Chat assistant - Writer AI Studio

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In this tutorial, you’ll use the Writer Framework to create a simple yet powerful chat assistant that can engage in conversations on various topics, provide answers to your questions, and maybe even help you when you’re experiencing writer’s block! The process will take only minutes using a drag-and-drop visual editor to build the user interface and Python for the back-end code. Here’s what the finished project will look like:

Prerequisites

Before starting, ensure you have:

Setting up your project

Create a Writer app and get its API key

First, you’ll need to create a new app within Writer.

  1. Create the app in Writer
    Log into Writer. From the Home screen, click on the Build an app button.
    The Start building menu will appear, presenting options for the types of apps you can create. Select Framework, located under Developer tools. This will create a brand new app based on Writer Framework.

  2. Copy your app's API key
    On the next screen, titled How to deploy an application, you can get the API key for the app by clicking on the Reveal key button, located under the text Authenticate with an API key. Your complete API key will be displayed, and a “copy” button will appear. Click this button to copy the key; you’ll use it in the next step.

Set up your computer and create the app’s project

The next step is to set up the Writer Framework environment on your computer. You’ll do this by creating a directory for the project, installing dependencies, and creating the project for the application using a template.

  1. Open your terminal application
    Open your terminal application. On macOS and Linux, this application goes by the name Terminal; on Windows, you can use either Windows PowerShell (which is preferred) or Command Prompt.

  2. Install the dependencies
    If you already have the writer and python-dotenv packages installed on your computer, you can skip this step.
    Install the writer and python-dotenv packages by entering the following commands in your terminal application:

    pip install writer python-dotenv

    This command tells pip, the Python package installer, to install two packages:

  1. Set the API key environment variable
    To pass your API key to the Writer Framework, you need to set an environment variable called WRITER_API_KEY. Select your operating system and terminal application below, then copy and paste the command into your terminal application, replacing [your_api_key] with the API key you copied earlier:

  1. Create the project
    Create the project by entering this command into your terminal application:

    writer create chat-assistant --template=ai-starter

    This command sets up a new project called chat-assistant using a starter template called ai-starter so that you’re not starting “from scratch.”

Build the UI

Now that you’ve created the project, it’s time to define the UI. The Writer Framework’s drag-and-drop capabilities make it easy — even if you haven’t done much UI work before! The project editor is a web application that runs on your computer and enables you to define and edit your app’s user interface. Launch it by typing the following into your terminal application:

writer edit chat-assistant

You’ll see a URL. Control-click it (command-click on macOS) to open it, or copy the URL and paste it into the address bar of a browser window. The browser window will contain the project editor, which will look like this:

Examine the header

Select the Header component by clicking it — it’s the component at the top, containing the title AI STARTER and a gray area labeled Empty Header. When you click it, you’ll see the properties panel appear on the right side of the page. This lets you view and edit the properties of the selected component.

The first property you’ll see in the panel is the Text property, which defines the text that appears as the header’s title. It should contain the value @{my_app.title}. The @{ and } indicate that my_app.title is a variable and that its contents should be the text displayed instead of the literal text “my_app.title”. You’ll set the value of this variable soon.

Clear the Section's default title

Select the Section component by clicking it — it’s just below the Header component and contains the title Section Title and a gray area labeled Empty Section. In the properties panel, clear out the value of the Title property. This will remove the Section’s default title.

Add a Text component to the Section

Drag a Text component from the Core toolkit panel on the left (it’s under Content, and you may need to scroll down a little to find it) and into the Section. Sections can act as containers for other components.

You can search for a specific component by using the search bar at the top of the Core toolkit panel.

Select the Text component. In the properties panel, set the Text property to provide instructions or context for your chat assistant. Here’s an example: Welcome to the Chat Assistant. Ask me anything!

Add a Chatbot component to the Section

The heart of this app is the Chatbot component, a pre-built component that displays the conversation between the LLM and the user and provides a text field where the user can enter prompts. Drag a Chatbot component from the Core toolkit panel (it’s under Content) into the Section, just below the Text box.

Add the back-end code

With the UI laid out, it’s time to work on the logic behind it. The logic behind the user interface is defined in a file named main.py, which is in your project’s directory. This file was automatically generated; you’ll update the code in it to define the behavior of your app. The simplest way to edit main.py is within the project editor. Click on the “toggle code” button (beside the word Code) near the lower left corner of the project editor page.

A pane with the name Code will appear at the bottom half of the screen, displaying an editor for the the contents of main.py.

If you’d rather use a code editor instead of coding in the browser, use it to open the main.py file in your project’s directory.

Import libraries and load the Writer Framework API key

You should see the following at the start of the file:

import writer as wf
import writer.ai

Replace that code with the following:

import os
import writer as wf
import writer.ai

# Set the API key
wf.api_key = os.getenv("WRITER_API_KEY")

This code imports the libraries that the application will need and then reads your Writer Framework API key in the WRITER_API_KEY environment variable.

Create a handler to respond to the user's input

The application needs a function to handle incoming chat messages. Find these comments in the code…

# Welcome to Writer Framework!
# This template is a starting point for your AI apps.
# More documentation is available at https://dev.writer.com/framework

…and replace them with the following function:

def generate_completion(state, payload):
  print(f"Here's what the user entered: {payload['content']}")
  state["conversation"] += payload
  print(f"Conversation: {state['conversation'].messages}")
  try:
      for index, chunk in enumerate(state["conversation"].stream_complete()):
          print(f"Chunk {index}: {chunk}")
          if not chunk.get("content"):
              chunk["content"] = ""
          state["conversation"] += chunk

print(f"state['conversation']:\n{state['conversation'].messages}")
  except Exception as e:
      print(f"Error during stream_complete: {e}")

The generate_completion() function will be called when the user enters a prompt, which is contained in the payload object. The payload object is added to the conversation object contained in the application’s state, which adds the user’s prompt to the record of the conversation between the user and the LLM. After adding the user’s prompt to the conversational record, generate_completion() calls the conversation object’s stream_complete() method, which generates an LLM completion based on the conversation so far. As its name implies, stream_complete() returns the completion as a stream of text chunks, which are captured and added to the conversation object.

The conversation object in the code above is an instance of Writer’s Conversation class. You can find out more about this class on our Writer AI module page.

Initialize the application

The final step is to set the application’s initial state. Find this code, which should be just after the generate_completion() function…

# Initialise the state
wf.init_state({
    "my_app": {
        "title": "AI STARTER"
    },
})

…and replace it with this:

# Initialize the state
wf.init_state({
    "conversation": writer.ai.Conversation(),
    "my_app": {
        "title": "CHAT ASSISTANT"
    },
})

The Writer Framework’s init_state() method sets the initial value of state, a dictionary containing values that define the state of the application. The key-value pairs in state are how you store values used by your app and how you pass data between the back-end code and the UI. The code above sets the initial value of state so that it has two key-value pairs:

Save the updated code and hide the code editor

That’s all the code. If you edited the code in the browser, save it by clicking the “save” button near the top right corner of the code editor. Click the “toggle code” button to hide the code editor.

Bind the UI to the back-end code

You’ve built the UI and written the code behind it. Let’s connect the two! Go back to the browser window with the project editor and do the following:

  1. Observe that the heading at the top of the app is now 'CHATBOT ASSISTANT'
    Earlier, you saw that the Header component’s Text property was set to @{my_app.title}, a value in the app’s state variable. You changed this value when you update the call to the Writer Framework’s init_state() method.

  2. Bind the Chatbot component to the 'state' variable's 'conversation' key
    Recall that the conversation object contained within the state variable contains the record of the conversation that the user is having with the LLM. Binding the Chatbot component to this object allows it to display the conversation to the user. Select the Chatbot component. In the properties panel, find the Conversation property and set its value to @{conversation}.

  3. Specify the Chatbot component's event handler
    You need to specify that the Chatbot component should call the generate_completion() function when the user enters a prompt. Do this by scrolling down the properties panel to the Events section until you see a property called wf_chatbot_message. Select generate_completion from its menu.

Test the application

You’ve completed all the steps to make a working chat assistant, and you can try using it right now, even while editing the user interface! Try entering some prompts into the text entry at the bottom of the Chatbot component. The LLM should respond accordingly: To get a better sense of what the experience will be like for the user, switch to the preview by changing the edit mode (located near the upper left corner of the page) from UI mode to Preview mode by selecting the Preview option: Here’s what the app looks like in Preview mode: You can see the output of any print() functions and error messages by clicking on the Log button located near the upper right corner of the page: Here’s what the app looks like when displaying the log: It’s very helpful to be able to test the application while editing it. As you continue to work with Writer Framework, you’ll find yourself alternating between making changes to your application and testing those changes without having to leave the project editor.

Run the application locally

Once you’ve tested the application, it’s time to run it locally. Switch back to your terminal application. Stop the project editor with ctrl-c, then run the application by entering the following command:

writer run chat-assistant

Note that the command starts with writer run as opposed to writer edit. This launches the application as your users will see it, without any of the editing tools. Even though you can preview your applications in the project editor, it’s still a good idea to test it by running it on your computer, outside the project editor, before deploying it. You’ll be able to access the application with your browser at the URL that appears on the command line. It should look like this:

Conclusion

That’s it — you’ve built a functional chat assistant using the Writer Framework! Feel free to modify this project! The Writer platform is flexible enough for you to customize, extend, and evolve your application into something completely different! To find out what else you can do, check out the documentation for Writer Framework and the Writer API.