fix(vector): normalize doc_id to str + add Qdrant keyword payload indexes

Production was logging two cascading classes of Qdrant errors against the
welcomed-malamute deployment:

1. HTTP 400 — "Bad request: Index required but not found for \"doc_id\" of
   one of the following types: [keyword]". The collection was created via
   create_collection() with no payload indexes, so any FieldCondition
   filter on doc_id failed at the Qdrant layer (placeholder writes/reads,
   eviction, search context lookups).

2. Compounding the missing index, producers wrote a mix of int and str
   doc_ids: webhook_parser stringified node_id, scanner stringified note
   IDs, news IDs, and deck card IDs — but the file scanner passed the
   numeric file_id through unchanged. A keyword index would not have
   covered both kinds even if it had existed.

This change:

- Normalizes doc_id to str at every producer site (scanner.py:459,
  DocumentTask.doc_id, indexed_*_ids reads from Qdrant).
- Tightens str|int annotations to str across placeholder.py,
  eviction.py, search/verification.py, search/context.py,
  SearchResult.id, and the auth/api visualization endpoints.
- Defensive str() coercion on doc_id reads in semantic.py /
  bm25_hybrid.py / vector/visualization.py for the transition window
  before the backfill runs.
- Adds an idempotent startup migration in get_qdrant_client():
  - _ensure_keyword_payload_indexes creates KEYWORD indexes for
    doc_id, user_id, and doc_type (tolerates "already exists" 400s).
  - _backfill_doc_id_to_string scrolls the collection once and rewrites
    int doc_ids to str. Skipped after a quick sample shows no legacy
    int payloads.
- Public API preserved: SemanticSearchResult.id stays int via explicit
  int(r.id) narrowing in server/semantic.py — surfaces a TypeError with
  actionable context if a future doc_type ships non-numeric ids.
- Documents the startup migration in docs/configuration.md.

Tests: 11 new unit tests in tests/unit/vector/test_qdrant_client.py
covering happy path / already-exists / unrelated-400 for the index
helpers, and sample-skip / mixed-batch rewrite / payload=None edge cases
for the backfill. 889 unit tests pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

nextcloud_mcp_server/search/algorithms.py

@@ -132,9 +132,12 @@ class SearchResult:
     """A single search result with metadata and score.
 
     Attributes:
-        id: Document ID. Numeric for indexed types today (notes, files,
-            deck cards, news items), but typed as ``int | str`` to allow
-            future doc types that use string identifiers (e.g., file paths).
+        id: Document ID — always a string. Producers stringify their native
+            ID before writing to Qdrant so the keyword payload index on
+            ``doc_id`` matches every point regardless of source doc_type.
+            Public response models (e.g. ``SemanticSearchResult``) re-narrow
+            this back to ``int`` at the MCP boundary via ``int(r.id)`` —
+            see ``server/semantic.py`` for the narrowing site.
         doc_type: Document type (note, file, calendar, contact, etc.)
         title: Document title
         excerpt: Content excerpt showing match context
@@ -151,7 +154,7 @@ class SearchResult:
         point_id: Qdrant point ID for batch vector retrieval (None if not from Qdrant)
     """
 
-    id: int | str
+    id: str
     doc_type: str
     title: str
     excerpt: str