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In a few previous PR's we restructured the ES|QL docs to make it possible to generate them dynamically. This PR just moves a few files around to make the query languages docs easier to work with, and a little more organized like the ES|QL docs. A bit part of this was setting up redirects to the new locations, so other repo's could correctly link to the elasticsearch docs.
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Term-level queries [term-level-queries]
You can use term-level queries to find documents based on precise values in structured data. Examples of structured data include date ranges, IP addresses, prices, or product IDs.
Unlike full-text queries, term-level queries do not analyze search terms. Instead, term-level queries match the exact terms stored in a field.
::::{note}
Term-level queries still normalize search terms for keyword fields with the normalizer property. For more details, see normalizer.
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Types of term-level queries [term-level-query-types]
existsquery- Returns documents that contain any indexed value for a field.
fuzzyquery- Returns documents that contain terms similar to the search term. {{es}} measures similarity, or fuzziness, using a Levenshtein edit distance.
idsquery- Returns documents based on their document IDs.
prefixquery- Returns documents that contain a specific prefix in a provided field.
rangequery- Returns documents that contain terms within a provided range.
regexpquery- Returns documents that contain terms matching a regular expression.
termquery- Returns documents that contain an exact term in a provided field.
termsquery- Returns documents that contain one or more exact terms in a provided field.
terms_setquery- Returns documents that contain a minimum number of exact terms in a provided field. You can define the minimum number of matching terms using a field or script.
wildcardquery- Returns documents that contain terms matching a wildcard pattern.