prompt

A lightweight interface to OpenAI's Chat-based LLMs

Features

• Edge compatible
• Streaming support (via ReadableStream)
  • Includes accurate token count estimation.
  • onToken and onComplete callbacks.

---

Input your prompts as a series of messages from the 3 possible roles: system, ai ("assistant") or user. prompt will read your OpenAI API key from process.env.OPENAI_API_KEY.

import { prompt, system, user } from "@adriancooney/prompt";

const response = await prompt([
  system("You are a helpful AI assistant"),
  user("If you drop a bowl of detergent, is it really a mess?"),
]);

console.log(response);

// {
//   model: 'gpt-4',
//   output: 'If you drop a bowl of detergent, it can be considered a mess in the sense that it requires cleaning up. Although detergent is a cleaning agent, it can create slippery surfaces, pose a risk to children or pets who may ingest it, and potentially damage certain materials. It is important to clean up any spilled detergent promptly to avoid potential hazards.',
//   prompts: [
//     { role: 'system', content: 'You are a helpful AI assistant' },
//     {
//       role: 'user',
//       content: 'If you drop a bowl of detergent, is it really a mess?'
//     },
//     {
//       role: 'ai',
//       content: 'If you drop a bowl of detergent, it can be considered a mess in the sense that it requires cleaning up. Although detergent is a cleaning agent, it can create slippery surfaces, pose a risk to children or pets who may ingest it, and potentially damage certain materials. It is important to clean up any spilled detergent promptly to avoid potential hazards.'
//     }
//   ],
//   timestamp: 1684781024503,
//   duration: 13577,
//   estimatedTokens: 100
// }

The response object contains the following properties:

• output - The full text output receive from the model. (string)
• model - The OpenAI Chat Completion model used. (gpt-4 or gpt-3.5-turbo (default))
• prompts - The input array of messages and the output ai message. (ChatMessage[])
• timestamp - The time the completion operation started at. (number ms)
• duration - The time it took to complete the operation. (number ms)
• estimatedTokens - The amount of tokens the operation used. Exact value when called via prompt, an accurate estimate when called via promptStream. (number)

Sending another message to the chat is a matter of appending the prompts returned from previous calls:

import { prompt, system, user } from "@adriancooney/prompt";

const { prompts } = await prompt([
  system("You are a helpful AI assistant"),
  user("If you drop a bowl of detergent, is it really a mess?"),
]);

const { output } = await prompt([
  ...prompts,
  user("Can you repeat your answer in a Batman monologue?"),
]);

console.log(output);

// *dramatic Batman voice* In the shadows of the night, a fallen bowl of detergent
// lurks. Though it cleans, it brings chaos, danger to unsuspecting victims. We must
// act swiftly to vanquish this foe, for the sake of Gotham's children and creatures.
// The darkness awaits, but justice will prevail..

To stream your response from an Edge function in Next.js, use the promptStream method. It's options are the same with the addition of two callbacks, onToken and onComplete.

import { promptStream, system, user } from "@adriancooney/prompt";

export const runtime = "edge";

export async function POST(request: Request) {
  return await promptStream(
    [
      system("You are a helpful AI assistant"),
      user("If you drop a bowl of detergent, is it really a mess?"),
    ],
    {
      onToken(token, response) {
        console.log(token);
      },

      onComplete(response) {
        await sql`
          INSERT INTO messages(content, estimated_tokens) VALUES(${response.output}, ${response.estimatedTokens});
        `;
      },
    }
  );
}

Configure the prompt:

await prompt(
  [
    system("You are a helpful AI assistant"),
    user("If you drop a bowl of detergent, is it really a mess?"),
  ],
  {
    apiKey: "...", // defaults process.env.OPENAI_API_KEY
    model: "gpt-3.5-turbo", // defaults process.env.OPENAI_MODEL
    frequencyPenalty: 1, // defaults process.env.OPENAI_FREQUENCY_PENALTY
    presencePenalty: 1, // defaults process.env.OPENAI_PRESENCE_PENALTY
    maxTokens: 7, // defaults process.env.OPENAI_MAX_TOKENS
    temperature: 0, // defaults process.env.OPENAI_TEMPERATURE
    topP: 1, // defaults process.env.OPENAI_TOP_P
  }
);

If an error is called, an OpenAIError will be thrown and a OpenAIModelOverloadedError when the server is overloaded.

import {
  prompt,
  system,
  user,
  OpenAIError,
  OpenAIModelOverloadedError,
} from "@adriancooney/prompt";

try {
  await prompt([
    // ...
  ]);
} catch (err) {
  if (err instanceof OpenAIModelOverloadedError) {
    console.log(
      "Open AI is overloaded and ignoring us! Please try again later."
    );
  }

  if (err instanceof OpenAIError) {
    console.log(err);
  }

  throw err;
}

Interface

type ChatCompletionOptions = {
  apiKey: string;
  model: "gpt-3.5-turbo" | "gpt-4";
  frequencyPenalty?: number;
  maxTokens?: number;
  presencePenalty?: number;
  temperature?: number;
  topP?: number;
};

type Message = {
  content: string;
  metadata?: any;
};

type SystemMessage = Message & {
  role: "system";
};

type UserMessage = Message & {
  role: "user";
};

type AIMessage = Message & {
  role: "ai";
};

type ChatMessage = SystemMessage | UserMessage | AIMessage;

type PromptResponse = {
  prompts: ChatMessage[];
  output: string;
  timestamp: number;
  duration: number;
  estimatedTokens: number;
  model: ChatCompletionOptions["model"];
};

declare function prompt(
  prompts: (string | ChatMessage)[],
  options?: Partial<ChatCompletionOptions>
): Promise<PromptResponse>;

declare function promptStream(
  prompts: (string | ChatMessage)[],
  options?: {
    onToken?: (token: string, res: PromptResponse) => void | Promise<void>;
    onComplete?: (res: PromptResponse) => void | Promise<void>;
  } & Partial<ChatCompletionOptions>
): Promise<ReadableStream>;

declare function system(content: string, metadata?: any): SystemMessage;
declare function user(content: string, metadata?: string): UserMessage;
declare function ai(content: string, metadata?: any): AIMessage;

declare class OpenAIError extends Error {
  type: string;
  code: string | null;
  constructor(type: string, code: string | null, message: string);
}

declare class OpenAIModelOverloadedError extends OpenAIError {}

export {
  AIMessage,
  ChatMessage,
  OpenAIError,
  OpenAIModelOverloadedError,
  SystemMessage,
  UserMessage,
  ai,
  prompt,
  promptStream,
  system,
  user,
};