feat(viz): Add dual-score display and improve UI controls

This commit enhances the vector visualization interface with better score
transparency and improved UX:

**Dual-Score Display:**
- Store original algorithm scores before normalization (viz_routes.py:203)
- Display both raw and normalized scores: "Raw Score: 0.842 (89% relative)"
- Update plot hover text with dual scores (userinfo_routes.py:740)
- Fixes issue where all queries showed at least one 100% match regardless
  of actual relevance (normalization artifact)

**UI Improvements:**
1. Fusion Method dropdown: Changed from x-show to :disabled
   - Prevents jarring layout shift when switching algorithms
   - Dropdown stays visible but grayed out when Semantic is selected
   - Better UX with opacity: 0.5 and cursor: not-allowed

2. Score Threshold: Changed step from 0.1 to "any"
   - Allows arbitrary float precision (0.7, 0.85, 0.123)
   - Users can now fine-tune threshold values

3. Document Types: Converted multi-select to checkbox grid
   - Replaced clunky Ctrl/Cmd multi-select listbox
   - Checkbox grid with cleaner layout
   - Positioned left of Score Threshold and Result Limit inputs
   - More intuitive UX

**Technical Details:**
- Raw score ranges vary by algorithm:
  - Semantic: 0.0-1.0 (cosine similarity)
  - BM25 RRF: ~0.001-0.033 (Reciprocal Rank Fusion)
  - BM25 DBSF: Can exceed 1.0 (Distribution-Based Score Fusion)
- Normalized scores (0-1) used for visual encoding (marker size, color)
- Original scores preserved in API response via getattr fallback

Files modified:
- nextcloud_mcp_server/auth/viz_routes.py (store original_score)
- nextcloud_mcp_server/auth/templates/vector_viz.html (UI controls)
- nextcloud_mcp_server/auth/userinfo_routes.py (plot hover text)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

nextcloud_mcp_server/auth/viz_routes.py

@@ -184,7 +184,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             search_results = all_results[:limit]
             search_duration = time.perf_counter() - search_start
 
-        # Normalize scores relative to this result set for better visualization
+        # Store original scores and normalize for visualization
         # (best result = 1.0, worst result = 0.0 within THIS result set)
         # This makes visual encoding meaningful regardless of RRF normalization
         if search_results:
@@ -197,8 +197,11 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 f"→ [0.0, 1.0]"
             )
 
-            # Rescale each result's score to 0-1 within this result set
+            # Store original score and rescale to 0-1 for visualization
             for r in search_results:
+                # Store original score before normalization
+                r.original_score = r.score
+                # Rescale for visual encoding
                 r.score = (r.score - min_score) / score_range
 
         if not search_results:
@@ -317,7 +320,10 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 "doc_type": r.doc_type,
                 "title": r.title,
                 "excerpt": r.excerpt,
-                "score": r.score,
+                "score": r.score,  # Normalized score for visual encoding (0-1)
+                "original_score": getattr(
+                    r, "original_score", r.score
+                ),  # Raw score from algorithm
                 "chunk_start_offset": r.chunk_start_offset,
                 "chunk_end_offset": r.chunk_end_offset,
             }