feat: Implement per-chunk vector visualization with context expansion

Major improvements to vector visualization page:
- Refactor PCA to display individual chunks instead of averaged documents
- Add context expansion module for fetching surrounding text from notes and PDFs
- Update deduplication to use (doc_id, doc_type, chunk_start, chunk_end) keys
- Fix Alpine.js rendering with chunk-specific keys including offsets
- Refactor authentication helper to return NextcloudClient for better reuse
- Add async context manager support to NextcloudClient

Technical details:
- viz_routes.py: Fetch specific chunk vectors instead of averaging per document
- context.py: New module supporting both notes and PDF text extraction via PyMuPDF
- search algorithms: Extract page_number, chunk_index, total_chunks from Qdrant
- vector-viz.js/html: Use chunk positions in expansion tracking keys

This enables users to see which specific chunks match their query
and view them with surrounding context in the PCA visualization.

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

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

nextcloud_mcp_server/search/semantic.py

@@ -50,6 +50,9 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
         Returns unverified results from Qdrant. Access verification should be
         performed separately at the final output stage using verify_search_results().
 
+        Deduplicates by (doc_id, doc_type, chunk_start_offset, chunk_end_offset)
+        to show multiple chunks from the same document while avoiding duplicate chunks.
+
         Args:
             query: Natural language search query
             user_id: User ID for filtering
@@ -123,21 +126,24 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
             top_scores = [p.score for p in search_response.points[:3]]
             logger.debug(f"Top 3 similarity scores: {top_scores}")
 
-        # Deduplicate by (doc_id, doc_type) - multiple chunks per document
-        seen_docs = set()
+        # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
+        # This allows multiple chunks from same doc, but removes duplicate chunks
+        seen_chunks = set()
         results = []
 
         for result in search_response.points:
             # doc_id can be int (notes) or str (files - file paths)
             doc_id = result.payload["doc_id"]
             doc_type = result.payload.get("doc_type", "note")
-            doc_key = (doc_id, doc_type)
+            chunk_start = result.payload.get("chunk_start_offset")
+            chunk_end = result.payload.get("chunk_end_offset")
+            chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
 
-            # Skip if we've already seen this document
-            if doc_key in seen_docs:
+            # Skip if we've already seen this exact chunk
+            if chunk_key in seen_chunks:
                 continue
 
-            seen_docs.add(doc_key)
+            seen_chunks.add(chunk_key)
 
             # Return unverified results (verification happens at output stage)
             results.append(
@@ -153,6 +159,9 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
                     },
                     chunk_start_offset=result.payload.get("chunk_start_offset"),
                     chunk_end_offset=result.payload.get("chunk_end_offset"),
+                    page_number=result.payload.get("page_number"),
+                    chunk_index=result.payload.get("chunk_index", 0),
+                    total_chunks=result.payload.get("total_chunks", 1),
                 )
             )