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>
This commit is contained in:
Chris Coutinho
@@ -1,3 +1,4 @@
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import asyncio
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import logging
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import os
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from collections.abc import AsyncIterator
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@@ -8,6 +9,7 @@ from typing import TYPE_CHECKING, Optional
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if TYPE_CHECKING:
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from nextcloud_mcp_server.auth.refresh_token_storage import RefreshTokenStorage
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import anyio
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import click
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import httpx
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import uvicorn
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@@ -32,6 +34,7 @@ from nextcloud_mcp_server.client import NextcloudClient
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from nextcloud_mcp_server.config import (
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LOGGING_CONFIG,
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get_document_processor_config,
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get_settings,
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setup_logging,
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)
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from nextcloud_mcp_server.context import get_client as get_nextcloud_client
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@@ -47,6 +50,7 @@ from nextcloud_mcp_server.server import (
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configure_webdav_tools,
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)
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from nextcloud_mcp_server.server.oauth_tools import register_oauth_tools
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from nextcloud_mcp_server.vector import processor_task, scanner_task
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logger = logging.getLogger(__name__)
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@@ -206,6 +210,9 @@ class AppContext:
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"""Application context for BasicAuth mode."""
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client: NextcloudClient
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document_queue: Optional[asyncio.Queue] = None
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shutdown_event: Optional[anyio.Event] = None
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scanner_wake_event: Optional[anyio.Event] = None
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@dataclass
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@@ -369,6 +376,9 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
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Creates a single Nextcloud client with basic authentication
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that is shared across all requests.
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If vector sync is enabled (VECTOR_SYNC_ENABLED=true), also starts
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background tasks for automatic document indexing (ADR-007).
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"""
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logger.info("Starting MCP server in BasicAuth mode")
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logger.info("Creating Nextcloud client with BasicAuth")
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@@ -379,11 +389,74 @@ async def app_lifespan_basic(server: FastMCP) -> AsyncIterator[AppContext]:
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# Initialize document processors
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initialize_document_processors()
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try:
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yield AppContext(client=client)
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finally:
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logger.info("Shutting down BasicAuth mode")
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await client.close()
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settings = get_settings()
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# Check if vector sync is enabled
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if settings.vector_sync_enabled:
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logger.info("Vector sync enabled - starting background tasks")
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# Get username from environment for BasicAuth mode
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username = os.getenv("NEXTCLOUD_USERNAME")
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if not username:
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raise ValueError(
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"NEXTCLOUD_USERNAME is required for vector sync in BasicAuth mode"
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)
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# Initialize shared state
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document_queue = asyncio.Queue(maxsize=settings.vector_sync_queue_max_size)
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shutdown_event = anyio.Event()
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scanner_wake_event = anyio.Event()
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# Start background tasks using anyio TaskGroup
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async with anyio.create_task_group() as tg:
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# Start scanner task
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tg.start_soon(
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scanner_task,
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document_queue,
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shutdown_event,
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scanner_wake_event,
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client,
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username,
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)
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# Start processor pool
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for i in range(settings.vector_sync_processor_workers):
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tg.start_soon(
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processor_task,
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i,
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document_queue,
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shutdown_event,
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client,
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username,
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)
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logger.info(
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f"Background sync tasks started: 1 scanner + {settings.vector_sync_processor_workers} processors"
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)
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# Yield with background tasks running
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try:
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yield AppContext(
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client=client,
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document_queue=document_queue,
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shutdown_event=shutdown_event,
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scanner_wake_event=scanner_wake_event,
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)
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finally:
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# Shutdown signal
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logger.info("Shutting down background sync tasks")
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shutdown_event.set()
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# TaskGroup automatically cancels all tasks on exit
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logger.info("Background sync tasks stopped")
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await client.close()
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else:
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# No vector sync - simple lifecycle
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try:
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yield AppContext(client=client)
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finally:
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logger.info("Shutting down BasicAuth mode")
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await client.close()
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async def setup_oauth_config():
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@@ -946,7 +1019,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
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"""Readiness probe endpoint.
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Returns 200 OK if the application is ready to serve traffic.
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Checks that required configuration is present.
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Checks that required configuration is present and Qdrant if vector sync enabled.
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"""
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checks = {}
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is_ready = True
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@@ -976,6 +1049,24 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
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checks["auth_configured"] = "error: credentials not set"
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is_ready = False
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# Check Qdrant status if vector sync is enabled
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vector_sync_enabled = (
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os.getenv("VECTOR_SYNC_ENABLED", "false").lower() == "true"
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)
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if vector_sync_enabled:
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try:
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qdrant_url = os.getenv("QDRANT_URL", "http://qdrant:6333")
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async with httpx.AsyncClient(timeout=2.0) as client:
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response = await client.get(f"{qdrant_url}/readyz")
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if response.status_code == 200:
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checks["qdrant"] = "ok"
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else:
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checks["qdrant"] = f"error: status {response.status_code}"
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is_ready = False
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except Exception as e:
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checks["qdrant"] = f"error: {str(e)}"
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is_ready = False
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status_code = 200 if is_ready else 503
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return JSONResponse(
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{
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@@ -79,19 +79,22 @@ async def register_client(
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client_name: str = "Nextcloud MCP Server",
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redirect_uris: list[str] | None = None,
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scopes: str = "openid profile email",
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token_type: str = "Bearer",
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token_type: str | None = "Bearer",
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resource_url: str | None = None,
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) -> ClientInfo:
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"""
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Register a new OAuth client with Nextcloud OIDC using dynamic client registration.
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Register a new OAuth client using RFC 7591 Dynamic Client Registration.
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This function supports both Nextcloud OIDC and standard OIDC providers like Keycloak.
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Args:
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nextcloud_url: Base URL of the Nextcloud instance
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nextcloud_url: Base URL of the OIDC provider
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registration_endpoint: Full URL to the registration endpoint
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client_name: Name of the client application
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redirect_uris: List of redirect URIs (default: http://localhost:8000/oauth/callback)
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scopes: Space-separated list of scopes to request
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token_type: Type of access tokens to issue (default: "Bearer", also supports "JWT")
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token_type: Type of access tokens (default: "Bearer", supports "JWT" for Nextcloud).
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Set to None to omit this field (required for Keycloak and other standard providers).
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resource_url: OAuth 2.0 Protected Resource URL (RFC 9728) - used for token introspection authorization
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Returns:
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@@ -100,6 +103,11 @@ async def register_client(
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Raises:
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httpx.HTTPStatusError: If registration fails
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ValueError: If response is invalid
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Note:
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The token_type parameter is a Nextcloud-specific extension and is not part of RFC 7591.
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Standard OIDC providers like Keycloak do not accept this field and will return a 400 error
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if it's included. Set token_type=None when registering with Keycloak or other standard providers.
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"""
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if redirect_uris is None:
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redirect_uris = ["http://localhost:8000/oauth/callback"]
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@@ -111,9 +119,12 @@ async def register_client(
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"grant_types": ["authorization_code", "refresh_token"],
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"response_types": ["code"],
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"scope": scopes,
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"token_type": token_type,
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}
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# Add token_type if provided (Nextcloud-specific, not RFC 7591 standard)
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if token_type is not None:
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client_metadata["token_type"] = token_type
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# Add resource_url if provided (RFC 9728)
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if resource_url:
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client_metadata["resource_url"] = resource_url
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@@ -156,6 +156,22 @@ class Settings:
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token_encryption_key: Optional[str] = None
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token_storage_db: Optional[str] = None
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# Vector sync settings (ADR-007)
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vector_sync_enabled: bool = False
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vector_sync_scan_interval: int = 3600 # seconds
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vector_sync_processor_workers: int = 3
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vector_sync_queue_max_size: int = 10000
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# Qdrant settings
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qdrant_url: str = "http://qdrant:6333"
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qdrant_api_key: Optional[str] = None
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qdrant_collection: str = "nextcloud_content"
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# Ollama settings (for embeddings)
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ollama_base_url: Optional[str] = None
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ollama_embedding_model: str = "nomic-embed-text"
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ollama_verify_ssl: bool = True
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def get_settings() -> Settings:
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"""Get application settings from environment variables.
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@@ -192,4 +208,23 @@ def get_settings() -> Settings:
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# Token settings
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token_encryption_key=os.getenv("TOKEN_ENCRYPTION_KEY"),
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token_storage_db=os.getenv("TOKEN_STORAGE_DB", "/tmp/tokens.db"),
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# Vector sync settings (ADR-007)
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vector_sync_enabled=(
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os.getenv("VECTOR_SYNC_ENABLED", "false").lower() == "true"
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),
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vector_sync_scan_interval=int(os.getenv("VECTOR_SYNC_SCAN_INTERVAL", "3600")),
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vector_sync_processor_workers=int(
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os.getenv("VECTOR_SYNC_PROCESSOR_WORKERS", "3")
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),
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vector_sync_queue_max_size=int(
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os.getenv("VECTOR_SYNC_QUEUE_MAX_SIZE", "10000")
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),
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# Qdrant settings
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qdrant_url=os.getenv("QDRANT_URL", "http://qdrant:6333"),
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qdrant_api_key=os.getenv("QDRANT_API_KEY"),
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qdrant_collection=os.getenv("QDRANT_COLLECTION", "nextcloud_content"),
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# Ollama settings
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ollama_base_url=os.getenv("OLLAMA_BASE_URL"),
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ollama_embedding_model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
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ollama_verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
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)
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@@ -0,0 +1,5 @@
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"""Embedding service package for generating vector embeddings."""
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from .service import EmbeddingService, get_embedding_service
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__all__ = ["EmbeddingService", "get_embedding_service"]
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@@ -0,0 +1,43 @@
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"""Abstract base class for embedding providers."""
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from abc import ABC, abstractmethod
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class EmbeddingProvider(ABC):
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"""Base class for embedding providers."""
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@abstractmethod
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async def embed(self, text: str) -> list[float]:
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"""
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Generate embedding vector for text.
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Args:
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text: Input text to embed
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Returns:
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Vector embedding as list of floats
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"""
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pass
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@abstractmethod
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async def embed_batch(self, texts: list[str]) -> list[list[float]]:
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"""
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Generate embeddings for multiple texts (optimized).
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Args:
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texts: List of texts to embed
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Returns:
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List of vector embeddings
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"""
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pass
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@abstractmethod
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def get_dimension(self) -> int:
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"""
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Get embedding dimension for this provider.
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Returns:
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Vector dimension (e.g., 768 for nomic-embed-text)
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"""
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pass
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@@ -0,0 +1,85 @@
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"""Ollama embedding provider."""
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import logging
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import httpx
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from .base import EmbeddingProvider
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logger = logging.getLogger(__name__)
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class OllamaEmbeddingProvider(EmbeddingProvider):
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"""Ollama embedding provider with TLS support."""
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def __init__(
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self,
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base_url: str,
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model: str = "nomic-embed-text",
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verify_ssl: bool = True,
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):
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"""
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Initialize Ollama embedding provider.
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Args:
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base_url: Ollama API base URL (e.g., https://ollama.internal.coutinho.io:443)
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model: Embedding model name (default: nomic-embed-text)
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verify_ssl: Verify SSL certificates (default: True)
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"""
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self.base_url = base_url.rstrip("/")
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self.model = model
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self.verify_ssl = verify_ssl
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self.client = httpx.AsyncClient(verify=verify_ssl, timeout=30.0)
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self._dimension = 768 # nomic-embed-text default
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logger.info(
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f"Initialized Ollama provider: {base_url} (model={model}, verify_ssl={verify_ssl})"
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)
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async def embed(self, text: str) -> list[float]:
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"""
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Generate embedding vector for text.
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Args:
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text: Input text to embed
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Returns:
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Vector embedding as list of floats
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"""
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response = await self.client.post(
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f"{self.base_url}/api/embeddings",
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json={"model": self.model, "prompt": text},
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)
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response.raise_for_status()
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return response.json()["embedding"]
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async def embed_batch(self, texts: list[str]) -> list[list[float]]:
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"""
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Generate embeddings for multiple texts (batched requests).
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Note: Ollama doesn't have native batch API, so we send requests sequentially.
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For better performance with large batches, consider using asyncio.gather().
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Args:
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texts: List of texts to embed
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Returns:
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List of vector embeddings
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"""
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embeddings = []
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for text in texts:
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embedding = await self.embed(text)
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embeddings.append(embedding)
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return embeddings
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def get_dimension(self) -> int:
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"""
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Get embedding dimension.
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Returns:
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Vector dimension (768 for nomic-embed-text)
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"""
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return self._dimension
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async def close(self):
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"""Close HTTP client."""
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await self.client.aclose()
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@@ -0,0 +1,102 @@
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"""Embedding service with provider detection."""
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|
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import logging
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import os
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from .base import EmbeddingProvider
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from .ollama_provider import OllamaEmbeddingProvider
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logger = logging.getLogger(__name__)
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class EmbeddingService:
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"""Unified embedding service with automatic provider detection."""
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def __init__(self):
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"""Initialize embedding service with auto-detected provider."""
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self.provider = self._detect_provider()
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def _detect_provider(self) -> EmbeddingProvider:
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"""
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Auto-detect available embedding provider.
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||||
|
||||
Checks environment variables in order:
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1. OLLAMA_BASE_URL - Use Ollama provider
|
||||
|
||||
Returns:
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Configured embedding provider
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||||
|
||||
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
|
||||
@@ -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",
|
||||
]
|
||||
@@ -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
|
||||
@@ -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)"
|
||||
)
|
||||
@@ -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
|
||||
@@ -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}")
|
||||
Reference in New Issue
Block a user