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Dialogflow → Intent and Slot Classifier with WHISPER
While your intent and slot classifier is in development, you can use Dialogflow, a powerful Google Cloud tool, to handle Automatic Speech Recognition (ASR) and Intent and Slot Classification for your conversational agent. Dialogflow allows you to optimize your agent's understanding of user input by creating intents with training phrases and entities that define the vocabulary and natural language understanding capabilities needed for recipe recommendations.
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With the replacement in place, your custom system will continue to interface with the MARBEL agent, providing it with the same intent and entity data, but with enhanced flexibility and scalability. This shift eliminates dependency on external tools like Dialogflow, ensuring better alignment with your project’s needs and granting your team full ownership of the conversational pipeline. By transitioning to your classifier and WHISPER, you’ll achieve a more robust, efficient, and adaptive solution for managing user interactions.
Create a Dialogflow agent
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Only one team member needs to do this. You need a Google account for this. Just provide a name for your Dialogflow agent. You do not need to change any other settings (you will use the default English language). |
Share the Dialogflow agent with your other team members (assign them the Developer role): https://cloud.google.com/dialogflow/es/docs/access-control.
Use the zip file found in the repository to make an agent follow the steps below to make a Dialogflow agent:
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Connect it to the MARBEL agent
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Create and download your Dialogflow agent’s JSON key file by following the instructions here: https://cloud.google.com/iam/docs/creating-managing-service-account-keys#get-key. Note that you will have to create a service account for the project (button at the top under the blue bar). When you have done this, you can click on the email for the service account. Note that the JSON file is automatically downloaded when it is created.
First, click on Enable API.
Click the purple link.
Then click IAM & Admin
Then Service Accounts is on the left menu.
Then Create Service Account.
Fill in whatever you want.
Then click blue link
Then choose Keys and Add Key - JSON.
All group members need to add the JSON file key to your local repository… it will not let you put it on GitHub because it is a key file. Rename the key file to dialogflow-keyfile.json, and add it under social-interaction-cloud/sic_framework/services/eis/dialogflow-keyfile.json
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Starting with Your MARBEL Agent
If your project installation is complete, you now should be able to run the code. Try it by following the instructions here: Run your Conversational Agent. You should see a start page.
A Pattern to Get Started
The dialog manager agent uses an agenda to structure the conversation with a user. An agenda consists of one or more conversational patterns; conversational patterns are templates for small sub-dialogs or mini-dialogs that can be viewed as building blocks to create a larger dialog or conversation. By inspecting this agenda, the agent can figure out what it should do and in what order things should be handled in the conversation. The agenda is managed by a MARBEL agent, which implements the dialog manager. There already is some code for initializing the agenda in the dialog_init.mod2g module. Open this file and navigate to the code line where the agenda is inserted into the agent’s database (state). As you can see, the agenda in the agent we provided to you as a starting point is still empty (it is the empty list []). We will use some of the patterns that we will create in the project to define the agent’s agenda by adding them to the list. To get started, we need to add something already to the agenda. We can use the start pattern for this which has already been defined in the patterns.pl file (check it out). This pattern just waits for the user to click a button on the screen. Below we will create such a screen where a user can click a start button to initiate the interaction. For now, let’s just add the start pattern to the agent’s agenda:
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Visuals
The conversational agent that we will create not only talks but also shows things using dynamic web pages that are displayed on a screen. These dynamic web pages will provide the user with some additional visual support to be able to keep track of some of the things that happened in the conversation. We want the agent, for example, to display information about where we are at in the conversation by, for example, displaying subtitles that refer to different parts (conversational patterns) of the conversation. We will also want the agent to display the preferences for or constraints on recipes that have already been added by the user and show how many recipes still fit those preferences. This will help the user understand what the agent is doing and help them remember which preferences they already indicated to the agent.
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