fix(searcher): clamp effective_distance to valid cosine range [0, 2]

``search_memories`` computes ``effective_dist = dist - boost`` where
``boost`` can be as large as ``CLOSET_RANK_BOOSTS[0] == 0.40`` for a
rank-0 closet hit. When the raw drawer distance is small — any
near-exact match — the subtraction goes negative.

Two downstream effects:

1. Line 418 returns ``round(max(0.0, 1 - effective_dist), 3)`` as
   ``similarity``. With ``effective_dist = -0.30`` that yields
   ``similarity = 1.30``, outside the documented ``[0, 1]`` range.
   The ``max(0.0, ...)`` only prevents negative similarities; it does
   not cap above 1.
2. Line 427 stores ``_sort_key: effective_dist`` and line 435 sorts
   ``scored`` ascending by that key. A negative key drops *below* the
   rest, so the strongest hybrid matches end up sorting after weaker
   ones — ranking inversion under the exact conditions hybrid retrieval
   is supposed to serve best.

Clamp ``effective_dist`` to the valid cosine-distance range ``[0, 2]``.
The boost still wins (closet-backed hit still ranks first), it just no
longer flips the order.

Test added: mock drawer_col (base dist 0.08 / 0.35 for two sources) +
closet_col (rank-0 closet for the 0.08 source) → assert all hits have
``0 <= similarity <= 1`` and ``0 <= effective_distance <= 2``, and that
the closet-boosted source still ranks first.

Relationship to other PRs:

* **#988** clamps the output ``similarity`` alone. That does not fix
  the sort-key inversion or the invalid ``effective_distance`` in the
  returned dict. This PR clamps at the arithmetic source so both
  downstream users of the value stay in range.
* Orthogonal to **#979** (``tool_check_duplicate`` negative similarity).

mempalace/searcher.py

@@ -825,7 +825,12 @@ def search_memories(
                 matched_via = "drawer+closet"
                 closet_preview = c_preview
 
-        effective_dist = dist - boost
+        # Clamp to the valid cosine-distance range [0, 2]. When a strong
+        # closet boost (up to 0.40) exceeds the raw distance, the subtraction
+        # can go negative — which (a) yields ``similarity > 1.0`` downstream
+        # and (b) makes the sort key land *below* ordinary positive distances,
+        # inverting the ranking so the best hybrid matches sort last.
+        effective_dist = max(0.0, min(2.0, dist - boost))
         entry = {
             "text": doc,
             "wing": meta.get("wing", "unknown"),