Term Vectorsedit

Returns information and statistics on terms in the fields of a particular document. The document could be stored in the index or artificially provided by the user [1.4.0.Beta1] Added in 1.4.0.Beta1. . Note that for documents stored in the index, this is a near realtime API as the term vectors are not available until the next refresh.

curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'

Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url

curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?fields=text,...'

or by adding the requested fields in the request body (see example below). Fields can also be specified with wildcards in similar way to the multi match query [1.4.0.Beta1] Added in 1.4.0.Beta1. .

Return valuesedit

Three types of values can be requested: term information, term statistics and field statistics. By default, all term information and field statistics are returned for all fields but no term statistics.

Term informationedit

  • term frequency in the field (always returned)
  • term positions (positions : true)
  • start and end offsets (offsets : true)
  • term payloads (payloads : true), as base64 encoded bytes

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.

Note

Added in 1.4.0.Beta1.

The ability to computed term vectors on the fly as well as support for artificial documents is only available from 1.4.0 onwards (see below example 2 and 3 respectively)

Warning

Start and end offsets assume UTF-16 encoding is being used. If you want to use these offsets in order to get the original text that produced this token, you should make sure that the string you are taking a sub-string of is also encoded using UTF-16.

Term statisticsedit

Setting term_statistics to true (default is false) will return

  • total term frequency (how often a term occurs in all documents)
  • document frequency (the number of documents containing the current term)

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

Field statisticsedit

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

  • document count (how many documents contain this field)
  • sum of document frequencies (the sum of document frequencies for all terms in this field)
  • sum of total term frequencies (the sum of total term frequencies of each term in this field)

Behaviouredit

The term and field statistics are not accurate. Deleted documents are not taken into account. The information is only retrieved for the shard the requested document resides in. The term and field statistics are therefore only useful as relative measures whereas the absolute numbers have no meaning in this context. By default, when requesting term vectors of artificial documents, a shard to get the statistics from is randomly selected. Use routing only to hit a particular shard.

Example 1. Returning stored term vectors

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

curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
  "mappings": {
    "tweet": {
      "properties": {
        "text": {
          "type": "string",
          "term_vector": "with_positions_offsets_payloads",
          "store" : true,
          "index_analyzer" : "fulltext_analyzer"
         },
         "fullname": {
          "type": "string",
          "term_vector": "with_positions_offsets_payloads",
          "index_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:

curl -XPUT 'http://localhost:9200/twitter/tweet/1?pretty=true' -d '{
  "fullname" : "John Doe",
  "text" : "twitter test test test "
}'

curl -XPUT 'http://localhost:9200/twitter/tweet/2?pretty=true' -d '{
  "fullname" : "Jane Doe",
  "text" : "Another twitter test ..."
}'

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

curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
  "fields" : ["text"],
  "offsets" : true,
  "payloads" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}'

Response:

{
    "_id": "1",
    "_index": "twitter",
    "_type": "tweet",
    "_version": 1,
    "found": true,
    "term_vectors": {
        "text": {
            "field_statistics": {
                "doc_count": 2,
                "sum_doc_freq": 6,
                "sum_ttf": 8
            },
            "terms": {
                "test": {
                    "doc_freq": 2,
                    "term_freq": 3,
                    "tokens": [
                        {
                            "end_offset": 12,
                            "payload": "d29yZA==",
                            "position": 1,
                            "start_offset": 8
                        },
                        {
                            "end_offset": 17,
                            "payload": "d29yZA==",
                            "position": 2,
                            "start_offset": 13
                        },
                        {
                            "end_offset": 22,
                            "payload": "d29yZA==",
                            "position": 3,
                            "start_offset": 18
                        }
                    ],
                    "ttf": 4
                },
                "twitter": {
                    "doc_freq": 2,
                    "term_freq": 1,
                    "tokens": [
                        {
                            "end_offset": 7,
                            "payload": "d29yZA==",
                            "position": 0,
                            "start_offset": 0
                        }
                    ],
                    "ttf": 2
                }
            }
        }
    }
}

Example 2. Generating term vectors on the fly

Term vectors which are not explicitly stored in the index are automatically computed on the fly [1.4.0.Beta1] Added in 1.4.0.Beta1. . 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.

curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
  "fields" : ["text", "some_field_without_term_vectors"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}'

Example 3. Artificial documents

Additionally, term vectors can also be generated for artificial documents [1.4.0.Beta1] Added in 1.4.0.Beta1. , that is for documents not present in the index. The syntax is similar to the percolator API. For example, the following request would return the same results as in example 1. The mapping used is determined by the index and type.

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

curl -XGET 'http://localhost:9200/twitter/tweet/_termvector' -d '{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "twitter test test test"
  }
}'