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Vector Store Retriever

KNIME LabsAIVector Stores
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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.

Node details

Input ports
  1. Type: org.knime.python3.nodes.PythonBinaryBlobFileStorePortObject
    Vector Store

    Vector store containing document embeddings.

  2. Type: Table
    Queries

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

Output ports
  1. Type: Table
    Result table

    Table containing the queries and their closest match from the vector store.

Extension

The Vector Store Retriever node is part of this extension:

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Related workflows & nodes

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