term_vectoredit
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.
The term_vector setting accepts:
|
|
No term vectors are stored. (default) |
|
|
Just the terms in the field are stored. |
|
|
Terms and positions are stored. |
|
|
Terms and character offsets are stored. |
|
|
Terms, positions, and character offsets are stored. |
|
|
Terms, positions, and payloads are stored. |
|
|
Terms, positions, offsets and payloads are stored. |
The fast vector highlighter requires with_positions_offsets.
The term vectors API can retrieve whatever is stored.
Setting with_positions_offsets will double the size of a field’s
index.
response = client.indices.create(
index: 'my-index-000001',
body: {
mappings: {
properties: {
text: {
type: 'text',
term_vector: 'with_positions_offsets'
}
}
}
}
)
puts response
response = client.index(
index: 'my-index-000001',
id: 1,
body: {
text: 'Quick brown fox'
}
)
puts response
response = client.search(
index: 'my-index-000001',
body: {
query: {
match: {
text: 'brown fox'
}
},
highlight: {
fields: {
text: {}
}
}
}
)
puts response
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": {}
}
}
}