> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Full-Text Search

> The fts() table function: match, fuzzy, phrase, boolean, boost and multi-match queries from SQL.

**Applies to:** Embedded ❌, Enterprise ✅

Full-text search is a **table function**, `fts(table, query)`, rather than a
predicate: it produces a relevance score alongside the matching rows, and a
`WHERE` clause has nowhere to put one. The two string-literal arguments are
the table name and a JSON-encoded query, built with the same query-builder
classes used from the SDK and serialized with `.to_json()`.

## Setup

Run these statements in order, sending one statement per SQL request.
Semicolons separate the statements for clients that execute a script.

```sql SQL icon="database" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
CREATE TABLE my_docs (id INT, text STRING, category STRING, author STRING);

INSERT INTO my_docs VALUES
    (1, 'The happy puppy runs merrily in the park', 'animals', 'Alice'),
    (2, 'A curious kitten jumps quickly over the fence', 'animals', 'Bob'),
    (3, 'The puppy catches a ball with great enthusiasm', 'sports', 'Alice'),
    (5, 'Puppy training requires patience and dedication', 'training', 'Alice');

CREATE INDEX ON my_docs USING fts (text);
```

> [!WARNING]
>
> Phrase queries need position information, which costs extra index size.
> Add `WITH (with_position = true)` to `CREATE INDEX`. Without it,
> [phrase queries](#phrase) find nothing. See
> [FTS parameters](https://docs.lancedb.com/indexing/fts-index.md#fts-parameters).

## Basic search

The query builder produces the JSON string used as the second SQL argument.
The SQL examples below spell out the equivalent match object so they can be
run directly after the setup above.

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from lancedb.query import MatchQuery

query = MatchQuery("puppy", "text")
json_query = query.to_json()
```

```sql SQL icon="database" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
SELECT id, text, category
FROM fts(
    'my_docs',
    '{"match":{"column":"text","terms":"puppy"}}'
)
LIMIT 5
```

By default the top 5 matches come back in **arbitrary order**. Select
`_score` and order by it for relevance ranking:

```sql SQL icon="database" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
SELECT id, text, _score
FROM fts(
    'my_docs',
    '{"match":{"column":"text","terms":"puppy"}}'
)
ORDER BY _score DESC
LIMIT 5
```

`_score` is a pseudo-column carrying the query's BM25 relevance for the row;
it behaves like any other column, but exists only in a relation that came
from `fts()`. See [Data types](https://docs.lancedb.com/sql/data-types.md#pseudo-columns).

## Fuzzy

Tolerates typos, up to a character-edit budget:

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
MatchQuery("pupy", "text", fuzziness=2)
```

## Phrase

Exact phrase, or with `slop` to allow words between terms:

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from lancedb.query import PhraseQuery

PhraseQuery("happy puppy", "text")
PhraseQuery("puppy park", "text", slop=2)   # up to 2 words between them
```

## Boolean

Combine queries with `&` (AND) or `|` (OR):

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
MatchQuery("puppy", "text") & MatchQuery("happy", "text")
MatchQuery("puppy", "text") | MatchQuery("kitten", "text")
```

## Boost

Weight a query up or down relative to another:

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from lancedb.query import BoostQuery

BoostQuery(
    positive=MatchQuery("puppy", "text"),
    negative=MatchQuery("kitten", "text"),
    negative_boost=0.2,
)
```

## Multi-match

Search several columns at once, optionally weighting them differently:

```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
from lancedb.query import MultiMatchQuery

MultiMatchQuery("puppy", ["text", "category"])
MultiMatchQuery("puppy", ["text", "category"], boosts=[2.0, 1.0])  # text weighted 2x
```

## Combining with SQL

`fts()` is a relation, so it composes with the rest of the SQL surface.
Filter, join, or aggregate over it same as any table:

```sql SQL icon="database" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
SELECT id, text, category
FROM fts(
    'my_docs',
    '{"match":{"column":"text","terms":"puppy"}}'
)
WHERE category = 'animals'
LIMIT 5
```

## What to read next

> [card] [Full-Text Search](https://docs.lancedb.com/search/full-text-search.md)
>
> The query-builder equivalent, and every query form in full.

> [card] [FTS Index](https://docs.lancedb.com/indexing/fts-index.md)
>
> Tokenizers, and the position information phrase queries need.


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