July 2021 Summaries

Vipshop, an online discount retailer in China, built a personalized search recommendation system to optimize their customers' shopping experience. The core function of the e-commerce search recommendation system is to retrieve suitable products from a large number of products and display them to users according to their search intent and preference. To achieve this, Vipshop used Milvus, an open source vector database, which supports distributed deployment, multi-language SDKs, read/write separation, etc., compared to the commonly used standalone Faiss. The overall architecture consists of two main parts: write process and read process. Data such as product information, user search intent, and user preferences are all unstructured data that were converted into feature vectors using various deep learning models and imported into Milvus. With the excellent performance of Milvus, Vipshop's e-commerce search recommendation system can efficiently query the TopK vectors that are similar to the target vectors. The average latency for recalling TopK vectors is about 30 ms.

Sound is an information dense data type, with 83% of Americans ages 12 or older listening to terrestrial radio in a given week in 2020. Sound can be classified into three categories: speech, music, and waveform. Audio retrieval systems are used for searching and monitoring online media in real-time to prevent intellectual property infringement and classify audio data. Feature extraction is crucial for audio similarity search, with deep learning-based models showing lower error rates than traditional ones. Milvus, an open-source vector database, can efficiently process feature vectors extracted by AI models and provides various common vector similarity calculations. The article demonstrates how to use an audio retrieval system powered by Milvus for non-speech audio data processing.

The new and improved Milvus 2.0 bootcamp offers updated guides and easier to follow code examples for testing, deploying, and building vector search solutions. Users can stress test their systems against 1 million and 100 million dataset benchmarks, explore popular vector similarity search use cases such as image, video, audio, recommendation system, molecular search, and question answering system. The bootcamp also provides quick deployment solutions for fully built applications on any system and scenario-specific notebooks to easily deploy pre-configured applications. Additionally, users can learn how to deploy Milvus in different environments like Mishards, Kubernetes, and load balancing setups.