Building an AI‑Ready, Multi‑Modal Database Foundation for the Digital Intelligence Era

AI Era Challenges

Large language models (LLMs) have shown impressive capabilities in the past two‑three years, yet they suffer from instability and uncertainty that conflict with production‑system requirements for reliability and determinism. A war‑simulation test reported that 95% of mainstream models (GPT, Claude, Gemini, etc.) would choose to use nuclear weapons, highlighting the need for a safety lock when systems fail. Additionally, LLMs consume massive resources: DeepSeek‑V4‑pro has 1.6 trillion parameters, while an estimate of human intelligence corresponds to 600 trillion parameters—a 375× gap. Ten GPT interactions on an 8‑card H200 AI server consume roughly the energy of a bottle of mineral water.

Data‑level challenges are equally severe. GPT‑3 pre‑training required 45 TB of data, and forecasts suggest that high‑quality public text will be exhausted by 2026‑2032, leading to a “garbage‑in, garbage‑out” problem that can cause up to 15% productivity loss. Enterprise private data, representing the hidden 90% of the iceberg, remains siloed, limiting the effectiveness of Retrieval‑Augmented Generation (RAG) and Graph‑RAG.

3V Data Management Challenges

The AI era faces three core data challenges: Volume, Variety, and Velocity.

Volume: Scalar computation is shifting to high‑dimensional vector computation. Meta’s open‑source Faiss vector index uses a pure‑memory architecture, which becomes a bottleneck as memory prices rise.

Variety: Data spans relational, text, graph, and unstructured types. Traditional siloed (chimney) management leads to high system complexity and poor maintainability.

Velocity: In the Agent era, dozens of agents per department generate rapid, concurrent updates. Database‑level capabilities are required for fast memory management, conflict detection, and concurrent processing.

YashanDB Original Theory Breakthroughs

YashanDB follows a "theory breakthrough → prototype → product" path. In shared‑cluster performance it surpasses international benchmarks by 30%.

It introduces a resource‑constrained computing theory, achieving a 25‑to‑100 000‑fold (5 orders of magnitude) improvement in communication‑data query speed.

For large‑scale complex data, Yashan combines HNSW vector indexing with relational attribute filtering, using precise sampling algorithms and a hierarchical architecture to enable efficient queries under resource limits.

To address Velocity, Yashan proposes an adaptive concurrent transaction scheduling mechanism. Unlike traditional MVCC locks that cause many retries under high conflict, Yashan moves the scheduling point from transaction commit to CPU execution, monitors conflict patterns in real time, and performs proactive transaction reordering and delay control. Experiments show a 137% throughput increase and a 42% reduction in retries; the results were published at SIGMOD.

These innovations—shared clusters, resource‑limited computing, adaptive transaction scheduling, and cross‑modal fusion queries—have been certified by the China Electronics Society as internationally advanced, with some components reaching international leading status, capable of replacing Oracle in core banking workloads.

Multi‑Modal Fusion Architecture

Based on the theoretical breakthroughs, Yashan builds an AI‑Ready data foundation. The architecture is layered from bottom to top:

Infrastructure layer: General‑purpose and AI‑specific compute servers, providing the necessary compute power.

Storage engine layer: Data buffer and background thread management, inheriting Yashan’s transaction, high‑availability, and data‑security capabilities.

Technical layer: Aggregated memory, data sandbox, metadata & DDL, segment‑page management, row‑column storage, transaction consistency, partitioning, redo/undo, etc.

Data‑model layer: A unified storage engine supports five models—Relation, Vector, GIS, Graph, and Document (XML/JSON/Full‑Text)—all sharing the same transaction and high‑availability mechanisms.

Computation engine layer: Heterogeneous, multi‑node parallel execution, batch processing, cross‑modal fusion computation, and semantic association, enabling a single SQL statement to join graph, relational, and vector data.

KSA Trustworthy Knowledge Engineering

Yashan proposes a three‑pillar KSA framework for AI‑native intelligence: Knowledge, Skill, and Agent.

The KSA stack includes four core modules: full‑link traceability, multi‑path recall with trustworthy re‑ranking, knowledge graph (semantic network & logical reasoning), and Knowledge Units (KUs) that are independently verifiable. The Skill layer provides a standardized workflow toolbox with strict safety controls to ensure agent behavior is controllable.

Knowledge management emphasizes four properties: completeness, traceability, consistency, and security. Retrieval must also guarantee accuracy, consistency, security, and traceability, with results exposing the matched entry, retrieval strategy, and ranking logic.

Each KU carries a unique identifier and a SHA‑256 content hash. Trust is established through five dimensions: source credibility, timeliness, relational strength, permission control, and confidence score—mirroring Git‑style version management. Incremental document updates affect only the impacted KUs, saving over 80% of token costs.

Real‑World Deployments

Data Sandbox & Time‑Travel: Provides isolated workspaces for each employee or agent, supporting up to 8 192 concurrent sandboxes with second‑level provisioning. The Time‑Travel feature restores data to any past point without extra storage or snapshots.

Smart‑City CIM Platform (Shenzhen): Handles 3.8 billion heterogeneous records, serving 1.8 million people and managing 790 000 buildings. It integrates relational, spatial, time‑series, and BIM models, enabling real‑time emergency‑scenario reasoning (e.g., fire evacuation) by linking building structures, personnel distribution, traffic, nearby facilities, and historical plans via a unified I/O and operator stack.

Energy‑Industry Data Foundation: Uses a “one‑library‑multiple‑abilities” (relation + text + vector) architecture. IoT devices stream pipeline data hourly; the same tuple stores relational fields for historical analysis and vector embeddings for anomaly detection. New data triggers automatic vector retrieval and relational correlation to quickly flag potential faults.

Q&A Highlights

Q1: How to build semantic connections for multi‑modal queries? The simplest method is to store basic info, text, and vector together in a tuple, achieving natural mapping. Yashan’s internal graph implementation follows Oracle’s approach, embedding nodes and edges directly in relational tables and supporting graph query languages.

Q2: How to avoid wasteful token consumption when enterprise documents are updated or deleted? Yashan adopts a multi‑version management mechanism similar to Git. Only the affected KUs are refreshed or retired, identified via content hashes, reducing token usage by more than 80%.

Conclusion

The presented innovations—shared clusters, resource‑limited computing, adaptive transaction scheduling, cross‑modal fusion queries, and the KSA trustworthy knowledge‑engineering stack—demonstrate that a modern database can serve as the reliable, deterministic backbone for AI‑driven applications across smart‑city, energy, and enterprise domains.