Build and Run an Automation
This tutorial is designed to walk you through the process of building your first automation. Whereas integrations consist of predefined workflows that end-users can easily activate via a web form, automations are a powerful tool for enhancing your internal business operations. They allow you to link applications and services used within your organization or to launch bespoke business logic that meets your unique requirements.
You can also build a flow using the AI copilot. When you start building a new flow, you can choose between two options: add a trigger manually, or build with AI.
Automation Overview
In this tutorial, we'll construct an automation designed to gather customer feedback via Typeform, relay each piece of feedback as a Slack notification, and automatically generate a GitHub issue based on the customer's response to the question: "What improvements or additional features would you like to see in future updates?". Utilizing OpenAI's ChatGPT, the generated ticket will be structured as a comprehensive user story, encompassing title, role, goal, reason, acceptance criteria, and test case.
Let's delve into the specifics of the automation flow:
Here's the first piece of customer feedback collected through our Typeform:
As a consequence, we received a notification in Slack:
Furthermore, a new GitHub Issue was created containing this content:
A Step-by-step Guide
The upcoming sections will walk you through the steps required to build the aforementioned automation from start to finish.
Select a trigger
Every automation begins with a trigger, which initiates the automation flow. Typically, triggers can capture data from external sources—like a Webhook trigger or a Gmail - New Email trigger—or they can be based on time events, such as a Scheduler or Timer. This setup allows you to program your automations to run at predetermined times, for instance, "every day" or "each week on Monday at 2pm". A unique trigger type is the "On Start" trigger, which activates immediately when your automation flow is started. This is particularly useful for batch operations that you intend to run only once or for debugging purposes.
To start building an automation, go to the Automations page and select "Create Automation". This will lead you to the Automation Designer. The first item you'll encounter there is the Trigger Selector. Look up "Typeform" and choose the "New Entry" trigger.
In the Configuration Inspector on the right, authenticate with Typeform and select one of your forms:
Add Actions
Click Add Step on the side of the New Entry trigger and find Slack. Pick the Send Channel Message action. Authenticate with your Slack account and select a Slack channel to direct your notifications:
Click the "+" button next to the Slack message field to include data placeholders for each Typeform question. These placeholders will be substituted with real data once the automation executes and retrieves customer feedback from Typeform:
Similarly, add the OpenAI - Send Prompt action. Configure the model, set the response type to json_object, and define the prompt as follows:
You can copy paste the prompt from here: From the below product feedback on improvements and additional features that was posted by a customer of our product Appmixer, create a user story for developers so that they have a concise description with a test case defining the feature. Format the user story as a JSON object with "title" and "content" properties. The "content" must contain a Github Markdown text with the following sections: "title", "role", "goal", "reason", "acceptance_criteria" and "test_case". Product feedback: <map your data variable from the form here>
The goal is to have ChatGPT generate a JSON comprising two fields: "title" and "content." This JSON will then be seamlessly integrated into the "Github.CreateIssue" action. The structure of our output JSON should resemble the following:
Create new variables and transform data with Modifiers
When your automation flow includes a component that outputs data you need to alter and then utilize the modified data across various configuration fields, employing the Control.SetVariable component proves beneficial. This component enables you to define custom variables, which can subsequently be referenced by name in other linked components. This approach is particularly advantageous when you aim to use transformed data in several locations, as it obviates the need to replicate the same modifications for a single variable across multiple fields.
In our scenario, we intend to capture the output of the ChatGPT completion (ChatGPT's response) in a variable named result. This allows us to conveniently reuse this data for both the title and description fields when we're ready to create our GitHub issue.
Given our interest in only the first choice, and considering that the message content of this choice is a JSON string (as specified in our prompt to ChatGPT), we need to manipulate the Choices variable to extract our desired JSON. To transform data within Appmixer, simply click on a variable to access the Modifiers panel and then sequentially apply the necessary modifiers, akin to how formulas are utilized in an Excel sheet. In our case, we will employ the sequence of modifiers: First Item -> JSON Path -> Parse. This sequence will allow us to select the first item from the Choices list, extract the message.content from this item, and finally parse the message.content into a JSON object for later use. It is crucial to note that ChatGPT outputs a JSON string, not a JSON object, necessitating the Parse modifier to convert the text into structured data.
Finalize our Workflow Automation
The final step involves creating a GitHub Issue using the title and content (in Markdown) generated by our AI. Navigate through the interface to locate the GitHub connector, then drag and drop it onto the canvas. Select the "CreateIssue" action and assign response.title and response.content (the JSON fields generated by ChatGPT) to the corresponding fields within the "Github.CreateIssue" action. As an additional step, choose the "enhancement" label from the selection box to categorize our GitHub issues as enhancements.
Align Your Diagram
The Designer renders flows with a redesigned diagram and a modern look, in both light and dark modes. If your canvas gets messy while building, click the Align Diagram button in the upper toolbar: the flow is re-arranged into a clean left-to-right layout and zoomed to fit. Only component positions change — connections and configuration stay untouched — and the alignment can be undone in a single step.
Test Your Automation
Before starting your automation, you can test it using the "Test flow" button in the upper toolbar. This runs the flow once so you can verify that each component behaves as expected without needing to start it live.
Once the test finishes, you can see the status of each component and preview its logs by hovering over the individual components on the canvas.
The test status notification also tells you what kind of data was used for the test:
Real data from the trigger component, if it was available.
Historical data from the logs, if no real data was found on the trigger component.
Dummy data generated for the test, if neither real nor historical data was available.
Start and Monitor Your Automation
Your automation is now set up and ready to be launched. Begin by giving it a distinctive name; this can be done by double-clicking on the default "New flow" title and renaming it to something more descriptive, like "Track Customer Feature Requests". To activate your automation, click the "Start flow" button located in the top right corner. You'll notice the log panel opens at the bottom displaying the initial log entry: "Flow started". As customer feedback starts coming in through our Typeform web form, the log panel will populate with entries detailing the actions taken within the flow. These logs provide insights into each component's input, the data transmitted between components, and the output generated, offering a comprehensive view of the automation's operation:
Clicking any log entry will reveal a panel that offers additional details.
Making Changes to an Active Automation
You can edit a running automation without stopping it. When an automation is active, an "Edit" button appears in the upper toolbar. Clicking it creates a draft, where you can make changes and test them just like on any other flow. Once you're happy with the draft, click "Publish changes" to replace the active version with it.
In addition to editing via a draft, you can view and manage the active version, the draft, and other versions in the versioning panel. Open it by clicking the Versioning icon on the left side of the upper toolbar, next to the automation's name. From the versioning panel you can browse through previous versions, create new versions manually as snapshots of the current state, or restore a previous version.
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