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Quickstart

Logging calls to custom models is currently supported via the Helicone NodeJS SDK.

1

To get started, install the `@helicone/helpers` package

npm install @helicone/helpers
2

Set `HELICONE_API_KEY` as an environment variable

export HELICONE_API_KEY=sk-<your-api-key>
You can also set the Helicone API Key in your code (See below)
3

Create a new HeliconeManualLogger instance

import { HeliconeManualLogger } from "@helicone/helpers";

const heliconeLogger = new HeliconeManualLogger({
  apiKey: process.env.HELICONE_API_KEY, // Can be set as env variable
  headers: {} // Additional headers to be sent with the request
});
4

Log your request

const reqBody = {
  model: "text-embedding-ada-002",
  input: "The food was delicious and the waiter was very friendly.",
  encoding_format: "float"
}
const res = await heliconeLogger.logRequest(
  reqBody,
  async (resultRecorder) => {
    const r = await fetch("https://api.openai.com/v1/embeddings", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${process.env.OPENAI_API_KEY}`
      },
      body: JSON.stringify(reqBody)
    })
    const resBody = await r.json();
    resultRecorder.appendResults(resBody);
    return results; // this will be returned by the logRequest function
  },
  {
   // Additional headers to be sent with the request
  }
);

API Reference

HeliconeManualLogger

class HeliconeManualLogger {
  constructor(opts: IHeliconeManualLogger)
}

type IHeliconeManualLogger = {
  apiKey: string;
  headers?: Record<string, string>;
  loggingEndpoint?: string; // defaults to https://api.hconeai.com/custom/v1/log
};

logRequest

logRequest<T>(
    request: HeliconeLogRequest,
    operation: (resultRecorder: HeliconeResultRecorder) => Promise<T>,
    additionalHeaders?: Record<string, string>
  ): Promise<T>

Parameters

  1. request: HeliconeLogRequest - The request object to log
type HeliconeLogRequest = ILogRequest | HeliconeCustomEventRequest; // ILogRequest is the type for the request object for custom model logging

// The name and structure of the prompt field depends on the model you are using.
// Eg: for chat models it is named "messages", for embeddings models it is named "input".
// Hence, the only enforced type is `model`, you need still add the respective prompt property for your model.
// You may also add more properties (eg: temperature, stop reason etc)
type ILogRequest = {
  model: string;
  [key: string]: any;
};
  1. operation: (resultRecorder: HeliconeResultRecorder) => Promise<T> - The operation to be executed and logged
class HeliconeResultRecorder {
  private results: Record<string, any> = {};

  appendResults(data: Record<string, any>): void {
    this.results = { ...this.results, ...data };
  }

  getResults(): Record<string, any> {
    return this.results;
  }
}
  1. additionalHeaders: Record<string, string>

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