The node generates a FAISS vector store that uses the given embeddings model to map documents to a numerical vector that captures the semantic meaning of the document.
By default, the node embeds the selected documents using the embeddings model but it is also possible to create the vector store from existing embeddings by specifying the corresponding embeddings column in the node dialog.
Downstream nodes, such as the Vector Store Retriever , utilize the vector store to find documents with similar semantic meaning when given a query.