diff --git a/Dockerfile b/Dockerfile
index d2199e7..18f3a77 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -12,5 +12,6 @@ COPY . .
 RUN uv sync --locked --no-dev
 
 ENV PYTHONUNBUFFERED=1
+ENV VIRTUAL_ENV=/app/.venv
 
 ENTRYPOINT ["/app/.venv/bin/nextcloud-mcp-server", "--host", "0.0.0.0"]
diff --git a/docker-compose.yml b/docker-compose.yml
index 15161c4..066e56c 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -74,6 +74,8 @@ services:
     depends_on:
       app:
         condition: service_healthy
+      qdrant:
+        condition: service_healthy
     ports:
       - 127.0.0.1:8000:8000
     environment:
@@ -81,6 +83,21 @@ services:
       - NEXTCLOUD_USERNAME=admin
       - NEXTCLOUD_PASSWORD=admin
 
+      # Vector sync configuration (ADR-007)
+      - VECTOR_SYNC_ENABLED=true
+      - VECTOR_SYNC_SCAN_INTERVAL=3600
+      - VECTOR_SYNC_PROCESSOR_WORKERS=3
+
+      # Qdrant configuration
+      - QDRANT_URL=http://qdrant:6333
+      - QDRANT_API_KEY=${QDRANT_API_KEY:-my_secret_api_key}
+      - QDRANT_COLLECTION=nextcloud_content
+
+      # Ollama configuration
+      - OLLAMA_BASE_URL=https://ollama.internal.coutinho.io:443
+      - OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+      - OLLAMA_VERIFY_SSL=true
+
   mcp-oauth:
     build: .
     command: ["--transport", "streamable-http", "--oauth", "--port", "8001", "--oauth-token-type", "jwt"]
@@ -183,6 +200,22 @@ services:
       - keycloak-tokens:/app/data
       - keycloak-oauth-storage:/app/.oauth
 
+  qdrant:
+    image: qdrant/qdrant:latest
+    restart: always
+    ports:
+      - 127.0.0.1:6333:6333  # REST API
+      - 127.0.0.1:6334:6334  # gRPC (optional)
+    volumes:
+      - qdrant-data:/qdrant/storage
+    environment:
+      - QDRANT__SERVICE__API_KEY=${QDRANT_API_KEY:-my_secret_api_key}
+    healthcheck:
+      test: ["CMD-SHELL", "curl -f http://localhost:6333/readyz || exit 1"]
+      interval: 10s
+      timeout: 5s
+      retries: 10
+
 volumes:
   nextcloud:
   db:
@@ -190,3 +223,4 @@ volumes:
   oauth-tokens:
   keycloak-tokens:
   keycloak-oauth-storage:
+  qdrant-data:
diff --git a/nextcloud_mcp_server/app.py b/nextcloud_mcp_server/app.py
index 197301c..d0a63e5 100644
--- a/nextcloud_mcp_server/app.py
+++ b/nextcloud_mcp_server/app.py
@@ -1,3 +1,4 @@
+import asyncio
 import logging
 import os
 from collections.abc import AsyncIterator
@@ -8,6 +9,7 @@ from typing import TYPE_CHECKING, Optional
 if TYPE_CHECKING:
     from nextcloud_mcp_server.auth.refresh_token_storage import RefreshTokenStorage
 
+import anyio
 import click
 import httpx
 import uvicorn
@@ -32,6 +34,7 @@ from nextcloud_mcp_server.client import NextcloudClient
 from nextcloud_mcp_server.config import (
     LOGGING_CONFIG,
     get_document_processor_config,
+    get_settings,
     setup_logging,
 )
 from nextcloud_mcp_server.context import get_client as get_nextcloud_client
@@ -47,6 +50,7 @@ from nextcloud_mcp_server.server import (
     configure_webdav_tools,
 )
 from nextcloud_mcp_server.server.oauth_tools import register_oauth_tools
+from nextcloud_mcp_server.vector import processor_task, scanner_task
 
 logger = logging.getLogger(__name__)
 
@@ -206,6 +210,9 @@ class AppContext:
     """Application context for BasicAuth mode."""
 
     client: NextcloudClient
+    document_queue: Optional[asyncio.Queue] = None
+    shutdown_event: Optional[anyio.Event] = None
+    scanner_wake_event: Optional[anyio.Event] = None
 
 
 @dataclass
@@ -369,6 +376,9 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
 
     Creates a single Nextcloud client with basic authentication
     that is shared across all requests.
+
+    If vector sync is enabled (VECTOR_SYNC_ENABLED=true), also starts
+    background tasks for automatic document indexing (ADR-007).
     """
     logger.info("Starting MCP server in BasicAuth mode")
     logger.info("Creating Nextcloud client with BasicAuth")
@@ -379,11 +389,74 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
     # Initialize document processors
     initialize_document_processors()
 
-    try:
-        yield AppContext(client=client)
-    finally:
-        logger.info("Shutting down BasicAuth mode")
-        await client.close()
+    settings = get_settings()
+
+    # Check if vector sync is enabled
+    if settings.vector_sync_enabled:
+        logger.info("Vector sync enabled - starting background tasks")
+
+        # Get username from environment for BasicAuth mode
+        username = os.getenv("NEXTCLOUD_USERNAME")
+        if not username:
+            raise ValueError(
+                "NEXTCLOUD_USERNAME is required for vector sync in BasicAuth mode"
+            )
+
+        # Initialize shared state
+        document_queue = asyncio.Queue(maxsize=settings.vector_sync_queue_max_size)
+        shutdown_event = anyio.Event()
+        scanner_wake_event = anyio.Event()
+
+        # Start background tasks using anyio TaskGroup
+        async with anyio.create_task_group() as tg:
+            # Start scanner task
+            tg.start_soon(
+                scanner_task,
+                document_queue,
+                shutdown_event,
+                scanner_wake_event,
+                client,
+                username,
+            )
+
+            # Start processor pool
+            for i in range(settings.vector_sync_processor_workers):
+                tg.start_soon(
+                    processor_task,
+                    i,
+                    document_queue,
+                    shutdown_event,
+                    client,
+                    username,
+                )
+
+            logger.info(
+                f"Background sync tasks started: 1 scanner + {settings.vector_sync_processor_workers} processors"
+            )
+
+            # Yield with background tasks running
+            try:
+                yield AppContext(
+                    client=client,
+                    document_queue=document_queue,
+                    shutdown_event=shutdown_event,
+                    scanner_wake_event=scanner_wake_event,
+                )
+            finally:
+                # Shutdown signal
+                logger.info("Shutting down background sync tasks")
+                shutdown_event.set()
+
+                # TaskGroup automatically cancels all tasks on exit
+                logger.info("Background sync tasks stopped")
+                await client.close()
+    else:
+        # No vector sync - simple lifecycle
+        try:
+            yield AppContext(client=client)
+        finally:
+            logger.info("Shutting down BasicAuth mode")
+            await client.close()
 
 
 async def setup_oauth_config():
@@ -946,7 +1019,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
         """Readiness probe endpoint.
 
         Returns 200 OK if the application is ready to serve traffic.
-        Checks that required configuration is present.
+        Checks that required configuration is present and Qdrant if vector sync enabled.
         """
         checks = {}
         is_ready = True
@@ -976,6 +1049,24 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
                 checks["auth_configured"] = "error: credentials not set"
                 is_ready = False
 
+        # Check Qdrant status if vector sync is enabled
+        vector_sync_enabled = (
+            os.getenv("VECTOR_SYNC_ENABLED", "false").lower() == "true"
+        )
+        if vector_sync_enabled:
+            try:
+                qdrant_url = os.getenv("QDRANT_URL", "http://qdrant:6333")
+                async with httpx.AsyncClient(timeout=2.0) as client:
+                    response = await client.get(f"{qdrant_url}/readyz")
+                    if response.status_code == 200:
+                        checks["qdrant"] = "ok"
+                    else:
+                        checks["qdrant"] = f"error: status {response.status_code}"
+                        is_ready = False
+            except Exception as e:
+                checks["qdrant"] = f"error: {str(e)}"
+                is_ready = False
+
         status_code = 200 if is_ready else 503
         return JSONResponse(
             {
diff --git a/nextcloud_mcp_server/auth/client_registration.py b/nextcloud_mcp_server/auth/client_registration.py
index 44451a9..f4e3797 100644
--- a/nextcloud_mcp_server/auth/client_registration.py
+++ b/nextcloud_mcp_server/auth/client_registration.py
@@ -79,19 +79,22 @@ async def register_client(
     client_name: str = "Nextcloud MCP Server",
     redirect_uris: list[str] | None = None,
     scopes: str = "openid profile email",
-    token_type: str = "Bearer",
+    token_type: str | None = "Bearer",
     resource_url: str | None = None,
 ) -> ClientInfo:
     """
-    Register a new OAuth client with Nextcloud OIDC using dynamic client registration.
+    Register a new OAuth client using RFC 7591 Dynamic Client Registration.
+
+    This function supports both Nextcloud OIDC and standard OIDC providers like Keycloak.
 
     Args:
-        nextcloud_url: Base URL of the Nextcloud instance
+        nextcloud_url: Base URL of the OIDC provider
         registration_endpoint: Full URL to the registration endpoint
         client_name: Name of the client application
         redirect_uris: List of redirect URIs (default: http://localhost:8000/oauth/callback)
         scopes: Space-separated list of scopes to request
-        token_type: Type of access tokens to issue (default: "Bearer", also supports "JWT")
+        token_type: Type of access tokens (default: "Bearer", supports "JWT" for Nextcloud).
+                    Set to None to omit this field (required for Keycloak and other standard providers).
         resource_url: OAuth 2.0 Protected Resource URL (RFC 9728) - used for token introspection authorization
 
     Returns:
@@ -100,6 +103,11 @@ async def register_client(
     Raises:
         httpx.HTTPStatusError: If registration fails
         ValueError: If response is invalid
+
+    Note:
+        The token_type parameter is a Nextcloud-specific extension and is not part of RFC 7591.
+        Standard OIDC providers like Keycloak do not accept this field and will return a 400 error
+        if it's included. Set token_type=None when registering with Keycloak or other standard providers.
     """
     if redirect_uris is None:
         redirect_uris = ["http://localhost:8000/oauth/callback"]
@@ -111,9 +119,12 @@ async def register_client(
         "grant_types": ["authorization_code", "refresh_token"],
         "response_types": ["code"],
         "scope": scopes,
-        "token_type": token_type,
     }
 
+    # Add token_type if provided (Nextcloud-specific, not RFC 7591 standard)
+    if token_type is not None:
+        client_metadata["token_type"] = token_type
+
     # Add resource_url if provided (RFC 9728)
     if resource_url:
         client_metadata["resource_url"] = resource_url
diff --git a/nextcloud_mcp_server/config.py b/nextcloud_mcp_server/config.py
index 73d86e4..da05108 100644
--- a/nextcloud_mcp_server/config.py
+++ b/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",
     )
diff --git a/nextcloud_mcp_server/embedding/__init__.py b/nextcloud_mcp_server/embedding/__init__.py
new file mode 100644
index 0000000..3b06aba
--- /dev/null
+++ b/nextcloud_mcp_server/embedding/__init__.py
@@ -0,0 +1,5 @@
+"""Embedding service package for generating vector embeddings."""
+
+from .service import EmbeddingService, get_embedding_service
+
+__all__ = ["EmbeddingService", "get_embedding_service"]
diff --git a/nextcloud_mcp_server/embedding/base.py b/nextcloud_mcp_server/embedding/base.py
new file mode 100644
index 0000000..b17e264
--- /dev/null
+++ b/nextcloud_mcp_server/embedding/base.py
@@ -0,0 +1,43 @@
+"""Abstract base class for embedding providers."""
+
+from abc import ABC, abstractmethod
+
+
+class EmbeddingProvider(ABC):
+    """Base class for embedding providers."""
+
+    @abstractmethod
+    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
+        """
+        pass
+
+    @abstractmethod
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts (optimized).
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+        """
+        pass
+
+    @abstractmethod
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension for this provider.
+
+        Returns:
+            Vector dimension (e.g., 768 for nomic-embed-text)
+        """
+        pass
diff --git a/nextcloud_mcp_server/embedding/ollama_provider.py b/nextcloud_mcp_server/embedding/ollama_provider.py
new file mode 100644
index 0000000..6050e8b
--- /dev/null
+++ b/nextcloud_mcp_server/embedding/ollama_provider.py
@@ -0,0 +1,85 @@
+"""Ollama embedding provider."""
+
+import logging
+
+import httpx
+
+from .base import EmbeddingProvider
+
+logger = logging.getLogger(__name__)
+
+
+class OllamaEmbeddingProvider(EmbeddingProvider):
+    """Ollama embedding provider with TLS support."""
+
+    def __init__(
+        self,
+        base_url: str,
+        model: str = "nomic-embed-text",
+        verify_ssl: bool = True,
+    ):
+        """
+        Initialize Ollama embedding provider.
+
+        Args:
+            base_url: Ollama API base URL (e.g., https://ollama.internal.coutinho.io:443)
+            model: Embedding model name (default: nomic-embed-text)
+            verify_ssl: Verify SSL certificates (default: True)
+        """
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.verify_ssl = verify_ssl
+        self.client = httpx.AsyncClient(verify=verify_ssl, timeout=30.0)
+        self._dimension = 768  # nomic-embed-text default
+        logger.info(
+            f"Initialized Ollama provider: {base_url} (model={model}, verify_ssl={verify_ssl})"
+        )
+
+    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
+        """
+        response = await self.client.post(
+            f"{self.base_url}/api/embeddings",
+            json={"model": self.model, "prompt": text},
+        )
+        response.raise_for_status()
+        return response.json()["embedding"]
+
+    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts (batched requests).
+
+        Note: Ollama doesn't have native batch API, so we send requests sequentially.
+        For better performance with large batches, consider using asyncio.gather().
+
+        Args:
+            texts: List of texts to embed
+
+        Returns:
+            List of vector embeddings
+        """
+        embeddings = []
+        for text in texts:
+            embedding = await self.embed(text)
+            embeddings.append(embedding)
+        return embeddings
+
+    def get_dimension(self) -> int:
+        """
+        Get embedding dimension.
+
+        Returns:
+            Vector dimension (768 for nomic-embed-text)
+        """
+        return self._dimension
+
+    async def close(self):
+        """Close HTTP client."""
+        await self.client.aclose()
diff --git a/nextcloud_mcp_server/embedding/service.py b/nextcloud_mcp_server/embedding/service.py
new file mode 100644
index 0000000..758744a
--- /dev/null
+++ b/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
diff --git a/nextcloud_mcp_server/vector/__init__.py b/nextcloud_mcp_server/vector/__init__.py
new file mode 100644
index 0000000..00c11cb
--- /dev/null
+++ b/nextcloud_mcp_server/vector/__init__.py
@@ -0,0 +1,16 @@
+"""Vector database and background sync package."""
+
+from .document_chunker import DocumentChunker
+from .processor import process_document, processor_task
+from .qdrant_client import get_qdrant_client
+from .scanner import DocumentTask, scan_user_documents, scanner_task
+
+__all__ = [
+    "get_qdrant_client",
+    "DocumentChunker",
+    "scanner_task",
+    "scan_user_documents",
+    "DocumentTask",
+    "processor_task",
+    "process_document",
+]
diff --git a/nextcloud_mcp_server/vector/document_chunker.py b/nextcloud_mcp_server/vector/document_chunker.py
new file mode 100644
index 0000000..5855154
--- /dev/null
+++ b/nextcloud_mcp_server/vector/document_chunker.py
@@ -0,0 +1,51 @@
+"""Document chunking for large texts."""
+
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class DocumentChunker:
+    """Chunk large documents for optimal embedding."""
+
+    def __init__(self, chunk_size: int = 512, overlap: int = 50):
+        """
+        Initialize document chunker.
+
+        Args:
+            chunk_size: Number of words per chunk (default: 512)
+            overlap: Number of overlapping words between chunks (default: 50)
+        """
+        self.chunk_size = chunk_size
+        self.overlap = overlap
+
+    def chunk_text(self, content: str) -> list[str]:
+        """
+        Split text into overlapping chunks.
+
+        Uses simple word-based chunking with configurable overlap to preserve
+        context across chunk boundaries.
+
+        Args:
+            content: Text content to chunk
+
+        Returns:
+            List of text chunks (may be single item if content is small)
+        """
+        # Simple word-based chunking
+        words = content.split()
+
+        if len(words) <= self.chunk_size:
+            return [content]
+
+        chunks = []
+        start = 0
+
+        while start < len(words):
+            end = start + self.chunk_size
+            chunk_words = words[start:end]
+            chunks.append(" ".join(chunk_words))
+            start = end - self.overlap
+
+        logger.debug(f"Chunked document into {len(chunks)} chunks ({len(words)} words)")
+        return chunks
diff --git a/nextcloud_mcp_server/vector/processor.py b/nextcloud_mcp_server/vector/processor.py
new file mode 100644
index 0000000..defc1d4
--- /dev/null
+++ b/nextcloud_mcp_server/vector/processor.py
@@ -0,0 +1,219 @@
+"""Processor task for vector database synchronization.
+
+Processes documents from queue: fetches content, generates embeddings, stores in Qdrant.
+"""
+
+import asyncio
+import logging
+import time
+
+import anyio
+from httpx import HTTPStatusError
+from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
+
+from nextcloud_mcp_server.client import NextcloudClient
+from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.embedding import get_embedding_service
+from nextcloud_mcp_server.vector.document_chunker import DocumentChunker
+from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+from nextcloud_mcp_server.vector.scanner import DocumentTask
+
+logger = logging.getLogger(__name__)
+
+
+async def processor_task(
+    worker_id: int,
+    document_queue: asyncio.Queue,
+    shutdown_event: anyio.Event,
+    nc_client: NextcloudClient,
+    user_id: str,
+):
+    """
+    Process documents from queue concurrently.
+
+    Each processor task runs in a loop:
+    1. Pull document from queue (with timeout)
+    2. Fetch content from Nextcloud
+    3. Tokenize and chunk text
+    4. Generate embeddings (I/O bound - external API)
+    5. Upload vectors to Qdrant
+    6. Mark task complete
+
+    Multiple processors run concurrently for I/O parallelism.
+
+    Args:
+        worker_id: Worker identifier for logging
+        document_queue: Queue to pull documents from
+        shutdown_event: Event signaling shutdown
+        nc_client: Authenticated Nextcloud client
+        user_id: User being processed
+    """
+    logger.info(f"Processor {worker_id} started")
+
+    while not shutdown_event.is_set():
+        try:
+            # Get document with timeout (allows checking shutdown)
+            doc_task = await asyncio.wait_for(
+                document_queue.get(),
+                timeout=1.0,
+            )
+
+            # Process document
+            await process_document(doc_task, nc_client)
+
+            # Mark complete
+            document_queue.task_done()
+
+        except asyncio.TimeoutError:
+            # No documents available, continue
+            continue
+
+        except Exception as e:
+            logger.error(
+                f"Processor {worker_id} error processing "
+                f"{doc_task.doc_type}_{doc_task.doc_id}: {e}",
+                exc_info=True,
+            )
+            # Mark task done even on error to prevent queue blocking
+            try:
+                document_queue.task_done()
+            except ValueError:
+                pass
+
+    logger.info(f"Processor {worker_id} stopped")
+
+
+async def process_document(doc_task: DocumentTask, nc_client: NextcloudClient):
+    """
+    Process a single document: fetch, tokenize, embed, store in Qdrant.
+
+    Implements retry logic with exponential backoff for transient failures.
+
+    Args:
+        doc_task: Document task to process
+        nc_client: Authenticated Nextcloud client
+    """
+    logger.debug(
+        f"Processing {doc_task.doc_type}_{doc_task.doc_id} "
+        f"for {doc_task.user_id} ({doc_task.operation})"
+    )
+
+    qdrant_client = await get_qdrant_client()
+    settings = get_settings()
+
+    # Handle deletion
+    if doc_task.operation == "delete":
+        await qdrant_client.delete(
+            collection_name=settings.qdrant_collection,
+            points_selector=Filter(
+                must=[
+                    FieldCondition(
+                        key="user_id",
+                        match=MatchValue(value=doc_task.user_id),
+                    ),
+                    FieldCondition(
+                        key="doc_id",
+                        match=MatchValue(value=doc_task.doc_id),
+                    ),
+                    FieldCondition(
+                        key="doc_type",
+                        match=MatchValue(value=doc_task.doc_type),
+                    ),
+                ]
+            ),
+        )
+        logger.info(
+            f"Deleted {doc_task.doc_type}_{doc_task.doc_id} for {doc_task.user_id}"
+        )
+        return
+
+    # Handle indexing with retry
+    max_retries = 3
+    retry_delay = 1.0
+
+    for attempt in range(max_retries):
+        try:
+            await _index_document(doc_task, nc_client, qdrant_client)
+            return  # Success
+
+        except (HTTPStatusError, Exception) as e:
+            if attempt < max_retries - 1:
+                logger.warning(
+                    f"Retry {attempt + 1}/{max_retries} for "
+                    f"{doc_task.doc_type}_{doc_task.doc_id}: {e}"
+                )
+                await anyio.sleep(retry_delay)
+                retry_delay *= 2  # Exponential backoff
+            else:
+                logger.error(
+                    f"Failed to index {doc_task.doc_type}_{doc_task.doc_id} "
+                    f"after {max_retries} retries: {e}"
+                )
+                raise
+
+
+async def _index_document(
+    doc_task: DocumentTask, nc_client: NextcloudClient, qdrant_client
+):
+    """
+    Index a single document (called by process_document with retry).
+
+    Args:
+        doc_task: Document task to index
+        nc_client: Authenticated Nextcloud client
+        qdrant_client: Qdrant client instance
+    """
+    settings = get_settings()
+
+    # Fetch document content
+    if doc_task.doc_type == "note":
+        document = await nc_client.notes.get_note(int(doc_task.doc_id))
+        content = f"{document['title']}\n\n{document['content']}"
+        title = document["title"]
+        etag = document.get("etag", "")
+    else:
+        raise ValueError(f"Unsupported doc_type: {doc_task.doc_type}")
+
+    # Tokenize and chunk
+    chunker = DocumentChunker(chunk_size=512, overlap=50)
+    chunks = chunker.chunk_text(content)
+
+    # Generate embeddings (I/O bound - external API call)
+    embedding_service = get_embedding_service()
+    embeddings = await embedding_service.embed_batch(chunks)
+
+    # Prepare Qdrant points
+    indexed_at = int(time.time())
+    points = []
+
+    for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+        points.append(
+            PointStruct(
+                id=f"{doc_task.doc_type}_{doc_task.doc_id}_{i}",
+                vector=embedding,
+                payload={
+                    "user_id": doc_task.user_id,
+                    "doc_id": doc_task.doc_id,
+                    "doc_type": doc_task.doc_type,
+                    "title": title,
+                    "excerpt": chunk[:200],
+                    "indexed_at": indexed_at,
+                    "modified_at": doc_task.modified_at,
+                    "etag": etag,
+                    "chunk_index": i,
+                    "total_chunks": len(chunks),
+                },
+            )
+        )
+
+    # Upsert to Qdrant
+    await qdrant_client.upsert(
+        collection_name=settings.qdrant_collection,
+        points=points,
+        wait=True,
+    )
+
+    logger.info(
+        f"Indexed {doc_task.doc_type}_{doc_task.doc_id} for {doc_task.user_id} "
+        f"({len(chunks)} chunks)"
+    )
diff --git a/nextcloud_mcp_server/vector/qdrant_client.py b/nextcloud_mcp_server/vector/qdrant_client.py
new file mode 100644
index 0000000..733d769
--- /dev/null
+++ b/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
diff --git a/nextcloud_mcp_server/vector/scanner.py b/nextcloud_mcp_server/vector/scanner.py
new file mode 100644
index 0000000..aa5c682
--- /dev/null
+++ b/nextcloud_mcp_server/vector/scanner.py
@@ -0,0 +1,172 @@
+"""Scanner task for vector database synchronization.
+
+Periodically scans enabled users' content and queues changed documents for processing.
+"""
+
+import asyncio
+import logging
+from dataclasses import dataclass
+
+import anyio
+from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+from nextcloud_mcp_server.client import NextcloudClient
+from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DocumentTask:
+    """Document task for processing queue."""
+
+    user_id: str
+    doc_id: str
+    doc_type: str  # "note", "file", "calendar"
+    operation: str  # "index" or "delete"
+    modified_at: int
+
+
+async def scanner_task(
+    document_queue: asyncio.Queue,
+    shutdown_event: anyio.Event,
+    wake_event: anyio.Event,
+    nc_client: NextcloudClient,
+    user_id: str,
+):
+    """
+    Periodic scanner that detects changed documents for enabled user.
+
+    For BasicAuth mode, scans a single user with credentials available at runtime.
+
+    Args:
+        document_queue: Queue to enqueue changed documents
+        shutdown_event: Event signaling shutdown
+        wake_event: Event to trigger immediate scan
+        nc_client: Authenticated Nextcloud client
+        user_id: User to scan
+    """
+    logger.info(f"Scanner task started for user: {user_id}")
+    settings = get_settings()
+
+    while not shutdown_event.is_set():
+        try:
+            # Scan user documents
+            await scan_user_documents(
+                user_id=user_id,
+                document_queue=document_queue,
+                nc_client=nc_client,
+            )
+
+        except Exception as e:
+            logger.error(f"Scanner error: {e}", exc_info=True)
+
+        # Sleep until next interval or wake event
+        try:
+            with anyio.move_on_after(settings.vector_sync_scan_interval):
+                # Wait for wake event or shutdown (whichever comes first)
+                await wake_event.wait()
+        except anyio.get_cancelled_exc_class():
+            # Shutdown, exit loop
+            break
+
+    logger.info("Scanner task stopped")
+
+
+async def scan_user_documents(
+    user_id: str,
+    document_queue: asyncio.Queue,
+    nc_client: NextcloudClient,
+    initial_sync: bool = False,
+):
+    """
+    Scan a single user's documents and queue changes.
+
+    Args:
+        user_id: User to scan
+        document_queue: Queue to enqueue changed documents
+        nc_client: Authenticated Nextcloud client
+        initial_sync: If True, queue all documents (first-time sync)
+    """
+    logger.info(f"Scanning documents for user: {user_id}")
+
+    # Fetch all notes from Nextcloud
+    notes = await nc_client.notes.list_notes()
+    logger.debug(f"Found {len(notes)} notes for {user_id}")
+
+    if initial_sync:
+        # Queue everything on first sync
+        for note in notes:
+            await document_queue.put(
+                DocumentTask(
+                    user_id=user_id,
+                    doc_id=str(note["id"]),
+                    doc_type="note",
+                    operation="index",
+                    modified_at=note["modified"],
+                )
+            )
+        logger.info(f"Queued {len(notes)} documents for initial sync: {user_id}")
+        return
+
+    # Get indexed state from Qdrant
+    qdrant_client = await get_qdrant_client()
+    scroll_result = await qdrant_client.scroll(
+        collection_name=get_settings().qdrant_collection,
+        scroll_filter=Filter(
+            must=[
+                FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                FieldCondition(key="doc_type", match=MatchValue(value="note")),
+            ]
+        ),
+        with_payload=["doc_id", "indexed_at"],
+        with_vectors=False,
+        limit=10000,
+    )
+
+    indexed_docs = {
+        point.payload["doc_id"]: point.payload["indexed_at"]
+        for point in scroll_result[0]
+    }
+
+    logger.debug(f"Found {len(indexed_docs)} indexed documents in Qdrant")
+
+    # Compare and queue changes
+    queued = 0
+    for note in notes:
+        doc_id = str(note["id"])
+        indexed_at = indexed_docs.get(doc_id)
+
+        # Queue if never indexed or modified since last index
+        if indexed_at is None or note["modified"] > indexed_at:
+            await document_queue.put(
+                DocumentTask(
+                    user_id=user_id,
+                    doc_id=doc_id,
+                    doc_type="note",
+                    operation="index",
+                    modified_at=note["modified"],
+                )
+            )
+            queued += 1
+
+    # Check for deleted documents (in Qdrant but not in Nextcloud)
+    nextcloud_doc_ids = {str(note["id"]) for note in notes}
+    for doc_id in indexed_docs:
+        if doc_id not in nextcloud_doc_ids:
+            await document_queue.put(
+                DocumentTask(
+                    user_id=user_id,
+                    doc_id=doc_id,
+                    doc_type="note",
+                    operation="delete",
+                    modified_at=0,
+                )
+            )
+            queued += 1
+
+    if queued > 0:
+        logger.info(f"Queued {queued} documents for incremental sync: {user_id}")
+    else:
+        logger.debug(f"No changes detected for {user_id}")
diff --git a/pyproject.toml b/pyproject.toml
index e48d876..a0da862 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,7 @@ dependencies = [
     "pyjwt[crypto]>=2.8.0",
     "aiosqlite>=0.20.0", # Async SQLite for refresh token storage
     "authlib>=1.6.5",
+    "qdrant-client>=1.7.0",  # Vector database for semantic search
 ]
 classifiers = [
     "Development Status :: 4 - Beta",
diff --git a/uv.lock b/uv.lock
index 85bf9f4..0f94096 100644
--- a/uv.lock
+++ b/uv.lock
@@ -537,6 +537,57 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e3/a5/6ddab2b4c112be95601c13428db1d8b6608a8b6039816f2ba09c346c08fc/greenlet-3.2.4-cp314-cp314-win_amd64.whl", hash = "sha256:e37ab26028f12dbb0ff65f29a8d3d44a765c61e729647bf2ddfbbed621726f01", size = 303425, upload-time = "2025-08-07T13:32:27.59Z" },
 ]
 
+[[package]]
+name = "grpcio"
+version = "1.76.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b6/e0/318c1ce3ae5a17894d5791e87aea147587c9e702f24122cc7a5c8bbaeeb1/grpcio-1.76.0.tar.gz", hash = "sha256:7be78388d6da1a25c0d5ec506523db58b18be22d9c37d8d3a32c08be4987bd73", size = 12785182, upload-time = "2025-10-21T16:23:12.106Z" }
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 version = "0.16.0"
@@ -989,6 +1040,7 @@ dependencies = [
     { name = "pydantic" },
     { name = "pyjwt", extra = ["crypto"] },
     { name = "pythonvcard4" },
+    { name = "qdrant-client" },
 ]
 
 [package.dev-dependencies]
@@ -1019,6 +1071,7 @@ requires-dist = [
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     { name = "pyjwt", extras = ["crypto"], specifier = ">=2.8.0" },
     { name = "pythonvcard4", specifier = ">=0.2.0" },
+    { name = "qdrant-client", specifier = ">=1.7.0" },
 ]
 
 [package.metadata.requires-dev]
@@ -1036,6 +1089,87 @@ dev = [
     { name = "ty", specifier = ">=0.0.1a25" },
 ]
 
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