Open Telemetry for Langchain

langchain_otel.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "from opentelemetry import trace as trace_api\n",
    "from opentelemetry.sdk import trace as trace_sdk\n",
    "from opentelemetry.sdk.trace.export import BatchSpanProcessor\n",
    "from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n",
    "\n",
    "from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter\n",
    "from openinference.instrumentation.langchain import LangChainInstrumentor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "APPLICATIONINSIGHTS_CONNECTION_STRING = os.getenv('APPLICATIONINSIGHTS_CONNECTION_STRING')\n",
    "AOAI_API_KEY = os.getenv('AOAI_API_KEY')\n",
    "AOAI_DEPLOYMENT_GPT3 = os.getenv('AOAI_DEPLOYMENT_GPT3')\n",
    "AOAI_API_VERSION = os.getenv('AOAI_API_VERSION')\n",
    "AOAI_ENDPOINT = os.getenv('AOAI_ENDPOINT')\n",
    "APPLICATIONINSIGHTS_ENABLED = True\n",
    "ARIZEPHOENIX_ENABLED = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import AzureChatOpenAI\n",
    "llm = AzureChatOpenAI(api_key=AOAI_API_KEY, azure_deployment=AOAI_DEPLOYMENT_GPT3, \n",
    "                      azure_endpoint=AOAI_ENDPOINT, api_version=AOAI_API_VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open telemetry setup\n",
    "tracer_provider = trace_sdk.TracerProvider()\n",
    "trace_api.set_tracer_provider(tracer_provider=tracer_provider)\n",
    "if APPLICATIONINSIGHTS_ENABLED:\n",
    "    # configure Azure Monitor exporter\n",
    "    azure_exporter = AzureMonitorTraceExporter(connection_string=APPLICATIONINSIGHTS_CONNECTION_STRING)\n",
    "    tracer_provider.add_span_processor(BatchSpanProcessor(span_exporter=azure_exporter))\n",
    "if ARIZEPHOENIX_ENABLED:\n",
    "    # configure Arize Phoenix exporter (launch phoenix from code/cli/docker)\n",
    "    # docker run -p 6006:6006 arizephoenix/phoenix\n",
    "    span_otlp_exporter = OTLPSpanExporter(endpoint=\"http://127.0.0.1:6006/v1/traces\")\n",
    "    tracer_provider.add_span_processor(BatchSpanProcessor(span_exporter=span_otlp_exporter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instruments langchain\n",
    "LangChainInstrumentor().instrument()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Why did the bicycle fall over? It was two-tired!"
     ]
    }
   ],
   "source": [
    "# operate with langchain as usual\n",
    "for chunk in llm.stream(\"Tell me a joke about bikes.\"):\n",
    "    print(chunk.content, end=\"\", flush=True)"
   ]
  }
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requirements.txt

langchain_openai
opentelemetry-sdk
opentelemetry-exporter-otlp
openinference-instrumentation-langchain
azure-monitor-opentelemetry