answer:Sure, here's an example of a `tsquery` in Postgres: Assume we have a table called "books" with a column "title" that contains the title of each book. We want to search for books that have the words "data" and "analytics" in their titles. Here's the query with the `tsquery`: ``` SELECT title FROM books WHERE to_tsvector('english', title) @@ to_tsquery('english', 'data & analytics'); ``` In this query, the `to_tsvector()` function is used to convert the "title" column to a `tsvector` data type, which is a document in a form that can be searched efficiently. The `'english'` argument specifies the language used to parse the text. The `to_tsquery()` function is used to convert the search query string `'data & analytics'` to a `tsquery` data type that can be matched against the `tsvector` document. The `@@` operator is used to match the `tsquery` against the `tsvector` and return the matching rows. This query will return the titles of the books that have both "data" and "analytics" in their titles.

answer:To sort the result based on the exact search using `tsquery`, you can use the `ts_rank_cd` function along with the `ORDER BY` clause. Here's an example: ``` SELECT title, ts_rank_cd(to_tsvector('english', title), to_tsquery('english', 'data & analytics'), 1) AS rank FROM books WHERE to_tsvector('english', title) @@ to_tsquery('english', 'data & analytics') ORDER BY rank DESC; ``` In this query, we're using the `ts_rank_cd` function to calculate the rank of each row based on the exact match of the search query against the `tsvector` of the "title" column. The `ts_rank_cd` function returns a value greater than zero for each row that matches the search query, with higher values indicating a stronger match. The `ORDER BY` clause is then used to sort the result set in descending order based on the calculated rank. This will ensure that the rows with exact matches are displayed at the top of the result set. Note that the third argument of the `ts_rank_cd` function is set to 1, which specifies the normalization method used to calculate the rank. In this case, we're using the "Cover Density" normalization method, which is useful for phrase queries like the one in this example. I hope this helps!

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