conversation-engine

Conversation Engine

A powerful wrapper around the OpenAI API, providing additional features and making it easier to interact with conversational models. Seamlessly chat with your AI assistant, include context strings, and manage conversation history.

Give it a message and it will handle the rest.

Features

• Automatically handle chat history and context injection
• Built-in support for summarizing long conversations
• Customizable system instructions for controlling AI behavior
• Works with OpenAI API, making it easy to integrate with existing projects
• Built with TypeScript, providing type safety and autocompletion

Installation

Install conversation-engine with npm

npm install conversation-engine

Usage

Minimal Example

import { configureChat, chat, ModelName } from 'conversation-engine';

// Configure the chat
configureChat({
	apiKey: 'your-openai-api-key',
	modelSelection: ModelName.GPT_3_5_TURBO,
});

// Send a message
async function yourChatBot() {
	const userMessage = { role: 'user', content: 'Tell me a joke!' };
	const response = await chat(userMessage);

	/*
	// The response object
	{
	    role: 'assistant',
	    content: "Why couldn't the bicycle stand up by itself? Because it was two tired!"
	}
	*/

	console.log(response.content);
}

yourChatBot();

Advanced Example

import { configureChat, chat, ModelName } from 'conversation-engine';

// Configure the chat with more options
configureChat({
	apiKey: 'your-openai-api-key',
	modelSelection: ModelName.GPT_4,
	historyLength: 6,
	historySummarizationModel: ModelName.GPT_3_5_TURBO,
	openaiOptions: {
		temperature: 0.8,
		maxTokens: 500,
		presencePenalty: 0,
		frequencyPenalty: 0,
	},
});

async function yourAdvancedChatBot() {
	const userMessage = { role: 'user', content: 'What are two benefits of exercise?' };
	const context = [
		'Exercise can reduce your risk of heart diseases',
		'Exercise can improve your mental health and mood. ',
	];
	const systemMessageContent = 'You are an AI health expert.';

	const response = await chat(userMessage, context, systemMessageContent);
	console.log(response.content);
}

yourAdvancedChatBot();

Note You should use dotenv to provide your apiKey.

API Reference

Message

A message object containing a role and content.

Property	Type	Description
role	string	The role of the message sender (system, user, or assistant)
content	string	The content of the message

ModelName

Enum for AI model names.

Value	Description
GPT_4	GPT-4 Model
GPT_4_32K	GPT-4-32K Model
GPT_3_5_TURBO	GPT-3.5-Turbo Model

ChatConfiguration

A chat configuration object containing various settings.

Property	Type	Description
apiKey	string	The API key for accessing the AI service
modelSelection	ModelName	The AI model to use for chat
historyLength	number	The number of recent messages to include without summarization
historySummarizationModel	ModelName	The AI model to use for history summarization
messageHistory	Message[]	The array of message objects representing the chat history

OpenAIOptions

Optional settings for the OpenAI API. Check out their documentation.

Property	Type	Description
temperature	number	The temperature for generating responses
topP	number	The top_p value for nucleus sampling
n	number	The number of responses to generate
stream (broken)	boolean	Whether to use streaming mode. (Streaming does not work currently)
stop	string | string[]	A string or array of strings to stop the response generation
maxTokens	number	The maximum number of tokens for the generated response
presencePenalty	number	The presence penalty for the generated response
frequencyPenalty	number	The frequency penalty for the generated response
logitBias	{ [token: string]: number }	An object of token strings and their biases
user	string	A user identifier to associate with this chat completion

FAQ

Q: Which AI Models Can I Use with This Module?

A: You can use any of the models available in the OpenAI API, such as GPT-4, GPT-4-32K, and GPT-3.5-Turbo. You can refer to the ModelName enum for a complete list of supported models.

Q: How Do I Provide Context for the Conversation?

A: You can provide context by passing an array of context strings to the chat() function. This will include the context messages in the conversation, making it easier for the AI to understand the context.

Q: How Does Message History Summarization Work?

A: The message history summarization feature helps to condense long conversations into a shorter, summarized form. This allows the AI to focus on the most relevant information and provide better responses. The summarization is done using the AI model specified in the ChatConfiguration object.

Future Features

• Working streaming responses
• Import past history
• Add tests