Why Vector Databases Are the Next Big Thing in AI: A Deep Dive into RAG and Baidu’s VectorDB

Database Evolution Over 70 Years

Databases have survived for more than seven decades by continuously adapting to changes in underlying hardware and emerging business needs, moving from mainframes to minicomputers, PCs, data‑center servers, the cloud, and now the AI era.

Each era produced a flagship database: Oracle in the PC era, MySQL during the Internet boom, and cloud‑native databases in the cloud era. In the AI era, hardware shifts to GPU + CPU and workloads migrate from CPU‑centric to GPU‑accelerated, making vector databases the most prominent technology.

AI Era: Vector Databases and RAG

Large language models (LLMs) and generative AI have created a symbiotic relationship with databases. Vector databases store high‑dimensional embeddings that enable fast similarity search, while LLMs provide the reasoning and generation capabilities that make the stored data actionable.

The hottest sub‑field today is vector databases, especially when combined with Retrieval‑Augmented Generation (RAG) to supplement LLMs with up‑to‑date, private knowledge.

RAG Workflow

Data Extraction – Convert structured and unstructured sources (PDFs, images, tables) into text and metadata.

Data Indexing – Split documents into appropriate chunks (typically 300‑400 characters) and embed them using models such as BGE, OpenAI text‑embedding‑3, or CLIP for multimodal data.

Retrieval – Process the query (intent detection, synonym generation, entity extraction) and perform vector and/or scalar retrieval with multi‑stage recall and re‑ranking.

Generation – Optimize the prompt (e.g., add step‑by‑step instructions) before feeding retrieved results to the LLM for final answer generation.

Challenges and Requirements for Vector Databases

Full‑lifecycle data management, including versioning and bulk updates.

Hybrid queries that combine scalar filters with vector similarity.

Support for both public‑cloud and on‑premises deployments, with multi‑tenant capabilities for private‑cloud scenarios.

Baidu VectorDB Architecture

VectorDB is a purpose‑built, AI‑native vector database that follows a three‑layer distributed design:

Stateless proxy nodes with automatic load balancing.

Raft‑based management nodes providing high availability and cluster topology management.

Data nodes handling CRUD operations, indexing, failover, and elastic scaling.

Core Capabilities of VectorDB

Strong schema support for both scalar and vector fields.

Secondary indexes enabling mixed scalar‑vector queries.

Multiple storage layouts (row, column, hybrid) with compression and snapshot/MTV recovery.

Hardware‑aware optimizations that leverage CPU instruction sets and compilers for high throughput.

Performance Comparison

Under identical recall conditions, VectorDB achieves 3‑7.5× higher QPS or throughput than leading open‑source solutions. The benchmark methodology, data, and reproducible test code are publicly documented.

Customer Use Cases

Document‑search platform migrated from an Elasticsearch plugin to VectorDB, reducing total cost of ownership by ~7× and improving query latency from hours to seconds.

Automotive knowledge‑base (“有驾智能体”) leveraged VectorDB for accurate vehicle‑spec queries, reaching 95% query accuracy and 85% recall.

Mobile search system replaced an ANN plugin with VectorDB, eliminating manual Excel‑based data pipelines and achieving near‑real‑time data updates.

Future Trends in the Data Stack

As LLMs become more capable, enterprises will increasingly value private knowledge bases. The data stack will evolve from pure data‑governance to full‑stack data engineering, supporting multimodal pipelines, end‑to‑end data cleaning, enrichment, and continuous model iteration.

Vector databases, as a mature and high‑performance component, are poised to become a foundational layer for AI‑native applications.