Full Text Search

Postgres has built-in functions to handle Full Text Search queries. This is like a "search engine" within Postgres.

Preparation

For this guide we'll use the following example data:

create table books (
  id serial primary key,
  title text,
  author text,
  description text
);

insert into books
  (title, author, description)
values
  (
    'The Poky Little Puppy',
    'Janette Sebring Lowrey',
    'Puppy is slower than other, bigger animals.'
  ),
  ('The Tale of Peter Rabbit', 'Beatrix Potter', 'Rabbit eats some vegetables.'),
  ('Tootle', 'Gertrude Crampton', 'Little toy train has big dreams.'),
  (
    'Green Eggs and Ham',
    'Dr. Seuss',
    'Sam has changing food preferences and eats unusually colored food.'
  ),
  (
    'Harry Potter and the Goblet of Fire',
    'J.K. Rowling',
    'Fourth year of school starts, big drama ensues.'
  );

Usage

The functions we'll cover in this guide are:

to_tsvector() [#to-tsvector]

Converts your data into searchable tokens. to_tsvector() stands for "to text search vector." For example:

select to_tsvector('green eggs and ham');
-- Returns 'egg':2 'green':1 'ham':4

Collectively these tokens are called a "document" which Postgres can use for comparisons.

to_tsquery() [#to-tsquery]

Converts a query string into tokens to match. to_tsquery() stands for "to text search query."

This conversion step is important because we will want to "fuzzy match" on keywords. For example if a user searches for eggs, and a column has the value egg, we probably still want to return a match.

Postgres provides several functions to create tsquery objects:

• to_tsquery() - Requires manual specification of operators (&, |, !)
• plainto_tsquery() - Converts plain text to an AND query: plainto_tsquery('english', 'fat rats') → 'fat' & 'rat'
• phraseto_tsquery() - Creates phrase queries: phraseto_tsquery('english', 'fat rats') → 'fat' <-> 'rat'
• websearch_to_tsquery() - Supports web search syntax with quotes, "or", and negation

Match: @@ [#match]

The @@ symbol is the "match" symbol for Full Text Search. It returns any matches between a to_tsvector result and a to_tsquery result.

Take the following example:

select *
from books
where title = 'Harry';

const { data, error } = await supabase.from('books').select().eq('title', 'Harry')

The equality symbol above (=) is very "strict" on what it matches. In a full text search context, we might want to find all "Harry Potter" books and so we can rewrite the example above:

select *
from books
where to_tsvector(title) @@ to_tsquery('Harry');

const { data, error } = await supabase.from('books').select().textSearch('title', `'Harry'`)

Basic full text queries

Search a single column

To find all books where the description contain the word big:

select
  *
from
  books
where
  to_tsvector(description)
  @@ to_tsquery('big');

const { data, error } = await supabase.from('books').select().textSearch('description', `'big'`)

Search multiple columns

Right now there is no direct way to use JavaScript or Dart to search through multiple columns but you can do it by creating computed columns on the database.

To find all books where description or title contain the word little:

select
  *
from
  books
where
  to_tsvector(description || ' ' || title) -- concat columns, but be sure to include a space to separate them!
  @@ to_tsquery('little');

create function title_description(books) returns text as $$
  select $1.title || ' ' || $1.description;
$$ language sql immutable;

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('title_description', `little`)

Match all search words

To find all books where description contains BOTH of the words little and big, we can use the & symbol:

select
  *
from
  books
where
  to_tsvector(description)
  @@ to_tsquery('little & big'); -- use & for AND in the search query

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', `'little' & 'big'`)

Match any search words

To find all books where description contain ANY of the words little or big, use the | symbol:

select
  *
from
  books
where
  to_tsvector(description)
  @@ to_tsquery('little | big'); -- use | for OR in the search query

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', `'little' | 'big'`)

Notice how searching for big includes results with the word bigger (or biggest, etc).

Partial search

Partial search is particularly useful when you want to find matches on substrings within your data.

Implementing partial search

You can use the :* syntax with to_tsquery(). Here's an example that searches for any book titles beginning with "Lit":

select title from books where to_tsvector(title) @@ to_tsquery('Lit:*');

Extending functionality with RPC

To make the partial search functionality accessible through the API, you can wrap the search logic in a database function.

After creating this function, you can invoke it from your application using the SDK for your platform. Here's an example:

create or replace function search_books_by_title_prefix(prefix text)
returns setof books AS $$
begin
  return query
  select * from books where to_tsvector('english', title) @@ to_tsquery(prefix || ':*');
end;
$$ language plpgsql;

const { data, error } = await supabase.rpc('search_books_by_title_prefix', { prefix: 'Lit' })

This function takes a prefix parameter and returns all books where the title contains a word starting with that prefix. The :* operator is used to denote a prefix match in the to_tsquery() function.

Handling spaces in queries

When you want the search term to include a phrase or multiple words, you can concatenate words using a + as a placeholder for space:

select * from search_books_by_title_prefix('Little+Puppy');

Web search syntax with websearch_to_tsquery() [#websearch-to-tsquery]

The websearch_to_tsquery() function provides an intuitive search syntax similar to popular web search engines, making it ideal for user-facing search interfaces.

Basic usage

select *
from books
where to_tsvector(description) @@ websearch_to_tsquery('english', 'green eggs');

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', 'green eggs', { type: 'websearch' })

Quoted phrases

Use quotes to search for exact phrases:

select * from books
where to_tsvector(description || ' ' || title) @@ websearch_to_tsquery('english', '"Green Eggs"');
-- Matches documents containing "Green" immediately followed by "Eggs"

OR searches

Use "or" (case-insensitive) to search for multiple terms:

select * from books
where to_tsvector(description) @@ websearch_to_tsquery('english', 'puppy or rabbit');
-- Matches documents containing either "puppy" OR "rabbit"

Negation

Use a dash (-) to exclude terms:

select * from books
where to_tsvector(description) @@ websearch_to_tsquery('english', 'animal -rabbit');
-- Matches documents containing "animal" but NOT "rabbit"

Complex queries

Combine multiple operators for sophisticated searches:

select * from books
where to_tsvector(description || ' ' || title) @@
  websearch_to_tsquery('english', '"Harry Potter" or "Dr. Seuss" -vegetables');
-- Matches books by "Harry Potter" or "Dr. Seuss" but excludes those mentioning vegetables

Creating indexes

Now that you have Full Text Search working, create an index. This allows Postgres to "build" the documents preemptively so that they don't need to be created at the time we execute the query. This will make our queries much faster.

Searchable columns

Create a new column fts inside the books table to store the searchable index of the title and description columns.

We can use a special feature of Postgres called Generated Columns to ensure that the index is updated any time the values in the title and description columns change.

alter table
  books
add column
  fts tsvector generated always as (to_tsvector('english', description || ' ' || title)) stored;

create index books_fts on books using gin (fts); -- generate the index

select id, fts
from books;

| id  | fts                                                                                                             |
| --- | --------------------------------------------------------------------------------------------------------------- |
| 1   | 'anim':7 'bigger':6 'littl':10 'poki':9 'puppi':1,11 'slower':3                                                 |
| 2   | 'eat':2 'peter':8 'rabbit':1,9 'tale':6 'veget':4                                                               |
| 3   | 'big':5 'dream':6 'littl':1 'tootl':7 'toy':2 'train':3                                                         |
| 4   | 'chang':3 'color':9 'eat':7 'egg':12 'food':4,10 'green':11 'ham':14 'prefer':5 'sam':1 'unus':8                |
| 5   | 'big':6 'drama':7 'ensu':8 'fire':15 'fourth':1 'goblet':13 'harri':9 'potter':10 'school':4 'start':5 'year':2 |

Search using the new column

Now that we've created and populated our index, we can search it using the same techniques as before:

select
  *
from
  books
where
  fts @@ to_tsquery('little & big');

const { data, error } = await supabase.from('books').select().textSearch('fts', `'little' & 'big'`)

Query operators

Visit Postgres: Text Search Functions and Operators to learn about additional query operators you can use to do more advanced full text queries, such as:

Proximity: <-> [#proximity]

The proximity symbol is useful for searching for terms that are a certain "distance" apart. For example, to find the phrase big dreams, where the a match for "big" is followed immediately by a match for "dreams":

select
  *
from
  books
where
  to_tsvector(description) @@ to_tsquery('big <-> dreams');

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', `'big' <-> 'dreams'`)

We can also use the <-> to find words within a certain distance of each other. For example to find year and school within 2 words of each other:

select
  *
from
  books
where
  to_tsvector(description) @@ to_tsquery('year <2> school');

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', `'year' <2> 'school'`)

Negation: ! [#negation]

The negation symbol can be used to find phrases which don't contain a search term. For example, to find records that have the word big but not little:

select
  *
from
  books
where
  to_tsvector(description) @@ to_tsquery('big & !little');

const { data, error } = await supabase
  .from('books')
  .select()
  .textSearch('description', `'big' & !'little'`)

Ranking search results [#ranking]

Postgres provides ranking functions to sort search results by relevance, helping you present the most relevant matches first. Since ranking functions need to be computed server-side, use RPC functions and generated columns.

Creating a search function with ranking [#search-function-ranking]

First, create a Postgres function that handles search and ranking:

create or replace function search_books(search_query text)
returns table(id int, title text, description text, rank real) as $$
begin
  return query
  select
    books.id,
    books.title,
    books.description,
    ts_rank(to_tsvector('english', books.description), to_tsquery(search_query)) as rank
  from books
  where to_tsvector('english', books.description) @@ to_tsquery(search_query)
  order by rank desc;
end;
$$ language plpgsql;

Now you can call this function from your client:

const { data, error } = await supabase.rpc('search_books', { search_query: 'big' })

select * from search_books('big');

Ranking with weighted columns [#weighted-ranking]

Postgres allows you to assign different importance levels to different parts of your documents using weight labels. This is especially useful when you want matches in certain fields (like titles) to rank higher than matches in other fields (like descriptions).

Understanding weight labels

Postgres uses four weight labels: A, B, C, and D, where:

• A = Highest importance (weight 1.0)
• B = High importance (weight 0.4)
• C = Medium importance (weight 0.2)
• D = Low importance (weight 0.1)

Creating weighted search columns

First, create a weighted tsvector column that gives titles higher priority than descriptions:

-- Add a weighted fts column
alter table books
add column fts_weighted tsvector
generated always as (
  setweight(to_tsvector('english', title), 'A') ||
  setweight(to_tsvector('english', description), 'B')
) stored;

-- Create index for the weighted column
create index books_fts_weighted on books using gin (fts_weighted);

Now create a search function that uses this weighted column:

create or replace function search_books_weighted(search_query text)
returns table(id int, title text, description text, rank real) as $$
begin
  return query
  select
    books.id,
    books.title,
    books.description,
    ts_rank(books.fts_weighted, to_tsquery(search_query)) as rank
  from books
  where books.fts_weighted @@ to_tsquery(search_query)
  order by rank desc;
end;
$$ language plpgsql;

Custom weight arrays

You can also specify custom weights by providing a weight array to ts_rank():

create or replace function search_books_custom_weights(search_query text)
returns table(id int, title text, description text, rank real) as $$
begin
  return query
  select
    books.id,
    books.title,
    books.description,
    ts_rank(
      '\{0.0, 0.2, 0.5, 1.0\}'::real[], -- Custom weights \{D, C, B, A\}
      books.fts_weighted,
      to_tsquery(search_query)
    ) as rank
  from books
  where books.fts_weighted @@ to_tsquery(search_query)
  order by rank desc;
end;
$$ language plpgsql;

This example uses custom weights where:

• A-labeled terms (titles) have maximum weight (1.0)
• B-labeled terms (descriptions) have medium weight (0.5)
• C-labeled terms have low weight (0.2)
• D-labeled terms are ignored (0.0)

Using the weighted search

// Search with standard weighted ranking
const { data, error } = await supabase.rpc('search_books_weighted', { search_query: 'Harry' })

// Search with custom weights
const { data: customData, error: customError } = await supabase.rpc('search_books_custom_weights', {
  search_query: 'Harry',
})

-- Standard weighted search
select * from search_books_weighted('Harry');

-- Custom weighted search
select * from search_books_custom_weights('Harry');

Practical example with results

Say you search for "Harry". With weighted columns:

1. "Harry Potter and the Goblet of Fire" (title match) gets weight A = 1.0
2. Books mentioning "Harry" in description get weight B = 0.4

This ensures that books with "Harry" in the title ranks significantly higher than books that only mention "Harry" in the description, providing more relevant search results for users.

Using ranking with indexes [#ranking-with-indexes]

When using the fts column you created earlier, ranking becomes more efficient. Create a function that uses the indexed column:

create or replace function search_books_fts(search_query text)
returns table(id int, title text, description text, rank real) as $$
begin
  return query
  select
    books.id,
    books.title,
    books.description,
    ts_rank(books.fts, to_tsquery(search_query)) as rank
  from books
  where books.fts @@ to_tsquery(search_query)
  order by rank desc;
end;
$$ language plpgsql;

const { data, error } = await supabase.rpc('search_books_fts', { search_query: 'little & big' })

select * from search_books_fts('little & big');

Using web search syntax with ranking [#websearch-ranking]

You can also create a function that combines websearch_to_tsquery() with ranking for user-friendly search:

create or replace function websearch_books(search_text text)
returns table(id int, title text, description text, rank real) as $$
begin
  return query
  select
    books.id,
    books.title,
    books.description,
    ts_rank(books.fts, websearch_to_tsquery('english', search_text)) as rank
  from books
  where books.fts @@ websearch_to_tsquery('english', search_text)
  order by rank desc;
end;
$$ language plpgsql;

// Support natural search syntax
const { data, error } = await supabase.rpc('websearch_books', {
  search_text: '"little puppy" or train -vegetables',
})

select * from websearch_books('"little puppy" or train -vegetables');

Resources

• Postgres: Text Search Functions and Operators