Production Troubleshooting - LangChain in Production
Learn how to diagnose, debug, and resolve issues in LangChain applications running in production environments
🛠️ Troubleshooting Overview
Production issues can impact reliability, performance, and user experience. This guide covers debugging techniques, error handling, incident response, and root cause analysis for LangChain systems.
🚨 Common Production Issues
- LLM API Failures: Timeouts, quota exceeded, invalid responses
- Chain Errors: Logic bugs, data mismatches, unexpected outputs
- Infrastructure Problems: Resource exhaustion, network failures, container crashes
- Vector DB Issues: Slow queries, index corruption, data loss
🧑💻 Debugging Techniques
- Enable verbose logging and structured error messages
- Use distributed tracing to follow request flow
- Capture stack traces and error context
- Reproduce issues in staging environments
🛡️ Error Handling Patterns
- Implement retries with exponential backoff
- Use circuit breakers for failing services
- Gracefully degrade features on failure
- Alert and escalate critical errors
python
import time
import logging
logger = logging.getLogger("langchain")
# Retry with exponential backoff
def retry_llm_call(func, max_attempts=3):
for attempt in range(max_attempts):
try:
return func()
except Exception as e:
logger.error(f"Attempt {attempt+1} failed: {e}")
time.sleep(2 ** attempt)
raise Exception("All attempts failed")🔍 Incident Response & RCA
- Set up incident response playbooks
- Automate alerting and escalation
- Perform root cause analysis (RCA) after incidents
- Document fixes and preventive actions
🧩 Example: FastAPI Error Handler
python
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
app = FastAPI()
@app.exception_handler(Exception)
def generic_exception_handler(request: Request, exc: Exception):
return JSONResponse(status_code=500, content={"error": str(exc)})🔗 Next Steps
Key Troubleshooting Takeaways:
- Monitor for common production issues
- Use logging, tracing, and error handling patterns
- Automate incident response and RCA
- Document and prevent future issues
- Continuously improve troubleshooting processes