How Vector Databases and Large Models are Transforming AI-Driven Database Operations

1 Database and Large Models

Databases have evolved for over 70 years, moving from mainframes to PCs, cloud, and now the AI era, where GPUs and large models drive new applications such as Copilot and Midjourney. The current hot topics are vector databases and AI‑powered intelligent operations like Baidu's DBSC (Database Smart Cockpit).

Large models now exhibit four core abilities—understanding, generation, reasoning, and memory—making their combination with databases more practical and generic, sparking a new wave of AI‑database integration.

The AI stack includes IaaS (CPU+GPU), PaaS (large models, Model Builder, Agent Builder, App Builder), and SaaS applications such as vector databases with RAG flow and intelligent driving‑cockpit services.

2 DB4AI: Vector Database

Vector databases originated from similarity search libraries like Faiss (2015) and serve three main scenarios: similarity search, semantic search, and Retrieval‑Augmented Generation (RAG) for knowledge bases, customer service, and model memory.

RAG requires four stages—data extraction, indexing, retrieval, and generation—each with specific challenges such as handling complex file formats, optimal chunk sizes, embedding models (e.g., BGE, OpenAI text‑embedding‑3, CLIP), query preprocessing, multi‑modal recall, and prompt optimization.

Compared with generic databases, dedicated vector databases provide higher performance, lower cost (CPU vs. GPU), stable answers, better handling of complex queries, and traceable pipelines.

Enterprise requirements include version management, mixed scalar‑vector queries, multi‑tenant private deployments, and full‑lifecycle data management.

Baidu Cloud developed its own AI‑native VectorDB with four key strengths: distributed architecture supporting billions of vectors, high‑performance indexing (including a proprietary puck algorithm), full‑stack capabilities (schema, scalar/vector storage, secondary indexes, compression, snapshots), and enterprise‑grade availability.

3 AI4DB: Database Operations

AI4DB leverages large models to enhance database operations through the DBSC product, which offers request analysis, intelligent maintenance, stress testing, and DevOps, all powered by RAG, embedding models from Wenxin Qianfan, and VectorDB for knowledge storage.

DBSC continuously absorbs operational knowledge, creating a data flywheel that improves service quality as usage grows.

Overall, Baidu Cloud's VectorDB and DBSC demonstrate a mature, high‑performance, full‑stack solution for AI‑driven database management.