{"id":2325,"date":"2026-11-02T16:04:54","date_gmt":"2026-11-02T16:04:54","guid":{"rendered":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/design-ai-data-infrastructure-rag-architecture-guide\/"},"modified":"2026-11-02T16:04:54","modified_gmt":"2026-11-02T16:04:54","slug":"design-ai-data-infrastructure-rag-architecture-guide","status":"publish","type":"post","link":"https:\/\/www.kingbaseglobal.com\/blog\/tech-blog\/design-ai-data-infrastructure-rag-architecture-guide\/","title":{"rendered":"Design AI Data Infrastructure_ RAG Architecture Guide"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/kingbase-bbs.oss-cn-beijing.aliyuncs.com\/qywx\/blogImage\/eebbf0a2-2620-46b7-8c50-d8d58874b52c.webp\" alt=\"Precision tools arranged in foam cutouts illustrating structured system design\" \/><\/p>\n<h2>Deconstructing the RAG Data Layer: System of Record vs. System of Insight<\/h2>\n<p>Enterprise AI applications often face a paradox: the AI model is sophisticated, but the data infrastructure supporting it is fragmented. Organizations frequently attempt to force all data into a single storage solution, leading to performance bottlenecks and compliance risks. The core issue is not the AI model itself, but the inability to handle high-throughput transactional ingestion and low-latency semantic retrieval simultaneously within a single engine.<\/p>\n<p>A robust <strong>AI application data infrastructure<\/strong> requires a decoupled architecture. This approach separates the &quot;System of Record&quot; from the &quot;System of Insight.&quot; The System of Record handles transactional integrity, structured metadata, user profiles, and Access Control Lists (ACLs). The System of Insight handles vector embeddings, semantic similarity search, and high-concurrency read operations.<\/p>\n<p>This separation is grounded in the architectural principle of Polyglot Persistence. Different parts of an application have different access characteristics. Relational data requires ACID compliance and complex joins. Vector data requires Approximate Nearest Neighbor (ANN) indexing for speed. Combining these into a monolithic system often sacrifices one for the other. By decoupling them, enterprises can optimize each layer independently while maintaining a unified view of the data.<\/p>\n<p>Conventional RAG systems follow a four-tier architecture: embedding conversion, similarity search, context integration, and response generation. The data layer must support this flow. It must ingest embeddings frequently, retrieve them with low latency, and return results alongside structured metadata. KingbaseES serves as the commercial, enterprise-grade System of Record. It manages the structured context. A separate vector store manages the semantic search. This guide details how to architect, implement, and validate this hybrid infrastructure.<\/p>\n<h2>Prerequisites: Designing the Hybrid Metadata Store<\/h2>\n<p>Before implementing the hybrid architecture, you must define the environment requirements. KingbaseES is designed for online transaction processing (OLTP), query-intensive data warehouses, and demanding internet applications. It is suitable for hosting the metadata layer of a RAG pipeline.<\/p>\n<p>The metadata store must handle:<\/p>\n<ul>\n<li>Document metadata (creation date, author, file size).<\/li>\n<li>User profiles and roles.<\/li>\n<li>Access Control Lists (ACLs) for row-level security.<\/li>\n<li>High-throughput ingestion during indexing phases.<\/li>\n<\/ul>\n<p>KingbaseES supports various cluster architectures, including master-slave, read-write splitting, and multi-active shared storage. These architectures allow you to scale the metadata layer independently of the vector layer. For example, you can use read-write splitting to handle the high volume of metadata updates during document ingestion, while the master node ensures data consistency.<\/p>\n<h3>Checklist for Environment Setup<\/h3>\n<ol>\n<li><strong>Define Metadata Schema<\/strong>: Create tables for documents, users, and permissions. Ensure foreign keys link document IDs to user IDs.<\/li>\n<li><strong>Select Cluster Topology<\/strong>: Choose a cluster architecture based on write\/read ratios. Master-slave is suitable for standard OLTP workloads. Multi-active shared storage is suitable for high-availability requirements.<\/li>\n<li><strong>Configure Access Control<\/strong>: Enable row-level security policies. Define rules for who can access which metadata records.<\/li>\n<li><strong>Establish Integration Points<\/strong>: Identify the API endpoints for the vector store. Plan the handshake protocol between the vector store and KingbaseES.<\/li>\n<li><strong>Verify Compatibility<\/strong>: Ensure the underlying operating system and CPU architecture are supported. KingbaseES supports deployment on mainstream CPUs, operating systems, and cloud platforms.<\/li>\n<\/ol>\n<h2>Step-by-Step: Implementing ACL-Based Metadata Filtering<\/h2>\n<p>The most critical aspect of a secure AI infrastructure is ensuring that the AI only returns information the user is authorized to see. This is achieved by storing ACLs in the relational database and using them to filter search results.<\/p>\n<p>In this pattern, the vector store performs a semantic search and returns a list of document IDs. The application then queries KingbaseES to fetch the metadata and check permissions for those specific IDs. This is known as post-filtering or hybrid filtering.<\/p>\n<h3>Implementation Steps<\/h3>\n<ol>\n<li>\n<p><strong>Create Metadata Tables<\/strong>:<br \/>\nDefine tables for <code>documents<\/code>, <code>users<\/code>, and <code>permissions<\/code>. Verify table structures with KingbaseES documentation.<\/p>\n<pre><code class=\"language-sql\">-- Verify table structure syntax with KingbaseES documentation\nCREATE TABLE documents (\n    doc_id VARCHAR(64) PRIMARY KEY,\n    title VARCHAR(255),\n    content_hash VARCHAR(64),\n    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE permissions (\n    doc_id VARCHAR(64),\n    user_role VARCHAR(50),\n    access_level VARCHAR(20),\n    PRIMARY KEY (doc_id, user_role)\n);\n<\/code><\/pre>\n<\/li>\n<li>\n<p><strong>Populate Metadata<\/strong>:<br \/>\nWhen a document is ingested, insert its metadata into KingbaseES. Generate a unique <code>doc_id<\/code>. This ID will be used as the primary key in the vector store.<\/p>\n<\/li>\n<li>\n<p><strong>Define Access Control Policies<\/strong>:<br \/>\nUse KingbaseES row-level security (RLS) or application-level checks. RLS is preferred for consistency. Verify specific RLS syntax with KingbaseES documentation, as syntax may differ from standard PostgreSQL implementations.<\/p>\n<pre><code class=\"language-sql\">-- Verify RLS policy syntax with KingbaseES documentation\n-- Example pseudocode for RLS policy definition\nCREATE POLICY user_access_policy ON documents\nUSING (\n    EXISTS (\n        SELECT 1 FROM permissions\n        WHERE permissions.doc_id = documents.doc_id\n        AND permissions.user_role = current_setting('app.current_role')\n    )\n);\n<\/code><\/pre>\n<\/li>\n<li>\n<p><strong>Integrate with Vector Store<\/strong>:<br \/>\nWhen the vector store returns results, pass the list of <code>doc_ids<\/code> to KingbaseES.<\/p>\n<pre><code class=\"language-sql\">SELECT doc_id, title, content_hash\nFROM documents\nWHERE doc_id IN ('id1', 'id2', 'id3')\nAND EXISTS (\n    SELECT 1 FROM permissions\n    WHERE permissions.doc_id = documents.doc_id\n    AND permissions.user_role = current_setting('app.current_role')\n);\n<\/code><\/pre>\n<\/li>\n<li>\n<p><strong>Validate Permissions<\/strong>:<br \/>\nEnsure that the query fails if the user does not have permission. The application should handle the empty result set gracefully.<\/p>\n<\/li>\n<\/ol>\n<h2>Orchestrating the Handshake: Vector Retrieval and Metadata Resolution<\/h2>\n<p>The operational flow of a hybrid RAG system involves a coordinated handshake between the vector store and the relational database. This section explains how to minimize latency while maintaining data integrity.<\/p>\n<h3>Workflow<\/h3>\n<ol>\n<li><strong>User Query<\/strong>: The user submits a query.<\/li>\n<li><strong>Embedding<\/strong>: The application converts the query into a vector embedding using an embedding model.<\/li>\n<li><strong>Vector Search<\/strong>: The application sends the embedding to the vector store. The vector store performs a similarity search and returns the top N document IDs.<\/li>\n<li><strong>Metadata Resolution<\/strong>: The application sends the list of document IDs to KingbaseES.<\/li>\n<li><strong>Permission Check<\/strong>: KingbaseES filters the results based on the user&#8217;s ACLs.<\/li>\n<li><strong>Context Assembly<\/strong>: The application retrieves the full text or summaries for the permitted documents.<\/li>\n<li><strong>Response Generation<\/strong>: The application sends the query and the retrieved context to the LLM.<\/li>\n<\/ol>\n<h3>Optimization Strategies<\/h3>\n<ul>\n<li><strong>Batching<\/strong>: Always query KingbaseES with a batch of IDs rather than one by one. This reduces network round-trips.<\/li>\n<li><strong>Connection Pooling<\/strong>: Use a connection pool for KingbaseES to handle concurrent requests efficiently.<\/li>\n<li><strong>Caching<\/strong>: Cache frequent metadata lookups if the data is relatively static. However, ensure that cache invalidation is handled correctly when metadata is updated.<\/li>\n<\/ul>\n<h2>Ensuring Consistency: Validating Index Freshness and Integrity<\/h2>\n<p>In a polyglot system, data consistency is challenging. You must ensure that the vector index reflects the current state of the metadata in KingbaseES. If a document is deleted or its permissions are changed in KingbaseES, the vector index must be updated accordingly.<\/p>\n<h3>Validation Methods<\/h3>\n<ol>\n<li>\n<p><strong>Timestamp Checks<\/strong>:<br \/>\nStore a <code>last_modified<\/code> timestamp in KingbaseES. When querying, compare this timestamp with the vector store&#8217;s index timestamp. If the metadata is newer, trigger a re-indexing process.<\/p>\n<\/li>\n<li>\n<p><strong>Checksums<\/strong>:<br \/>\nStore a checksum of the document content in KingbaseES. Compare this checksum with the vector store&#8217;s stored checksum. If they differ, the content has changed.<\/p>\n<\/li>\n<li>\n<p><strong>Transactional Consistency<\/strong>:<br \/>\nUse KingbaseES transactions to ensure that metadata updates are consistent within the database. Note that KingbaseES ensures transactional integrity for its own data; it does not natively coordinate transactions with external vector stores. External consistency must be managed via application logic or message queues.<\/p>\n<\/li>\n<li>\n<p><strong>Reconciliation Jobs<\/strong>:<br \/>\nSchedule a periodic job to compare the list of document IDs in KingbaseES with the list in the vector store. Identify orphaned records in the vector store and stale records in KingbaseES.<\/p>\n<\/li>\n<\/ol>\n<h2>Troubleshooting: Latency Spikes and Metadata Mismatches<\/h2>\n<p>Hybrid architectures introduce new failure modes. Latency spikes often occur during the handshake phase. Metadata mismatches occur when the vector store and KingbaseES are out of sync.<\/p>\n<h3>Common Failure Modes<\/h3>\n<ul>\n<li>\n<p><strong>Slow Metadata Resolution<\/strong>:<br \/>\n<em>Cause<\/em>: Large batch sizes or complex permission checks.<br \/>\n<em>Solution<\/em>: Optimize the SQL query. Ensure that the <code>doc_id<\/code> and <code>user_role<\/code> columns are indexed. Reduce batch sizes if the network latency is high.<\/p>\n<\/li>\n<li>\n<p><strong>Permission Mismatches<\/strong>:<br \/>\n<em>Cause<\/em>: The vector store returns a document that the user no longer has access to.<br \/>\n<em>Solution<\/em>: Implement strict post-filtering. Never trust the vector store&#8217;s metadata. Always verify permissions in KingbaseES.<\/p>\n<\/li>\n<li>\n<p><strong>Index Staleness<\/strong>:<br \/>\n<em>Cause<\/em>: The vector index is not updated immediately after metadata changes.<br \/>\n<em>Solution<\/em>: Implement a message queue to trigger vector index updates. Ensure that the vector index update is idempotent.<\/p>\n<\/li>\n<\/ul>\n<h3>Verification Checklist<\/h3>\n<ol>\n<li>Measure the latency of the metadata resolution step.<\/li>\n<li>Verify that the vector store returns the correct number of results after permission filtering.<\/li>\n<li>Check for orphaned records in the vector store.<\/li>\n<li>Ensure that the timestamp checks are accurate.<\/li>\n<\/ol>\n<h2>Migration and Rollback: Safeguarding Legacy Data<\/h2>\n<p>Migrating from legacy heterogeneous databases to a hybrid AI-ready infrastructure requires careful planning. KingbaseES provides an extensible compatibility framework to assist in migration from Oracle or other heterogeneous databases.<\/p>\n<h3>Migration Strategy<\/h3>\n<ol>\n<li>\n<p><strong>Assess Legacy Data<\/strong>:<br \/>\nIdentify the structured data that needs to be migrated. Define the metadata schema for the new system.<\/p>\n<\/li>\n<li>\n<p><strong>Extract and Transform<\/strong>:<br \/>\nExtract data from the legacy database. Transform it to fit the new metadata schema. Ensure that ACLs are correctly mapped.<\/p>\n<\/li>\n<li>\n<p><strong>Load into KingbaseES<\/strong>:<br \/>\nLoad the transformed data into KingbaseES. Use batch inserts to improve performance.<\/p>\n<\/li>\n<li>\n<p><strong>Index Vector Data<\/strong>:<br \/>\nGenerate embeddings for the migrated documents. Load them into the vector store.<\/p>\n<\/li>\n<li>\n<p><strong>Validate Consistency<\/strong>:<br \/>\nCompare the number of records in the legacy database with KingbaseES. Verify that the ACLs are correctly applied.<\/p>\n<\/li>\n<\/ol>\n<h3>Rollback Plan<\/h3>\n<ol>\n<li>\n<p><strong>Keep Legacy System Active<\/strong>:<br \/>\nDo not decommission the legacy system until the hybrid system is fully validated.<\/p>\n<\/li>\n<li>\n<p><strong>Point-in-Time Recovery<\/strong>:<br \/>\nUse KingbaseES point-in-time recovery to restore the metadata store to a previous state if errors are detected. Verify specific recovery procedures with KingbaseES documentation.<\/p>\n<\/li>\n<li>\n<p><strong>Data Reconciliation<\/strong>:<br \/>\nIf a mismatch is detected, re-run the migration for the affected records. Use the legacy system as the source of truth.<\/p>\n<\/li>\n<\/ol>\n<h2>FAQ<\/h2>\n<h3>How do we balance real-time inference latency with data consistency in a hybrid AI data architecture?<\/h3>\n<p>Balance latency and consistency by decoupling the storage layers. Use KingbaseES for strong consistency in metadata and ACLs. Use the vector store for low-latency semantic search. Optimize the handshake by batching metadata queries and using connection pooling. Validate consistency through periodic reconciliation jobs rather than real-time synchronization.<\/p>\n<h3>What are the risks of data fragmentation when migrating legacy data to an AI-ready vector-relational infrastructure?<\/h3>\n<p>Data fragmentation risks include orphaned records in the vector store and stale metadata in the relational database. These risks are mitigated by using transactional integrity in KingbaseES and implementing a robust reconciliation process. KingbaseES&#8217;s extensible compatibility framework helps ensure data consistency during the transition.<\/p>\n<h3>How does KingbaseES handle Access Control Lists (ACLs) for secure RAG retrieval?<\/h3>\n<p>KingbaseES stores ACLs in relational tables. It enforces access control through row-level security policies or application-level checks. During retrieval, the application queries KingbaseES to filter results based on the user&#8217;s role. This ensures that the AI only returns authorized information.<\/p>\n<h3>What is the recommended rollback strategy for hybrid database migrations?<\/h3>\n<p>The recommended strategy is to keep the legacy system active until the hybrid system is fully validated. Use KingbaseES point-in-time recovery to restore the metadata store if errors occur. Perform data reconciliation to identify and fix mismatches. If the hybrid system fails, redirect traffic back to the legacy system.<\/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>Deconstructing the RAG Data Layer: System of Record vs. System of Insight Enterprise AI applications often face a paradox: the AI model is sophisticated, but the data infrastructure supporting it&#8230;<\/p>\n","protected":false},"author":318,"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-2325","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/2325","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\/318"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/comments?post=2325"}],"version-history":[{"count":0,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/posts\/2325\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/media?parent=2325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/categories?post=2325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kingbaseglobal.com\/blog\/wp-json\/wp\/v2\/tags?post=2325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}