How to use Similarity Queries in Elasticsearch In Databases
Elasticsearch offers a powerful query type called “more like this” (MLT). This feature helps find documents similar to a given text or a set of documents. It’s especially useful for recommendations, searching for related content, and other tasks that need to match text similarity.
Syntax:
GET /index_name/_search
{
"query": {
"more_like_this": {
"fields": ["field1", "field2", ...],
"like": "text to find similar documents",
"min_term_freq": 1,
"max_query_terms": 12
}
}
}
In this query:
- fields specify the fields to analyze for similarity (title and plot).
- like is the text to find similar documents .
- min_term_freq is the minimum term frequency (1 means all terms are considered).
- max_query_terms limits the number of query terms (12 is a good starting point).
Similarity Queries in Elasticsearch
Elasticsearch, a fast open-source search and analytics, employs a “more like this” query. This query helps identify relevant documents based on the topics and concepts, or even close text match of the input document or set of documents.
The more like this query is useful especially when coming up with a set of results or a list of recommendations when you get some results closely associated with other contents. This can be useful when a particular query requires identifying semantic relations that do not necessarily relate to the keywords used for the search.