Conversational filtering developer's guide

Cloud console

The console allows retailers to manage generative questions in a conversational product filtering experience. Learn more about using generative questions in conversational product filtering.

Steps to use the generative question service

1. Satisfy data requirements.

2. Configure manual question overrides.

3. Turn the feature on.

4. Preview and test.

Data requirements

To find out if your search data is ready for conversational product filtering, in the console, under Conversational product filtering and browse, or under Data quality > Conversation, go to the Coverage checks tab.

To enable conversational product filtering, you need to meet certain data requirements.

These are:

• 1,000 queries per day: After you reach this first threshold, a conversation plan is generated that evaluates your inputs and outputs:
• Inputs: filter count in events
• Outputs: conversational coverage
• 25% conversational coverage: Calculated by AI Commerce Search models, conversational coverage means the percentage of queries that have one question. A frequency-weighted 25% (by volume) of queries should have at least a first question that matches it.

If you don't have 25% conversational coverage yet, but have the first prerequisite 1000 queries per day, blocking and advisory checks begin to be applied to your outputs and inputs, respectively. Here, AI Commerce Search begins to calculate by how much of a percentage your user-event-applied filters have to increase in order to reach the 25% conversational coverage threshold. The more filters that are uploaded, the higher the coverage reached.

To view your conversational readiness:

1. Go to the Conversation tab in the Data quality page in the AI Commerce Search in Gemini Enterprise for Customer Experience console. This provides you with a critical check of whether a minimum of 25% of search queries have at least one follow-up question, as well as advisory checks as to what percentage of user events with valid filters is needed to reach that conversational coverage goal.

Figure 1. Conversational readiness check.

1. If you pass the critical check, with sufficient user events with valid filters, proceed to the next step.

2. To control how generative questions are served, go to the Conversational product filtering and browse page in the AI Commerce Search console.

Generative question controls

The generative AI writes a question for every indexable attribute in the catalog, using both names and values of attributes for system and custom attributes. These questions are generated by an LLM and aim to enhance the search experience. For example, for furniture type, values can be indoor or outdoor, the AI synthesizes a question about what type of furniture you are looking for.

Each facet has one generated question. Based on historic user events and facet engagement from past search event data, the questions are sorted by expected frequency of the question appearing. The AI first looks at the questions on top, then finds what is relevant by attribute. The list of questions is generated once. If a new attribute is added, it is reflected in the list in two hours.

1. Go to the Shopping agent page in the AI Commerce Search in Gemini Enterprise for Customer Experience console.
   
   Go to the Conversational search and browse page.

2. Under the Manage AI generated questions tab, view all the questions sorted by how often they are used, in query-weighted frequency, meaning how often they are served with common queries. The ranking uses the frequency field in the GenerativeQuestionConfig configuration. This field is responsible for sorting the AI-generated questions by how often they are used.

3. You can use the filter option to filter the questions.

4. Check the box to enable question visibility for each attribute.

5. Click edit at the end of each row to open an edit panel for each question.

To make bulk edits, follow these steps:

1. Select or clear the boxes next to the questions that you want to include or exclude in conversation.

2. Click either the addAllow in conversation or the removeDisallow in conversation buttons that appear at the top of the list. Alternatively, to edit an individual question, click edit and clear or recheck the box next to Allowed in conversation in the pane that opens:

Figure 2. Edit each AI-generated question.

Use generative questions in conversational product filtering

The generative question service API provides controls to mitigate potential inconsistencies in the LLM output. These can be managed from the console. Here, retailers can also configure conversational product filtering by toggling its enabled state and setting the minimum number of products required to trigger it.

You can define the questions, specifying the question itself, potential answers, and whether the question is allowed in the conversation. Individual questions can be generated by an LLM or overridden by the retailer. The console supports reviewing AI-generated questions, allowing retailers to override them or toggle their conversational status. Questions can also be bulk edited.

Edit individual questions

You can also use controls to curate the individual questions. It is recommended to do this before you turn conversational product filtering on.

For each question, there are two options. Click edit in in the last column to access the questions visible to the users panel:

1. Turn off a question for all queries: The question is enabled by default. Clear (or check again) the box next to Allowed in conversation. This option skips the question altogether. A retailer can opt to disable a question entirely if it doesn't relate to the queried attributes or could be misconstrued as inappropriate (a question, such as What dress size are you looking for?, might be perceived as prying about weight.)
2. Rewrite a question: In the pane, you can see the AI-generated question, what attribute it is attached to and what values the attribute has. Click the pencil to rewrite it.

Turn on conversational filtering

After you have edited your generative AI questions in the console, you are ready to turn on conversational product filtering.

To enable conversational product filtering, go to the Conversational product filtering and browse page in the AI Commerce Search in Gemini Enterprise for Customer Experience console.

1. Go to the Shopping agent page in the AI Commerce Search in Gemini Enterprise for Customer Experience console.
   
   Go to the Conversational search and browse page.

2. Consider the minimum amount of products in your catalog you want returned in the search before questions are generated. This number can be higher but never lower than 2. One row to a page is often the right amount for triggering a conversation.

3. Configure the number and switch the toggle to On. If fewer products match the number, they get filtered out.

Figure 3. Switch toggle to Enable conversational search.

This page provides information as to the status of your blocking and advisory checks. If you have enough search queries with at least one follow-up question, your site is now conversational search-enabled.

Evaluate and test

Evaluate lets you preview the serving experience by running a test search and testing your questions against displayed facets. This part of the console provides you with a preview of your serving experience with conversational product filtering.

To evaluate and test, follow these steps. In the Evaluate section on the Search or Browse tabs on the Evaluate page of the AI Commerce Search in Gemini Enterprise for Customer Experience console.

1. Go to the Evaluate page in the AI Commerce Search in Gemini Enterprise for Customer Experience console.
   
   Go to the Evaluate page

2. Click Search or Browse.

3. In the Search Evaluation field, enter a test query that makes sense based on the catalog you have uploaded to search, such as shoes if your catalog consists of clothing items.

4. Click Search preview to see search results.

Figure 4. Preview results.

If you have conversational product filtering enabled, generative questions are enabled.

Generative Question API

This section describes how to use the generative question API to integrate the Conversational API into your web interface, manage the generative questions, and serve conversational product filtering on your site.

API integration

Objects:

• GenerativeQuestionsFeatureConfig
• GenerativeQuestionConfig
• GenerativeQuestions Service
  • UpdateGenerativeQuestionsFeatureConfiguration
  • UpdateGenerativeQuestionConfig
  • ListGenerativeQuestionConfigs
  • GetGenerativeQuestionFeatureConfig
  • BatchUpdateGenerativeQuestionConfigs

The core to integrating conversational product filtering is defining the question resource. This includes the question itself and whether the question is allowed in the conversation. The question is by default generated by an LLM but can be overridden by the administrator.

Enable conversational product filtering

Object:

• GenerativeQuestionsFeatureConfig

This object is a control configuration file for enabling conversational product filtering for generative questions to manage the overall serving experience. GenerativeQuestionsFeatureConfig uses a GET method to obtain attribute information and whether the attributes are indexable or not from the catalog associated with the project.

The feature_enabled switch determines whether questions are used at serving time. It manages the top-level toggles in the console.

// Configuration for overall generative question feature state.
message GenerativeQuestionsFeatureConfig {
...

// Resource name of the affected catalog.
// Format: projects/{project}/locations/{location}/catalogs/{catalog}
string catalog = 1 [(google.api.field_behavior) = REQUIRED];

// Determines whether questions are used at serving time.
// Note: You cannot enable this until initial data requirements are
// satisfied.
bool feature_enabled = 2 [(google.api.field_behavior) = OPTIONAL];

// Minimum number of products in the response to trigger follow-up questions.
// Value must be 0 or positive.
int32 minimum_products = 3 [(google.api.field_behavior) = OPTIONAL];
}