Intelligent Incident Response (FIXED)

Scenario Overview

Organization: Tier-1 Telecom Service Provider with nationwide 5G network Challenge: 99.999% uptime SLA for critical infrastructure Scale: 50,000+ cell towers, 500+ edge data centers, multi-vendor RAN Complexity: Network slicing, service orchestration, cascading failures

Technical Workflow

Step 1: Initialize Incident Response System

# Setup incident response environment
mkdir -p incident-response/{runbooks,topology,logs,analysis}
cd incident-response

# Initialize FalkorDB for incident knowledge
netintel-ocr kg init \
  --falkordb-host localhost \
  --falkordb-port 6379 \
  --graph-name incident_response

# Set environment for incident processing
export FALKORDB_GRAPH=incident_response
export KG_MODEL=RotatE
export KG_EPOCHS=100

Step 2: Ingest Operational Documentation

# Process network topology documents with KG
netintel-ocr process batch topology/ \
  --pattern "*.pdf" \
  --kg-model RotatE \
  --extract-tables \
  --output-dir analysis/topology/

# Process each runbook document
for runbook in runbooks/*.pdf; do
  netintel-ocr kg process \
    --model RotatE \
    --epochs 100 \
    "$runbook"
done

# Build service dependency graph using Cypher
netintel-ocr kg query \
  "LOAD CSV WITH HEADERS FROM 'file:///dependencies.csv' AS row
   CREATE (s:Service {name:row.service})
   MERGE (d:Service {name:row.dependency})
   CREATE (s)-[:DEPENDS_ON {criticality:row.criticality}]->(d)" \
  --format json

# Verify extraction
netintel-ocr kg stats --format table

# View dependency statistics
netintel-ocr kg stats --format json > analysis/dependency_stats.json

Step 3: Incident Analysis and Correlation

# Create incident data in graph
cat > test_incident.json << EOF
{
  "id": "INC-2024-1847",
  "severity": "P1",
  "timestamp": "2024-01-15T14:23:00Z",
  "affected_services": ["5g-core-amf", "CELL-NYC-4521"],
  "symptoms": ["BBU connectivity loss", "S1-MME timeout"]
}
EOF

# Import incident into graph
netintel-ocr kg query \
  "CREATE (i:Incident {id:'INC-2024-1847', severity:'P1'})
   SET i.timestamp = datetime('2024-01-15T14:23:00Z')
   CREATE (s1:Service {name:'5g-core-amf'})
   CREATE (s2:Service {name:'CELL-NYC-4521'})
   CREATE (i)-[:AFFECTS]->(s1)
   CREATE (i)-[:AFFECTS]->(s2)" \
  --format json

# Find similar past incidents using embeddings
netintel-ocr kg find-similar "INC-2024-1847" \
  --limit 10 \
  --threshold 0.7 > analysis/similar_incidents.json

# Use RAG to analyze the incident
netintel-ocr kg rag-query \
  "Analyze incident INC-2024-1847 with BBU connectivity loss and S1-MME timeout. What are the likely causes and recommended actions?" \
  --mode hybrid \
  --context-depth 3 > analysis/incident_analysis.txt

# Find affected services using path analysis
netintel-ocr kg path-find "CELL-NYC-4521" "5g-core-amf" \
  --max-depth 5 \
  --bidirectional > analysis/affected_paths.json

Step 4: Runbook Retrieval and Execution

# Find relevant runbooks using hybrid search
netintel-ocr kg hybrid-search \
  "BBU connectivity loss S1-MME interface timeout troubleshooting" \
  --strategy adaptive \
  --limit 5 > analysis/relevant_runbooks.json

# Get specific runbook procedures using RAG
netintel-ocr kg rag-query \
  "What are the step-by-step procedures for resolving BBU connectivity loss on CELL-NYC-4521?" \
  --mode hybrid \
  --temperature 0.3 > analysis/runbook_procedures.txt

# Query for specific remediation steps
netintel-ocr kg query \
  "MATCH (r:Runbook)-[:ADDRESSES]->(s:Symptom)
   WHERE s.name IN ['BBU connectivity loss', 'S1-MME timeout']
   RETURN r.name, r.procedure, r.priority
   ORDER BY r.priority" \
  --format json > analysis/remediation_steps.json

# Generate automation script using RAG
netintel-ocr kg rag-query \
  "Generate a bash script to restart BBU services and verify S1-MME connectivity" \
  --mode hybrid \
  --temperature 0.2 > analysis/remediation_script.sh

Step 5: Root Cause Analysis

# Trace dependencies to find root cause
netintel-ocr kg query \
  "MATCH path = (i:Incident {id:'INC-2024-1847'})-[:AFFECTS]->
         (s:Service)-[:DEPENDS_ON*1..5]->(root:Service)
   WHERE NOT EXISTS((root)-[:DEPENDS_ON]->())
   RETURN path, root.name as potential_root_cause" \
  --format json > analysis/dependency_trace.json

# Use clustering to identify incident patterns
netintel-ocr kg cluster \
  --n-clusters 5 \
  --method kmeans > analysis/incident_patterns.json

# Analyze root cause using RAG
netintel-ocr kg rag-query \
  "Based on the dependency graph and incident history, what is the most likely root cause of INC-2024-1847?" \
  --mode hybrid \
  --context-depth 5 > analysis/root_cause_analysis.txt

# Find common failure points
netintel-ocr kg query \
  "MATCH (i:Incident)-[:AFFECTS]->(s:Service)
   WITH s, count(i) as incident_count
   WHERE incident_count > 3
   RETURN s.name, incident_count
   ORDER BY incident_count DESC" \
  --format json > analysis/common_failure_points.json

Step 6: Impact Assessment

# Find all downstream dependencies
netintel-ocr kg query \
  "MATCH (start:Service {name:'5g-core-amf'})-[:DEPENDS_ON*]->(downstream)
   RETURN DISTINCT downstream.name, downstream.criticality" \
  --format json > analysis/downstream_impact.json

# Calculate business impact using RAG
netintel-ocr kg rag-query \
  "What is the business impact of 5g-core-amf degradation affecting CELL-NYC-4521 and surrounding cells?" \
  --mode hybrid > analysis/business_impact.txt

# Find affected customers
netintel-ocr kg query \
  "MATCH (c:Cell {id:'CELL-NYC-4521'})-[:SERVES]->(area:Area)
         -[:CONTAINS]->(customer:Customer)
   RETURN count(customer) as affected_customers,
          collect(DISTINCT customer.tier) as customer_tiers" \
  --format json > analysis/customer_impact.json

# Generate impact timeline
netintel-ocr kg query \
  "MATCH (i:Incident {id:'INC-2024-1847'})-[r:AFFECTS]->(s:Service)
   RETURN s.name, r.detected_at, r.resolved_at
   ORDER BY r.detected_at" \
  --format json > analysis/impact_timeline.json

Step 7: Pattern Analysis and Prevention

# Analyze incident patterns using embeddings
netintel-ocr kg train-embeddings \
  --model ComplEx \
  --epochs 150 \
  --force

# Find incident clusters
netintel-ocr kg cluster \
  --n-clusters 10 \
  --method dbscan \
  --min-samples 3 \
  --eps 0.5 > analysis/incident_clusters.json

# Visualize incident patterns
netintel-ocr kg visualize \
  --method tsne \
  --dimensions 2 \
  --color-by severity \
  --save-plot analysis/incident_landscape.png

# Compare with similar incidents
netintel-ocr kg find-similar "INC-2024-1847" \
  --limit 20 \
  --threshold 0.6 > analysis/historical_similar.json

# Generate prevention recommendations
netintel-ocr kg rag-query \
  "Based on the incident patterns and root cause analysis, what preventive measures should be implemented to avoid similar incidents?" \
  --mode hybrid \
  --context-depth 4 > analysis/prevention_recommendations.txt

Step 8: Automated Response Workflows

# Create batch queries for common incident checks
cat > incident_queries.txt << EOF
What services are currently affected?
What is the root cause of the current incident?
Which runbooks should be executed?
What is the expected recovery time?
What preventive measures are recommended?
EOF

netintel-ocr kg batch-query incident_queries.txt \
  --output analysis/incident_batch_analysis.json \
  --parallel 4

# Generate incident report using RAG
netintel-ocr kg rag-query \
  "Generate a comprehensive incident report for INC-2024-1847 including timeline, root cause, impact, and remediation steps" \
  --mode hybrid \
  --temperature 0.5 > analysis/incident_report.md

# Export incident graph for visualization
netintel-ocr kg export \
  --format graphml \
  --output analysis/incident_graph.graphml

# Export full incident data with embeddings
netintel-ocr kg export \
  --format json \
  --include-embeddings \
  --output analysis/incident_full_export.json

Step 9: Continuous Learning

# Update runbook effectiveness based on incident
netintel-ocr kg query \
  "MATCH (r:Runbook {name:'BBU-Recovery-v2.3'})<-[:USED_IN]-(i:Incident)
   SET r.success_count = r.success_count + 1,
       r.avg_resolution_time =
         (r.avg_resolution_time * r.use_count + 45) / (r.use_count + 1),
       r.use_count = r.use_count + 1
   RETURN r" \
  --format json

# Retrain embeddings with new incident data
netintel-ocr kg train-embeddings \
  --model RotatE \
  --epochs 100

# Generate lessons learned
netintel-ocr kg rag-query \
  "What lessons were learned from incident INC-2024-1847 and how should runbooks be updated?" \
  --mode hybrid > analysis/lessons_learned.txt

# Update incident knowledge base
netintel-ocr kg process \
  --model RotatE \
  --epochs 50 \
  analysis/incident_report.md

Python Integration Example

from netintel_ocr.kg import HybridRetriever, FalkorDBManager
import asyncio
import json

async def analyze_incident(incident_id: str, symptoms: list):
    # Initialize components
    manager = FalkorDBManager(
        host="localhost",
        port=6379,
        graph_name="incident_response"
    )

    retriever = HybridRetriever(
        falkor_manager=manager,
        milvus_client=None
    )

    # Create incident in graph
    cypher = f"""
    CREATE (i:Incident {{id:'{incident_id}', timestamp:datetime()}})
    """
    manager.execute_cypher(cypher)

    # Find similar incidents
    results = await retriever.hybrid_search(
        query=" ".join(symptoms),
        strategy="adaptive",
        limit=10
    )

    return results

# Example usage
incident = "INC-2024-1848"
symptoms = ["packet loss", "latency spike", "BGP flapping"]
analysis = asyncio.run(analyze_incident(incident, symptoms))

Performance Metrics

Actual Achievable Performance

• Incident correlation time: 2-5 seconds
• Runbook retrieval: 1-2 seconds
• Root cause analysis: 3-5 seconds (using RAG)
• Impact assessment: 1-3 seconds
• Pattern analysis: 5-10 seconds (with clustering)

Accuracy Metrics

• Incident similarity matching: 70-80%
• Root cause identification: 60-70%
• Runbook relevance: 75-85%
• Impact prediction: 65-75%

Commands Reference (Only Valid Commands)

# Essential incident response commands that actually work
netintel-ocr kg init                          # Initialize KG system
netintel-ocr kg process document.pdf          # Process runbooks/docs
netintel-ocr kg query "[Cypher]"             # Query incident data
netintel-ocr kg find-similar "[incident]"     # Find similar incidents
netintel-ocr kg path-find "[from]" "[to]"    # Trace dependencies
netintel-ocr kg rag-query "[question]"       # Analyze incidents
netintel-ocr kg hybrid-search "[symptoms]"   # Find relevant info
netintel-ocr kg cluster                      # Identify patterns
netintel-ocr kg train-embeddings             # Learn from incidents
netintel-ocr kg export --format json         # Export incident data
netintel-ocr kg batch-query queries.txt      # Batch analysis
netintel-ocr kg visualize                    # Visualize patterns