Monitoring Patterns - LangChain in Production

Learn advanced monitoring strategies for LangChain applications, including metrics, dashboards, and alerting

📈 Monitoring Patterns Overview

Monitoring ensures reliability, performance, and rapid incident response. This guide covers metrics, dashboards, alerting, and best practices for production systems.

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🏛️ Metrics to Track

• LLM latency and throughput
• Error rates and exceptions
• Resource usage (CPU, memory, GPU)
• Vector DB query performance

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🧑‍💻 Dashboards & Visualization

• Use Grafana, Azure Dashboards, or AWS CloudWatch
• Visualize key metrics and trends
• Set up real-time alerts for anomalies

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🚨 Alerting & Incident Response

• Automate alerts for high error rates, latency, or resource exhaustion
• Integrate with incident response tools
• Document and review incidents for improvement

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🧩 Example: FastAPI Health Check Endpoint

from fastapi import FastAPI

app = FastAPI()

@app.get("/health")
def health():
    return {"status": "ok"}

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🔗 Next Steps

• Callback Patterns

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Key Monitoring Takeaways:

• Track key metrics and visualize trends
• Automate alerts and incident response
• Continuously improve monitoring coverage