Chroma Vector Store Creator

The node generates a Chroma vector store by processing a string column containing documents with the provided embeddings model. For each document, the embeddings model extracts a numerical vector that represents the semantic meaning of the document. These embeddings are then stored in the vector store, along with their corresponding documents. Downstream nodes, such as the Vector Store Retriever node, 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.

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 Chroma vector store.

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Links

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Installation

To use this node in KNIME, install the extension KNIME AI Extension (Labs) from the below update site following our NodePit Product and Node Installation Guide:

v5.2

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

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.2.1.202402071416
On NodePit since: 2024-02-07
Last update: 2024-04-27
KNIME versions: Since v5.2

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