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Enterprise AI applications require a distributed data fabric. The architecture consists of four distinct layers. Each layer serves a specific purpose. Separating these layers ensures data sovereignty, consistency, and performance.<\/p>\n
This layer stores structured business data. It handles customer records, inventory, and financial transactions. It requires strict ACID compliance. Data integrity is paramount here.<\/p>\n
Commercial databases, such as KingbaseES, function as transactional Systems of Record. They provide authoritative access to structured data. They do not inherently provide native vector search or RAG orchestration.<\/p>\n
This layer handles semantic search. It stores vector embeddings. These embeddings represent the semantic meaning of text chunks. The layer uses Approximate Nearest Neighbor (ANN) algorithms.<\/p>\n
ANN search is faster than exact search. It sacrifices some precision for speed. This layer supports low-latency inference.<\/p>\n
This layer stores the original source text. It holds large unstructured documents. It manages versioning and chunking. It provides the raw context for LLMs.<\/p>\n
The document store must support efficient retrieval of full text.<\/p>\n
This layer connects the other three. It manages the flow of data. It handles prompt engineering. It coordinates the retrieval and generation steps. It enforces access control policies. It ensures the correct data reaches the LLM.<\/p>\n
Mixing these layers in a single system creates complexity. Transactional locks can block vector updates. Vector index maintenance can slow down transactional writes. Separation allows each layer to scale independently.<\/p>\n
Enterprise databases must handle two conflicting workloads. One workload requires strict consistency. The other requires high-speed approximate matching. Combining these workloads degrades performance.<\/p>\n
ACID stands for Atomicity, Consistency, Isolation, and Durability. These properties ensure data reliability. A transaction either completes fully or not at all. This is critical for financial data. It is also critical for customer records. Transactional databases optimize for this consistency. They use locking and logging mechanisms.<\/p>\n
Vector search finds similar items. It does not find exact matches. It uses mathematical approximations. This approach is fast. It is not strictly consistent in the ACID sense. Vector indices may be eventually consistent. Updates to the vector index can lag behind data updates.<\/p>\n
When you mix transactional data and vector embeddings in a single system, you face a trade-off. Strong consistency slows down vector updates. Fast vector updates weaken transactional consistency. This leads to stale data in AI responses. It also causes performance bottlenecks during peak loads.<\/p>\n
Dedicated vector stores optimize for throughput. Transactional databases optimize for integrity. A unified SQL architecture may reduce cross-system replication. It does not eliminate the need for embedding generation. It does not eliminate the need for chunk regeneration. It does not eliminate the need for model-version migration.<\/p>\n
KingbaseES is a commercial database software. It is not open-source or source-available. It is not community-supported. It serves as the authoritative source for structured business data.<\/p>\n
In a four-layer architecture, KingbaseES anchors the Transactional System of Record. It stores the metadata that drives vector retrieval. It holds the user identity and permission records. It maintains the document references.<\/p>\n
KingbaseES does not replace specialized AI components. It integrates with them. It provides the structured data that the orchestration layer uses. It ensures that the data feeding the AI is accurate and up-to-date.<\/p>\n
Verify whether the target KingbaseES version supports specific vector extensions. Do not infer vector capabilities from B-tree, GIN, GiST, SP-GiST, or BRIN index support. Do not claim serverless, pod-based deployment, or namespaces without exact evidence. Do not claim billion-vector scale or HNSW\/IVF support without evidence. Do not claim low latency or real-time upserts without evidence. Do not claim native hybrid search without evidence.<\/p>\n
When evidence is missing, write: "Verify whether the target KingbaseES version supports…" This keeps the claim precise and bounded.<\/p>\n
Enterprise security requires precise access control. Vector search provides approximate retrieval. It does not inherently enforce strict access control.<\/p>\n
SQL queries use exact matching. They filter by specific values. Row-level security policies apply directly to the query execution. The database engine enforces these policies. Access control is deterministic.<\/p>\n
Vector search finds similar embeddings. It does not filter by exact values. It uses metadata filtering to narrow results. However, this filtering happens after or during the ANN search. The vector index itself does not enforce security policies.<\/p>\n
Access control depends on query execution identity. It depends on policy configuration. It depends on application service accounts. It depends on caching layers. It depends on reranking steps. It depends on context assembly.<\/p>\n
Do not claim row-level security automatically protects every RAG component. Do not claim the AI cannot retrieve unauthorized information. Protection requires identity propagation from the application to the vector layer. It requires explicit policy enforcement at each retrieval step.<\/p>\n
Metadata filtering and access control requirements differ between vector search and traditional SQL queries. SQL provides strict guarantees. Vector search provides efficient similarity search. Both are necessary. They require different implementation strategies.<\/p>\n
Data accuracy depends on two types of freshness. They are often confused. They require different maintenance strategies.<\/p>\n
This refers to the stored source text. Is the text current? Updates to the document store ensure document freshness.<\/p>\n
This refers to the embedding vector. Was the embedding regenerated from the current document content? Does it use the intended embedding-model version? Document updates do not regenerate the embedding. The application must supply a new embedding. Or an automated pipeline must regenerate it.<\/p>\n
This refers to the physical search index. Is the updated vector value reflected in the index? Updating an embedding column may cause a native database index to be maintained through normal DML behavior. Whether a separate rebuild, refresh, or maintenance command is required depends on the exact vector extension and index implementation.<\/p>\n
Do not prescribe "refresh the index" as a universal step. Do not claim automatic index updates. Index freshness lags behind embedding freshness. This lag can cause stale search results.<\/p>\n
Transaction consistency, embedding freshness, and vector-index freshness are three different concepts. They must not be merged. A unified database may reduce cross-system replication. It does not eliminate embedding generation. It does not eliminate retries. It does not eliminate failed update handling. It does not eliminate backfills. It does not eliminate stale-vector detection. It does not eliminate orphan-vector cleanup.<\/p>\n
Performance requirements vary by workload. Real-time inference needs low latency. Batch processing needs high throughput. The choice of architecture affects both.<\/p>\n