# Full-Text Queries: match, multi_match, match_phrase — Elasticsearch

Source: https://www.skillbyai.com/en/elasticsearch/q-fulltext

> Analysed queries that score.

## Queries that understand text

`match` analyses the query text and finds documents containing the terms; by default any term can match (`operator: or`), and you can require all with `operator: and` or a `minimum_should_match` such as `"75%"`. `fuzziness: "AUTO"` tolerates small typos. `multi_match` runs a match over several fields with optional boosts (`title^3`); its `type` controls how per-field scores combine: `best_fields` (default, the best single field wins), `most_fields` (sum across fields), `cross_fields` (treat fields as one big field, good for names split across first/last), and `phrase`/`phrase_prefix`. `match_phrase` requires the terms in order and adjacent, with `slop` allowing some distance between them.

## Asking precise questions

Full-text queries score relevance, term-level queries filter exactly, and bool combines them.

![Three ideas: full-text queries, bool with filters, relevance tuning.](assets/figures/elasticsearch/section-4-map.svg) — Figure 4.1 — Query text, matching documents and scoring.

## Three full-text queries

Kibana Dev Tools console syntax; send the same requests with curl or a client library.

```http
GET /products/_search
{
  "query": {
    "match": {
      "name": { "query": "waterproof jaket", "operator": "and", "fuzziness": "AUTO" }
    }
  }
}

GET /products/_search
{
  "query": {
    "multi_match": {
      "query": "trail running shoe",
      "fields": [ "name^3", "brand^2", "description" ],
      "type": "best_fields"
    }
  }
}

GET /products/_search
{
  "query": {
    "match_phrase": { "description": { "query": "machine washable", "slop": 1 } }
  }
}
```

## Start with multi_match and boosts

For a typical search box, multi_match over a few fields with a higher boost on the title is a solid baseline; tune with real queries rather than guesses.

**Quiz:** Which query requires its terms to appear in order and near each other?

- [x] match_phrase
- [ ] match with operator or
- [ ] term
- [ ] exists

*Answer:* match_phrase. match_phrase uses term positions; slop relaxes adjacency.