feat(search): ACL-aware vector filter via Nextcloud Shares lookup

The vector index has always been strictly per-user: every Qdrant payload
carries a `user_id` and the search filter is `user_id == querying_user`.
A file Alice indexed cannot be discovered by Bob even if she has shared
it with him — Bob would have to re-index it under his own user_id to
make it searchable, which means duplicate index entries for every share
recipient.

Switch to ownership-with-ACL-expansion:

- New `nextcloud_mcp_server.search.access_filter` module:
  - `list_accessible_owners(sharing_client, user_id)` calls the OCS
    Sharing API (`shared_with_me=true`) and returns
    `{user_id} ∪ {uid_owner of each share}`. Fails open to `[user_id]`
    so a misbehaving Sharing API doesn't black-hole search.
  - `build_ownership_filter(user_id, accessible_owners)` returns a
    Qdrant `Filter` whose `should` branch matches either the new
    `owner_id IN accessible_owners` field or the legacy `user_id` field.
    The legacy branch keeps points indexed before this change reachable
    without a migration backfill.
- Indexer payload (`vector/processor.py`) now writes `owner_id` alongside
  `user_id`. `DocumentTask` gains an optional `owner_id` field; today the
  scanner always runs as the owner so the processor falls back to
  `user_id`, but the field is plumbed so a future shared-with-me crawler
  can set the true owner without reshaping the payload contract.
- `SemanticSearchAlgorithm.search` and `BM25HybridSearchAlgorithm.search`
  accept `accessible_owners` via kwargs and use the new ownership filter.
  Default behaviour with no kwarg is unchanged (self-only).
- Both user-facing callers — the MCP tool path (`server/semantic.py`) and
  the visualization Starlette route (`auth/viz_routes.py`) — compute
  `accessible_owners` from the authenticated Nextcloud client before
  invoking the search algorithm. Eviction, scanner deletion, placeholder,
  and chunk-context paths intentionally keep the legacy `user_id`
  semantics (those are "operations on a specific user's records", not
  cross-user reads).
- 10 new unit tests in `tests/unit/search/test_access_filter.py` cover
  self-only default, owner expansion, dedup, fallback fields, OCS
  failure, and the legacy `should`-branch shape.

Pairs with cbcoutinho/astrolabe#89 — together they let an Astrolabe user
find content owners have shared with them without going through any
re-authorization flow or re-indexing.

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

nextcloud_mcp_server/search/semantic.py

@@ -8,6 +8,7 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue
 from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.embedding import get_embedding_service
 from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
+from nextcloud_mcp_server.search.access_filter import build_ownership_filter
 from nextcloud_mcp_server.search.algorithms import (
     SearchAlgorithm,
     SearchResult,
@@ -65,7 +66,11 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
             user_id: User ID for filtering
             limit: Maximum results to return
             doc_type: Optional document type filter
-            **kwargs: Additional parameters (score_threshold override)
+            **kwargs:
+                - score_threshold (float): override the instance default
+                - accessible_owners (list[str]): owner UIDs the user can read
+                  (self + share senders). Pre-computed by the caller from the
+                  OCS Sharing API. Defaults to ``[user_id]`` when omitted.
 
         Returns:
             List of unverified SearchResult objects ranked by similarity score
@@ -75,6 +80,7 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
         """
         settings = get_settings()
         score_threshold = kwargs.get("score_threshold", self.score_threshold)
+        accessible_owners: list[str] | None = kwargs.get("accessible_owners")
 
         logger.info(
             "Semantic search: query='%s', user=%s, limit=%s, score_threshold=%s, doc_type=%s",
@@ -97,10 +103,7 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
         # Build Qdrant filter
         filter_conditions = [
             get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
-            FieldCondition(
-                key="user_id",
-                match=MatchValue(value=user_id),
-            ),
+            build_ownership_filter(user_id, accessible_owners),
         ]
 
         # Add doc_type filter if specified