How to Make 4 Popular AI Applications with Milvus

This post demonstrates the practical application of Milvus in building four distinct AI applications: NLP chatbots, reverse image search, audio search, and video object detection. It outlines the specific ML models (BERT, VGG, PANNs, YOLOv3, ResNet50) used in conjunction with Milvus for each use case, detailing the data processing steps (vectorization and storage) and search mechanisms. For NLP, it explains how BERT converts questions to vectors, and Milvus retrieves similar questions, with answers stored in PostgreSQL. For image search, VGG converts images to vectors, Milvus finds similar vectors, and image paths are stored in CacheDB. For audio search, PANNs convert audio to vectors, Milvus finds similar vectors using IP distance, and audio paths are stored in PostgreSQL. For video object detection, ResNet50 converts object images to vectors, Milvus finds similar objects, and image paths are stored in MySQL, with OpenCV and YOLOv3 used for video processing.