opentelemetry-instrumentation-langchain

import os from opentelemetry import trace from opentelemetry.sdk.resources import Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor from opentelemetry.instrumentation.langchain import LangchainInstrumentor from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate from langchain_core.output_parsers import StrOutputParser # 1. Configure OpenTelemetry SDK (essential for any OTel instrumentation) # Set up a basic console exporter for demonstration resource = Resource.create({"service.name": "my-langchain-app"}) tracer_provider = TracerProvider(resource=resource) tracer_provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter())) trace.set_tracer_provider(tracer_provider) # 2. Instrument Langchain LangchainInstrumentor().instrument() # Optional: Disable sensitive content logging if needed (e.g., for production) # os.environ['TRACELOOP_TRACE_CONTENT'] = 'false' # 3. Use Langchain as usual os.environ['OPENAI_API_KEY'] = os.environ.get('OPENAI_API_KEY', 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx') # Replace with actual key or secure handling llm = ChatOpenAI(temperature=0) prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful AI assistant."), ("user", "Tell me a short story about {topic}.") ]) output_parser = StrOutputParser() chain = prompt | llm | output_parser if __name__ == "__main__": print("Running LangChain application...") response = chain.invoke({"topic": "a cat detective"}) print("\nLangChain Response:") print(response) print("\nCheck console output for OpenTelemetry traces.")