Chat application

From start to finish, set up a chat-based application with NLX

Checklist

You'll complete the following to successfully launch your chat application:


Pre-setup: Integrations

A one-time integration of a Natural Language Processing (NLP) engine must be completed in your workspace:


Step 1: Create a flow

Begin by identifying the tasks your conversational AI application will automate and organize them into individual topics handled by a flow. Determine the sequence of steps and messaging that the conversational application follows to assist a user with the task. The conversation workflow is assembled in a flow's Canvas with a pattern of nodes similar to a flow diagram.

Each flow is invoked when your chosen AI model identifies customer intent from a user's query ("I want to order room service") and matches it to a flow you've created (OrderRoomService). This match is done based on the training data you provide for your AI model.

  • Enter a descriptive name (no spaces or special characters) > Select Save

  • Complete Flow setup by attaching training data and attaching slots

  • Add nodes by right-clicking on the Canvas & choosing New node (see available node types)

  • Connect first node to Start node > Connect sequential nodes via node edges or stacking

  • Add conversational AI messages to nodes by selecting + Add message on a node's side panel

  • Click Save

Repeat for any additional flows your application will help automate.

🧠 Looking for more? See Flows


Step 2: Set up application

Now you'll create the conversational AI application users will interface with. This step involves attaching all intent flows you want your application to access, defining flows to handle certain behaviors, and setting up the channel your application supports.

  • Select Applications from workspace menu > Choose New application

  • Enter a descriptive name > Click Save

  • Click Flows tab of application > Select Attach flows > Attach one or more flows created to make available to your application > Click Attach selected then Save

  • Select Default behaviors tab of application > Assign any attached flows to the application's behaviors > Click Save

  • Select Channels tab of application > Expand API option > Click + Create channel

  • Click + Add domain under Whitelisted domains > Add your webpage's URL and https://developers.nlx.ai

  • Click Create channel

🧠 Looking for more? See Manage channels


Step 3: Build & deploy

A build constructs the array of flows that make up your conversational AI application and updates any changes made to your flows, while deploying makes a successful build live:

  • Click Deployment tab of application > Select Create or Review & build

  • Wait for validation to complete > Select Create build*

  • When satisfied with a successful build, click Deploy

*After a build status appears as 🟢 Built, you may use the Test feature to test the conversation with your application using the latest build.

🔧 Need to troubleshoot your build? See how to troubleshoot build errors


Step 4: Install SDK

To make your application available through your frontend and adjust the look, feel, and chat experience with theming and custom UI components, install the NLX chat SDK:

  • From Deployment tab of application, select Details next to the Deployed status

  • Expand the API section under Setup instructions in the pop-up modal

  • Expand the API section under Setup instructions in the pop-up > Select Open Touchpoint configurator

  • Make adjustments to the theming > Copy Setup snippet > Install on your site

🧠 Looking for more? See Developer SDK


Optional: Retrieve external data

To pull in and reference information from an external system, you may first set up a data request integration and invoke it in a flow using the Data request node.

  • Select Data requests under Resources of workspace menu > Choose New data request > Provide a name

  • Switch to External implementation under the Implementation tab > Choose the appropriate HTTPS method

  • Expand Production endpoint > Enter the URL and any applicable Header(s)*

  • Select the Response model tab > Choose <> Auto-generate option > Enter a snippet of your schema

Optional: If you require sending a payload when the API call is made in a workflow, select the Request model tab > Click Create from example JSON > Enter a snippet of the payload schema

  • Click Save

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