func (q *Queries) ListCandidatesForIntel(ctx context.Context, limit int32) ([]Candidate, error)

	SourceFirstTouch      string           `json:"source_first_touch"`
	SourceLastTouch       string           `json:"source_last_touch"`
	SourceDetail          string           `json:"source_detail"`
	RrfScore              float64          `json:"rrf_score"`
	Snippet               string           `json:"snippet"`
}

// Hybrid retrieval: pgvector cosine kNN fused with full-text rank via Reciprocal Rank Fusion (k=60).
// RLS already scopes both legs to the caller's org. $1=query embedding, $2=limit, $3=text query.
// The vector arm is MATERIALIZED so the relaxed_order HNSW output is re-sorted by TRUE cosine distance
// before it is ranked: under hnsw.iterative_scan = relaxed_order the index returns rows slightly out of
// distance order, and ranking them without the re-sort would mis-rank the nearest neighbors at scale
// (pgvector 0.8 README; AWS pgvector 0.8 Aurora guidance). Every ORDER BY carries a unique tiebreaker
// (id) so results are byte-stable for fixed inputs, the AEDT reproducibility mandate. The caller SETs
// LOCAL hnsw.ef_search and hnsw.iterative_scan first.
func (q *Queries) SearchCandidatesHybrid(ctx context.Context, arg SearchCandidatesHybridParams) ([]SearchCandidatesHybridRow, error) {
	rows, err := q.db.Query(ctx, searchCandidatesHybrid, arg.Column1, arg.Limit, arg.WebsearchToTsquery)
	if err != nil {

Hybrid retrieval: pgvector cosine kNN fused with full-text rank via Reciprocal Rank Fusion (k=60).