Although proximity queries are useful, the fact that they require all terms to be present can make them overly strict. It’s the same issue that we discussed in Controlling Precision in Full-Text Search: if six out of seven terms match, a document is probably relevant enough to be worth showing to the user, but the match_phrase query would exclude it.

Instead of using proximity matching as an absolute requirement, we can use it as a signal—as one of potentially many queries, each of which contributes to the overall score for each document (see Most Fields).

The fact that we want to add together the scores from multiple queries implies that we should combine them by using the bool query.

We can use a simple match query as a must clause. This is the query that will determine which documents are included in our result set. We can trim the long tail with the minimum_should_match parameter. Then we can add other, more specific queries as should clauses. Every one that matches will increase the relevance of the matching docs.

GET /my_index/my_type/_search
{
  "query": {
    "bool": {
      "must": {
        "match": { 
          "title": {
            "query":                "quick brown fox",
            "minimum_should_match": "30%"
          }
        }
      },
      "should": {
        "match_phrase": { 
          "title": {
            "query": "quick brown fox",
            "slop":  50
          }
        }
      }
    }
  }
}

We could, of course, include other queries in the should clause, where each query targets a specific aspect of relevance.