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.
Select the column containing the documents to be embedded.
Select the column containing existing embeddings if available.
Define whether missing values in the document column should be skipped or whether the node execution should fail on missing values.
Available options:
Selection of columns used as metadata for each document. The documents column will be ignored.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
To use this node in KNIME, install the extension KNIME Python Extension Development (Labs) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
Try NodePit Runner!Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter or botsin.space/@nodepit on Mastodon.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.