feat(ccip): tunable match threshold, default 0.85 (#114)

Live data showed the v1 flat 0.75 cosine over-fired — ~64% of matched images got
3-10 character guesses dominated by the most-referenced characters (a 27-ref
character clears a low bar on many images). A sweep showed 0.85 collapses the
noise (noisy multi-matches 47→3) while keeping the confident single-character
matches.

- ml_settings.ccip_match_threshold (migration 0063, default 0.85); match_image
  reads it (override still accepted). DEFAULT_SIM_THRESHOLD fallback 0.75→0.85.
- Exposed in GET/PATCH /api/ml/settings (validated 0.5–0.999).
- Slider in the GPU agent card ("Character-match strictness") — tune live, no
  redeploy, same observe-and-tune loop as auto-apply.

Test: a ~0.9-cosine figure matches at 0.85, dropped at 0.95.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

tests/test_ccip.py

@@ -86,3 +86,20 @@ async def test_no_figure_vectors_means_no_match(db):
     query = await _img(db, "g" * 64)
     await db.commit()
     assert await match_image(db, query.id) == []
+
+
+@pytest.mark.asyncio
+async def test_threshold_gates_borderline_match(db):
+    # A figure ~0.9 cosine from the reference: matched at 0.85, dropped at 0.95.
+    raven = await TagService(db).find_or_create("Raven", TagKind.character)
+    ref = await _img(db, "h" * 64)
+    await _figure(db, ref.id, _ccip(0))                 # e0
+    await _tag_image(db, ref.id, raven.id)
+    near = [0.0] * 768
+    near[0], near[1] = 0.9, 0.4359                       # |·|=1, cos(e0)=0.9
+    query = await _img(db, "i" * 64)
+    await _figure(db, query.id, near)
+    await db.commit()
+
+    assert any(m["tag_id"] == raven.id for m in await match_image(db, query.id, 0.85))
+    assert await match_image(db, query.id, 0.95) == []