How Vector Databases Power AI and RAG: Insights from Baidu’s DTCC 2024

Databases have survived over 70 years by continuously evolving with changes in underlying infrastructure and business needs, moving from mainframes to PCs, servers, cloud, and now AI.

In the AI era, hardware shifts to GPU + CPU and workloads migrate accordingly, making vector databases the most prominent technology, especially when combined with large language models for intelligent operations and data engineering.

RAG and Vector Databases

Retrieval‑Augmented Generation (RAG) uses vector similarity search to retrieve relevant documents, forms prompts for large models, and mitigates issues like outdated knowledge and hallucinations. The RAG pipeline includes data extraction, indexing, retrieval, and generation, each with specific challenges.

Data extraction must handle complex formats (e.g., PDFs with mixed text, tables, images) and preserve contextual metadata.

Data indexing requires proper chunking (300‑400 bytes) and suitable embedding models (BGE, OpenAI text‑embedding‑3, CLIP for multimodal).

Retrieval involves query preprocessing (intent, synonym, entity generation) and vector/text recall with re‑ranking.

Generation needs prompt optimization, such as step‑by‑step instructions.

Effective RAG depends on the large model's comprehension, reasoning, and generation capabilities.

Vector Database Requirements

Enterprise RAG scenarios demand vector databases that support full lifecycle data management, versioning, mixed scalar‑vector queries, multi‑tenant deployments, and both public‑cloud and on‑premises options.

Why Build a Native Vector Database?

Traditional vector extensions on relational databases suffer from suboptimal architecture, leading to lower write performance, higher latency, and limited concurrency, which cannot meet the demands of large‑scale AI workloads.

Baidu Intelligent Cloud developed a purpose‑built VectorDB, released in June 2024 (v1.0), featuring:

Distributed architecture supporting billions of vectors and >4096‑dimensional data.

High‑performance access with proprietary Puck indexing, achieving 3‑7.5× higher QPS and lower latency than open‑source solutions.

Full‑stack capabilities, including schema support, scalar‑vector hybrid queries, row/column/mixed storage, compression, snapshots, multi‑version recovery, and hardware‑aware optimizations.

Robust retrieval engine offering pre‑filter, runtime filter, post‑filter, and statistical filter mechanisms.

Benchmark tests show VectorDB surpasses open‑source alternatives by 3‑7.5× in throughput under equal recall conditions.

Real‑World Deployments

Examples include:

Baidu Wenku migrated from an Elasticsearch vector plugin to VectorDB, reducing system cost by ~7× while handling massive unstructured data.

Automotive knowledge base "Youjia Smart Agent" achieved 95% query accuracy and 85% recall by leveraging VectorDB for precise vector retrieval.

Baidu Mobile Search replaced an ANN plugin with VectorDB, cutting data sync latency from 8 hours to seconds and improving operational efficiency.

These cases demonstrate that a mature, high‑performance vector database can significantly lower costs and simplify complex business workflows.

Future Outlook

As large models become more capable, enterprises will increasingly value private knowledge bases, driving further adoption of vector databases. In the broader data domain, traditional data platforms will evolve toward data engineering pipelines that handle multimodal data end‑to‑end, supporting continuous model iteration and real‑world AI applications.