Get term vector information

POST /{index}/_termvectors

Get information and statistics about terms in the fields of a particular document.

You can retrieve term vectors for documents stored in the index or for artificial documents passed in the body of the request. You can specify the fields you are interested in through the fields parameter or by adding the fields to the request body. For example:

GET /my-index-000001/_termvectors/1?fields=message

Fields can be specified using wildcards, similar to the multi match query.

Term vectors are real-time by default, not near real-time. This can be changed by setting realtime parameter to false.

You can request three types of values: term information, term statistics, and field statistics. By default, all term information and field statistics are returned for all fields but term statistics are excluded.

Term information

  • 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.


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.

Behaviour

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.

Path parameters

  • index string Required

    The name of the index that contains the document.

Query parameters

  • fields string | array[string]

    A comma-separated list or wildcard expressions of fields to include in the statistics. It is used as the default list unless a specific field list is provided in the completion_fields or fielddata_fields parameters.

  • If true, the response includes:

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

    If true, the response includes term offsets.

  • payloads boolean

    If true, the response includes term payloads.

  • positions boolean

    If true, the response includes term positions.

  • The node or shard the operation should be performed on. It is random by default.

  • realtime boolean

    If true, the request is real-time as opposed to near-real-time.

  • routing string

    A custom value that is used to route operations to a specific shard.

  • If true, the response includes:

    • The total term frequency (how often a term occurs in all documents).
    • The 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.

  • version number

    If true, returns the document version as part of a hit.

  • The version type.

    Values are internal, external, external_gte, or force.

application/json

Body

  • doc object

    An artificial document (a document not present in the index) for which you want to retrieve term vectors.

    Additional properties are allowed.

  • filter object

    Additional properties are allowed.

    Hide filter attributes Show filter attributes object
    • Ignore words which occur in more than this many docs. Defaults to unbounded.

    • The maximum number of terms that must be returned per field.

    • Ignore words with more than this frequency in the source doc. It defaults to unbounded.

    • The maximum word length above which words will be ignored. Defaults to unbounded.

    • Ignore terms which do not occur in at least this many docs.

    • Ignore words with less than this frequency in the source doc.

    • The minimum word length below which words will be ignored.

  • Override the default per-field analyzer. This is useful in order to generate term vectors in any fashion, especially when using artificial documents. When providing an analyzer for a field that already stores term vectors, the term vectors will be regenerated.

    Hide per_field_analyzer attribute Show per_field_analyzer attribute object
    • * string Additional properties

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • found boolean Required
    • _id string
    • _index string Required
    • Hide term_vectors attribute Show term_vectors attribute object
      • * object Additional properties

        Additional properties are allowed.

        Hide * attributes Show * attributes object
    • took number Required
    • _version number Required
POST /{index}/_termvectors
curl \
 --request POST http://api.example.com/{index}/_termvectors \
 --header "Content-Type: application/json" \
 --data '"{\n  \"fields\" : [\"text\"],\n  \"offsets\" : true,\n  \"payloads\" : true,\n  \"positions\" : true,\n  \"term_statistics\" : true,\n  \"field_statistics\" : true\n}"'
Run `GET /my-index-000001/_termvectors/1` to return all information and statistics for field `text` in document 1.
{
  "fields" : ["text"],
  "offsets" : true,
  "payloads" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}
Run `GET /my-index-000001/_termvectors/1` to set per-field analyzers. A different analyzer than the one at the field may be provided by using the `per_field_analyzer` parameter.
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  },
  "fields": ["fullname"],
  "per_field_analyzer" : {
    "fullname": "keyword"
  }
}
Run `GET /imdb/_termvectors` to filter the terms returned based on their tf-idf scores. It returns 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.
{
  "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
  }
}
Run `GET /my-index-000001/_termvectors/1`. Term vectors which are not explicitly stored in the index are automatically computed on the fly. This 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 regenerated.
{
  "fields" : ["text", "some_field_without_term_vectors"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}
Run `GET /my-index-000001/_termvectors`. Term vectors can be generated for artificial documents, that is for documents not present in the index. If dynamic mapping is turned on (default), the document fields not in the original mapping will be dynamically created.
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  }
}
Response examples (200)
A successful response from `GET /my-index-000001/_termvectors/1`.
{
  "_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=="
            }
          ]
        }
      }
    }
  }
}
A successful response from `GET /my-index-000001/_termvectors` with `per_field_analyzer` in the request body.
{
  "_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
                }
            ]
          }
      }
    }
  }
}
A successful response from `GET /my-index-000001/_termvectors` with a `filter` in the request body.
{
  "_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
            }
        }
      }
  }
}