npm install @arizeai/openinference-instrumentation-langchain

npm install @opentelemetry/exporter-trace-otlp-grpc @grpc/grpc-js

# Import open-telemetry dependencies
from arize.otel import register

# Setup OTel via our convenience function
tracer_provider = register(
    space_id = "your-space-id", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    project_name = "your-project-name", # name this to whatever you would like
)

# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.langchain import LangChainInstrumentor

# Finish automatic instrumentation
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)

/*instrumentation.ts */
import { LangChainInstrumentation } from "@arizeai/openinference-instrumentation-langchain";
import { ConsoleSpanExporter } from "@opentelemetry/sdk-trace-base";
import {
  NodeTracerProvider,
  SimpleSpanProcessor,
} from "@opentelemetry/sdk-trace-node";
import { Resource } from "@opentelemetry/resources";
import { OTLPTraceExporter as GrpcOTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-grpc"; // Arize specific
import { diag, DiagConsoleLogger, DiagLogLevel } from "@opentelemetry/api";
import { Metadata } from "@grpc/grpc-js";
import * as CallbackManagerModule from "@langchain/core/callbacks/manager";

// For troubleshooting, set the log level to DiagLogLevel.DEBUG
diag.setLogger(new DiagConsoleLogger(), DiagLogLevel.DEBUG);

// Arize specific - Create metadata and add your headers
const metadata = new Metadata();

// Your Arize Space and API Keys, which can be found in the UI
metadata.set('space_id', 'your-space-id');
metadata.set('api_key', 'your-api-key');

const provider = new NodeTracerProvider({
  resource: new Resource({
    // Arize specific - The name of a new or preexisting model you 
    // want to export spans to
    "model_id": "your-model-id",
    "model_version": "your-model-version"
  }),
});

provider.addSpanProcessor(new SimpleSpanProcessor(new ConsoleSpanExporter()));
provider.addSpanProcessor(
  new SimpleSpanProcessor(
    new GrpcOTLPTraceExporter({
      url: "https://otlp.arize.com/v1",
      metadata,
    }),
  ),
);

const lcInstrumentation = new LangChainInstrumentation();
// LangChain must be manually instrumented as it doesn't have
// a traditional module structure
lcInstrumentation.manuallyInstrument(CallbackManagerModule);

provider.register();

import { 
  OTLPTraceExporter as ProtoOTLPTraceExporter 
} from "@opentelemetry/exporter-trace-otlp-proto";

// add as another SpanProcessor below the previous SpanProcessor
provider.addSpanProcessor(
  new SimpleSpanProcessor(
    new ProtoOTLPTraceExporter({
      // This is the url where your phoenix server is running
      url: "http://localhost:6006/v1/traces",
    }),
  ),
);

docker compose up --build

import { ChatOpenAI } from "@langchain/openai";

const chatModel = new ChatOpenAI({
  openAIApiKey: "my-api-key",
  modelName: "gpt-3.5-turbo",
});

async function run() {
  // First message invocation
  const response1 = await chatModel.invoke("Hello, how are you?", {
    metadata: {
      thread_id: "thread-456",
    },
  });

  // Second message invocation
  const response2 = await chatModel.invoke("What can you do?", {
    metadata: {
      thread_id: "thread-456",
    },
  });

  // Print thread_id and session_id from the responses
  console.log("Response 1 Metadata:");
  console.log(`Thread ID: thread-456`);
  console.log(`Session ID: ${response1.metadata?.session_id || "Not available"}`);

  console.log("Response 2 Metadata:");
  console.log(`Thread ID: thread-456`);