Vector Store Retriever

This node specializes in retrieving embeddings from a vector store based on their relevance to user queries.

Note: Dissimilarity scores calculated using FAISS or Chroma with L2 distance are not bound to a specific range, therefore allowing only for ordinal comparison of scores. These scores also depend on the embeddings model used to generate the embeddings, as different models produce embeddings with varying scales and distributions. Therefore, understanding or comparing similarity across different models or spaces without contextual normalization is not meaningful.

Options

Queries column

Column containing the queries.

Number of results

Number of top results to get from vector store search. Ranking from best to worst.

Retrieved document column name

The name for the appended column containing the retrieved documents.

Retrieve metadata from documents

Whether or not to retrieve document metadata, if provided.

Retrieve dissimilarity scores

Whether or not to retrieve dissimilarity scores for the retrieved documents. FAISS and Chroma use L2 distance by default to calculate dissimilarity scores. Lower score represents more similarity.

Dissimilarity scores column name

The name for the appended column containing the dissimilarity scores.

Input Ports

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Vector store containing document embeddings.

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Table containing a string column with the queries for the vector store.

Output Ports

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Table containing the queries and their closest match from the 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: 2024-11-20
KNIME versions: Since v5.2

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