How Milvus Powers Billion-Scale Vector Search for AI at TAL Education

Background

With the rapid rise of large models and generative AI (AIGC), vector retrieval has become essential for semantic understanding, multimodal content processing, and intelligent search. TAL Education’s vector database, built on the open‑source Milvus, provides a complete solution covering data ingestion, resource isolation, cluster management, monitoring alerts, and cost optimization, serving multiple business lines with billions of vectors.

Why Vector Databases?

Traditional relational databases excel at structured data but cannot efficiently handle high‑dimensional vector similarity search, limited indexing, and lack flexible APIs. Vector databases offer a dedicated, non‑structured data stack that supports large‑scale ANN search, making them indispensable for AI workloads.

Introducing Milvus

Created in 2019, Milvus is designed to store, index, and manage massive embedding vectors generated by deep neural networks. It can index trillions of vectors and is built from the ground up for non‑structured data. Milvus is cloud‑native, separating storage and compute, and all components are stateless, running on Kubernetes via the Milvus Operator or Helm.

Key Features

High‑performance retrieval for massive vector datasets

Hybrid search with scalar filtering and vector similarity (supports keyword search)

Rich index types: HNSW, FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, SCANN, DiskANN, RabitQ

Multiple similarity metrics: Cosine, L2, IP, Hamming, Jaccard

Multi‑language support and toolchains

Cloud‑native and highly scalable

Typical Application Scenarios

Image, video, and audio similarity search

Natural Language Processing (NLP)

Multimodal (image‑text) search

Recommendation systems

Bioinformatics / medical imaging analysis

Milvus in Practice at TAL Education

TAL Education runs hybrid‑cloud Milvus clusters (self‑built data‑center and public‑cloud) and uses the Attu web UI for management. Monitoring is handled with Prometheus + Alertmanager, providing minute‑level alerts for retrieval latency, service health, proxy failures, and storage capacity.

Business use cases include intelligent grading, resource recommendation, and document‑based vector recall. The system architecture consists of:

Data Processing Layer : Ingests exam questions via APIs, batch import, or OCR, normalizes data, extracts features, and converts text and images to vectors.

Milvus Core Layer : Distributed ANN engine storing billions of vectors, using multi‑level indexes and MySQL for metadata.

Business Application Layer : Provides intelligent grading, similar‑question recommendation, and duplicate detection, turning vector similarity into concrete educational tools.

Quality evaluation is performed through confidence scoring, feedback loops, and continuous model improvement, ensuring high‑quality grading and recommendations.

Results and Impact

After integrating Milvus, TAL Education achieved millisecond‑level similarity retrieval on billion‑scale vectors, dramatically improving recall accuracy and reducing latency compared to traditional fuzzy matching. This has accelerated AI adoption across grading, recommendation, and knowledge‑base search, enhancing teaching efficiency and fairness.

Future Outlook

TAL Education will continue to contribute to the Milvus community, focusing on easier data migration, further retrieval performance gains, deeper community collaboration, and leveraging the commercial Zilliz Cloud version for high‑availability scenarios.