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.
Column containing the queries.
Number of top results to get from vector store search. Ranking from best to worst.
The name for the appended column containing the retrieved documents.
Whether or not to retrieve document metadata, if provided.
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.
The name for the appended column containing the dissimilarity scores.
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