feat: implement vector sync scanner and processor (ADR-007 Phase 2)

Implements background vector database synchronization using anyio
TaskGroups for BasicAuth mode with single-user credentials.

Scanner Implementation:
- Periodic document discovery (hourly, configurable)
- Timestamp-based change detection (Nextcloud vs Qdrant)
- Wake event for immediate scanning on-demand
- Supports both initial sync (all docs) and incremental sync (changes only)
- Detects deleted documents and queues for removal

Processor Implementation:
- Concurrent document processing pool (3 workers default)
- I/O-bound embedding generation via Ollama API
- Retry logic with exponential backoff (3 retries)
- Document chunking (512 words, 50-word overlap)
- Handles both index and delete operations
- Upserts vectors to Qdrant with rich metadata

App Lifespan Integration:
- Extended AppContext with background task state
- Modified app_lifespan_basic() to start tasks via anyio TaskGroups
- Graceful shutdown with coordinated task cancellation
- Only activates when VECTOR_SYNC_ENABLED=true

Embedding Service:
- OllamaEmbeddingProvider with TLS support
- Singleton pattern for shared client instances
- Batch embedding support for efficiency
- Auto-detects embedding dimension (768 for nomic-embed-text)

Qdrant Client:
- Async client wrapper with singleton pattern
- Auto-creates collection on first use
- COSINE distance metric for semantic similarity
- Integrates with embedding service for dimension detection

Health Check Enhancement:
- Added Qdrant status check to /health/ready endpoint
- Only checks when VECTOR_SYNC_ENABLED=true
- 2-second timeout for health probe
- Reports connection errors with details

Configuration:
- VECTOR_SYNC_ENABLED: Enable background sync
- VECTOR_SYNC_SCAN_INTERVAL: Scanner frequency (3600s default)
- VECTOR_SYNC_PROCESSOR_WORKERS: Concurrent processors (3 default)
- QDRANT_URL, QDRANT_API_KEY, QDRANT_COLLECTION: Vector DB config
- OLLAMA_BASE_URL, OLLAMA_EMBEDDING_MODEL: Embedding service config

Dependencies Added:
- qdrant-client>=1.7.0: Vector database client

Docker Compose:
- Added Qdrant service with health check
- Exposed ports 6333 (REST) and 6334 (gRPC)
- Configured MCP service with vector sync environment
- Added qdrant-data volume for persistence

Known Issue:
- FastMCP lifespan not triggering for streamable-http transport
- Background tasks will start once lifespan integration is complete
- Lifespan triggers on MCP session establishment, not server startup

Related: ADR-007 Background Vector Database Synchronization

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

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

nextcloud_mcp_server/vector/qdrant_client.py

@@ -0,0 +1,66 @@
+"""Qdrant client wrapper."""
+
+import logging
+import os
+
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.models import Distance, VectorParams
+
+logger = logging.getLogger(__name__)
+
+
+# Singleton instance
+_qdrant_client: AsyncQdrantClient | None = None
+
+
+async def get_qdrant_client() -> AsyncQdrantClient:
+    """
+    Get singleton Qdrant client instance.
+
+    Automatically creates collection on first use if it doesn't exist.
+
+    Returns:
+        Configured AsyncQdrantClient instance
+
+    Raises:
+        Exception: If Qdrant connection fails or collection creation fails
+    """
+    global _qdrant_client
+
+    if _qdrant_client is None:
+        url = os.getenv("QDRANT_URL", "http://qdrant:6333")
+        api_key = os.getenv("QDRANT_API_KEY")
+
+        _qdrant_client = AsyncQdrantClient(
+            url=url,
+            api_key=api_key,
+            timeout=30,
+        )
+
+        # Ensure collection exists
+        collection_name = os.getenv("QDRANT_COLLECTION", "nextcloud_content")
+
+        # Import here to avoid circular dependency
+        from nextcloud_mcp_server.embedding import get_embedding_service
+
+        embedding_service = get_embedding_service()
+        dimension = embedding_service.get_dimension()
+
+        try:
+            await _qdrant_client.get_collection(collection_name)
+            logger.info(f"Using existing Qdrant collection: {collection_name}")
+        except Exception:
+            # Collection doesn't exist, create it
+            await _qdrant_client.create_collection(
+                collection_name=collection_name,
+                vectors_config=VectorParams(
+                    size=dimension,
+                    distance=Distance.COSINE,
+                ),
+            )
+            logger.info(
+                f"Created Qdrant collection: {collection_name} "
+                f"(dimension={dimension}, distance=COSINE)"
+            )
+
+    return _qdrant_client