FAISS Vector Store Creator

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.

Options

Document column

Select the column containing the documents to be embedded.

Embeddings column

Select the column containing existing embeddings if available.

Handle missing values in the document column

Define whether missing values in the document column should be skipped or whether the node execution should fail on missing values.

Available options:

  • Skip rows: Rows with missing values will be ignored.
  • Fail: This node will fail during the execution.

Metadata

Metadata columns

Selection of columns used as metadata for each document. The documents column will be ignored.

Input Ports

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The embeddings model to use for the vector store.

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Table containing a string column representing documents that will be used in the vector store.

Output Ports

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The created FAISS vector store.

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Installation

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:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.3.3.v202410291554
On NodePit since: 2024-07-09
Last update: 2024-11-21
KNIME versions: Since v5.2

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