Product Selector

Fusion 5.11
    Fusion 5.11

    Use a deep learning model to encode and clusterize queries into fixed dense vector space

    skip - boolean

    Set to true to skip this stage.

    Default: false

    label - string

    A unique label for this stage.

    <= 255 characters

    condition - string

    Define a conditional script that must result in true or false. This can be used to determine if the stage should process or not.

    modelId - stringrequired

    The ID of the DL encoder model bundled to the MLeap format and stored in the Fusion blob store.

    >= 1 characters

    documentFeatureField - stringrequired

    Name of the document field to feed into the encoder.

    >= 1 characters

    Default: body_t

    vectorField - stringrequired

    Name of the field to store the encoded vector in the document. "_ds" suffix will be added if not already present

    >= 1 characters

    Default: document_vector_ds

    clustersField - stringrequired

    Name of the field to store document extracted clusters. "_ss" suffix will be added if not already present

    >= 1 characters

    Default: document_clusters_ss

    distancesField - stringrequired

    Name of the field to store document extracted distances to the clusters. "_ds" suffix will be added if not already present

    >= 1 characters

    Default: document_distances_ds

    numClusters - integerrequired

    Number of document clusters to be stored in clusters field. Should be less or equal to the amount of clusters from the encoder model.

    Default: 1

    failOnError - boolean

    Flag to indicate if this stage should throw an exception if an error occurs.

    Default: false

    storeInContext - boolean

    Flag to indicate that the encoded document vector and clusters should be stored in the Context instead of the documents fields.

    Default: false