Term vectors API

If the requested information wasn’t stored in the index, it will be computed on the fly if possible. Additionally, term vectors could be computed for documents not even existing in the index, but instead provided by the user.

Setting term_statistics to true (default is false) will return

By default these values are not returned since term statistics can have a serious performance impact.

Setting field_statistics to false (default is true) will omit :

With the parameter filter, the terms returned could also be filtered based on their tf-idf scores. This could be useful in order find out a good characteristic vector of a document. This feature works in a similar manner to the second phase of the More Like This Query. See example 5 for usage.

The following sub-parameters are supported:

First, we create an index that stores term vectors, payloads etc. :

PUT /my-index-000001
{ "mappings": {
    "properties": {
      "text": {
        "type": "text",
        "term_vector": "with_positions_offsets_payloads",
        "store" : true,
        "analyzer" : "fulltext_analyzer"
       },
       "fullname": {
        "type": "text",
        "term_vector": "with_positions_offsets_payloads",
        "analyzer" : "fulltext_analyzer"
      }
    }
  },
  "settings" : {
    "index" : {
      "number_of_shards" : 1,
      "number_of_replicas" : 0
    },
    "analysis": {
      "analyzer": {
        "fulltext_analyzer": {
          "type": "custom",
          "tokenizer": "whitespace",
          "filter": [
            "lowercase",
            "type_as_payload"
          ]
        }
      }
    }
  }
}

Second, we add some documents:

PUT /my-index-000001/_doc/1
{
  "fullname" : "John Doe",
  "text" : "test test test "
}

PUT /my-index-000001/_doc/2?refresh=wait_for
{
  "fullname" : "Jane Doe",
  "text" : "Another test ..."
}

The following request returns all information and statistics for field text in document 1 (John Doe):

GET /my-index-000001/_termvectors/1
{
  "fields" : ["text"],
  "offsets" : true,
  "payloads" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}

Response:

{
  "_index": "my-index-000001",
  "_id": "1",
  "_version": 1,
  "found": true,
  "took": 6,
  "term_vectors": {
    "text": {
      "field_statistics": {
        "sum_doc_freq": 4,
        "doc_count": 2,
        "sum_ttf": 6
      },
      "terms": {
        "test": {
          "doc_freq": 2,
          "ttf": 4,
          "term_freq": 3,
          "tokens": [
            {
              "position": 0,
              "start_offset": 0,
              "end_offset": 4,
              "payload": "d29yZA=="
            },
            {
              "position": 1,
              "start_offset": 5,
              "end_offset": 9,
              "payload": "d29yZA=="
            },
            {
              "position": 2,
              "start_offset": 10,
              "end_offset": 14,
              "payload": "d29yZA=="
            }
          ]
        }
      }
    }
  }
}

Term vectors which are not explicitly stored in the index are automatically computed on the fly. The following request returns all information and statistics for the fields in document 1, even though the terms haven’t been explicitly stored in the index. Note that for the field text, the terms are not re-generated.

GET /my-index-000001/_termvectors/1
{
  "fields" : ["text", "some_field_without_term_vectors"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}

Term vectors can also be generated for artificial documents, that is for documents not present in the index. For example, the following request would return the same results as in example 1. The mapping used is determined by the index.

If dynamic mapping is turned on (default), the document fields not in the original mapping will be dynamically created.

GET /my-index-000001/_termvectors
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  }
}

GET /my-index-000001/_termvectors
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  },
  "fields": ["fullname"],
  "per_field_analyzer" : {
    "fullname": "keyword"
  }
}

{
  "_index": "my-index-000001",
  "_version": 0,
  "found": true,
  "took": 6,
  "term_vectors": {
    "fullname": {
       "field_statistics": {
          "sum_doc_freq": 2,
          "doc_count": 4,
          "sum_ttf": 4
       },
       "terms": {
          "John Doe": {
             "term_freq": 1,
             "tokens": [
                {
                   "position": 0,
                   "start_offset": 0,
                   "end_offset": 8
                }
             ]
          }
       }
    }
  }
}

Finally, the terms returned could be filtered based on their tf-idf scores. In the example below we obtain the three most "interesting" keywords from the artificial document having the given "plot" field value. Notice that the keyword "Tony" or any stop words are not part of the response, as their tf-idf must be too low.

GET /imdb/_termvectors
{
  "doc": {
    "plot": "When wealthy industrialist Tony Stark is forced to build an armored suit after a life-threatening incident, he ultimately decides to use its technology to fight against evil."
  },
  "term_statistics": true,
  "field_statistics": true,
  "positions": false,
  "offsets": false,
  "filter": {
    "max_num_terms": 3,
    "min_term_freq": 1,
    "min_doc_freq": 1
  }
}

Response:

{
   "_index": "imdb",
   "_version": 0,
   "found": true,
   "term_vectors": {
      "plot": {
         "field_statistics": {
            "sum_doc_freq": 3384269,
            "doc_count": 176214,
            "sum_ttf": 3753460
         },
         "terms": {
            "armored": {
               "doc_freq": 27,
               "ttf": 27,
               "term_freq": 1,
               "score": 9.74725
            },
            "industrialist": {
               "doc_freq": 88,
               "ttf": 88,
               "term_freq": 1,
               "score": 8.590818
            },
            "stark": {
               "doc_freq": 44,
               "ttf": 47,
               "term_freq": 1,
               "score": 9.272792
            }
         }
      }
   }
}