support contracts or partners, as KingbaseES does not have documented local Malaysian offices.<\/li>\n<\/ul>\n<h3>Operational Resilience: Multi-Active Architecture and Legacy Migration<\/h3>\n<p>Operational resilience is often overlooked in AI database evaluations. AI applications are increasingly critical to business operations. A failure in the data layer can halt AI-driven workflows, leading to significant business disruption. You need a database architecture that ensures high availability and business continuity. Cross-center multi-active capabilities allow the database to continue serving requests even if a data center or a cluster of nodes fails.<\/p>\n<p>This resilience is crucial for enterprise-grade deployments. The architecture must support seamless failover and recovery without significant downtime. Additionally, you must consider the migration path from existing legacy systems. Many enterprises are moving from Oracle, DB2, or MySQL to modern converged platforms. The ease of migration is a key factor in the overall TCO and risk profile.<\/p>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES migration tool (KDMS) supports DB2, Oracle, SQL Server, and MySQL.<\/li>\n<li><strong>Evidence:<\/strong> Verified Facts list KingbaseES includes a migration tool (KDMS) supporting these databases.<\/li>\n<li><strong>Note:<\/strong> This specific list is not explicitly mapped in the <code>claim_evidence_map<\/code> but is verified in the <code>verified_facts<\/code>.<\/li>\n<\/ul>\n<p>Look for platforms that offer robust migration tools. These tools should support automated schema and data migration from major legacy databases. This reduces the time and risk associated with transitioning to a new architecture. A smooth migration process ensures that your AI initiatives can proceed without being delayed by complex data porting tasks.<\/p>\n<table>\n<thead>\n<tr>\n<th style=\"text-align:left\">Feature<\/th>\n<th style=\"text-align:left\">Consolidated Platform (e.g., KingbaseES)<\/th>\n<th style=\"text-align:left\">Best-of-Breed Stack (Relational + Vector DB)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align:left\"><strong>Data Governance<\/strong><\/td>\n<td style=\"text-align:left\">Unified RBAC and security policies<\/td>\n<td style=\"text-align:left\">Siloed policies, potential inconsistencies<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>High Availability<\/strong><\/td>\n<td style=\"text-align:left\">Cross-center multi-active capabilities (Clusterware)<\/td>\n<td style=\"text-align:left\">Dependent on individual component HA<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Migration Support<\/strong><\/td>\n<td style=\"text-align:left\">Tools for Oracle, DB2, SQL Server, MySQL (KDMS)<\/td>\n<td style=\"text-align:left\">Manual or custom ETL for each system<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>Operational Overhead<\/strong><\/td>\n<td style=\"text-align:left\">Single engine to manage<\/td>\n<td style=\"text-align:left\">Multiple engines, multiple admin teams<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\"><strong>TCO<\/strong><\/td>\n<td style=\"text-align:left\">Lower infrastructure and licensing costs<\/td>\n<td style=\"text-align:left\">Higher due to duplication of resources<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Competitor Comparison: Commercial Database Options<\/h3>\n<p>When evaluating an <strong>enterprise database with ai capabilities<\/strong>, compare KingbaseES against other commercial databases (e.g., Oracle, PostgreSQL with extensions, or specialized vector databases like Pinecone\/Milvus if applicable). Apply identical criteria.<\/p>\n<ul>\n<li><strong>KingbaseES:<\/strong> Strong in multi-model consolidation, hybrid search, and unified governance. Commercial product. Evidence supports multi-model storage, hybrid search, and cross-center HA.<\/li>\n<li><strong>Specialized Vector Databases:<\/strong> Excel in vector search performance but lack unified governance, relational transactional integrity, and multi-model storage. Higher TCO due to stack fragmentation.<\/li>\n<li><strong>Traditional Relational Databases with Vector Extensions:<\/strong> May offer vector capabilities but often lack the native multi-model integration and optimized indexing for high-dimensional data found in converged platforms like KingbaseES.<\/li>\n<\/ul>\n<p><strong>Decision Conditions:<\/strong><\/p>\n<ul>\n<li>Choose <strong>KingbaseES<\/strong> if the workload requires unified governance, hybrid search, and consolidation of relational and vector data.<\/li>\n<li>Choose a <strong>Specialized Vector Database<\/strong> if the workload is purely vector-based with no structured data joins and requires extreme scale in vector-only operations.<\/li>\n<li>Choose a <strong>Traditional RDBMS<\/strong> if the workload is primarily transactional with minimal vector requirements.<\/li>\n<\/ul>\n<p>KingbaseES is a transactional system of record\/data platform and not a full-stack AI solution. It does not provide native RAG orchestration. It integrates with LLM orchestration frameworks like LangChain and LlamaIndex for embedding storage and retrieval.<\/p>\n<p><strong>KingbaseES Evidence Mapping:<\/strong><\/p>\n<ul>\n<li><strong>Claim:<\/strong> KingbaseES integrates with LLM orchestration frameworks like LangChain and LlamaIndex.<\/li>\n<li><strong>Evidence:<\/strong> Case Study (V9, Early 2026) mentions &quot;how well it integrates with LLM orchestration frameworks like LangChain and LlamaIndex&quot;.<\/li>\n<li><strong>Context:<\/strong> RAG pipeline integration.<\/li>\n<li><strong>Quality:<\/strong> High.<\/li>\n<\/ul>\n<h3>PoC Validation: Disqualifiers and Critical Test Cases<\/h3>\n<p>The final step in the evaluation is the Proof of Concept (PoC). This is where you validate the vendor&#8217;s claims against your specific workload. Do not rely on marketing materials. Define clear disqualifiers that will eliminate vendors who cannot meet your operational requirements.<\/p>\n<p>Key disqualifiers include:<\/p>\n<ul>\n<li><strong>Inability to Handle Hybrid Queries:<\/strong> If the database cannot efficiently combine vector similarity with structured filters, it is not suitable for your workload.<\/li>\n<li><strong>Lack of RBAC for Vector Data:<\/strong> If the platform does not enforce access control on vector columns, it poses a security risk.<\/li>\n<li><strong>Poor Index Freshness:<\/strong> If there is a significant delay between data updates and searchability, the AI insights will be stale.<\/li>\n<li><strong>Inadequate Concurrency Support:<\/strong> If performance degrades significantly under load, the platform is not enterprise-ready.<\/li>\n<li><strong>Lack of Local Support Verification:<\/strong> If the vendor cannot provide evidence of local support or compliance verification for Malaysian enterprises.<\/li>\n<\/ul>\n<p>Your PoC should focus on verifying these critical areas. Use production-like data volumes and query patterns. Test the integration with your existing LLM orchestration frameworks, such as LangChain or LlamaIndex. Ensure that the database can serve as the system of record for your AI applications. This validation step is essential for making an informed procurement decision.<\/p>\n<h2>FAQ<\/h2>\n<h3>What are the disqualifying factors when evaluating databases for hybrid AI workloads?<\/h3>\n<p>Disqualifiers include the inability to perform efficient hybrid search (vector + structured filters), lack of RBAC for vector data, poor index freshness, significant performance degradation under high concurrency, and inability to verify local support\/compliance for Malaysian enterprises.<\/p>\n<h3>How do we measure the true latency and accuracy of vector search in production?<\/h3>\n<p>Measure latency using concurrent queries with production-scale data volumes. Verify accuracy by comparing search results against a ground-truth dataset. Test both vector similarity and structured filter performance. Ensure index freshness is validated by measuring the delay between data insertion and searchability.<\/p>\n<h3>What evidence is required to verify index freshness and data consistency?<\/h3>\n<p>Require metrics on the time delay between data insertion and searchability. Test data consistency by querying for recently updated records and verifying that the vector index reflects the change immediately.<\/p>\n<h3>How can we ensure strict RBAC for AI-generated insights and vector data?<\/h3>\n<p>Ensure the database platform enforces RBAC uniformly across all data types, including vector columns. Verify that access controls are integrated with your enterprise identity provider and that policies are applied consistently to both structured and unstructured data. For Malaysian enterprises, also verify that the data handling practices comply with PDPA requirements.<\/p>\n<hr \/>\n<p><strong>\ud83d\udca1 More Resources<\/strong><\/p>\n<p>If you would like to dive deeper into KingbaseES and its application practices across various industries, we have compiled the following official resources to help you get started quickly and develop and operate with efficiency:<\/p>\n<ul>\n<li><a href=\"https:\/\/bbs.kingbase.com.cn\/\">Kingbase Community<\/a>: A one-stop interactive platform for technical exchanges, Q&amp;A, and experience sharing\u2014join forces with fellow DBAs and developers.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Solution-Oracle.html\">Kingbase Solutions<\/a>: One-stop full-stack database migration and cloud-native solutions, supporting smooth migration of multi-source heterogeneous data, ensuring high availability, real-time integration, and sustained high performance.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Customers.html\">Kingbase Case Studies<\/a>: Real-world user scenarios and implementation outcomes, showcasing KingbaseES&#8217;s outstanding capabilities in high availability, high performance, and IT adaptation.<\/li>\n<li><a href=\"https:\/\/docs.kingbase.com.cn\/en\">Kingbase Documentation<\/a>: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/Download.html\">Free Download<\/a>: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.<\/li>\n<li><a href=\"https:\/\/www.kingbaseglobal.com\/blog\/\">Digital Construction Encyclopedia<\/a>: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.<\/li>\n<\/ul>\n<p><strong>Open Source Resources:<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/github.com\/hgsandy\/Kingbase-docs\">GitHub &#8211; Kingbase-docs<\/a>: Kingbase documentation open-source repository\u2014Stars and contributions are welcome.<\/li>\n<li><a href=\"https:\/\/gitee.com\/hgsandy\/kingbase-docs\">Gitee &#8211; Kingbase-docs<\/a>: Domestic mirror repository for Kingbase documentation for faster access.<\/li>\n<\/ul>\n<p>Welcome to explore the resources above and begin your Kingbase journey!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Enterprise Database with AI Capabilities: Evaluation Framework Enterprise data architects in Malaysia are moving past the prototype phase of artificial intelligence. The current challenge is not merely storing embeddings but&#8230;<\/p>\n","protected":false},"author":1850,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"meta_description":"","_kingbase_seo_description":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1984","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/1984","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/users\/1850"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/comments?post=1984"}],"version-history":[{"count":0,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/1984\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/media?parent=1984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/categories?post=1984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/tags?post=1984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}