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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
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);

注意

Phrase queries need position information, which costs extra index size. Add WITH (with_position = true) to CREATE INDEX. Without it, phrase queries find nothing. See FTS parameters.

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.

Fuzzy ​

Tolerates typos, up to a character-edit budget:

python
MatchQuery("pupy", "text", fuzziness=2)

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 ​

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

python
MatchQuery("puppy", "text") & MatchQuery("happy", "text")
MatchQuery("puppy", "text") | MatchQuery("kitten", "text")

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 ​

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 ​

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
Full-Text SearchThe query-builder equivalent, and every query form in full.FTS IndexTokenizers, and the position information phrase queries need.