Unlocking AI with Vector Databases: Architecture, Optimization, and Real-World Cases

Why Vector Databases Matter in the AI Era

In the age of large AI models, vector databases act as the "memory" for these models, providing not only storage but also enabling knowledge enhancement through data retrieval and analysis. This creates a new paradigm for generative AI application development.

Embedding and Core Definition

When searching with images or text, the database stores and compares extracted "features" rather than raw media. The process of extracting these features is called Embedding, and the resulting vectors enable similarity‑based retrieval of unstructured data. A vector database is a system that produces, stores, indexes, and analyzes massive vector data generated by machine‑learning models. Typical use cases include intelligent customer service powered by large language models, enterprise knowledge‑base Q&A, and tools like Chatdoc.

Distributed Architecture with Compute‑Storage Separation

Within Douyin, early vector retrieval engines were built for search, recommendation, and advertising, handling billions of items. For example, storing 100 million 128‑dimensional float vectors requires about 48 GB of memory. To address this, the team designed a distributed system that separates storage and computation, enabling vector sharding, batch building, and real‑time online retrieval. This architecture supports multiple indexes, reduces resource consumption, speeds up index construction, and improves service stability.

Kernel Performance Optimization

Building an enterprise‑grade vector retrieval service for billions of vectors with sub‑10 ms latency requires high‑performance kernels. Optimizations focused on throughput, cost reduction, and stability, including memory usage reduction, index performance tuning, CPU instruction set enhancements, and filtering/re‑ranking improvements. These efforts yielded more than a three‑fold increase in throughput and latency compared to open‑source baselines, leading to widespread adoption across Douyin’s services.

The framework was further refactored for cloud‑native, multi‑tenant deployment with automated scheduling, reducing configuration complexity and error rates.

Hybrid Vector‑Scalar Retrieval Capability

Vector databases often need to combine vector data with structured attributes (e.g., document department for permission filtering). Two common strategies exist: post‑filtering (retrieve a larger set of vectors, then filter by structured fields) and pre‑filtering (apply structured filters before vector ranking). To automatically choose the optimal path, a DSL‑directed engine was developed, supporting simultaneous vector search and structured filtering with high performance and logical completeness.

Accelerating Large‑Model Knowledge Bases

As large‑model applications expand, converting enterprise data into vectors becomes critical. The team provides ready‑to‑use vectorization methods, allowing businesses to write raw data directly into the vector database and query with the same model‑generated vectors, speeding up knowledge‑base construction.

Full‑View Vector Database Technology

The final architecture, built on cloud infrastructure, offers end‑to‑end solutions from multimodal data ingestion, vector generation, online retrieval, to elastic scheduling and monitoring.

Scenario Implementations

Vector databases now support over 50 business lines within Douyin, covering scenarios such as intelligent search, AIGC cross‑modal retrieval, recommendation and deduplication, intelligent Q&A, relevance ranking, clustering analysis, and data mining, many at hundred‑billion‑scale.

Intelligent Search – TuChong Image Search

TuChong manages 460 million images and 20 million videos, requiring robust vector retrieval. The solution provides an end‑to‑end image search pipeline: upload source images, vectorize and store them, then vectorize query images and perform similarity search to return the most relevant results.

Enterprise Knowledge Base – Volcano Engine Oncall Q&A

Oncall assists frontline customer service by handling large volumes of queries. Documents are vectorized and stored, then combined with large language models (LLMs) for knowledge‑base retrieval, enabling a specialized chatbot that provides accurate, timely answers and generates training data for future model improvements.

Having been refined through extensive internal practice, the vector database is now offered externally, continuously adding features such as algorithm optimization, multimodal vectorization models, and cross‑modal retrieval to support diverse user needs. It has become foundational infrastructure for the broader large‑model ecosystem.