#Milvus

Enhance traditional surveillance with advanced AI functionalities like facial and person recognition, fall detection, and more. Supports a wide range of devices including Nvidia Jetson and Raspberry Pi, and integrates with systems like Home-Assistant for smart home solutions. Offers seamless local and cloud deployment through SharpAI Hub.

Milvus, an open-source vector database, enhances AI application capabilities with millisecond search over trillion vector datasets. It delivers a consistent user experience across cloud, local clusters, and laptops, ensuring reliable data management. Featuring built-in replication, failover, and elastic scalability, Milvus excels in real-time analytics and offers a hybrid search framework for multiple vector fields. Its unified Lambda architecture simplifies vector similarity searches. Backed by the LF AI & Data Foundation and a proactive community, Milvus is ideal for incorporating similarity search into diverse applications.

Discover how vector databases enhance the processing of unstructured data such as images, audio, and video through neural network feature extraction. The platform supports the development of similarity search applications, offering tutorials for building AI tools like chatbots and recommendation systems. Perform benchmark tests and deploy applications using Jupyter Notebooks or Docker. Access practical examples and in-depth articles to deepen your understanding of unstructured data analytics.

This guide offers detailed instructions for compiling a C++ project from source, specifically designed as an internal core for Milvus. It invites contributions from developers, especially those using Ubuntu, CentOS, and macOS systems. The document outlines system prerequisites, package installation, source code assembly, and testing procedures. Furthermore, it includes steps for creating Python wheels, performing clean-up tasks, and executing pre-commit checks. This comprehensive guide assists developers in enhancing their Knowhere building experience, promoting cross-platform support and collaborative progress without bias.