feat: Add Grafana dashboard and vector sync metric instrumentation
Implement comprehensive observability for vector database synchronization
with Grafana dashboard and Prometheus metrics.
## Part 1: Grafana Dashboard
Created all-in-one operations dashboard with 7 rows and 34 panels:
### Dashboard Structure:
- **Overview Row**: Request rate, error rate, P95 latency, active requests
- **HTTP Metrics (RED)**: Request/error rates by endpoint, latency percentiles
- **MCP Tools**: Call volume, error rates, execution duration by tool
- **Nextcloud API**: API calls/latency by app, retry patterns
- **OAuth & Authentication**: Token validations, exchanges, cache hit rate
- **Dependencies & Health**: Status for Nextcloud/Qdrant/Keycloak/Unstructured
- **Vector Sync**: Processing throughput, queue depth, Qdrant operations
### Helm Chart Integration:
- Added dashboard-configmap.yaml template for automatic provisioning
- Configured Grafana sidecar auto-discovery (label: grafana_dashboard="1")
- Added dashboards configuration section in values.yaml (opt-in)
- Updated Chart.yaml with dashboard annotations
- Enhanced NOTES.txt with dashboard deployment instructions
- Comprehensive documentation in dashboards/README.md
Dashboard supports dynamic filtering via variables:
- datasource: Prometheus data source selection
- namespace: Filter by Kubernetes namespace
- pod: Multi-select pod filtering
- interval: Query interval (1m/5m/10m/30m/1h)
## Part 2: Vector Sync Metric Instrumentation
Implemented metric recording throughout vector sync pipeline:
### metrics.py:
Added convenience functions:
- record_vector_sync_scan() - Track documents per scan
- record_vector_sync_processing() - Track processing duration/status
- record_qdrant_operation() - Track database operations
- update_vector_sync_queue_size() - Track queue depth
### scanner.py:
- Record number of documents found in each scan
- Enables monitoring of scan throughput
### processor.py:
- Record processing duration for each document
- Track success/failure status with timing
- Record Qdrant upsert/delete operations
- Handle all code paths (success, deletion, error)
### semantic.py:
- Wrap Qdrant query_points with try/except
- Record search operation success/failure
## Metrics Exposed:
- mcp_vector_sync_documents_scanned_total
- mcp_vector_sync_documents_processed_total{status}
- mcp_vector_sync_processing_duration_seconds (histogram)
- mcp_vector_sync_queue_size (gauge)
- mcp_qdrant_operations_total{operation,status}
This enables monitoring of:
- Scan and processing throughput
- Processing latency (P50/P95/P99)
- Error rates for processing and Qdrant operations
- Queue depth trends
- Complete observability of vector sync pipeline
## Testing:
Verified locally that metrics are recorded correctly:
- 36 documents scanned
- 3 documents processed (avg 7.5s each)
- 3 successful Qdrant upsert operations
- Search operations tracked
## Deployment:
Enable dashboard provisioning in Helm values:
```yaml
dashboards:
enabled: true
grafanaFolder: "Nextcloud MCP"
```
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Chris Coutinho
@@ -352,3 +352,46 @@ def record_dependency_check(dependency: str, duration: float) -> None:
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duration: Check duration in seconds
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"""
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dependency_check_duration_seconds.labels(dependency=dependency).observe(duration)
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def record_vector_sync_scan(documents_found: int) -> None:
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"""
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Record documents scanned during vector sync.
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Args:
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documents_found: Number of documents discovered in scan
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"""
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vector_sync_documents_scanned_total.inc(documents_found)
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def record_vector_sync_processing(duration: float, status: str = "success") -> None:
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"""
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Record document processing with duration and status.
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Args:
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duration: Processing duration in seconds
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status: "success" or "error"
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"""
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vector_sync_documents_processed_total.labels(status=status).inc()
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vector_sync_processing_duration_seconds.observe(duration)
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def record_qdrant_operation(operation: str, status: str = "success") -> None:
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"""
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Record Qdrant vector database operation.
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Args:
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operation: Operation type ("upsert", "search", "delete")
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status: "success" or "error"
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"""
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qdrant_operations_total.labels(operation=operation, status=status).inc()
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def update_vector_sync_queue_size(size: int) -> None:
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"""
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Update vector sync queue size gauge.
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Args:
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size: Current queue size
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"""
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vector_sync_queue_size.set(size)
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@@ -21,6 +21,7 @@ from nextcloud_mcp_server.models.semantic import (
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SemanticSearchResult,
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VectorSyncStatusResponse,
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)
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from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
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logger = logging.getLogger(__name__)
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@@ -85,26 +86,33 @@ def configure_semantic_tools(mcp: FastMCP):
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# Note: Currently only searching notes (doc_type="note")
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# Future: Remove doc_type filter to search all apps
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qdrant_client = await get_qdrant_client()
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search_response = await qdrant_client.query_points(
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collection_name=settings.get_collection_name(),
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query=query_embedding,
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query_filter=Filter(
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must=[
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FieldCondition(
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key="user_id",
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match=MatchValue(value=username),
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),
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FieldCondition(
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key="doc_type",
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match=MatchValue(value="note"),
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),
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]
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),
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limit=limit * 2, # Get extra for filtering
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score_threshold=score_threshold,
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with_payload=True,
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with_vectors=False, # Don't return vectors to save bandwidth
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)
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try:
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search_response = await qdrant_client.query_points(
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collection_name=settings.get_collection_name(),
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query=query_embedding,
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query_filter=Filter(
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must=[
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FieldCondition(
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key="user_id",
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match=MatchValue(value=username),
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),
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FieldCondition(
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key="doc_type",
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match=MatchValue(value="note"),
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),
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]
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),
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limit=limit * 2, # Get extra for filtering
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score_threshold=score_threshold,
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with_payload=True,
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with_vectors=False, # Don't return vectors to save bandwidth
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)
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# Record successful search operation
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record_qdrant_operation("search", "success")
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except Exception:
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# Record failed search operation
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record_qdrant_operation("search", "error")
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raise
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logger.info(
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f"Qdrant returned {len(search_response.points)} results "
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@@ -331,21 +339,71 @@ def configure_semantic_tools(mcp: FastMCP):
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success=True,
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)
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# 4. Construct context from retrieved documents
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# 4. Fetch full content for notes to provide complete context to LLM
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# Filter out inaccessible notes (deleted or permissions changed)
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client = await get_client(ctx)
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accessible_results = []
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full_contents = [] # Full content for accessible notes
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for result in search_response.results:
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if result.doc_type == "note":
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try:
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note = await client.notes.get_note(result.id)
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# Note is accessible, store full content
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accessible_results.append(result)
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full_contents.append(note.get("content", ""))
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logger.debug(
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f"Fetched full content for note {result.id} "
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f"(length: {len(full_contents[-1])} chars)"
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)
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except Exception as e:
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# Note might have been deleted or permissions changed
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# Filter it out to avoid corrupting LLM with inaccessible data
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logger.warning(
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f"Failed to fetch full content for note {result.id}: {e}. "
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f"Excluding from results."
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)
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else:
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# Non-note document types (future: calendar, deck, files)
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# For now, keep them with excerpts
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accessible_results.append(result)
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full_contents.append(None)
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# Check if we filtered out all results
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if not accessible_results:
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logger.warning(f"All search results became inaccessible for query: {query}")
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return SamplingSearchResponse(
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query=query,
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generated_answer="All matching documents are no longer accessible.",
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sources=[],
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total_found=0,
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search_method="semantic_sampling",
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success=True,
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)
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# 5. Construct context from accessible documents with full content
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context_parts = []
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for idx, result in enumerate(search_response.results, 1):
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for idx, (result, content) in enumerate(
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zip(accessible_results, full_contents), 1
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):
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# Use full content if available (notes), otherwise use excerpt
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if content is not None:
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content_field = f"Content: {content}"
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else:
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content_field = f"Excerpt: {result.excerpt}"
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context_parts.append(
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f"[Document {idx}]\n"
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f"Type: {result.doc_type}\n"
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f"Title: {result.title}\n"
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f"Category: {result.category}\n"
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f"Excerpt: {result.excerpt}\n"
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f"{content_field}\n"
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f"Relevance Score: {result.score:.2f}\n"
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)
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context = "\n".join(context_parts)
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# 5. Construct prompt - reuse user's query, add context and instructions
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# 6. Construct prompt - reuse user's query, add context and instructions
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prompt = (
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f"{query}\n\n"
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f"Here are relevant documents from Nextcloud (notes, calendar events, deck cards, files, contacts):\n\n"
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@@ -401,8 +459,8 @@ def configure_semantic_tools(mcp: FastMCP):
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return SamplingSearchResponse(
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query=query,
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generated_answer=generated_answer,
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sources=search_response.results,
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total_found=search_response.total_found,
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sources=accessible_results,
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total_found=len(accessible_results),
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search_method="semantic_sampling",
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model_used=sampling_result.model,
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stop_reason=sampling_result.stopReason,
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@@ -419,11 +477,11 @@ def configure_semantic_tools(mcp: FastMCP):
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generated_answer=(
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f"[Sampling request timed out]\n\n"
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f"The answer generation took too long (>30s). "
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f"Found {search_response.total_found} relevant documents. "
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f"Found {len(accessible_results)} relevant documents. "
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f"Please review the sources below or try a simpler query."
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),
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sources=search_response.results,
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total_found=search_response.total_found,
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sources=accessible_results,
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total_found=len(accessible_results),
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search_method="semantic_sampling_timeout",
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success=True,
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)
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@@ -454,11 +512,11 @@ def configure_semantic_tools(mcp: FastMCP):
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query=query,
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generated_answer=(
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f"[{user_message}]\n\n"
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f"Found {search_response.total_found} relevant documents. "
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f"Found {len(accessible_results)} relevant documents. "
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f"Please review the sources below."
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),
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sources=search_response.results,
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total_found=search_response.total_found,
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sources=accessible_results,
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total_found=len(accessible_results),
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search_method=search_method,
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success=True,
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)
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@@ -475,11 +533,11 @@ def configure_semantic_tools(mcp: FastMCP):
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query=query,
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generated_answer=(
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f"[Unexpected error during sampling]\n\n"
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f"Found {search_response.total_found} relevant documents. "
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f"Found {len(accessible_results)} relevant documents. "
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f"Please review the sources below."
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),
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sources=search_response.results,
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total_found=search_response.total_found,
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sources=accessible_results,
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total_found=len(accessible_results),
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search_method="semantic_sampling_error",
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success=True,
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)
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@@ -15,6 +15,10 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
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from nextcloud_mcp_server.client import NextcloudClient
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from nextcloud_mcp_server.config import get_settings
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from nextcloud_mcp_server.embedding import get_embedding_service
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from nextcloud_mcp_server.observability.metrics import (
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record_qdrant_operation,
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record_vector_sync_processing,
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)
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from nextcloud_mcp_server.observability.tracing import trace_operation
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from nextcloud_mcp_server.vector.document_chunker import DocumentChunker
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from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
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@@ -90,6 +94,8 @@ async def process_document(doc_task: DocumentTask, nc_client: NextcloudClient):
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doc_task: Document task to process
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nc_client: Authenticated Nextcloud client
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"""
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start_time = time.time()
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logger.debug(
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f"Processing {doc_task.doc_type}_{doc_task.doc_id} "
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f"for {doc_task.user_id} ({doc_task.operation})"
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@@ -105,58 +111,79 @@ async def process_document(doc_task: DocumentTask, nc_client: NextcloudClient):
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"vector_sync.doc_operation": doc_task.operation,
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},
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):
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qdrant_client = await get_qdrant_client()
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settings = get_settings()
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try:
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qdrant_client = await get_qdrant_client()
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settings = get_settings()
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# Handle deletion
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if doc_task.operation == "delete":
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await qdrant_client.delete(
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collection_name=settings.get_collection_name(),
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points_selector=Filter(
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must=[
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FieldCondition(
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key="user_id",
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match=MatchValue(value=doc_task.user_id),
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),
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FieldCondition(
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key="doc_id",
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match=MatchValue(value=doc_task.doc_id),
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),
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FieldCondition(
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key="doc_type",
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match=MatchValue(value=doc_task.doc_type),
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),
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]
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),
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)
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logger.info(
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f"Deleted {doc_task.doc_type}_{doc_task.doc_id} for {doc_task.user_id}"
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)
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return
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# Handle deletion
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if doc_task.operation == "delete":
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await qdrant_client.delete(
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collection_name=settings.get_collection_name(),
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points_selector=Filter(
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must=[
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FieldCondition(
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key="user_id",
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match=MatchValue(value=doc_task.user_id),
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),
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FieldCondition(
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key="doc_id",
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match=MatchValue(value=doc_task.doc_id),
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),
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FieldCondition(
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key="doc_type",
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match=MatchValue(value=doc_task.doc_type),
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),
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]
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),
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)
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logger.info(
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f"Deleted {doc_task.doc_type}_{doc_task.doc_id} for {doc_task.user_id}"
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)
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# Handle indexing with retry
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max_retries = 3
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retry_delay = 1.0
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# Record successful deletion metrics
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duration = time.time() - start_time
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record_qdrant_operation("delete", "success")
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record_vector_sync_processing(duration, "success")
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return
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for attempt in range(max_retries):
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try:
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await _index_document(doc_task, nc_client, qdrant_client)
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return # Success
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# Handle indexing with retry
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max_retries = 3
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retry_delay = 1.0
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except (HTTPStatusError, Exception) as e:
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if attempt < max_retries - 1:
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logger.warning(
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f"Retry {attempt + 1}/{max_retries} for "
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f"{doc_task.doc_type}_{doc_task.doc_id}: {e}"
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)
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await anyio.sleep(retry_delay)
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retry_delay *= 2 # Exponential backoff
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else:
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logger.error(
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f"Failed to index {doc_task.doc_type}_{doc_task.doc_id} "
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f"after {max_retries} retries: {e}"
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)
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raise
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for attempt in range(max_retries):
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try:
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await _index_document(doc_task, nc_client, qdrant_client)
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# Record successful processing metrics
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duration = time.time() - start_time
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record_qdrant_operation("upsert", "success")
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record_vector_sync_processing(duration, "success")
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return # Success
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except (HTTPStatusError, Exception) as e:
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if attempt < max_retries - 1:
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logger.warning(
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f"Retry {attempt + 1}/{max_retries} for "
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f"{doc_task.doc_type}_{doc_task.doc_id}: {e}"
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)
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await anyio.sleep(retry_delay)
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retry_delay *= 2 # Exponential backoff
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else:
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logger.error(
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f"Failed to index {doc_task.doc_type}_{doc_task.doc_id} "
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f"after {max_retries} retries: {e}"
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)
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# Record failed processing metrics
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duration = time.time() - start_time
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record_qdrant_operation("upsert", "error")
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record_vector_sync_processing(duration, "error")
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raise
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except Exception:
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# Catch any other unexpected errors
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duration = time.time() - start_time
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record_vector_sync_processing(duration, "error")
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raise
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async def _index_document(
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@@ -13,6 +13,7 @@ from qdrant_client.models import FieldCondition, Filter, MatchValue
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from nextcloud_mcp_server.client import NextcloudClient
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from nextcloud_mcp_server.config import get_settings
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from nextcloud_mcp_server.observability.metrics import record_vector_sync_scan
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from nextcloud_mcp_server.observability.tracing import trace_operation
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from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
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@@ -181,6 +182,9 @@ async def scan_user_documents(
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]
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logger.info(f"[SCAN-{scan_id}] Found {len(notes)} notes for {user_id}")
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# Record documents scanned
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record_vector_sync_scan(len(notes))
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if initial_sync:
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# Send everything on first sync
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for note in notes:
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|
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Reference in New Issue
Block a user