Building a Production Monitoring Suite: The Ops View

A production Graph RAG system is a living thing. Its database changes with every ingestion. Its AI "Personality" changes with every model update. Without a Monitoring Suite, you are flying blind. When a system is slow, is it the Graph DB? Is it the LLM? Is it a "Zombie" ingestion worker? You need a single Source of Operational Truth.

In this final lesson of Module 17, we will build the Monitoring Blueprint. We will learn how to integrate Prometheus (for database metrics), LangSmith (for AI traces), and ELK (for logs) into a single view. We will see how to build "Alerting Thresholds" that catch a "Database CPU Spike" before it disconnects your users.

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1. The Three Pillars of Graph Ops

Pillar 1: Infrastructure (The Hardware)

• Metrics: CPU, RAM usage, Page Cache Hit Ratio, Disk I/O.
• Tool: Prometheus + Neo4j Exporter.

Pillar 2: Pipeline (The Workers)

• Metrics: Queue Depth (How many docs are waiting?), Processing Time per doc, Error Rate.
• Tool: Redis Desktop / Grafana.

Pillar 3: Logic (The AI)

• Metrics: Token usage per query, Hallucination Rate (Module 12), Average # of Hops per answer.
• Tool: LangSmith / Honeycomb.

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2. The "Health Check" Endpoint

Your API should have a special URL (e.g., /health) that performs a Deep Probe:

1. Can it ping the Graph DB?
2. Can it perform a simple MATCH (n) RETURN count(n) query?
3. Can it reach the LLM provider (OpenAI/Gemini)?

If any of these fail, the load balancer should automatically stop sending traffic to that instance.

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3. The "Incident Replay" Dashboard

When a user reports a "Bad Answer," your dashboard should allow you to Replay the Traversal.

• You enter the Request ID.
• The UI shows you: The User Query -> The Generated Cypher -> The Graph Result -> The LLM Output.
• Goal: Identify which part of the "Pipeline" failed.

graph TD
    API[RAG API] -->|Metrics| P[Prometheus]
    API -->|Traces| LS[LangSmith]
    API -->|Logs| ELK[Log Pool]
    
    P & LS & ELK --> DASH[Unified Operations Dashboard]
    
    style DASH fill:#34A853,color:#fff
    note[One screen to rule them all]

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4. Implementation: A Unified Metric Logger

def log_operational_metrics(request_id, start_time, graph_time, llm_time):
    total_time = time.time() - start_time
    
    # Store in Prometheus / InfluxDB
    push_metric("rag_total_latency", total_time)
    push_metric("rag_graph_latency", graph_time)
    push_metric("rag_llm_latency", llm_time)
    
    # Calculate percentages
    graph_pct = (graph_time / total_time) * 100
    print(f"System Check: Graph took up {graph_pct:.1f}% of the total request time.")

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5. Summary and Exercises

Monitoring is the "Insurance Policy" for your AI infrastructure.

• Infrastructure metrics prevent hardware crashes.
• Pipeline metrics ensure knowledge remains fresh.
• Logic metrics prevent the erosion of AI reliability.
• Unified dashboards allow for rapid troubleshooting during incidents.

Exercises

1. Dashboard Design: Which metric would you put in the "Biggest Widget" on your screen? Why?
2. Alerting Setup: If the average "Hops per query" suddenly jumps from 2 to 20, what is happening in your system? (Hint: The LLM might be writing inefficient Cypher or getting stuck in a loop).
3. Visualization: Draw a graph representing "A Perfect Query" timeline. Mark the Start, the Graph call, and the LLM response.

Congratulations! You have completed Module 17: Graph RAG System Architecture. You now have the blueprint for a complete, production-ready system.

In Module 18: Advanced Graph RAG Patterns, we will look at the experimental frontier: Multi-graphs, Temporal Reasoning, and more.