Get multiple term vectors | Elasticsearch API documentation (v8)

Get multiple term vectors Generally available

GET /_mtermvectors

Get multiple term vectors with a single request. You can specify existing documents by index and ID or provide artificial documents in the body of the request. You can specify the index in the request body or request URI. The response contains a docs array with all the fetched termvectors. Each element has the structure provided by the termvectors API.

Artificial documents

You can also use mtermvectors to generate term vectors for artificial documents provided in the body of the request. The mapping used is determined by the specified _index.

Required authorization

Index privileges: read

Query parameters

ids array[string]

A comma-separated list of documents ids. You must define ids as parameter or set "ids" or "docs" in the request body
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.
field_statistics boolean

If true, the response includes the document count, sum of document frequencies, and sum of total term frequencies.
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.
preference string

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 used to route operations to a specific shard.
term_statistics boolean

If true, the response includes term frequency and document frequency.
version number

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

The version type.

Values are internal, external, external_gte, or force.

application/json

Body

docs array[object]

An array of existing or artificial documents.
Hide docs attributes Show docs attributes object
- _id string
- _index string
- doc object
  
  An artificial document (a document not present in the index) for which you want to retrieve term vectors.
- fields string | array[string]
- field_statistics boolean
  
  If true, the response includes the document count, sum of document frequencies, and sum of total term frequencies.
- filter object
  Hide filter attributes Show filter attributes object
  
  max_doc_freq number
  
  Ignore words which occur in more than this many docs. Defaults to unbounded.
  
  max_num_terms number
  
  The maximum number of terms that must be returned per field.
  
  max_term_freq number
  
  Ignore words with more than this frequency in the source doc. It defaults to unbounded.
  
  max_word_length number
  
  The maximum word length above which words will be ignored. Defaults to unbounded.
  
  min_doc_freq number
  
  Ignore terms which do not occur in at least this many docs.
  
  min_term_freq number
  
  Ignore words with less than this frequency in the source doc.
  
  min_word_length number
  
  The minimum word length below which words will be ignored.
- 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.
- routing string
- term_statistics boolean
  
  If true, the response includes term frequency and document frequency.
- version number
- version_type string
  
  Values are internal, external, external_gte, or force.
ids array[string]

A simplified syntax to specify documents by their ID if they're in the same index.

Responses

200 application/json
Hide response attribute Show response attribute object
- docs array[object] Required
  
  Hide docs attributes Show docs attributes object
  
  _id string
  
  _index string Required
  
  _version number
  
  took number
  
  found boolean
  
  term_vectors object
  
  Hide term_vectors attribute Show term_vectors attribute object
  
  * object Additional properties
  
  Hide * attributes Show * attributes object
  
  field_statistics object
  
  Hide field_statistics attributes Show field_statistics attributes object
  
  doc_count number Required
  
  sum_doc_freq number Required
  
  sum_ttf number Required
  
  terms object Required
  
  Hide terms attribute Show terms attribute object
  
  * object Additional properties
  
  error object
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Hide error attributes Show error attributes object
  
  type string Required
  
  The type of error
  
  reason string | null
  
  A human-readable explanation of the error, in English.
  
  One of:
  string-1 string string-2 string | null
  
  stack_trace string
  
  The server stack trace. Present only if the error_trace=true parameter was sent with the request.
  
  caused_by object
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  root_cause array[object]
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  suppressed array[object]
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.
  
  Cause and details about a request failure. This class defines the properties common to all error types. Additional details are also provided, that depend on the error type.

GET /_mtermvectors

curl \
 --request GET 'http://api.example.com/_mtermvectors' \
 --header "Authorization: $API_KEY" \
 --header "Content-Type: application/json" \
 --data '"{\n  \"docs\": [\n      {\n        \"_id\": \"2\",\n        \"fields\": [\n            \"message\"\n        ],\n        \"term_statistics\": true\n      },\n      {\n        \"_id\": \"1\"\n      }\n  ]\n}"'

Request examples

Run `POST /my-index-000001/_mtermvectors`. When you specify an index in the request URI, the index does not need to be specified for each documents in the request body.

{
  "docs": [
      {
        "_id": "2",
        "fields": [
            "message"
        ],
        "term_statistics": true
      },
      {
        "_id": "1"
      }
  ]
}

Run `POST /my-index-000001/_mtermvectors`. If all requested documents are in same index and the parameters are the same, you can use a simplified syntax.

{
  "ids": [ "1", "2" ],
  "fields": [
    "message"
  ],
  "term_statistics": true
}

Run `POST /_mtermvectors` to generate term vectors for artificial documents provided in the body of the request. The mapping used is determined by the specified `_index`.

{
  "docs": [
      {
        "_index": "my-index-000001",
        "doc" : {
            "message" : "test test test"
        }
      },
      {
        "_index": "my-index-000001",
        "doc" : {
          "message" : "Another test ..."
        }
      }
  ]
}

Get multiple term vectors Generally available

Required authorization

Query parameters

Body

Responses

reason string | null