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Elastic Docs ›Elasticsearch Guide [2.0] ›Search APIs

Suggesters

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Suggesters

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The suggest feature suggests similar looking terms based on a provided text by using a suggester. Parts of the suggest feature are still under development.

The suggest request part is either defined alongside the query part in a _search request or via the REST _suggest endpoint.

curl -s -XPOST 'localhost:9200/_search' -d '{
  "query" : {
    ...
  },
  "suggest" : {
    ...
  }
}'

Suggest requests executed against the _suggest endpoint should omit the surrounding suggest element which is only used if the suggest request is part of a search.

curl -XPOST 'localhost:9200/_suggest' -d '{
  "my-suggestion" : {
    "text" : "the amsterdma meetpu",
    "term" : {
      "field" : "body"
    }
  }
}'

Several suggestions can be specified per request. Each suggestion is identified with an arbitrary name. In the example below two suggestions are requested. Both my-suggest-1 and my-suggest-2 suggestions use the term suggester, but have a different text.

"suggest" : {
  "my-suggest-1" : {
    "text" : "the amsterdma meetpu",
    "term" : {
      "field" : "body"
    }
  },
  "my-suggest-2" : {
    "text" : "the rottredam meetpu",
    "term" : {
      "field" : "title"
    }
  }
}

The below suggest response example includes the suggestion response for my-suggest-1 and my-suggest-2. Each suggestion part contains entries. Each entry is effectively a token from the suggest text and contains the suggestion entry text, the original start offset and length in the suggest text and if found an arbitrary number of options.

{
  ...
  "suggest": {
    "my-suggest-1": [
      {
        "text" : "amsterdma",
        "offset": 4,
        "length": 9,
        "options": [
           ...
        ]
      },
      ...
    ],
    "my-suggest-2" : [
      ...
    ]
  }
  ...
}

Each options array contains an option object that includes the suggested text, its document frequency and score compared to the suggest entry text. The meaning of the score depends on the used suggester. The term suggester’s score is based on the edit distance.

"options": [
  {
    "text": "amsterdam",
    "freq": 77,
    "score": 0.8888889
  },
  ...
]

Global suggest text

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To avoid repetition of the suggest text, it is possible to define a global text. In the example below the suggest text is defined globally and applies to the my-suggest-1 and my-suggest-2 suggestions.

"suggest" : {
  "text" : "the amsterdma meetpu",
  "my-suggest-1" : {
    "term" : {
      "field" : "title"
    }
  },
  "my-suggest-2" : {
    "term" : {
      "field" : "body"
    }
  }
}

The suggest text can in the above example also be specified as suggestion specific option. The suggest text specified on suggestion level override the suggest text on the global level.

Other suggest example

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In the below example we request suggestions for the following suggest text: devloping distibutd saerch engies on the title field with a maximum of 3 suggestions per term inside the suggest text. Note that in this example we set size to 0. This isn’t required, but a nice optimization. The suggestions are gather in the query phase and in the case that we only care about suggestions (so no hits) we don’t need to execute the fetch phase.

curl -s -XPOST 'localhost:9200/_search' -d '{
  "size": 0,
  "suggest" : {
    "my-title-suggestions-1" : {
      "text" : "devloping distibutd saerch engies",
      "term" : {
        "size" : 3,
        "field" : "title"
      }
    }
  }
}'

The above request could yield the response as stated in the code example below. As you can see if we take the first suggested options of each suggestion entry we get developing distributed search engines as result.

{
  ...
  "suggest": {
    "my-title-suggestions-1": [
      {
        "text": "devloping",
        "offset": 0,
        "length": 9,
        "options": [
          {
            "text": "developing",
            "freq": 77,
            "score": 0.8888889
          },
          {
            "text": "deloping",
            "freq": 1,
            "score": 0.875
          },
          {
            "text": "deploying",
            "freq": 2,
            "score": 0.7777778
          }
        ]
      },
      {
        "text": "distibutd",
        "offset": 10,
        "length": 9,
        "options": [
          {
            "text": "distributed",
            "freq": 217,
            "score": 0.7777778
          },
          {
            "text": "disributed",
            "freq": 1,
            "score": 0.7777778
          },
          {
            "text": "distribute",
            "freq": 1,
            "score": 0.7777778
          }
        ]
      },
      {
        "text": "saerch",
        "offset": 20,
        "length": 6,
        "options": [
          {
            "text": "search",
            "freq": 1038,
            "score": 0.8333333
          },
          {
            "text": "smerch",
            "freq": 3,
            "score": 0.8333333
          },
          {
            "text": "serch",
            "freq": 2,
            "score": 0.8
          }
        ]
      },
      {
        "text": "engies",
        "offset": 27,
        "length": 6,
        "options": [
          {
            "text": "engines",
            "freq": 568,
            "score": 0.8333333
          },
          {
            "text": "engles",
            "freq": 3,
            "score": 0.8333333
          },
          {
            "text": "eggies",
            "freq": 1,
            "score": 0.8333333
          }
        ]
      }
    ]
  }
  ...
}
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