codex/cli.py

@@ -102,7 +102,8 @@ def search_paper(
     cite_boost: bool = typer.Option(
         False,
         "--cite-boost",
-        help="Weight results by citation PageRank (F-15). Graceful when corpus < 5 papers.",
+        help="Re-rank the top results by citation PageRank — a within-page "
+        "tie-breaker, not a hard re-ranking (F-15). Graceful when corpus < 5 papers.",
     ),
 ) -> None:
     """Semantic similarity search over paper abstracts."""

codex/embed.py

@@ -1,4 +1,4 @@
-"""Hybrid dense + sparse embeddings via BGE-M3 (ADR-0002).
+"""Dense (+ optional sparse) embeddings via BGE-M3 (ADR-0002).
 
 Uses :class:`FlagEmbedding.BGEM3FlagModel` for encoding because the
 ``return_dense`` / ``return_sparse`` kwargs are part of the FlagEmbedding
@@ -7,6 +7,11 @@ vanilla ``SentenceTransformer`` load of ``BAAI/bge-m3`` (same weights,
 same model); sparse output is a list of ``{token_id: weight}`` dicts per
 text.
 
+Status: only the **dense** path is wired into ingest/search today; the search
+"hybrid" is dense + Postgres FTS. :meth:`Embedder.encode_sparse` / :meth:`encode`
+are provided for a future sparse-retrieval layer but are not yet consumed by the
+pipeline (audit C-14).
+
 Notes for callers
 -----------------
 * Empty input is handled explicitly — no model call is issued.