مع تحول النماذج اللغوية الكبيرة (LLMs) إلى محور التطبيقات الحديثة، أصبح ضمان موثوقيتها وكفاءتها من حيث التكلفة وأدائها أمراً بالغ الأهمية. غالباً ما تفشل أدوات المراقبة التقليدية في تتبع سير العمل المعقد وغير الحتمي للذكاء الاصطناعي التوليدي. هنا تبرز قوة OpenTelemetry (OTel)، حيث توفر معياراً موحداً ومستقلاً عن المزود لبيانات التيليمتري (Telemetry).
تحديات مراقبة أداء نماذج LLM
تختلف عملية مراقبة أداء نماذج LLM بشكل كبير عن مراقبة واجهات برمجة التطبيقات (APIs) التقليدية. يجب عليك التقاط ليس فقط زمن الاستجابة (Latency) وأكواد الحالة، بل أيضاً قوالب المطالبة (Prompt templates)، وعدد الرموز (Token counts)، وإصدارات النماذج، وبيانات التعريف للاستجابة. وبدون نهج موحد، تخاطر بالحبس داخل SDK مزود معين، مما يجعل من الصعب التبديل بين المزودين أو تجميع البيانات عبر خدمات ذكاء اصطناعي متعددة.
البدء باستخدام مكتبات OpenTelemetry للـ LLM
تقوم مكتبات مراقبة أداء نماذج LLM الخاصة بـ OpenTelemetry (خاصة لنظامي Python و Node.js) بتجريد تعقيد إنشاء مسارات تتبع (Traces) لمزودي نماذج مختلفين. فهي تنشئ تلقائياً مسارات (Spans) للمطالبات، والإجابات، والتضمينات (Embeddings)، مما يضمن الاتساق بغض النظر عما إذا كنت تستخدم OpenAI، أو Anthropic، أو مثيل Ollama المحلي.
التطبيق العملي في Python
لنلقِ نظرة على مثال عملي باستخدام SDK الخاص بـ Python. أولاً، تحتاج إلى تثبيت الحزم اللازمة:
pip install opentelemetry-api opentelemetry-sdk \
opentelemetry-exporter-otlp \
opentelemetry-instrumentation-llmبعد ذلك، قم بتكوين أداة التتبع (Tracer) ومراقبة عميل LLM الخاص بك. يوضح الكود التالي كيفية إعداد المصدر (Exporter) وتطبيق المراقبة:
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.llm import OpenTelemetryLLMInstrumentor
# إعداد مزود أداة التتبع
trace.set_tracer_provider(TracerProvider())
# تكوين مصدر OTLP لإرسال البيانات إلى الخلفية الخاصة بك
exporter = OTLPSpanExporter(endpoint="localhost:4317")
trace.get_tracer_provider().add_span_processor(
trace.get_tracer_provider().get_tracer(__name__).get_tracer_provider().add_span_processor(
trace.get_tracer_provider().get_tracer(__name__).get_tracer_provider().add_span_processor(
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