Hi,
I am trying out vector search. While creating a Machine Learning Inference Pipeline, I see that some fields in the index are not recognised. Only the top level fields are shown.
Consider the below example with field mappings:
{
"mappings": {
"properties": {
"core": {
"properties": {
"propertyA::keyword": {
"type": "keyword",
"normalizer": "lowercase_normalizer",
"fields": {
"exact": {
"type": "keyword"
}
}
},
"propertyB::keyword": {
"type": "keyword",
"normalizer": "lowercase_normalizer",
"fields": {
"exact": {
"type": "keyword"
}
}
}
}
"propertyC::keyword": {
"type": "keyword",
"normalizer": "lowercase_normalizer",
"fields": {
"exact": {
"type": "keyword"
}
}
}
}
}
}
Only propertyC is recognised while adding an inference pipeline. The fields core.propertyA and core.propertyB are not recognised.
If I edit the pipeline processor definition with the field core.PropertyB, the following error is seen while linking to the index:
This pipeline cannot be selected because the source field does not exist on the index.
How do we configure the inference pipeline with such inner fields?
