قابلية الرصد: السجلات والمقاييس والتتبّع
ادمجوا تسجيلًا شاملًا وجمعًا للمقاييس وتتبعًا موزّعًا للحصول على رؤى معمّقة حول سلوك تطبيق نماذج اللغة الكبيرة الخاص بكم.
قابلية الرصد: السجلات والمقاييس والتتبّع درس مجاني في LLM Apps in Production (RAG + Vector DB + Caching) على CoddyKit. هذا هو الدرس 2 من أصل 4. يمكنك قراءة الدرس كاملاً أدناه مجاناً — ثم تمرن عليه مباشرة في المتصفح باستخدام محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7. هذا الدرس جزء من مسار التعلم في LLM Apps in Production (RAG + Vector DB + Caching)، وتقدمك يتزامن عبر الويب وتطبيق CoddyKit. تتضمن دورة LLM Apps in Production (RAG + Vector DB + Caching) 4 دروس في المجموع.
بعض أجزاء هذا الدرس لم تُترجم بعد وتظهر باللغة الإنجليزية.
What is Observability?
In this lesson, we'll explore observability, a crucial concept for managing complex software systems, especially LLM applications.
Observability means understanding the internal state of a system by examining the data it produces. Think of it as having X-ray vision into your application's behavior.
For LLM apps, this helps us answer critical questions like:
- Why is a request slow?
- Is the RAG retrieval working as expected?
- Are we incurring unexpected costs?
Logs: Recording Events
Logs are timestamped records of events that happen within your application. They are like a diary of your system's activities.
For LLM applications, logs are essential for:
- Tracking incoming user prompts.
- Storing responses from the LLM.
- Recording intermediate steps in a RAG pipeline (e.g., documents retrieved).
- Capturing errors or warnings.
They provide detailed contextual information for debugging and post-mortem analysis.
Logging LLM Interactions
Here's a simple Python example demonstrating how to log an LLM interaction. We're using Python's built-in logging module.
This helps you see exactly what prompts were sent and what responses were received, which is vital for debugging and improving your application.
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
def call_llm(prompt):
logging.info(f"LLM Request: '{prompt[:40]}...' ")
# Simulate LLM processing
response = f"Simulated response to: {prompt}"
logging.info(f"LLM Response: '{response[:40]}...' ")
return response
if __name__ == "__main__":
user_prompt = "Explain observability simply."
result = call_llm(user_prompt)
print(f"Application output: {result}")Metrics: Measuring Performance
Metrics are numerical measurements collected over time, providing aggregated insights into your system's health and performance.
Unlike logs, which are individual events, metrics are typically quantitative values that can be visualized as graphs and dashboards. Key metrics for LLM apps include:
- Latency: How long it takes for the LLM to respond.
- Token Usage: Input/output tokens consumed per request.
- Error Rate: Percentage of failed LLM calls or RAG retrievals.
- Cache Hit Rate: How often cached responses are used.
Collecting Custom Metrics
You can collect custom metrics to understand specific aspects of your LLM application. This example shows how to track the number of LLM calls and their average latency.
In a real-world scenario, you'd send these metrics to a monitoring system like Prometheus or Datadog.
import time
class LLMMetrics:
def __init__(self):
self.total_calls = 0
self.total_latency = 0.0
def record_call(self, duration):
self.total_calls += 1
self.total_latency += duration
def get_avg_latency(self):
if self.total_calls == 0:
return 0.0
return self.total_latency / self.total_calls
metrics_store = LLMMetrics()
def call_llm_with_metrics(prompt):
start_time = time.time()
# Simulate LLM processing
time.sleep(0.05) # simulate 50ms work
response = f"Simulated reply to: {prompt}"
end_time = time.time()
metrics_store.record_call(end_time - start_time)
return response
if __name__ == "__main__":
print("Collecting LLM call metrics...")
call_llm_with_metrics("Hi")
call_llm_with_metrics("How are you?")
print(f"Total calls: {metrics_store.total_calls}")
print(f"Avg latency: {metrics_store.get_avg_latency():.3f}s")Tracing: Following Request Paths
Tracing is about following a single request as it flows through multiple services and components in a distributed system. This is especially vital for RAG applications that involve many steps: user input, embedding generation, vector DB lookup, LLM call, etc.
A trace visualizes the entire journey of a request, showing the exact path it took and the time spent in each operation.
Traces, Spans, and Context
A trace is a complete end-to-end journey of a request. It's composed of multiple spans.
- A span represents a single operation or unit of work within a trace (e.g., 'retrieve documents', 'call embedding model', 'invoke LLM').
- Spans have a parent-child relationship, forming a tree structure that shows dependencies.
- Context propagation ensures that a unique trace ID follows the request across different services, linking all related spans together.
This helps pinpoint bottlenecks or failures across microservices.
OpenTelemetry for Tracing
While implementing tracing from scratch is complex, tools like OpenTelemetry (an open-source observability framework) provide standardized ways to instrument your code.
You'd use OpenTelemetry SDKs to:
- Start a new trace when a request comes in.
- Create new spans for each significant operation (e.g., a function call to a vector database or an LLM API).
- Propagate the trace context to downstream services.
This allows you to visualize the full request flow in a tracing UI.
The Observability Triangle
Logs, metrics, and traces are often called the "observability triangle" because they offer complementary views of your system:
- Logs: The granular details and events.
- Metrics: The aggregated numbers and trends.
- Traces: The end-to-end journey of a request.
Together, they provide a comprehensive understanding of your LLM application's behavior, making it easier to diagnose issues, optimize performance, and ensure reliability in production.
Quick Check: Observability
You've learned about the three pillars of observability. Let's see if you can distinguish their primary uses.
Recap: Deep Insights
Congratulations! You've explored the world of observability for LLM applications.
- We defined observability as understanding internal system state from external data.
- We learned about logs for detailed event recording.
- We covered metrics for aggregated performance measurements.
- We understood traces for visualizing end-to-end request flows.
By integrating these three pillars, you gain powerful insights, enabling you to build more reliable, performant, and cost-efficient LLM systems.
الأسئلة الشائعة
هل درس «قابلية الرصد: السجلات والمقاييس والتتبّع» مجاني؟
نعم — نص درس «قابلية الرصد: السجلات والمقاييس والتتبّع» كامل متاح مجاناً هنا على الويب. لتمرينه بشكل تفاعلي (محرر أكواد مدمج ومدرس ذكاء اصطناعي متاح 24/7) وفتح باقي دورة LLM Apps in Production (RAG + Vector DB + Caching)، انتقل إلى CoddyKit PRO. تتضمن دورة LLM Apps in Production (RAG + Vector DB + Caching) 4 دروس في المجموع.
ماذا ستتعلم في «قابلية الرصد: السجلات والمقاييس والتتبّع»؟
ادمجوا تسجيلًا شاملًا وجمعًا للمقاييس وتتبعًا موزّعًا للحصول على رؤى معمّقة حول سلوك تطبيق نماذج اللغة الكبيرة الخاص بكم. تتمرن على LLM Apps in Production (RAG + Vector DB + Caching) مع أكواد عملية تشغلها مباشرة في المتصفح، ومدرس ذكاء اصطناعي متاح 24/7 يجيب على أسئلتك أثناء عملك.
هل أحتاج إلى خبرة سابقة لأبدأ LLM Apps in Production (RAG + Vector DB + Caching)؟
لا تُشترط خبرة سابقة. LLM Apps in Production (RAG + Vector DB + Caching) على CoddyKit منظم للمبتدئين حتى المتقدمين، لذا يمكنك البدء من هنا أو من البداية والتقدم بسرعتك الخاصة. هذا هو الدرس 2 من أصل 4.
كم من الوقت يستغرق درس «قابلية الرصد: السجلات والمقاييس والتتبّع»؟
معظم دروس CoddyKit تستغرق حوالي 5–10 دقائق. كل منها موجز وتفاعلي، لذا تحرز تقدماً مستمراً وتستأنف من حيث توقفت عبر الويب والتطبيق.
هل يمكنني كتابة وتشغيل أكواد في درس LLM Apps in Production (RAG + Vector DB + Caching) هذا؟
نعم. كل درس في LLM Apps in Production (RAG + Vector DB + Caching) يتضمن محرر أكواد مدمج، لذا تكتب وتشغل أكواداً حقيقية مباشرة في متصفحك وتحصل على تعليقات فورية من الذكاء الاصطناعي — بدون إعداد محلي.
جميع الدروس في هذه الدورة
- التوسعة الأفقية لمكوّنات RAG
- قابلية الرصد: السجلات والمقاييس والتتبّع
- التنبيهات والاستجابة للحوادث في عمليات نماذج اللغة الكبيرة
- اختبار التحميل وتخطيط السعة