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An extractor which can match a field from ranking event over an item field. In practice, it can be useful in search related tasks, when you need to match a search query over multiple separate fields in document, like title-tags-category.
Given this metadata event:
{
"event": "item",
"id": "81f46c34-a4bb-469c-8708-f8127cd67d27",
"item": "item1",
"timestamp": "1599391467000",
"fields": [
{"name": "title", "value": "red socks"},
{"name": "category", "value": "socks"},
{"name": "brand", "value": "buffalo"},
{"name": "description", "value": "lorem ipsum dolores sit amet"}
]
}
And a following ranking event:
{
"event": "ranking",
"id": "81f46c34-a4bb-469c-8708-f8127cd67d27",
"timestamp": "1599391467000",
"user": "user1",
"session": "session1",
"fields": [
{"name": "query", "value": "sock"}
],
"items": [
{"id": "item3"},
{"id": "item1"},
{"id": "item2"}
]
}
With the following config file snippet you can do a per-field matching of
ranking.query
field over item.title
field of the items in the ranking with 3-grams:- name: title_match
type: field_match
itemField: item.title // must be a string
rankingField: ranking.query // must be a string
method:
type: ngram // for now only ngram and term are supported
language: en // ISO-639-1 language code
n: 3
refresh: 0s // optional, how frequently we should update the value, 0s by default
ttl: 90d // optional, how long should we store this field
In a similar way you can do the same with term matching:
- name: title_match
type: field_match
itemField: item.title // must be a string
rankingField: ranking.query // must be a string
method:
type: term // for now only ngram and term are supported
language: en // ISO-639-1 language code
Both term and ngram matching methods leverage Lucene for text analysis and support the following set of languages:
- generic: no language specific transformations
- en: English
- cz: Czech
- da: Danish
- nl: Dutch
- et: Estonian
- fi: Finnish
- fr: French
- de: German
- gr: Greek
- it: Italian
- no: Norwegian
- pl: Polish
- pt: Portuguese
- es: Spanish
- sv: Swedish
- tr: Turkish
- ar: Arabic
- zh: Chinese
- ja: Japanese
Both term and ngram method share the same approach to the text analysis:
- text line is split into terms (using language-specific method)
- stopwords are removed
- for non-generic languages each term is stemmed
- then terms/ngrams from item and ranking are scored using intersection/union method.
Last modified 1mo ago