How do you combine pgvector with full-text search (hybrid keyword + semantic search)?

-- Hybrid search: combine semantic similarity with full-text ranking
-- using Reciprocal Rank Fusion (RRF) to merge the two ranked lists

CREATE TABLE documents (
    id         BIGSERIAL PRIMARY KEY,
    content    TEXT NOT NULL,
    embedding  VECTOR(1536),
    content_ts TSVECTOR GENERATED ALWAYS AS
               (to_tsvector('english', content)) STORED  -- auto-updated FTS index
);
CREATE INDEX ON documents USING gin(content_ts);   -- FTS index
CREATE INDEX ON documents USING hnsw(embedding vector_cosine_ops);  -- vector index

-- Reciprocal Rank Fusion hybrid search:
WITH
semantic AS (
    SELECT id, ROW_NUMBER() OVER (ORDER BY embedding <=> '[...]') AS rank
    FROM documents
    ORDER BY embedding <=> '[...]'
    LIMIT 50
),
keyword AS (
    SELECT id, ROW_NUMBER() OVER (ORDER BY ts_rank(content_ts, query) DESC) AS rank,
           ts_rank(content_ts, query) AS ts_score
    FROM documents,
         to_tsquery('english', 'pgvector & PostgreSQL') AS query
    WHERE content_ts @@ query
    LIMIT 50
),
fused AS (
    SELECT
        COALESCE(s.id, k.id) AS id,
        COALESCE(1.0 / (60 + s.rank), 0) +
        COALESCE(1.0 / (60 + k.rank), 0) AS rrf_score
    FROM semantic s FULL OUTER JOIN keyword k USING (id)
)
SELECT d.id, d.content, f.rrf_score
FROM fused f JOIN documents d ON d.id = f.id
ORDER BY f.rrf_score DESC
LIMIT 10;