OpenChat

Opensource chatting framework for generative models.

    ____   ____   ______ _   __   ______ __  __ ___   ______
   / __ \ / __ \ / ____// | / /  / ____// / / //   | /_  __/
  / / / // /_/ // __/  /  |/ /  / /    / /_/ // /| |  / /   
 / /_/ // ____// /___ / /|  /  / /___ / __  // ___ | / /    
 \____//_/    /_____//_/ |_/   \____//_/ /_//_/  |_|/_/
  • OpenChat is opensource chatting framework for generative models.
  • You can talk with AI with only one line of code.

Installation

pip install openchat

Model Supports

  • DialoGPT : [small, medium, large]
  • BlenderBot : [small, medium, large, xlarge]
  • Coming Soon...

Usage

1. Terminal environment

  • Chatting on terminal.
  • Type .exit if you want to exit dialogue.
  • Type .clear if you want to clear all histories.
  • You can add additional user keywords. (Details are described below.)
>>> from openchat import OpenChat
>>> OpenChat(model="blenderbot", size="large")

Chat with facebook/blenderbot-1B-distill !
user : Hi. It is nice day.
bot : It is a nice day out. I am going to go for a walk. What are you up to?
user : I'll play computer game. Do you like playing game?
bot : I love playing video games. What kind of games do you like to play on your computer?
user : I love racing game. It is so exited. How about you?
bot : I love racing games as well. I like to play on the Playstation 4. What about you?
user : Usually I like to play on PC. I have a question. Who is CEO of Apple?
bot : The CEO of Apple is Steve Jobs. He founded the company in 1976.
user : That's right. I have an IPhone. It is my favorite phone. What is your phone?
bot : I have a Samsung Galaxy S8. I love it. It's the best phone I've ever had.
user : .exit
bot : good bye.
  • If you want to use GPU, use argument device="cuda"
>>> from openchat import OpenChat
>>> OpenChat(model="blenderbot", size="large", device="cuda")

2. Your own environment (not terminal)

  • You can use user id to manage user-specific history.
  • This can be useful when deployed on Facebook messenger or WhatsApp.
  • There is a web demo implementation in the /demo folder.

2.1. Write your own environment class

  • Make your own environment class inherited from BaseEnv
  • And implement your own run(model: BaseModel) method like below.
from typing import Dict
from flask import Flask, render_template
from flask_cors import CORS
from openchat.envs import BaseEnv
from openchat.models import BaseModel


class WebDemoEnv(BaseEnv):

    def __init__(self):
        super().__init__()
        self.app = Flask(__name__)
        CORS(self.app)

    def run(self, model: BaseModel):

        @self.app.route("/")
        def index():
            return render_template("index.html", title=model.name)

        @self.app.route('/send/<user_id>/<text>', methods=['GET'])
        def send(user_id, text: str) -> Dict[str, str]:

            if text in self.keywords:
                # Format of self.keywords dictionary
                # self.keywords['/exit'] = (exit_function, 'good bye.')

                _out = self.keywords[text][1]
                # text to print when keyword triggered

                self.keywords[text][0](user_id, text)
                # function to operate when keyword triggered

            else:
                _out = model.predict(user_id, text)

            return {"output": _out}

        self.app.run(host="0.0.0.0", port=8080)

2.2. Start to run application.

from openchat import OpenChat
from demo.web_demo_env import WebDemoEnv

OpenChat(model="blenderbot", size="large", env=WebDemoEnv())

3. Additional Options

3.1. Add custom Keywords

  • You can add new manual keyword such as .exit, .clear,
  • call the self.add_keyword('.new_keyword', 'message to print', triggered_function)' method.
  • triggered_function should be form of function(user_id:str, text:str)
from openchat.envs import BaseEnv


class YourOwnEnv(BaseEnv):
    
    def __init__(self):
        super().__init__()
        self.add_keyword(".new_keyword", "message to print", self.function)

    def function(self, user_id: str, text: str):
        """do something !"""

3.2. Modify generation options

  • You can modify max_context_length (number of input history tokens, default is 128).
>>> OpenChat(size="large", device="cuda", max_context_length=256)
  • You can modify generation options ['num_beams', 'top_k', 'top_p'].
>>> model.predict(
...     user_id="USER_ID",
...     text="Hello.",
...     num_beams=5,
...     top_k=20,
...     top_p=0.8,
... )

3.3. Check histories

  • You can check all dialogue history using self.histories
from openchat.envs import BaseEnv


class YourOwnEnv(BaseEnv):
    
    def __init__(self):
        super().__init__()
        print(self.histories)

{
    user_1 : {'user': [] , 'bot': []},
    user_2 : {'user': [] , 'bot': []},
    ...more...
    user_n : {'user': [] , 'bot': []},
}

3.4. Clear histories

  • You can clear all dialogue histories
from flask import Flask
from openchat.envs import BaseEnv
from openchat.models import BaseModel

class YourOwnEnv(BaseEnv):
    
    def __init__(self):
        super().__init__()
        self.app = Flask(__name__)

    def run(self, model: BaseModel):
        
        @self.app.route('/send/<user_id>/<text>', methods=['GET'])
        def send(user_id, text: str) -> Dict[str, str]:
            
            self.clear(user_id, text)
            # clear all histories !

GitHub