OpenInference Vercel


This package provides a set of utilities to ingest Vercel AI SDK(>= 3.3) spans into platforms like Arize and Phoenix.

> Note: This package requires you to be using the Vercel AI SDK version 3.3 or higher.

Installation

``shell
npm install --save @arizeai/openinference-vercel
`

You will also need to install OpenTelemetry and Vercel packages to your project.

`shell
npm i @opentelemetry/api @vercel/otel @opentelemetry/exporter-trace-otlp-proto @arizeai/openinference-semantic-conventions
`

Usage

@arizeai/openinference-vercel provides a set of utilities to help you ingest Vercel AI SDK spans into platforms and works in conjunction with Vercel's OpenTelemetry support. To get started, you will need to add OpenTelemetry support to your Vercel project according to their guide

To process your Vercel AI SDK Spans add a OpenInferenceSimpleSpanProcessor or OpenInferenceBatchSpanProcessor to your OpenTelemetry configuration.

> [!NOTE]
> The OpenInferenceSpanProcessor does not handle the exporting of spans so you will pass it an exporter as a parameter.

`typescript
// instrumentation.ts
import { registerOTel } from "@vercel/otel";
import { diag, DiagConsoleLogger, DiagLogLevel } from "@opentelemetry/api";
import {
isOpenInferenceSpan,
OpenInferenceSimpleSpanProcessor,
} from "@arizeai/openinference-vercel";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto";
import { SEMRESATTRS_PROJECT_NAME } from "@arizeai/openinference-semantic-conventions";

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

export function register() {
registerOTel({
serviceName: "phoenix-next-app",
attributes: {
// This is not required but it will ensure your traces get added to a specific project in Arize Phoenix
[SEMRESATTRS_PROJECT_NAME]: "your-next-app",
},
spanProcessors: [
new OpenInferenceSimpleSpanProcessor({
exporter: new OTLPTraceExporter({
headers: {
// API key if you are sending it to Phoenix Cloud
api_key: process.env["PHOENIX_API_KEY"] || "",
// API key if you are sending it to local Phoenix
Authorization: Bearer ${process.env["PHOENIX_API_KEY"]} || "",
},
url:
process.env["PHOENIX_COLLECTOR_ENDPOINT"] ||
"https://app.phoenix.arize.com/v1/traces",
}),
spanFilter: (span) => {
// Only export spans that are OpenInference to negate non-generative spans
// This should be removed if you want to export all spans
return isOpenInferenceSpan(span);
},
}),
],
});
}
`

Now enable telemetry in your AI SDK calls by setting the experimental_telemetry parameter to true.

`typescript
const result = await generateText({
model: openai("gpt-4-turbo"),
prompt: "Write a short story about a cat.",
experimental_telemetry: { isEnabled: true },
});
``

For details on Vercel AI SDK telemetry see the Vercel AI SDK Telemetry documentation.

Examples

To see an example go to the Next.js OpenAI Telemetry Example in the examples directory of this repo.

For more information on Vercel OpenTelemetry support see the Vercel AI SDK Telemetry documentation.