gpt-tools v1.1.5

GPT Tools

This is a ChatGPT client that contains a set of JavaScript tools that interact with OpenAI's ChatGPT models.

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Setup

To obtain your API key, create an account at OpenAI.com

Create your key in the API Keys section

MAKE SURE TO COPY YOUR KEY as you won't get another chance to look at it!

Install this package in your project via npm or yarn:

npm install gpt-tools

yarn add gpt-tools

To use this client, you will need to create a .env file to contain your API Key, like so:
.env:

OPENAI_API_KEY=YourApiKeyGoesHere

In a React application, you can set it with the REACT_APP prefix

REACT_APP_OPENAI_API_KEY=YourApiKeyGoesHere

If you're using Vite, you'll want to use the VITE prefix:

VITE_OPENAI_API_KEY=YourApiKeyGoesHere

You can import models and the GPT client from the package like so:

import GPTClient from 'gpt-tools';
import { ChatMessage, ChatResponse } from 'gpt-tools/dist/gpt-models.js';

Example Usage:

Coming soon...

Tools

GPTClient

The GPT Client is the container class for the tool set. This contains all the functions. Create an instance of the client using your OpenAI API key.

const apiKey = process.env.REACT_APP_API_KEY;
const client = new GPTClient(apiKey);

Functions

Chatroom Functions

Add System Message

Add a message to the chat with the role of System

addSystemMessage(message: string)

Add Host Message

Add a message to the chat with the role of User

addHostMessage(message: string)

Add Message

Add a message to the chat with the given role and new participant name

addMessage(message: string, role: Role, name: string, setupPrompt?: string, introPrompt?: string, temperature?: number)

Add Message For

Add a message to the chat directed towards a particlar participant

addMessageFor(message: string, participant: ChatAgent, role?: Role)

Add System Message For

Prompt a particular participant for a response

addSystemMessageFor(message: string, participant: ChatAgent)

Get Conversation

Get the current chatroom conversation history

getConversation(): Array<ChatroomMessage>

Get Conversation Messages

Get the chatroom history as an array of chat messages only

• Names may be included in the message content, since different participant messages are not differentiated otherwise

  getConversationMessages(includeNames?: boolean): Array

Empty the chatroom conversation history

clearChat()

Participant (Agent) Methods

Add Participant

Add a new participant to the chatroom Participants must have a unique name and a setup prompt that defines the participant.

addParticipant(name: string, setupPrompt: string, introPrompt?: string, temperature?: number)

Add Participant Agent

Add an existing chat agent to the chatroom

addParticipantAgent(participant: ChatAgent)

Remove Participant

Remove a participant from chat with the given name, if they are in the chat

removeParticipant(name: string)

Remove Participant Agent

Remove a participant from the chat

removeParticipantAgent(participant: ChatAgent)

Is Participant In Chat?

Determine whether a participant with a particular name is currently in the chatroom

isParticipantInChat(name: string): boolean

Get Setup Message

Get a participant's setup prompt, as a chat message

getSetupMessage(participant: ChatAgent): ChatMessage

Get Setup Chatroom Message

Get a participant's setup prompt, as a system chatroom message

getSetupChatroomMessage(participant: ChatAgent): ChatroomMessage

Get Intro Message

Get a participant's intro prompt, if it exists, as a system chat message

getIntroMessage(participant: ChatAgent): ChatMessage

Does Participant Have Intro Message?

Determine if a participant has an intro message prompt

doesParticipantHaveIntro(participant: ChatAgent): boolean

Get Intro Chatroom Message

Get a participant's intro prompt, if it exists, as a system chatroom message

getIntroChatroomMessage(participant: ChatAgent): ChatroomMessage

Get Participants

Get all current participants in the chat

getParticipants(): Array<ChatAgent>

Clear Participants

Clear out the list of participants

clearParticipants()

Prompt Methods

Prompt Participant

Prompt a particular participant in the chat for a response

promptParticipant(participant: ChatAgent)

Prompt All Participants

Prompt a response from every participant

promptAllParticipants()

Introduce Participant

Introduce a participant - add their intro message and prompt a response

introduceParticipant(participant: ChatAgent)

Basic Helper Methods

Continue Chat

Take in a set of Chatroom messages and prompt a particular participant to continue the chat

async continueChat(messages: Array<ChatroomMessage>, participant: ChatAgent): Promise<ChatroomMessage>

Continue Conversation Full

Return the entire response body of a conversation completion request

async continueConversationFull(messages: Array<ChatMessage>, model?: string, temperature?: number, maxTokens?: number): Promise<ChatResponseBody>

Continue Conversation

Return just the first response from a conversation completion request

async continueConversation(messages: Array<ChatMessage>, model?: string, temperature?: number, maxTokens?: number): Promise<string>

Single Prompt

A simple one-time text prompt that returns a the first response content

async singlePrompt(message: string, model?: string, temperature?: number, maxTokens?: number): Promise<string>

Single Prompt Full

A one-time text prompt that returns the entire response body

async singlePromptFull(message: string, model?: string, temperature?: number, maxTokens?: number): Promise<ChatResponseBody>

PTM (Pre-Trained Model) Helper Methods

Returns the command necessary to create a pre-trained model given a local path to a file the contains the necessary training data

createPTM(filePath: string): string

Single Chat Methods

Reply To Conversation

Prompt the default model for a response to a conversation with a new message added

async replyToConversation(messages: Array<ChatMessage>, nextMessage: string): Promise<string>

Reply To Conversation Full

Prompt the default model for a response to a conversation given a next user message

async replyToConversationFull(messages: Array<ChatMessage>, nextMessage: string): Promise<ChatResponseBody>

Create Agent

Prompt single chat agent for a response given a conversation

async createAgent(agent: Agent, model?: string, conversation?: Array<ChatMessage>, temperature?: number): Promise<string>

Chat Message Roles

Roles

• system
• user
• assistant
• function

ChatGPT Request Models

ChatRequestBody

The body of the request required by the OpenAI API documentation // The conversation to which the model is supposed to reply messages: Array // The specific GPT model being targeted model?: string // The maximum number of tokens allowed for this completion max_tokens?: number // Temperature or randomness of the response (0.0 - 2.0) temperature?: number

ChatResponseBody

The body of the response as given in the OpenAI API documentation // The ID of this particular response (set) id?: string // Generally, "chat.completion" - the type of completion object // This can be different for function requests object?: string // The date and time the response was generated created?: number // The GPT model used model?: string // The responses generated // (the same request can be submitted multiple times to generate multiple responses) choices?: Array // The number of tokens used in the prompt and the response usage?: Usage

ChatMessage

The basic chat message model used in requests and responses // The origin of this message role: Role // The text content of a prompt or a response content: string

ChatResponse

A specific conversation completion, an item in the Choices array that // is part of the completion Response Body object export class ChatResponse { // The index of this particular response index?: number // The actual response message message?: ChatMessage // The reason this response was completed - finished, error, or token limit reached, etc. finish_reason?: string

Usage

A record of the number of tokens used in a completion request export class Usage { // The number of tokens in the prompt prompt_tokens?: number // The number of tokens in the response completion_tokens?: number // The sum of these two numbers total_tokens?: number

Dataset preparation/PTM generation models

Dataset

Used to train a model to create a PTM // A set of ideal prompt/response pairs data: Array

IdealPrompt

A single training datum - a prompt and response // A prompt intended for the PTM prompt: string // An ideal response from the PTM this data is intended to train completion: string

Simple Chat Models

ChatAgent

A primed agent meant to take part in a conversation // The name of the participant - this must me unique and serves as their ID name: string // The system prompt given to a chat agent that creates and identifies the agent // - Added to the beginning of the conversation when prompted for a response // - Always appears first, and only for this particular agent setupPrompt: string // The system prompt given to a chat agent only once, when introduced. // This message is added to the conversation introPrompt?: string // The randomness of this agent (0.0 - 2.0) temperature?: number // Whether or not the participant has been introduced - the intro prompt should only be used once wasIntroduced: boolean = false getSetupPrompt()

ChatroomMessage

A message that's part of a chat between one or more agents and a user/host // The message itself message: ChatMessage // The agent from whom this message originates (can be System or Host as well as a participant) speaker: ChatAgent

Agent

Intended to be a single-use agent, not part of a multi-agent chat. Can be extended, and the getSystemPrompt method can be overridden name?: string role?: string task?: string format?: string restrictions?: string getSystemPrompt()

Simple Prompt Functions

Single Prompt

This function takes a single prompt as a string, optionally directed at a specific model, optionally at a specific temperature. The prompt is not given any context outside of the model, and the request is coming from the User.

async singlePrompt(message: string): Promise<string>

Continue Conversation

This function takes an existing set of messages and prompts the given model for a response with the message history as the context.

async continueConversation(messages: Array<ChatMessage>, model?: string, temperature?: number): Promise<string>

A ChatMessage is a prompt to or response from the model. Together, a list of ChatMessages makes a conversation. A conversation history can be given to a model to provide context for a response. A chat message has a role (System, User, Assistant, or Function), and some content (a string). The roles a message can have are:

• user - The user prompting the model
• assistant - The model's response
• system - Contextual information given to the model
• function_call - Some models have built-in functions. This will call a particular function.

Create Agent

This function takes a prompt for an agent, optionally given a specific model and temperature

async createAgent(agent: Agent, model?: string, conversation?: Array<ChatMessage>, temperature?: number): Promise<string>

By default, an Agent has the (string) properties:

• Name
• Role
• Task
• Format
• Restrictions

These properties are used to build a system prompt that prepares an Agent. This is done with the getSystemPrompt function.

The Agent class can be extended to add properties or modify the getSystemPrompt function.

Creating an Agent also comes with the option of specifying a model, including an existing conversation, and applying a temperature.

Create Pre-Trained Model

This function returns the npm command necessary to create a new model from the OpenAI CLI.

async createPTM(filePath: string): string

Add this script to your package.json to use the command from a server-side application:

"scripts":{
    "train-model": "openai tools fine_tunes.prepare_data -f $npm_config_file",
    ...
}

Terms

Raw Data

This is any collection of data that has not been formatted into prompts and responses.

flowchart TD
    D[Raw Data]
    style D fill:#def

Dataset

This is a collection of data that has been formatted for use with GPT models

flowchart TD
    DS[(Dataset)]
    style DS fill:#edf

Model

This is a base, "vanilla" GPT model (not a pre-trained model)

flowchart TD
    M{Model}
    style M fill:#fde

OpenAI Models

Agent

This is a prepared conversation in which the model has been provided with a role via a system prompt, and possibly an existing conversation history.

flowchart TD
    Smith[/Agent\]
    style Smith fill:#fed

Human

All model interaction has to be prompted and moderated by humans, since models can make mistakes and produce garbage data. Human curation and model refinement can help decrease the frequency of mistakes, but it will never go to 0.

flowchart TD
    H((Human))
    style H fill:#ffc

Flavor

Flavor refers to the purpose or format of a dataset, and/or a pre-trained model based on such a dataset. A model can be trained, for instance, to generate Agent data, or to generate sets of prompts for creating other models based on variable parameters, or to emulate a particular style of writing. Examples:

• Prompt Generator
• Agent Generator
• Style Emulator

flowchart TD
    P{P}
    A{A}
    S{S}
    style P fill:#ffddfa,stroke-width:4
    style A fill:#ffe8ee,stroke-width:4
    style S fill:#ffeeee,stroke-width:4

Interactions

Training

This is when a model is created using some set of training data. Once a model is created, it cannot "unlearn" a dataset, or be altered in any way. New models can, however, be created from this model, using new datasets. When a base model is combined with a specific flavor of data to create a new model for a specific purpose, this is called a pre-trained model.

flowchart LR
    DS[(Dataset)] --> M{Model}
    E[e.g.:]
    I[(Internet)] --> C{ChatGPT}
    style DS fill:#edf
    style M fill:#fde
    style I fill:#edf
    style C fill:#fde
    style E fill:#fff0,stroke:#fff0

Pre-Trained Model:

flowchart LR
    D[(Dataset)] --> C2{ChatGPT}
    C2 --> P{PTM}
    style D fill:#edf
    style P fill:#fef,stroke-width:4
    style C2 fill:#fde

Agent Preparation

Agents are prepared by selecting a model, a setup, and an optional conversation history for context.

flowchart LR
    D["A(x)"] --> M{M} --> A[/A\]
    style D fill:#def
    style M fill:#fde
    style A fill:#fed

Trained Agent

Agent data is prepared to produce an agent given a specific Agent Generator model

flowchart LR
    D2["A(x)"] --> M2{A} --> A2[/"TA(x)"\]
    style D2 fill:#def
    style M2 fill:#fde,stroke-width:4
    style A2 fill:#fed,stroke-width:4

To Do

Extend to include other models besides ChatGPT
Mode robust control of model parameters
Refactor all model parameters into a ChatOptions model or ModelOptions model??# GPT Tools
This is a ChatGPT client that contains a set of typescript tools that interact with OpenAI's ChatGPT models.