term_vector [term-vector]

Term vectors contain information about the terms produced by the analysis process, including:

• a list of terms.
• the position (or order) of each term.
• the start and end character offsets mapping the term to its origin in the original string.
• payloads (if they are available) — user-defined binary data associated with each term position.

These term vectors can be stored so that they can be retrieved for a particular document.

Refer to the term vectors API examples page for usage examples.

The term_vector setting accepts:

no : No term vectors are stored. (default)

yes : Just the terms in the field are stored.

with_positions : Terms and positions are stored.

with_offsets : Terms and character offsets are stored.

with_positions_offsets : Terms, positions, and character offsets are stored.

with_positions_payloads : Terms, positions, and payloads are stored.

with_positions_offsets_payloads : Terms, positions, offsets and payloads are stored.

The fast vector highlighter requires with_positions_offsets. The term vectors API can retrieve whatever is stored.

::::{warning} Setting with_positions_offsets will double the size of a field’s index. ::::

PUT my-index-000001
{
  "mappings": {
    "properties": {
      "text": {
        "type":        "text",
        "term_vector": "with_positions_offsets"
      }
    }
  }
}

PUT my-index-000001/_doc/1
{
  "text": "Quick brown fox"
}

GET my-index-000001/_search
{
  "query": {
    "match": {
      "text": "brown fox"
    }
  },
  "highlight": {
    "fields": {
      "text": {} <1>
    }
  }
}

1. The fast vector highlighter will be used by default for the text field because term vectors are enabled.