Minh Vu
Software Engineer
Hi 👋, I'm a software engineer (backend, applied AI, Elastic Stack). My blog shares practical tutorials, mostly on the Elastic Stack and Next.js. LeetCode 1756 (top 10.4% globally, Sep 2026). Actively seeking SDE roles — let's get in touch!
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In this tutorial, we will learn how to find similar documents in Elasticsearch using the more_like_this query.
The more_like_this query finds documents that are similar to a given set of documents.
This can be useful when you want to query similar blog posts, products, or any other documents.
For example, at this site (dminhvu.com), I use the more_like_this query to find similar blog posts to the one you are reading, which is shown in the "You Might Also Like" section at the right side of the page.
more_like_this Query in Elasticsearch to Find Similar DocumentsThe more_like_this query can be used in 3 ways:
Let's discover the syntax and usage of each of these ways.
I will use the following documents for the examples throughout this tutorial:
{ "id": 1, "title": "How to Find Similar Documents in Elasticsearch", "description": "In this tutorial, we will learn how to find similar documents in Elasticsearch using the more_like_this query." } { "id": 2, "title": "Partial Update in Elasticsearch Guide (with Examples)", "description": "Learn how to perform partial updates in Elasticsearch 8.x to update only specific fields in a document using the Update API." } { "id": 3, "title": "Logstash Input from JSON File", "description": "Learn how to parse logs from a JSON file in Logstash using the multiline codec plugin." } { "id": 4, "title": "Python Extract Year from Date", "description": "Learn how to extract year from date in Python, including string date, date object, timestamp, and current year." }
The syntax of the more_like_this query to find similar documents to a given text is as follows:
GET <index>/_search { "query": { "more_like_this": { "fields": ["field1", "field2", ...], "like": "text to find similar documents to", "min_term_freq": 1, "max_query_terms": 10 } } }
where:
<index>: the name of the index to search.fields: the fields to search for similar documents.like: the text to find similar documents.min_term_freq: the minimum term frequency below which the terms will be ignored from the input document.max_query_terms: the maximum number of query terms that will be selected, the higher the number, the higher the accuracy but slower the query.For example, to find similar documents to the text "python extract year from date", we can use the following query:
GET posts/_search { "query": { "more_like_this": { "fields": ["title", "description"], "like": "python extract year from date", "min_term_freq": 1, "max_query_terms": 10 } } }
The above query will return the following results:
{ "took": 1, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 1, "relation": "eq" }, "max_score": 0.2876821, "hits": [ { "_index": "posts", "_type": "_doc", "_id": "4", "_score": 0.2876821, "_source": { "id": 4, "title": "Python Extract Year from Date", "description": "Learn how to extract year from date in Python, including string date, date object, timestamp, and current year." } } ] } }
As you can see, the query returns the document with id = 4 because it contains the text "python extract year from date" in the title field.
You can also use the more_like_this query to find similar documents to a given document or set of documents.
The syntax of the more_like_this query to find similar documents to a given document or set of documents is as follows:
GET <index>/_search { "query": { "more_like_this": { "fields": ["field1", "field2", ...], "like": [ { "_index": "<index>", "_id": "<id>" }, { "_index": "<index>", "_id": "<id>" }, ... ], "min_term_freq": 1, "max_query_terms": 10 } } }
where:
<index>: the name of the index to search._index: the name of the index of the document to find similar documents to._id: the id of the document to find similar documents to, those documents will be ignored from the search results.For example, to find similar documents to the document with id = 1 in the posts index, we can use the following query:
GET posts/_search { "query": { "more_like_this": { "fields": ["title", "description"], "like": [ { "_index": "posts", "_id": "1" } ], "min_term_freq": 1, "max_query_terms": 10 } } }
The above query will return the following results:
{ "took": 1, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 3, "relation": "eq" }, "max_score": 0.2876821, "hits": [ { "_index": "posts", "_type": "_doc", "_id": "2", "_score": 0.2876821, "_source": { "id": 2, "title": "Partial Update in Elasticsearch Guide (with Examples)", "description": "Learn how to perform partial updates in Elasticsearch 8.x to update only specific fields in a document using the Update API." } } ] } }
As you can see, the query returns the document with id = 2 because it is related to "elasticsearch", which is also related to the document with id = 1.
To find similar documents to a mixed set of documents and text, you can use the more_like_this query as follows:
GET <index>/_search { "query": { "more_like_this": { "fields": ["field1", "field2", ...], "like": [ { "_index": "<index>", "doc": { "some_field": "some value", "_doc": "some text to find similar documents to", ... } }, { "_index": "<index>", "_id": "<id>" }, ... ], "min_term_freq": 1, "max_query_terms": 10 } } }
where:
<index>: the name of the index to search._index: the name of the index of the document to find similar documents to._id: the id of the document to find similar documents to, those documents will be ignored from the search results.doc: the values of artificial fields to find similar documents._doc: the text to find similar documents.For example, to find similar documents to the document with id = 1 in the posts index and the text "python extract year from date", we can use the following query:
GET posts/_search { "query": { "more_like_this": { "fields": ["title", "description"], "like": [ { "_index": "posts", "_id": "1" }, { "_index": "posts", "doc": { "title": "python extract year from date", "description": "python extract year from date" } } ], "min_term_freq": 1, "max_query_terms": 10 } } }
The above query will return the following results:
{ "took": 1, "timed_out": false, "_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": { "value": 2, "relation": "eq" }, "max_score": 0.2876821, "hits": [ { "_index": "posts", "_type": "_doc", "_id": "2", "_score": 0.2876821, "_source": { "id": 2, "title": "Partial Update in Elasticsearch Guide (with Examples)", "description": "Learn how to perform partial updates in Elasticsearch 8.x to update only specific fields in a document using the Update API." } }, { "_index": "posts", "_type": "_doc", "_id": "4", "_score": 0.2876821, "_source": { "id": 4, "title": "Python Extract Year from Date", "description": "Learn how to extract year from date in Python, including string date, date object, timestamp, and current year." } } ] } }
As you can see, the query returns the documents with id = 2 and id = 4 because they are related to "elasticsearch" and "python extract year from date".
In this tutorial, we have learned how to find similar documents in Elasticsearch using the more_like_this query.
more_like_this query finds documents that are similar to a given set of documents.more_like_this query can be used in 3 ways:
more_like_this query is useful when you want to query similar blog posts, products, or any other documents.
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