GitHub user Yuukadesu added a comment to the discussion: Feat:Support Milvus as DatabaseSink
Milvus is an open-source high-performance vector database specifically designed for managing, indexing, and searching large-scale unstructured data such as text, images, audio, and more. Its core advantage lies in handling embeddings, which convert unstructured data into numerical vector representations that can effectively perform similarity searches. Milvus supports multiple types of search functions, including approximate nearest neighbor (ANN) search, filtered search, range search, and keyword search. Milvus has high scalability and can handle billions of vectors in distributed environments. It supports multiple hardware architectures such as AVX512, GPU, and NVMe SSD through hardware aware optimization, thereby improving performance. The system provides multiple deployment options ranging from lightweight versions (for rapid prototyping) to large-scale cloud native deployments (based on Kubernetes). Its distributed architecture is designed for scalability, ensuring that Milvus can seamlessly scale as data grows. Milvus' performance is optimized through advanced algorithms such as IVF, HNSW, and DiskANN, while its modular components support flexible scaling and efficient resource management. You can integrate Milvus into your application through various SDKs such as Python, Java, and Go to easily access its powerful search functionality. This is the official website of milvus https://milvus.io I think integrating vector databases like Milvus can enhance the functionality of streampipes. GitHub link: https://github.com/apache/streampipes/discussions/3333#discussioncomment-11318581 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
