Extensible package for creating machine learning powered chatbots.
Package supports Linux and Windows. Mac is not explicitly supported, although it is possible some, or many parts of this will still work.
NOTE: Some components require additional dependencies. See below for more information.
pip install -U chat-toolkit
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}]]
[--text-to-speech [{pyttsx3}]]
options:
-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.
--text-to-speech [{pyttsx3}] Text to speech model to use. Without additional arguments, defaults to pyttsx3. Defaults to
None when argument is not present.
To quickly start up a Text to Text conversation (default models):
python -m chat_toolkit
To quickly start up a Speech to Text conversation (default models):
python -m chat_toolkit --speech-to-text
To quickly start up a Text to Speech conversation (default models):
python -m chat_toolkit --text-to-speech
To quickly start up a Speech to Speech conversation (default models):
python -m chat_toolkit --speech-to-text --text-to-speech
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
These components send and receive text messages.
| Class | Requirements | Model | Default Cost | Reference |
|---|---|---|---|---|
| OpenAIChatBot | OPENAI_API_KEY | gpt-3.5-turbo (ChatGPT) | $0.002/1k tokens | 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
These components record speech and transform it into text.
| Class | Requirements | Model | Default Cost | Reference |
|---|---|---|---|---|
| OpenAISpeechToText | OPENAI_API_KEY, libportaudio2 (linux) | whiper-1 | $0.006/1k tokens | OpenAI |
Basic Usage:
from chat_toolkit import OpenAISpeechToText
speech_to_text = OpenAISpeechToText()
text, _ = speech_to_text.transcribe_speech()NOTE: Recording quality is very sensitive to your hardware. Things can go wrong, for example, if the input volume on your microphone is too loud.
Advanced Usage: You can create your own speech to text components by subclassing
chat_toolkit.base.SpeechToTextComponentBase
These components say pieces of text.
| ClassTextToSpeech | Requirements | Model | Default Cost | Reference |
|---|---|---|---|---|
| Pyttsx3TextToSpeech | espeak (linux) | n/a | Free | Pyttsx3 |
NOTE: Pyttsx3TextToSpeech currently defaults to English, but it may be configured using set_pyttsx3_property() method. See pyttsx3's documentation for more information.
Basic Usage:
from chat_toolkit import Pyttsx3TextToSpeech
text_to_speech = Pyttsx3TextToSpeech()
text_to_speech.say_text("hello")Advanced Usage: You can create your own text to speech components by subclassing
chat_toolkit.base.TextToSpeechComponentBase
The Orchestrator class also allow you to chat from the terminal. The Orchestrator 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.
Basic usage:
from chat_toolkit import OpenAIChatBot, Orchestrator
chat = Orchestrator(OpenAIChatBot())
chat.terminal_conversation()Basic usage:
from chat_toolkit import OpenAIChatBot, OpenAISpeechToText, Orchestrator
chat = Orchestrator(OpenAIChatBot(), OpenAISpeechToText())
chat.terminal_conversation()Basic usage:
from chat_toolkit import OpenAIChatBot, Orchestrator, Pyttsx3TextToSpeech
chat = Orchestrator(OpenAIChatBot(), text_to_speech_component=Pyttsx3TextToSpeech())
chat.terminal_conversation()Basic usage:
from chat_toolkit import OpenAIChatBot, OpenAISpeechToText, Orchestrator, Pyttsx3TextToSpeech
chat = Orchestrator(OpenAIChatBot(), OpenAISpeechToText(), Pyttsx3TextToSpeech())
chat.terminal_conversation()