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/config.py

@@ -156,6 +156,22 @@ class Settings:
     token_encryption_key: Optional[str] = None
     token_storage_db: Optional[str] = None
 
+    # Vector sync settings (ADR-007)
+    vector_sync_enabled: bool = False
+    vector_sync_scan_interval: int = 3600  # seconds
+    vector_sync_processor_workers: int = 3
+    vector_sync_queue_max_size: int = 10000
+
+    # Qdrant settings
+    qdrant_url: str = "http://qdrant:6333"
+    qdrant_api_key: Optional[str] = None
+    qdrant_collection: str = "nextcloud_content"
+
+    # Ollama settings (for embeddings)
+    ollama_base_url: Optional[str] = None
+    ollama_embedding_model: str = "nomic-embed-text"
+    ollama_verify_ssl: bool = True
+
 
 def get_settings() -> Settings:
     """Get application settings from environment variables.
@@ -192,4 +208,23 @@ def get_settings() -> Settings:
         # Token settings
         token_encryption_key=os.getenv("TOKEN_ENCRYPTION_KEY"),
         token_storage_db=os.getenv("TOKEN_STORAGE_DB", "/tmp/tokens.db"),
+        # Vector sync settings (ADR-007)
+        vector_sync_enabled=(
+            os.getenv("VECTOR_SYNC_ENABLED", "false").lower() == "true"
+        ),
+        vector_sync_scan_interval=int(os.getenv("VECTOR_SYNC_SCAN_INTERVAL", "3600")),
+        vector_sync_processor_workers=int(
+            os.getenv("VECTOR_SYNC_PROCESSOR_WORKERS", "3")
+        ),
+        vector_sync_queue_max_size=int(
+            os.getenv("VECTOR_SYNC_QUEUE_MAX_SIZE", "10000")
+        ),
+        # Qdrant settings
+        qdrant_url=os.getenv("QDRANT_URL", "http://qdrant:6333"),
+        qdrant_api_key=os.getenv("QDRANT_API_KEY"),
+        qdrant_collection=os.getenv("QDRANT_COLLECTION", "nextcloud_content"),
+        # Ollama settings
+        ollama_base_url=os.getenv("OLLAMA_BASE_URL"),
+        ollama_embedding_model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
+        ollama_verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
     )