Get term vector information

POST also GET /{index}/_termvectors/{id}
POST also GET /{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.

warn 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.

Required authorization

Index privileges: read

Path Parameters

Query Parameters

fields union Fields

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.

One of: Field or Field[]

field_statistics boolean · default: true

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 · default: true

If true, the response includes term offsets.

payloads boolean · default: true

If true, the response includes term payloads.

positions boolean · default: true

If true, the response includes term positions.

preference string

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

realtime boolean · default: true

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

routing string Routing

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

term_statistics boolean · default: false

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 VersionNumber

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

version_type enum VersionType

The version type.

Supported values include:

internal: Use internal versioning that starts at 1 and increments with each update or delete.
external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.
external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document. NOTE: The external_gte version type is meant for special use cases and should be used with care. If used incorrectly, it can result in loss of data.

Values: internal external external_gte

Request

doc object

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

filter {} Filter

Filter terms 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.

Reference

per_field_analyzer map string to string

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.

fields [] string Field

A list 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.

field_statistics boolean · default: true

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 · default: true

If true, the response includes term offsets.

payloads boolean · default: true

If true, the response includes term payloads.

positions boolean · default: true

If true, the response includes term positions.

term_statistics boolean · default: false

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.

routing string Routing

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

version number VersionNumber

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

version_type enum VersionType

The version type.

Supported values include:

internal: Use internal versioning that starts at 1 and increments with each update or delete.
external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.
external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document. NOTE: The external_gte version type is meant for special use cases and should be used with care. If used incorrectly, it can result in loss of data.

Values: internal external external_gte

Response

application/json

Examples

Run POST /my-index-000001/_termvectors/1 to return all information and statistics for field text in document 1.

Request Console 1 / 8 languages

Console Kibana Dev Tools

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

JavaScript Elasticsearch JavaScript Client

const response = await client.termvectors({
  index: "my-index-000001",
  id: 1,
  fields: ["text"],
  offsets: true,
  payloads: true,
  positions: true,
  term_statistics: true,
  field_statistics: true,
});

Python Elasticsearch Python Client

resp = client.termvectors(
    index="my-index-000001",
    id="1",
    fields=[
        "text"
    ],
    offsets=True,
    payloads=True,
    positions=True,
    term_statistics=True,
    field_statistics=True,
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/my-index-000001/_termvectors/1" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "fields": [
      "text"
    ],
    "offsets": true,
    "payloads": true,
    "positions": true,
    "term_statistics": true,
    "field_statistics": true
  }'

Java Elasticsearch Java Client

client.termvectors(t -> t
    .fieldStatistics(true)
    .fields("text")
    .id("1")
    .index("my-index-000001")
    .offsets(true)
    .payloads(true)
    .positions(true)
    .termStatistics(true)
);

C# Elasticsearch .NET Client

var response = await client
    .TermvectorsAsync<MyDocument>(index: "my-index-000001", id: "1", d1 => d1
        .Fields(x => x.Text)
        .FieldStatistics(true)
        .Offsets(true)
        .Payloads(true)
        .Positions(true)
        .TermStatistics(true)
    );

PHP Elasticsearch PHP Client

$resp = $client->termvectors([
    "index" => "my-index-000001",
    "id" => "1",
    "body" => [
        "fields" => array(
            "text",
        ),
        "offsets" => true,
        "payloads" => true,
        "positions" => true,
        "term_statistics" => true,
        "field_statistics" => true,
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.termvectors(
  index: "my-index-000001",
  id: "1",
  body: {
    "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=="
            }
          ]
        }
      }
    }
  }
}

Run POST /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.

Request Console 1 / 2 languages

Console Kibana Dev Tools

POST /my-index-000001/_termvectors/1
{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  },
  "fields": ["fullname"],
  "per_field_analyzer" : {
    "fullname": "keyword"
  }
}

curl Shell

curl -X POST "$ELASTICSEARCH_URL/my-index-000001/_termvectors/1" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  },
  "fields": ["fullname"],
  "per_field_analyzer" : {
    "fullname": "keyword"
  }
}'

Response

{
  "_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
                }
            ]
          }
      }
    }
  }
}

Run POST /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.

Request Console 1 / 2 languages

Console Kibana Dev Tools

POST /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
  }
}

curl Shell

curl -X POST "$ELASTICSEARCH_URL/imdb/_termvectors" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "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
            }
        }
      }
  }
}

Run POST /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.

Request Console 1 / 2 languages

Console Kibana Dev Tools

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

curl Shell

curl -X POST "$ELASTICSEARCH_URL/my-index-000001/_termvectors/1" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "fields" : ["text", "some_field_without_term_vectors"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}'

Run POST /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.

Request Console 1 / 2 languages

Console Kibana Dev Tools

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

curl Shell

curl -X POST "$ELASTICSEARCH_URL/my-index-000001/_termvectors" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "doc" : {
    "fullname" : "John Doe",
    "text" : "test test test"
  }
}'

Required authorization

Path Parameters { index , id , }

Query Parameters { fields , field_statistics , offsets , payloads , positions , preference , realtime , routing , term_statistics , version , version_type , }

Request { doc , filter , per_field_analyzer , fields , field_statistics , offsets , payloads , positions , term_statistics , routing , version , version_type , }

Response

Path Parameters

Query Parameters

Request