chat-toolkit 1.0.0

Extensible package for creating machine learning powered chatbots.

• Chat Toolkit
  • Installation
  • Quick Usage
  • Components
    • Chatbots
    • Speech to Text
  • Orchestrators
    • TextToTextOrchestrator
    • SpeechToTextOrchestrator

Chat Toolkit

Extensible package for creating machine learning powered chatbots.

NOTE: Linux users may need to install PortAudio. Please check their documentation for the best way to install on your system. For Ubuntu users, sudo apt-get install libportaudio2 should do the trick.

Installation

pip install -U chat-toolkit

Quick Usage

The main script has been provided for convenience. This allows you to easily start a conversation in your terminal.

Usage:

usage: A script for quickly starting a conversation in your terminal. [-h] [--chatbot {chatgpt}] [--speech-to-text [{whisper}]]

optional arguments:
  -h, --help                        show this help message and exit
  --chatbot {chatgpt}               Chatbot to use. Default: chatgpt.
  --speech-to-text [{whisper}]      Speech to text model to use. Without additional arguments, defaults to whisper. Defaults to None when argument is not present.

To quickly start up a TextToTextOrchestrator (both are equivalent):

python -m chat_toolkit OR python -m chat_toolkit --chatbot chatgpt

To quickly start up a SpeechToTextOrchestrator (all are equivalent):

python -m chat_toolkit --speech-to-text OR python -m chat_toolkit --speech-to-text whisper OR python -m chat_toolkit --chatbot chatgpt --speech-to-text whisper

Components

Components are ML powered objects that accomplish tasks. Components should be able to estimate session costs. You can build your own components to use in isolation or as part of an orchestrator object.

NOTE: Cost estimates are based on pricing rates provided by the user. Users should do their own due dilligence and are responsible for their own costs and estimations.

Advanced Usage: You can create your own component types by subclassing chat_toolkit.base.ComponentBase

Chatbots

These components send and receive text messages.

Class	Requirements	Models Available	Reference
OpenAIChatBot	OPENAI_API_KEY	gpt-3.5-turbo (ChatGPT)	OpenAI

Basic Usage:

from chat_toolkit import OpenAIChatBot

chatbot = OpenAIChatBot()
chatbot.prompt_chatbot("You are a butler named Jeeves.")
chatbot_response, _ = chatbot.send_message("Hello, what is your name?")

Advanced Usage: You can create your own chatbot components by subclassing chat_toolkit.base.ChatbotComponentBase

Speech to Text

These components record speech and transform it into text.

Class	Requirements	Models Available	Reference
OpenAISpeechToText	OPENAI_API_KEY	whiper-1	OpenAI

Basic Usage:

from chat_toolkit import OpenAISpeechToText

speech_to_text = OpenAISpeechToText()
text, _ = speech_to_text.record_and_transcribe()

Advanced Usage: You can create your own speech to text components by subclassing chat_toolkit.base.SpeechToTextComponentBase

Orchestrators

Orchestrators are modes of chatting that orchestrate one or more components differently. They also allow you to chat from the terminal. Orchestrators should work such that you can replace any component with another of the same type, or a custom-built one, and still be able to use the orchestrator.

Advanced Usage: You can create your own orchestration classes by subclassing chat_toolkit.base.OrchestratorBase

TextToTextOrchestrator

Basic usage:

from chat_toolkit.components import OpenAIChatBot
from chat_toolkit.orchestrators import TextToTextOrchestrator

chat = TextToTextOrchestrator(OpenAIChatBot())
chat.terminal_conversation()

SpeechToTextOrchestrator

Basic usage:

from chat_toolkit.components import OpenAIChatBot, OpenAISpeechToText
from chat_toolkit.orchestrators import SpeechToTextOrchestrator

chat = SpeechToTextOrchestrator(OpenAIChatBot(), OpenAISpeechToText())
chat.terminal_conversation()