Defining the RAG Database Boundary: System of Record vs. Retrieval Layer

A production-grade Retrieval-Augmented Generation (RAG) system relies on a clear separation of concerns. The database architecture must distinguish between the transactional System of Record (SOR) and the Vector Retrieval layer. The SOR maintains data consistency, handles structured metadata, and enforces access controls. The Retrieval layer focuses on low-latency semantic search and embedding storage.

Confusing these layers often leads to fragmented stacks. Organizations may deploy a specialized vector store for embeddings while keeping structured data in a traditional relational database. This creates data silos, increases engineering overhead for data synchronization, and complicates governance.

KingbaseES is a commercial enterprise-grade general-purpose database management system. It is designed for transactional processing and query-intensive data warehouse scenarios. As a commercial product, it provides the foundational stability required for the System of Record component of a RAG architecture. It supports high-availability cluster architectures, including primary-standby, read-write separation, and multi-active shared storage clusters. This ensures that the structured data and metadata governing the RAG pipeline remain available and consistent.

The decision to use a converged database versus a specialized vector store depends on workload scale. For mid-scale workloads, such as a product documentation knowledge base with 10 million embedded documents processing 500 queries per second (QPS), traditional databases can handle the workload effectively. They manage hybrid search, log collection, and query analytics within a single platform. This consolidation reduces the operational burden of managing multiple database engines.

Hybrid Search & Metadata Filtering: Balancing Latency with Precision

RAG pipelines require databases to ingest and update embeddings frequently, retrieve them with low latency, and return results alongside structured metadata. Pure vector similarity is rarely sufficient for enterprise applications. Precise retrieval requires filtering embeddings by attributes such as document type, date range, or user permissions.

Hybrid search combines keyword-based retrieval with vector similarity. This approach improves accuracy by ensuring that results match both the semantic intent and specific textual keywords. Metadata filtering further refines results by applying structural constraints before or during the vector search.

The performance impact of hybrid search and metadata filtering must be evaluated against latency requirements. High-concurrency RAG applications demand low-latency query responses. Traditional databases have demonstrated the ability to handle 10 million embedded documents at 500 QPS while performing hybrid search. This capability suggests that for many enterprise use cases, a converged database can meet latency and throughput demands without requiring a separate vector-specific engine.

However, index freshness is critical. As source documents update, embeddings must be regenerated and the index updated. The database must support efficient upserts and index maintenance to prevent stale data from being returned in RAG responses. The choice of database affects how easily these updates propagate to the retrieval layer.

Converged vs. Specialized: The Infrastructure Consolidation Trade-off

Selecting between a dedicated vector database and a converged relational database with vector extensions involves weighing performance against operational simplicity.

Purpose-built vector databases excel at vector search. However, infrastructure consolidation often outweighs specialization when the workload does not strictly demand dedicated vector database capabilities. For enterprises with 10 million embedded documents and moderate QPS, a converged database can effectively manage the entire data lifecycle. This includes log collection, full-text search, and query analytics, reducing the need for specialized tools.

The trade-off is not absolute. If the workload involves extreme vector dimensions or requires sub-millisecond latency for billions of vectors, a specialized solution may be necessary. For most enterprise knowledge management and customer service RAG applications, a converged approach offers a balanced solution.

Ensuring Data Freshness and Consistency in Real-Time RAG

Maintaining data freshness is a critical operational requirement for RAG systems. The vector index must reflect the current state of the System of Record. Stale embeddings lead to inaccurate or outdated responses from the LLM.

Enterprises can ensure consistency through a structured operational approach:

1. Define Update Triggers: Identify when source documents change. Use database triggers or application-level events to initiate embedding regeneration.
2. Batch or Real-Time Ingestion: Choose an ingestion strategy based on latency requirements. Real-time ingestion updates the index immediately. Batch ingestion processes updates periodically to reduce load.
3. Index Maintenance: Regularly optimize the vector index to maintain query performance. This includes compaction and re-indexing as data volume grows.
4. Validation: Verify that updated embeddings are correctly indexed and retrievable. Implement monitoring to detect drift between the SOR and the vector index.

KingbaseES supports high-availability cluster architectures, which aids in maintaining data consistency during updates. Its compatibility with domestic Chinese hardware and operating systems, such as Kylin, UOS, and OpenEuler, ensures that the underlying infrastructure is stable and supported. This stability is essential for maintaining the integrity of the transactional data that feeds the RAG pipeline.

Governance, Access Control, and Compliance in Enterprise RAG

Regulated industries require strict governance over RAG data. Access control must prevent unauthorized users from retrieving sensitive information. Audit logging is necessary to track data access and ensure compliance with local regulations.

Key governance mechanisms for enterprise RAG include:

• Row-Level Security (RLS): Restrict data access based on user roles or attributes. This ensures that the LLM only retrieves information the user is authorized to see.
• Encryption: Encrypt data at rest and in transit. This protects embeddings and metadata from interception or unauthorized access.
• Audit Logging: Record all data access and modification events. This provides a trail for compliance reviews and security investigations.
• Data Classification: Tag data with sensitivity levels. This helps in applying appropriate access controls and retention policies.

KingbaseES is an enterprise-grade database suitable for transactional data. Specific RAG-related governance features, such as row-level security for vector metadata, require verification against product documentation. Its general enterprise-grade status suggests it can support foundational security requirements, but specific implementations must be confirmed via official product specs.

For enterprises operating in Malaysia, compliance with data privacy regulations is critical. The database must support mechanisms to enforce data residency and access policies. The choice of a commercial database like KingbaseES can simplify compliance efforts by providing a single platform for data management and governance, subject to verification of specific regulatory alignment features.

Integration Complexity: Connecting Databases to LLM Orchestration

The choice of database significantly impacts integration complexity with LLM orchestration frameworks. Seamless integration reduces engineering overhead and accelerates deployment.

Most RAG architectures use frameworks like LangChain or LlamaIndex to manage the retrieval process. These frameworks connect to databases via standard protocols. The database must support these protocols to enable efficient data exchange.

KingbaseES supports standard database interfaces such as JDBC and ODBC. However, specific native connectors for popular LLM frameworks are not evidenced in the available documentation. Enterprises should verify the availability of JDBC, ODBC, or API-based integration patterns to ensure compatibility with their chosen orchestration layer.

To minimize integration complexity, consider the following:

1. Standard Protocols: Use standard database drivers and protocols. This ensures compatibility with a wide range of orchestration frameworks.
2. API Gateways: Implement API gateways to abstract database access. This simplifies changes to the underlying database technology.
3. Vector Extension Support: If the database supports vector extensions, ensure they are compatible with the chosen LLM framework.
4. Testing: Conduct thorough integration testing to validate latency and throughput under real-world conditions.

The goal is to reduce the friction between the database and the LLM pipeline. A converged database that supports both structured and vector data can simplify this integration by reducing the number of connections and protocols required.

Commercial Evaluation: TCO and Vendor Support

Evaluating database vendors for RAG support requires considering Total Cost of Ownership (TCO) and vendor support. Commercial databases offer dedicated support, which is critical for enterprise deployments.

Key evaluation criteria include:

• Licensing Model: Understand the licensing structure. Commercial databases often charge per core or per user. Evaluate this against the expected workload scale.
• Global Support: Assess the availability of global commercial support teams. Prompt response to technical issues is essential for minimizing downtime.
• Compliance: Ensure the database supports compliance with local regulations. This includes data privacy and security standards.
• Hardware Compatibility: Verify compatibility with existing hardware infrastructure. KingbaseES supports domestic Chinese hardware such as Hygon, Zhaoxin, Loongson, and Phytium/Kunpeng. This compatibility is relevant for enterprises using specific hardware environments.
• Scalability: Evaluate the database’s ability to scale with growing data volumes. High-availability architectures, such as those supported by KingbaseES, ensure continuous operation during scaling events.

The decision to choose a commercial database over an open-source alternative often hinges on the need for guaranteed support and compliance.

FAQ

How do I balance data freshness and query latency in RAG database systems?

Prioritize low-latency retrieval for user queries while scheduling embedding updates during off-peak hours or using batch processing. Implement monitoring to detect staleness and trigger updates based on source data changes.

What are the trade-offs between dedicated vector databases and extended relational databases for RAG?

Dedicated vector databases offer superior performance for pure vector search but increase integration complexity. Extended relational databases offer infrastructure consolidation, better metadata handling, and lower TCO for mid-scale workloads. Choose based on workload scale and governance needs.

Which database features are critical for secure enterprise RAG implementations?

Row-level security, encryption at rest and in transit, and comprehensive audit logging are critical. These features ensure that only authorized users can access sensitive data and that all access is tracked for compliance.

How does database choice impact total cost of ownership for AI platforms?

A converged database reduces TCO by eliminating the need for separate vector stores and simplifying data management. Commercial databases may have higher licensing costs but offer lower operational costs through dedicated support and reduced engineering overhead.

Disclaimer on KingbaseES RAG Capabilities

The provided evidence does not contain specific technical documentation on KingbaseES‘s vector search performance, index types (e.g., HNSW/IVF), or latency benchmarks for RAG workloads. Additionally, documentation on KingbaseES‘s integration with LLM orchestration frameworks or specific data governance features relevant to RAG compliance is not explicitly verified. Users must verify these specific vector/RAG capabilities against official product documentation before deployment.

💡 More Resources

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:

• Kingbase Community: A one-stop interactive platform for technical exchanges, Q&A, and experience sharing—join forces with fellow DBAs and developers.
• Kingbase Solutions: 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.
• Kingbase Case Studies: Real-world user scenarios and implementation outcomes, showcasing KingbaseES’s outstanding capabilities in high availability, high performance, and IT adaptation.
• Kingbase Documentation: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.
• Free Download: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.
• Digital Construction Encyclopedia: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.

Open Source Resources:

• GitHub – Kingbase-docs: Kingbase documentation open-source repository—Stars and contributions are welcome.
• Gitee – Kingbase-docs: Domestic mirror repository for Kingbase documentation for faster access.

Welcome to explore the resources above and begin your Kingbase journey!

The Enterprise RAG Architecture: Decoupling Record from Retrieval

A production-grade Retrieval-Augmented Generation (RAG) system is not a single monolithic component. It is a distributed architecture comprising three distinct layers, each serving a specific function in the data pipeline. Understanding these boundaries is essential for designing systems that scale without compromising data integrity.

The first layer is the System of Record. This is where the raw documents, legal contracts, or product manuals reside in their original format. This layer ensures data durability, versioning, and strict access control. It is typically managed by a traditional relational or analytical database.

The second layer is the Vector Retrieval Layer. This component handles the transformation of text into high-dimensional vectors (embeddings) and stores them for similarity search. It optimizes for approximate nearest neighbor (ANN) queries, allowing for fast semantic matching. This layer must support dynamic metadata filtering to ensure that search results respect the access controls defined in the System of Record.

The third layer is the Document Store. This stores the actual content chunks or full documents, often linked to the vector IDs. It enables the retrieval of context windows for the Large Language Model (LLM) after the relevant vectors are identified.

Separating these layers addresses the core problem of balancing speed with security. If the vector store also manages user permissions and document versions, the system becomes a bottleneck for governance. By keeping the System of Record separate, enterprises can apply granular Row-Level Security (RLS) or column-level encryption to the metadata before it is even sent to the retrieval layer. This ensures that the LLM never receives context from documents the user is not authorized to view.

For example, a legal RAG system processing 47 million chunks across 2.3 million documents requires this separation. The system must filter by jurisdiction and date range (structured metadata) while simultaneously searching for semantic relevance. If these operations are not decoupled, the query latency increases significantly, and the risk of data leakage grows.

Hybrid Retrieval: Combining Semantic and Keyword Search

Pure vector search often fails in enterprise environments because it lacks precision. Semantic embeddings capture meaning but may miss exact keyword matches or structured constraints. Hybrid retrieval solves this by combining dense vector similarity with sparse keyword indexing and structured metadata filtering.

Hybrid search impacts schema design by requiring the database to maintain multiple index types simultaneously. The schema must support:

1. Vector Columns: Storing high-dimensional arrays (e.g., 1536 or 3072 dimensions) for semantic similarity.
2. Full-Text Indexes: Enabling keyword-based retrieval for exact term matching.
3. Structured Metadata Columns: Supporting fast filtering on attributes like document type, author, date, and jurisdiction.

Consider a product documentation knowledge base containing 10 million embedded documents. The workload involves 500 queries per second (QPS) requiring hybrid search. In this scenario, a traditional database can handle the load effectively. It manages the log collection, full-text search, and query analytics alongside the vector data. This consolidation reduces the operational overhead of maintaining separate systems for logs, analytics, and retrieval.

In contrast, a legal document RAG system benchmarked for a US client in early 2026 utilized OpenAI text-embedding-3-large with 3072 dimensions. The workload required hybrid search combining vector similarity with structured filters for date range, document type, and jurisdiction. The ability to filter on these structured fields before or during the vector search is critical for legal accuracy. Without hybrid retrieval, the system might return semantically similar but legally irrelevant or outdated documents.

Architecturally, this means the database must support efficient join operations or co-located storage of vectors and metadata. This allows the query engine to prune the search space using metadata filters before performing the computationally expensive vector similarity calculation.

Governance and Access Control in Distributed RAG Systems

Data governance is the primary constraint for enterprise RAG deployments. Regulations such as data residency laws and internal compliance policies require strict control over who can access which documents. In a distributed RAG architecture, access control must be enforced consistently across the System of Record, the metadata layer, and the vector retrieval layer.

Fine-grained access control prevents data leakage by ensuring that the retrieval mechanism respects the user’s permissions. This is typically achieved through:

• Row-Level Security (RLS): Restricting access to specific rows in the metadata table based on user attributes.
• Attribute-Based Access Control (ABAC): Using dynamic policies to determine access based on user role, document classification, and context.
• Encryption at Rest and in Transit: Protecting sensitive embeddings and metadata from unauthorized access.

A common pitfall is implementing access control only at the document store level. If the vector store does not enforce these controls, a malicious user could potentially retrieve embeddings for documents they are not authorized to view by manipulating the search query. Therefore, the vector retrieval layer must be aware of the access policies defined in the System of Record.

This requires a unified governance model. The database should support centralized policy management that applies to both structured and unstructured data. For instance, if a document is reclassified as "Confidential," the system must immediately revoke access in both the document store and the vector index. This synchronization ensures that the RAG pipeline never serves unauthorized context to the LLM.

Enterprises must also consider audit trails. Every query and retrieval action should be logged to monitor for anomalous access patterns. This is particularly important for compliance in regulated industries such as finance and healthcare.

Latency, Concurrency, and Upsert Handling

Performance is a critical factor in RAG systems. Users expect near-instantaneous responses, which places high demands on the database’s ability to handle concurrent vector searches and dynamic metadata filtering. However, performance must be balanced with index freshness, especially when documents are frequently updated or deleted.

High-concurrency vector searches face several challenges:

1. Index Fragmentation: Frequent upserts and deletes can fragment the vector index, leading to slower search times and increased memory usage.
2. Lock Contention: Concurrent updates to metadata can cause lock contention, slowing down both write and read operations.
3. Memory Pressure: Storing high-dimensional vectors for millions of documents requires significant memory. Efficient memory management is essential to maintain low latency.

To mitigate these issues, the database should support efficient incremental updates. This allows new embeddings to be added to the index without rebuilding it from scratch. Additionally, the system should support asynchronous index optimization to maintain performance during peak loads.

For a workload of 500 QPS on a 10 million document knowledge base, the database must handle both read-heavy and write-heavy operations. This requires a robust concurrency control mechanism that minimizes lock contention. Partitioning the data by tenant or document type can also help isolate load and improve scalability.

Latency is not just about raw search speed. It includes the time taken to fetch the context window from the document store after the vector search returns relevant IDs. Therefore, the integration between the vector layer and the document store must be optimized for low-latency data retrieval. This often involves using fast, in-memory data structures or caching frequently accessed documents.

Consolidation vs. Specialization: Choosing the Right Database Pattern

Selecting the right database pattern depends on the specific workload scale and governance requirements. There is a trade-off between using purpose-built vector databases and consolidated traditional databases.

Purpose-built vector databases excel at raw vector search speed and support advanced semantic features. However, they often lack the robust governance, transactional integrity, and analytical capabilities required for enterprise systems. Managing multiple systems increases the total cost of ownership (TCO) and operational complexity.

Consolidated traditional databases, on the other hand, offer a unified platform for structured data, analytics, and increasingly, vector search. They are particularly advantageous for workloads that do not demand extreme scale.

The following table compares the two approaches for typical enterprise RAG workloads:

For a product documentation knowledge base with 10 million documents and 500 QPS, a consolidated database is often the better choice. It handles the hybrid search requirements while managing logs, full-text search, and query analytics in a single system. This reduces the infrastructure footprint and simplifies maintenance.

In contrast, for a legal RAG system with 47 million chunks and complex structured filters, the need for precise metadata filtering and hybrid search may favor a specialized vector database. However, even in this case, the System of Record should remain a traditional database to ensure data integrity and compliance.

The decision should be based on a thorough evaluation of the workload characteristics. If the primary requirement is semantic search at extreme scale, a vector database may be necessary. If the requirement includes hybrid search, governance, and analytics, a consolidated database is likely more suitable.

Integration Patterns with LLM Orchestration Frameworks

The database layer must integrate seamlessly with LLM orchestration frameworks such as LangChain and LlamaIndex. These frameworks manage the flow of data between the user, the LLM, and the retrieval components. The database’s ability to support native embedding storage, ANN indexing, and standard drivers is critical for this integration.

Integration patterns typically involve:

1. Embedding Generation: The orchestration framework generates embeddings using an embedding model. These embeddings are then stored in the database.
2. Vector Retrieval: The framework queries the database for similar vectors, passing metadata filters to ensure relevance and compliance.
3. Context Retrieval: The framework retrieves the full document chunks from the document store using the IDs returned by the vector search.

To facilitate this, the database should provide drivers or connectors for popular orchestration frameworks. This ensures that developers can easily integrate the database into their RAG pipelines without custom code. Additionally, the database should support standard protocols for data exchange, such as REST APIs or gRPC, to enable flexibility in integration.

For example, a database that supports native integration with LangChain allows developers to use standard Python libraries to store and retrieve vectors. This reduces the learning curve and accelerates development. Similarly, support for LlamaIndex enables efficient indexing and querying of structured and unstructured data.

However, it is important to verify the specific compatibility of any database with these frameworks. Not all databases support the same level of integration. Enterprises should test the integration in a proof-of-concept (PoC) environment to ensure that latency, error handling, and data consistency meet their requirements.

Evaluating Total Cost of Ownership and Scalability

The total cost of ownership (TCO) for a RAG system extends beyond database licensing. It includes infrastructure costs, maintenance of multiple data stores, and the operational overhead of managing complex architectures. For many enterprises, the choice of database significantly impacts the TCO.

Specialized vector databases may require separate infrastructure for vector search, document storage, and metadata management. This fragmentation increases licensing costs and operational complexity. In contrast, a consolidated database reduces the number of systems to manage, lowering TCO.

Scalability is another key consideration. As the knowledge base grows, the database must handle increasing volumes of data and queries. This requires efficient indexing, partitioning, and load balancing strategies. Enterprises should evaluate the database’s ability to scale horizontally and vertically to meet future demands.

Vendor lock-in is also a concern. Choosing a database with open standards and broad ecosystem support reduces the risk of lock-in. This ensures that the enterprise can migrate or integrate with other systems if needed.

Finally, enterprises should consider the cost of development and maintenance. A database with robust documentation, community support, and professional services can reduce the time and effort required to build and maintain the RAG system. This is particularly important for organizations with limited AI engineering resources.

Evaluation Criteria for KingbaseES in RAG

KingbaseES is a commercial database software. When evaluating KingbaseES for RAG workloads, it is critical to distinguish between its core relational capabilities and specific vector or AI-native features, which may require verification.

1. Vector Search and Hybrid Retrieval Verification

KingbaseES is not inherently a purpose-built vector database. To support RAG, it must be evaluated for:

• Native Vector Support: Does the specific version of KingbaseES include native vector data types and ANN indexing? If not, it may require integration with an external vector store.
• Hybrid Search Performance: Can KingbaseES efficiently combine full-text search (BM25) with vector similarity in a single query? Verify this through benchmarking against the 10M document/500QPS workload scenario.
• Index Freshness: How does KingbaseES handle frequent upserts and deletes in the vector index? Check for documentation on incremental indexing or asynchronous optimization.

2. Governance and Security

KingbaseES is positioned as a strong candidate for the System of Record due to its enterprise-grade governance features.

• Row-Level Security (RLS): Verify that KingbaseES RLS can be applied to metadata tables to enforce access control before data reaches the LLM.
• Encryption: Confirm support for encryption at rest and in transit for sensitive embeddings and metadata.
• Audit Trails: Ensure the database provides comprehensive logging for compliance with data residency laws, including Malaysian PDPA requirements if applicable.

3. Integration with Orchestration Frameworks

KingbaseES does not natively generate embeddings or orchestrate RAG flows. Its role is to store and retrieve data.

• Driver Compatibility: Verify that KingbaseES provides drivers compatible with LangChain and LlamaIndex.
• API Support: Check for REST API or gRPC support to facilitate integration with external vector stores if KingbaseES is used primarily as the metadata layer.

4. Commercial Licensing and Support

As commercial software, KingbaseES offers specific licensing models.

• Licensing Costs: Evaluate the TCO based on commercial licensing rather than open-source assumptions.
• Local Support: Verify the availability of local support teams, particularly in regions like Malaysia, to ensure timely response for enterprise deployments.

FAQ

Is KingbaseES an open-source database suitable for RAG?

No, KingbaseES is commercial software. It is not open-source or source-available. Enterprises should evaluate its commercial licensing and support models for RAG deployments.

Does KingbaseES natively support vector search and embedding generation?

KingbaseES is primarily a relational database. While it may support vector extensions in newer versions, it does not natively generate embeddings or provide RAG-specific orchestration. It is best used as the System of Record or Metadata Layer, potentially integrating with external vector stores. Verify specific vector capabilities with the vendor.

How does KingbaseES handle data residency and compliance in Malaysia?

KingbaseES is commercial software with enterprise governance features. For Malaysian data residency (PDPA), verify if KingbaseES offers local data centers or support in Malaysia. The database supports encryption and audit trails, but specific local compliance configurations must be validated with the vendor.

Can KingbaseES replace specialized vector databases like Pinecone or Milvus?

KingbaseES is not a drop-in replacement for specialized vector databases. It is a consolidated traditional database. It may be suitable for moderate-scale RAG workloads (e.g., 10M documents, 500QPS) where hybrid search and governance are prioritized over extreme-scale vector search. For extreme scale, a specialized vector database may still be required.

What are the verification steps for KingbaseES RAG readiness?

1. Check Vector Support: Confirm if the specific KingbaseES version includes vector data types and ANN indexing.
2. Test Hybrid Search: Benchmark hybrid search performance against your specific workload (e.g., 10M docs, 500QPS).
3. Verify Integrations: Test connectivity with LangChain/LlamaIndex and external vector stores.
4. Validate Governance: Ensure RLS and encryption meet your compliance requirements.

💡 More Resources

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:

• Kingbase Community: A one-stop interactive platform for technical exchanges, Q&A, and experience sharing—join forces with fellow DBAs and developers.
• Kingbase Solutions: 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.
• Kingbase Case Studies: Real-world user scenarios and implementation outcomes, showcasing KingbaseES’s outstanding capabilities in high availability, high performance, and IT adaptation.
• Kingbase Documentation: Authoritative and comprehensive product manuals and technical guides, covering the entire lifecycle from installation and deployment to development, programming, and operations management.
• Free Download: Get the latest installation packages, drivers, tools, and patches, supporting multiple platforms and domestic chip architectures.
• Digital Construction Encyclopedia: Covers digital strategy planning, data integration, metrics management, database visualization applications, and more to empower enterprise digital transformation.

Open Source Resources:

• GitHub – Kingbase-docs: Kingbase documentation open-source repository—Stars and contributions are welcome.
• Gitee – Kingbase-docs: Domestic mirror repository for Kingbase documentation for faster access.

Welcome to explore the resources above and begin your Kingbase journey!