@imaginary-dev/openai v0.6.1

DEPRECATED: @imaginary-dev/openai

This package has been deprecated, please migrate to https://www.npmjs.com/package/@libretto/openai.

TypeScript wrapper around openai library to send events to templatest

Installation

npm install @imaginary-dev/openai

Usage

To use this library, you need to patch the openai library. This will time calls to OpenAI, and report them to Templatest.

You'll need an API key from Templatest. Set it in the environment variable PROMPT_API_KEY or pass it directly to the patch() call. You'll also probably want to name which template you are using.

import { patch, objectTemplate } from "@imaginary-dev/openai";
import OpenAI from "openai";

async function main() {
  patch({
    apiKey: "XXX", // defaults to process.env.PROMPT_API_KEY
    // You can set this here or in the `create` call:
    // promptTemplateName: "my-template-test"
    OpenAI,
  });
  const openai = new OpenAI({
    apiKey: "YYY", // defaults to process.env.OPENAI_API_KEY
  });

  const completion = await openai.chat.completions.create({
    // Instead of a chat message array, you can pass objectTemplate instead.
    messages: objectTemplate([
      { role: "user", content: "Give a hearty welcome to our new user {name}" },
    ]) as any,
    model: "gpt-3.5-turbo",
    // Uniquely identifies this prompt within your project. Equivalent to
    // passing `promptTemplateName` to `patch()`.
    ip_prompt_template_name: "ts-client-test-chat",
    // The parameters to fill in the template.
    ip_template_params: { name: "John" },
  });

  console.log(completion.choices);
}

main();

Advanced Usage

You can "unpatch" the library by calling unpatch(). This will restore the original create method on the chat.completions object.

import { patch, objectTemplate } from "@imaginary-dev/openai";
import OpenAI from "openai";

const unpatch = patch({ OpenAI });

try {
    const completion = await openai.chat.completions.create({...});
} finally {
    unpatch();
}

Configuration

The following options may be passed to patch:

• promptTemplateName: A default name to associate with prompts. If provided, this is the name that will be associated with any create call that's made without an ip_prompt_template_name parameter.
• allowUnnamedPrompts: When set to true, every prompt will be sent to Templatest even if no prompt template name as been provided (either via the promptTemplateName option on patch or via the ip_prompt_template_name parameter added to create).
• redactPii: When true, certain personally identifying information (PII) will be attempted to be redacted before being sent to the Templatest backend. See the pii package for details about the types of PII being detected/redacted. false by default.

Additional Parameters

The following parameters are added to the create call:

• ip_template_params: The parameters to use for template strings. This is a dictionary of key-value pairs.
• ip_chat_id: The id of a "chat session" - if the chat API is being used in a conversational context, then the same chat id can be provided so that the events are grouped together, in order. If not provided, this will be left blank.
• ip_template_chat: The chat template to record for chat requests. This is a list of dictionaries with the following keys:
  • role: The role of the speaker. Either "system", "user" or "ai".
  • content: The content of the message. This can be a string or a template string with {} placeholders.
• ip_template_text: The text template to record for non-chat completion requests. This is a string or a template string with {} placeholders,
• ip_parent_event_id: The UUID of the parent event. All calls with the same parent id are grouped as a "Run Group".
• ip_feedback_key: The optional key used to send feedback on the prompt, for use with sendFeedback() later. This is normally auto-generated, and the value is returned in the OpenAI response.

Sending Feedback

Sometimes the answer provided by the LLM is not ideal, and your users may be able to help you find better responses. There are a few common cases:

• You might use the LLM to suggest the title of a news article, but let the user edit it. If they change the title, you can send feedback to Templatest that the answer was not ideal.
• You might provide a chatbot that answers questions, and the user can rate the
  answers with a thumbs up (good) or thumbs down (bad).

You can send this feedback to Tepmlatest by calling sendFeedback(). This will send a feedback event to Templatest about a prompt that was previously called, and let you review this feedback in the Templatest dashboard. You can use this feedback to develop new tests and improve your prompts.

import { patch, sendFeedback } from "@imaginary-dev/openai";
import crypto from "crypto";
import OpenAI from "openai";

async function main() {
  patch({ OpenAI });

  // Must be unique for each call to OpenAI
  const completion = await openai.chat.completions.create({
    // ...
  });

  // Maybe the user didn't like the answer, so ask them for a better one.
  const betterAnswer = await askUserForBetterResult(completion.choices[0].text);

  // If the user provided a better answer, send feedback to Templatest
  if (betterAnswer !== completion.choices[0].text) {
    // feedback key is automatically injected into OpenAI response object.
    const feedbackKey = completion.ip_feedback_key;
    await sendFeedback({
      apiKey,
      feedbackKey,
      // Better answer from the user
      betterResponse: betterAnswer,
      // Rating of existing answer, from 0 to 1
      rating: 0.2,
    });
  }
}

Note that feedback can include either rating, betterResponse, or both.

Parameters:

• rating - a value from 0 (meaning the result was completely wrong) to 1 (meaning the result was correct)
• betterResponse - the better response from the user