--- title: "Full-Text Search" sidebarTitle: "Full-Text Search" description: "The fts() table function — match, fuzzy, phrase, boolean, boost and multi-match queries from SQL." icon: magnifying-glass keywords: ["fts", "sql", "full text search", "fuzzy", "phrase", "boolean", "boost", "multi-match"] --- Enterprise [SQL runs on a deployment.](/sql) 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 {#setup} Run these statements in order, sending one statement per SQL request. Semicolons separate the statements for clients that execute a script. ```sql 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); ``` 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](/indexing/fts-index#fts-parameters). ## Basic search {#basic} 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 from lancedb.query import MatchQuery query = MatchQuery("puppy", "text") json_query = query.to_json() ``` ```sql 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 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](/sql/data-types#pseudo-columns). ## Fuzzy {#fuzzy} Tolerates typos, up to a character-edit budget: ```python MatchQuery("pupy", "text", fuzziness=2) ``` ## Phrase {#phrase} Exact phrase, or with `slop` to allow words between terms: ```python from lancedb.query import PhraseQuery PhraseQuery("happy puppy", "text") PhraseQuery("puppy park", "text", slop=2) # up to 2 words between them ``` ## Boolean {#boolean} Combine queries with `&` (AND) or `|` (OR): ```python MatchQuery("puppy", "text") & MatchQuery("happy", "text") MatchQuery("puppy", "text") | MatchQuery("kitten", "text") ``` ## Boost {#boost} Weight a query up or down relative to another: ```python from lancedb.query import BoostQuery BoostQuery( positive=MatchQuery("puppy", "text"), negative=MatchQuery("kitten", "text"), negative_boost=0.2, ) ``` ## Multi-match {#multi-match} Search several columns at once, optionally weighting them differently: ```python from lancedb.query import MultiMatchQuery MultiMatchQuery("puppy", ["text", "category"]) MultiMatchQuery("puppy", ["text", "category"], boosts=[2.0, 1.0]) # text weighted 2x ``` ## Combining with SQL {#combining} `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 SELECT id, text, category FROM fts('my_docs', '{"match":{"column":"text","terms":"puppy"}}') WHERE category = 'animals' LIMIT 5 ``` ## What to read next {#next} The query-builder equivalent, and every query form in full. Tokenizers, and the position information phrase queries need.