Text Embedder

This node applies the provided embedding model to create embeddings of the texts contained in a string column of the input table.

A text embedding is a dense vector of floating point values capturing the semantic meaning of the text by mapping it to a high-dimensional space. Similarities between embedded entities are then derived by how close they are to each other in said space. These embeddings are often used to find semantically similar documents e.g. in vector stores.

Different embedding models encode text differently, resulting in incomparable embeddings. If this node fails to execute with 'Execute failed: Error while sending a command.', refer to the description of the node that provided the embedding model.