IMAGE VECTORIZER API HOW TO

For example, the qna-transformers module adds a question answering feature, which can be used to query data using GraphQL. Modules can also add a certain functionality to Weaviate. An image vectorization module can be combined with at max one text vectorization module. This means that you can either enable the text2vec-contextionary, the text2vec-transformers or no text vectorization module. Note: at the moment, text vectorization modules cannot be combined in a single setup. Depending on the type of data you want to store and search (text, images, etc), and depending on the use case domain (science, healthcare, common daily language, etc), you can choose and attach a module that best fits your use case. Vectorization modules, like the text2vec-contextionary, text2vec-transformers and img2vec-neural, transform data into vectors. One or more modules can be attached, for example to vectorize data or other functionalities like question answering. The functionality of the vector-native database can be enriched by modules. Data is stored as vectors, and these vectors are searchable by the provide vector index (ANN) algorithm. The Core of Weaviate, without any modules attached, is a pure vector-native database and search engine.

IMAGE VECTORIZER API HOW TO

How to use Weaviate with your own vectors? (No modules, pure vector storage and search).

Quick start with the text2vec-contextionary module.

Enterprise Usage Collector Vector Index (ANN) Plugins.

Image vectorizer api