Exercise 2: tsvector, tsquery, @@, and the Worked Example's Components — Possible Solution
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THE ROLE OF tsvector
------------------------------
Per this chapter, "a tsvector is a preprocessed, normalized
representation of a document's searchable text — it converts raw text
into lexemes (normalized word forms), strips out stop words, and can
optionally weight different parts of a document differently." In the
worked example, the articles.search_vector column is a generated
tsvector built from both the title and body columns, so the document
being searched is preprocessed once and stored, rather than reparsed
on every search.

THE ROLE OF tsquery
------------------------------
Per this chapter, "a tsquery is a processed search query, converted
into the same lexeme form, supporting boolean operators." In the
worked example, websearch_to_tsquery('english', 'postgres indexing')
converts the user's plain search text into a tsquery automatically.

THE ROLE OF THE @@ OPERATOR
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Per this chapter, "the @@ match operator tests whether a tsvector
satisfies a tsquery." In the worked example's WHERE clause,
search_vector @@ query filters the articles down to only those whose
tsvector actually matches the search tsquery.

HOW THE WORKED EXAMPLE'S SPECIFIC ELEMENTS FIT TOGETHER
------------------------------
Per this chapter, setweight(to_tsvector('english', title), 'A') ||
setweight(to_tsvector('english', body), 'B') builds the tsvector with
title matches given a higher weight ('A') than body matches ('B'), so
"title matches rank higher than body matches." websearch_to_tsquery
"parses ordinary user search input (rather than requiring the user to
type boolean operators directly)," converting a plain search phrase
into a real tsquery. Finally, ts_rank(search_vector, query) "orders
results by actual relevance, not just by whether they matched at
all" — using the weighting set up by setweight to produce a
meaningful ORDER BY rank DESC.

WHY THIS WORKS AS AN ANSWER
------------------------------
It defines all three core concepts using the chapter's own exact
wording, and connects each of the worked example's named functions
(setweight, websearch_to_tsquery, ts_rank) to its specific role,
rather than describing tsvector/tsquery/@@ in the abstract only.