Ollama with the OpenAI Node.js SDK

npm install --save @arizeai/openinference-instrumentation-openai openai
npm install --save @opentelemetry/api@~1.9.0 @opentelemetry/sdk-trace-node@2.12.0 \
  @opentelemetry/sdk-trace-base@2.12.0 \
  @opentelemetry/resources@2.12.0 \
  @opentelemetry/semantic-conventions \
  @opentelemetry/instrumentation@0.212.0 \
  @opentelemetry/exporter-trace-otlp-proto@0.212.0 \
  @arizeai/openinference-semantic-conventions

import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
import { resourceFromAttributes } from "@opentelemetry/resources";
import { SimpleSpanProcessor } from "@opentelemetry/sdk-trace-base";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import { ATTR_SERVICE_NAME } from "@opentelemetry/semantic-conventions";
import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import OpenAI from "openai";
import { OpenAIInstrumentation } from "@arizeai/openinference-instrumentation-openai";

const endpoint = (process.env.PHOENIX_COLLECTOR_ENDPOINT ?? "http://localhost:6006").replace(/\/$/, "");
const projectName = process.env.PHOENIX_PROJECT_NAME ?? "ollama-node-app";

export const provider = new NodeTracerProvider({
  resource: resourceFromAttributes({
    [ATTR_SERVICE_NAME]: projectName,
    [SEMRESATTRS_PROJECT_NAME]: projectName,
  }),
  spanProcessors: [
    new SimpleSpanProcessor(new OTLPTraceExporter({ url: `${endpoint}/v1/traces` })),
  ],
});

provider.register();
const instrumentation = new OpenAIInstrumentation();
instrumentation.manuallyInstrument(OpenAI);
registerInstrumentations({ instrumentations: [instrumentation] });

import { provider } from "./instrumentation.js";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://127.0.0.1:11434/v1/",
  apiKey: "ollama",
});

const response = await client.chat.completions.create({
  model: "llama3.2:1b",
  messages: [{ role: "user", content: "Explain why local language models are useful." }],
});

console.log(response.choices[0].message.content);
await provider.forceFlush();
await provider.shutdown();