This talk is an introduction to the vector search engine Weaviate. You will learn how storing data using vectors enables semantic search and automatic data classification. Topics like the underlying vector storage mechanism and how the pre-trained language vectorization model enables this are touched. In addition, this presentation consists of live demos to show the power of Weaviate and how you can get started with your own datasets. No prior technical knowledge is required; all concepts are illustrated with real use case examples and live demos.
Most of all data is unstructured. Additionally, data is often stored without context, meaning and relation to concepts in the real world. This means that all this data is difficult to index, classify and search through. While this is traditionally solved by manual effort or expensive machine learning models, Weaviate takes another approach to this problem. Weaviate is a vector search engine, which stores data as vectors and automatically adds context and meaning to new data. This enables to search through the data without using exact matching keywords. Moreover, data can be automatically classified.
Weaviate is completely open source, has a built-in machine learning model, has a graph-like data model, completely API-based and is cloud-native. Weaviate uses a GraphQL API next to RESTful endpoints to interact with the data in an intuitive manner. Additionally, Python, Go, Java and JavaScript clients are available to facilitate interaction between Weaviate and your applications. GraphQL and client examples will be shown in the presentation.
