Milvus is an open source vector database built for similarity search

published on 2023/03/26

As a database specifically designed to handle queries over input vectors, it is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data.

As the Internet grew and evolved, unstructured data became more and more common, including emails, papers, IoT sensor data, Facebook photos, protein structures, and much more. In order for computers to understand and process unstructured data, these are converted into vectors using embedding techniques. Milvus stores and indexes these vectors. Milvus is able to analyze the correlation between two vectors by calculating their similarity distance. If the two embedding vectors are very similar, it means that the original data sources are similar as well.

The .NET SDK for Milvus is available here.

This is a good article introducing the concept of a vector database.

A vector database is, at its core, a full-fledged solution for unstructured data. As we’ve already seen in the previous section, this means that user-friendly features present in today’s database management systems for structured/semi-structured data - cloud-nativity, multi-tenancy, scalability, etc - should also be attributes for a mature vector database as well. All of these features will become clear as we dive deeper into this tutorial.

On the other hand, projects such as FAISS, ScaNN, and HNSW are lightweight ANN libraries rather than managed solutions. The intention of these libraries is to aid in the construction of vector indices - data structures designed to significantly speed up nearest neighbor search for multi-dimensional vectors[1]. If your dataset is small and limited, these libraries can prove to be sufficient for unstructured data processing, even for systems running in production. However, as dataset sizes increase and more users are onboarded, the problem of scale becomes increasingly difficult to solve.