Baidu’s Enterprise Vector Database: Architecture, Performance, and RAG Secrets

1. Baidu Cloud Vector Database R&D Practice

Generative AI has driven large‑scale pre‑training models, making vector databases a crucial component for storing and retrieving vector representations of unstructured data such as text, images, and video. For large models, a vector database enables more efficient and accurate applications.

Capital interest in vector databases has surged, with companies like Weaviate and Pinecone raising significant funding. The high attention stems from the benefits vector databases bring to large‑model deployments.

Large language models suffer from outdated knowledge, hallucinations, lack of domain‑specific or private knowledge, and safety concerns. Introducing a vector storage module as long‑term memory allows low‑cost mitigation of these issues.

In early 2023, Baidu Cloud launched VectorDB (VDB) 1.0, a professional enterprise‑grade vector database featuring a newly designed kernel, elastic scaling to billions of records, and QPS improvements of 1‑10× over comparable open‑source products.

We interviewed Baidu’s chief database architect Zhu Jie and senior architect Guo Bo to uncover the technical story behind VDB.

Interview Highlights

InfoQ: Baidu previously used ES+FAISS for Wenxin Yiyan. Why develop a dedicated vector database now?

Guo Bo explained that Baidu’s BES team built a custom C++ HNSW‑based vector retrieval engine, which better fits their large‑scale needs. He emphasized that enterprise customers require not only vector search but also full database capabilities such as multi‑tenant isolation, audit, high availability, and low‑cost operation.

Zhu Jie added that enterprise users demand support for diverse data types, high reliability, multi‑region deployment, and advanced security features—capabilities often missing in open‑source vector stores.

InfoQ: Is a dedicated vector database essential?

Guo Bo affirmed it is, noting that relational databases lack vector‑optimized storage and indexing, while NoSQL solutions like MongoDB or Elasticsearch face licensing and performance limitations for large‑scale vector workloads.

The team built the entire stack in C++ 17, covering storage format, engine, retrieval engine, schema, distributed architecture, and management console.

Performance testing showed VDB outperforming leading open‑source vector databases by up to 10× in certain scenarios, while maintaining linear improvements as recall thresholds are relaxed.

Technical Challenges

Key challenges included developing a column‑store engine tailored for vector data, integrating a custom retrieval engine with the storage layer, and ensuring distributed robustness through functional and chaos testing, as well as formal verification.

The project timeline: design started in August 2022, intensive development began mid‑September, first version completed and publicly tested by end of January 2023.

Optimization efforts focused on columnar storage to isolate vector fields, instruction‑set and compiler tuning, and minimizing non‑floating‑point overhead.

RAG and Multi‑Modal Support

While vector databases provide the storage and retrieval backbone, RAG (Retrieval‑Augmented Generation) is viewed as an application layer that leverages vector search. Baidu plans to support rich data types and integrate Baidu Cloud’s Qianfan large‑model embeddings for text, image, and video.

Future work will emphasize RAG capabilities, cost‑effective indexing, and AI‑native toolkits to lower development barriers.

Future Outlook

The team foresees continued demand for vector databases driven by enterprise data diversity, the need for secure, high‑performance, and low‑cost solutions, and deeper integration with large‑model ecosystems.

Baidu Cloud launches dedicated vector database VDB 1.0

How to build a hundred‑billion‑scale vector retrieval system?

BES’s exploration of large‑scale vector database scenarios