Agent Assist

Creation of conversation dataset

Transcripts of conversations are part of a conversation dataset. With the help of this data, a Smart Reply model is trained to suggest text replies to human chat agents when they are interacting with end users. In order to establish a discussion dataset to test your integration or observe how Smart Reply performs before uploading your own data, Agent Assist also offers publicly accessible conversation data.

Step 1: The following page displays when you create a new chat dataset:

Source: Agent Assist

Step 2: Give your new dataset a name and optional description. Enter the URI of the storage bucket containing your conversation transcripts in the Conversation data section. The usage of the * sign for wildcard matching is supported by Agent Assist. The URI should be formatted as follows:

gs://<bucket name>/<object name>

Step 3: Press Create. On the Data menu page's Conversation datasets tab, your new dataset is now visible in the dataset list.

Creation of knowledge base

Step 1: Go to the Agent Assist Console. Click on the Data menu item on the page's far left side after choosing your GCP project. All of your data is displayed under the Data menu. There are two tabs, one for knowledge bases and the other for conversation datasets:

Source: Agent Assist

Step 2: At the top of the knowledge bases page, click the +Create new button after selecting knowledge bases: Enter a name for the knowledge base and select a language from the pop-up menu

Step 3: A list of all the documents in this knowledge base will appear, which is now empty. Click +Create New to add a document.

Source: Agent Assist

Step 4: If you're submitting FAQ papers, pick FAQ under choose a knowledge type.

Step 5: Select the location of the document you're adding under choose a file source.

Step 6: To add the document, click Create.

Create a conversation profile

A conversation profile sets up a number of settings that regulate the recommendations presented to an agent. The suggestions that are presented during runtime are controlled by these settings. Each profile sets either a human agent or a virtual agent from Dialogflow for a chat.

Creating and editing a conversation profile 

Step 1: Open the Agent Assist console, choose your project from the list, and then click Conversation profiles on the left sidebar menu.

Step 2: In the top right corner of the page, click +Create new. The next page is displayed:

Source: Agent Assist

Step 3: In the Display name box in the pop-up menu, give your discussion profile a distinctive name.

Step 4: From the list of available possibilities, select one or more suggestion-type options.

Step 5: Decide whether to use inline retrieval or pub/sub retrieval.

Step 6: (Optional) Switch to a Dialogflow virtual agent or enable sentiment analysis.

Step 7: Press Create. Before the conversation profile is usable, it can take a while.

How to use the Agent Assist simulator

Before putting your model into practice, you can preview its performance using the simulator included in the Agent Assist Console. Any Agent Assist feature can be used with the simulator. We'll go over the steps needed to run the Agent Assist simulator in this section of the blog.

Testing a conversation profile's performance

Step 1: Go to the Agent Assist Console and select Conversation profiles from the menu on the left-hand side of the screen.

Step 2: A list of your conversation profiles is visible on the conversation profiles page. Click the Use simulator button after selecting the three vertical dots.

Step 3: The window that displays offers input fields for responses from the consumer and the agent. Text input can be used to test your model. An article suggestion discussion profile is being tested using the example below:

Source: Agent Assist

Step 4: For Smart Reply models, possible human agent responses are presented in bubbles underneath the Agent text field. The suggested responses are regularly updated as the debate goes on. The conversation profile and model parameters influence the outcomes.

Frequently Asked Questions

Describe VPC.

Virtual Private Cloud is referred to as VPC. This virtual network provides access to numerous resources, including the Google Kubernetes Engine clusters, and computes Engine's VM instances. The VPC provides a lot of flexibility for managing the connections between workloads on a global or regional scale.

What is load balancing?

In a cloud-based computing environment, load balancing is a mechanism for allocating workloads and computer resources in order to manage requests. Because the workload is properly controlled through resource allocation, it offers a high return on investment at lower costs.

What does Object Versioning entail?

For the purpose of restoring overwritten and deleted items, object versioning is used. Versioning things makes them secure when rewritten or deleted, but it raises storage costs. When object versioning is enabled in the GCP bucket, anytime the item is deleted or overwritten, a non-common version of the object is created.

Conclusion

To sum it up, in this agent assist blog, firstly, we discussed how to train a smart reply model and manage all the allowlists. Then we discussed the creation of a conversation dataset, knowledge base, and conversation profile, and lastly, we saw how to use the agent assist simulator. 

For more content, Refer to our guided paths on Coding Ninjas Studio to upskill yourself.

Do upvote our blogs if you find them helpful and engaging!

Happy Learning!