Embeddings are vectors representing entire documents or fragment of them into a continuous vector space. This numerical representation of is required to efficiently infer similitudes between documents and implement features like Semantic Search.

This means that LogicalDOC must calculate all these embeddings for the documents in your repository and save them into the Vectors Store.

The process of calculating an embedding of a document is not unique, but depends on what embedding model you use and LogicalDOC supports the definition of different embedding schemes that make use of specific directly implemented inside the system or externally implemented embedding models.