Fusion 5.9
Product Selector
Use a TensorFlow deep learning model to encode and clusterize queries into a fixed dense vector space.
This stage is used in the default query pipeline that enables Smart Answers. See Detailed Pipeline Setup for configuration guidelines.
| If you are using a dynamic schema, make sure this stage is added after the Solr Dynamic Field Name Mapping stage. |
Use a deep learning model to encode and clusterize queries into fixed dense vector space
Set to true to skip this stage.
Default: false
A unique label for this stage.
<= 255 characters
Define a conditional script that must result in true or false. This can be used to determine if the stage should process or not.
The ID of the DL encoder model bundled to the MLeap format and stored in the Fusion blob store.
>= 1 characters
Name of the query field to feed into the encoder.
>= 1 characters
Default: q
Context key used to store query encoded vector.
>= 1 characters
Default: query_vector
Context key used to store query clusters.
>= 1 characters
Default: query_clusters
Context key used to store distances to the clusters.
>= 1 characters
Default: query_distances
Number of query clusters to be used for additional Filter Query. Can be overwritten by `num_clusters` query parameter. Should be less or equal to the amount of clusters from the encoder model. Set to 0 to disable clusters.
Default: 0
Document clusters Solr field name. Filter Query with query cluster(s) are constructed against this field.
>= 1 characters
Default: document_clusters_ss
Flag to indicate if this stage should throw an exception if an error occurs.
Default: false
Flag to indicate that the encoded query vector and clusters should be stored in the context for later use by downstream query pipeline stages
Default: true