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Unlocking the Power of AI Chatbots with FastChat
A recent survey by Salesforce reveals that 23% of service companies are currently leveraging chatbots, with this number expected to more than double in the next 18 months. FastChat, a platform for training, serving, and evaluating large language models, is at the forefront of this revolution. In this article, we’ll explore how to harness FastChat to implement a simple AI chatbot in a JavaScript web app.
Setting Up FastChat
FastChat is available as an open-source Python library, which can be installed using pip. This command installs fschat along with two optional modules: model_worker, to handle different models, and webui, to host a web interface for interacting with the chatbot model.
Choosing the Right Language Model
When it comes to selecting a suitable language model, consider factors such as hardware resources, cloud service costs, and the type of generation required. There are various commercially available LLMs to choose from, including GPT-3, BERT, and T5. For this tutorial, we’ll use the google/flan-t5-large model, which is inspired by Google’s T5 model and can run comfortably on an average laptop.
Launching the FastChat Controller
The controller is the central component of the FastChat architecture, orchestrating calls to model workers and checking their health with a periodic heartbeat. To launch the controller, use the following command, which will listen for the model worker to connect.
Creating the UI
The fastest way to build the chatbot’s UI is to use the webui module, which utilizes Gradio, a library of web components designed to simplify the deployment of UI for interacting with AI pipelines and chatting with chatbots. The webui module provides a simple UI that will address the correct model worker.
Building a Simple Chatbot Web App
Let’s build a simple web app, written in JavaScript, that uses the OpenAI API hosted by FastChat. We’ll need the model worker, as well as a controller to handle the worker. The code for this example is available on GitHub. The three Python scripts must each be run in a different shell window.
Testing the API Server
Once you have the OpenAI API server up and running, you’ll be able to interact with it on http://localhost:8000. A quick curl will be enough to test it. In the /node-webui directory, you’ll find a single index.html file containing the smallest amount of code required to interact with the OpenAI API through a user interface using simple JavaScript.
The Future of AI Chatbots
FastChat is an incredible package designed to benchmark, interact, and experiment with a plethora of LLMs. It offers a quick way to host a chat web interface using Gradio, a standard framework for building machine learning web apps. With FastChat, you can test models, host your own application, and even accommodate pre-existing code compatible with the OpenAI API. Its architecture is also highly efficient in terms of expandability, allowing you to host multiple LLMs for testing or specialized tasks.