How Vector Databases Power AI‑Driven Retrieval: Inside Baidu’s VectorDB

1. Database and Large‑Model Evolution

Databases have a 70‑year history, evolving from mainframes to PCs, cloud data centers, and now the AI era. Hardware shifted from CPU‑only to CPU + GPU, and applications moved toward AI‑native workloads such as Copilot and generative models. The most prominent trend today is the convergence of databases with large models, especially vector databases and intelligent database operation platforms (e.g., DBSC – Database Smart Cockpit).

2. DB4AI – Vector Database

Vector databases originated with Facebook’s Faiss library in 2015 and now serve three primary scenarios:

Similarity search for multimodal retrieval, recommendation, and classification.

Semantic search that combines text and vector queries, often used for internal enterprise search.

Retrieval‑Augmented Generation (RAG), which enhances large‑model answers by grounding them in vector‑indexed knowledge bases.

RAG consists of four stages—data extraction, data indexing, retrieval, and generation—each with its own challenges.

Data extraction: parse structured and unstructured sources (e.g., PDFs with mixed text, tables, and images) and retain contextual metadata for accurate chunking.

Data indexing: split documents into 300‑400‑byte chunks, embed them with models such as BGE, OpenAI text‑embedding‑3, or CLIP for multimodal data, then store vectors in a vector database.

Retrieval: preprocess queries (intent detection, synonym generation, entity extraction) and perform hybrid vector‑scalar recall with re‑ranking.

Generation: craft prompts that include retrieved passages, optionally applying step‑by‑step prompting to improve answer quality.

RAG offers distinct advantages over feeding raw data to a large model: lower cost (CPU‑based vector search vs. GPU‑based inference), reduced latency, more stable answers, better handling of complex filtering, and full traceability of the retrieval pipeline.

3. AI4DB – Database Operations Powered by LLMs

The DBSC (Database Smart Cockpit) integrates large‑model capabilities into all aspects of database operations, including request analysis, intelligent maintenance, performance testing, and DevOps. Knowledge accumulated from years of Baidu database experience is stored in VectorDB and accessed via RAG, using Baidu’s “Wenxin Qianfan” text model for embeddings.

By continuously feeding operational data back into the system, DBSC creates a knowledge flywheel that improves over time.

4. Baidu VectorDB Technical Highlights

VectorDB is built on four core pillars:

Distributed architecture : stateless proxy nodes, Raft‑based management nodes for high availability, and data nodes that handle CRUD, indexing, and failover.

High‑performance data engine : supports strong schema, scalar + vector storage, secondary indexes, row/column/mixed storage, compression, snapshots, multi‑version recovery, and hardware‑aware optimizations.

Hybrid scalar‑vector retrieval : combines vector similarity with scalar filters, offering pre‑filter, in‑retrieval filter, post‑filter, and statistics‑driven index pruning.

Enterprise‑grade reliability : elastic scaling, multi‑tenant isolation, and robust high‑availability mechanisms.

Benchmark tests show VectorDB achieving 3–7.5× higher QPS and over 90 % lower memory consumption compared with open‑source alternatives under identical recall targets.

Key strengths summarized:

Significantly higher throughput and lower resource usage.

Full‑stack capabilities from ingestion to query.

Transactional‑level high availability.

Massive storage for billions of high‑dimensional vectors.

Cross‑platform compatibility with self‑developed code.