A rank_features field can index numeric feature vectors, so that they can
later be used to boost documents in queries with a
rank_feature query.
It is analogous to the rank_feature datatype but is better suited
when the list of features is sparse so that it wouldn’t be reasonable to add
one field to the mappings for each of them.
PUT my_index
{
"mappings": {
"properties": {
"topics": {
"type": "rank_features" 
}
}
}
}
PUT my_index/_doc/1
{
"topics": { 
"politics": 20,
"economics": 50.8
}
}
PUT my_index/_doc/2
{
"topics": {
"politics": 5.2,
"sports": 80.1
}
}
GET my_index/_search
{
"query": {
"rank_feature": {
"field": "topics.politics"
}
}
}
|
Rank features fields must use the |
| Rank features fields must be a hash with string keys and strictly positive numeric values |
rank_features fields only support single-valued features and strictly
positive values. Multi-valued fields and zero or negative values will be rejected.
rank_features fields do not support sorting or aggregating and may
only be queried using rank_feature queries.
rank_features fields only preserve 9 significant bits for the
precision, which translates to a relative error of about 0.4%.