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
@@ -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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