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/embedding/service.py

@@ -0,0 +1,102 @@
+"""Embedding service with provider detection."""
+
+import logging
+import os
+
+from .base import EmbeddingProvider
+from .ollama_provider import OllamaEmbeddingProvider
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    """Unified embedding service with automatic provider detection."""
+
+    def __init__(self):
+        """Initialize embedding service with auto-detected provider."""
+        self.provider = self._detect_provider()
+
+    def _detect_provider(self) -> EmbeddingProvider:
+        """
+        Auto-detect available embedding provider.
+
+        Checks environment variables in order:
+        1. OLLAMA_BASE_URL - Use Ollama provider
+
+        Returns:
+            Configured embedding provider
+
+        Raises:
+            ValueError: If no embedding provider is configured
+        """
+        # Ollama provider (for this deployment)
+        ollama_url = os.getenv("OLLAMA_BASE_URL")
+        if ollama_url:
+            return OllamaEmbeddingProvider(
+                base_url=ollama_url,
+                model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
+                verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
+            )
+
+        raise ValueError(
+            "No embedding provider configured. "
+            "Set OLLAMA_BASE_URL environment variable."
+        )
+
+    async def embed(self, text: str) -> list[float]:
+        """
+        Generate embedding vector for text.
+
+        Args:
+            text: Input text to embed
+
+        Returns:
+            Vector embedding as list of floats
+        """
+        return await self.provider.embed(text)
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts.
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+        """
+        return await self.provider.embed_batch(texts)
+
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension.
+
+        Returns:
+            Vector dimension
+        """
+        return self.provider.get_dimension()
+
+    async def close(self):
+        """Close provider resources."""
+        if hasattr(self.provider, "close") and callable(
+            getattr(self.provider, "close")
+        ):
+            close_method = getattr(self.provider, "close")
+            await close_method()
+
+
+# Singleton instance
+_embedding_service: EmbeddingService | None = None
+
+
+def get_embedding_service() -> EmbeddingService:
+    """
+    Get singleton embedding service instance.
+
+    Returns:
+        Global EmbeddingService instance
+    """
+    global _embedding_service
+    if _embedding_service is None:
+        _embedding_service = EmbeddingService()
+    return _embedding_service