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.

Note: If you use the Credentials Configuration node and do not select the "Save password in configuration (weakly encrypted)" option for passing the API key for the embeddings connector node, the Credentials Configuration node will need to be reconfigured upon reopening the workflow, as the credentials flow variable was not saved and will therefore not be available to downstream nodes.

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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