This engine configuration uses the Hybrid Search Model, which simultaneously processes text and image data inputs and intelligently retrieve the most relevant information, offering a comprehensive and integrated search experience.
Query Settings
Query Settings are options that can be enabled to augment the query before it hits the models that you have selected.
Spell Correction
The XSearch spelling correction plugin compares the query to a comprehensive language library (that is custom-trained on your individual product dataset) to fix spelling mistakes before running the search.
When to use Spell Correction
Both DeepSearch and GenSearch have built-in features to accommodate for spelling issues and typos automatically, and the Spell Correction plugin should only be used as needed. DeepSearch has fuzzy searching (which accommodates for deviations in copy), and GenSearch has built-in LLM understanding of the query regardless of spelling mistakes.
Negative Tone
If you find that users are using negatives in their searches (“Dress that is not white”), you’ll find that traditional search methodology doesn’t understand how to process this, and will ignore the negative term (“not”). Turning this feature on will use AI to adjust the query automatically and help the Engine respect the negative and provide more relevant results in these use cases.
Query Guard
Due to the open and interpretative nature of GenSearch, it is technically possible to return search results for inappropriate queries (political topics, illegal topics, and other potentially controversial subjects). To solve for this, Query Guard can be enabled to restrict or adjust any such queries.
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Query Guard operates at either the subject or keyword level, and you have the ability to:
Add specific keywords to redact
Select from a list of pre-defined subjects to avoid
Create your own subject to avoid
Add keywords to never-alter (if there is a word that is in a product title or IS relevant to your product line, this allows you to bypass Query Guard for any related terms)
Once you have configured what to avoid, you can then determine what is done with queries that are identified:
Return zero search results
Zero search results PLUS a text response
Edit the query to remove the flagged topic
The GenSearch Model leverages generative AI to facilitate semantic searching, accommodating natural language queries as input, and responding with inferences gathered from LLMs and images within your catalog. The engine settings provide you control over the way inputs are handled, and how responses are provided.
Query Settings
Query Settings are options that can be enabled to augment the query before it hits the models that you have selected.
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Spell Correction
The XSearch spelling correction plugin compares the query to a comprehensive language library (that is custom-trained on your individual product dataset) to fix spelling mistakes before running the search.
When to use Spell Correction
Both DeepSearch and GenSearch have built-in features to accommodate for spelling issues and typos automatically, and the Spell Correction plugin should only be used as needed. DeepSearch has fuzzy searching (which accommodates for deviations in copy), and GenSearch has built-in LLM understanding of the query regardless of spelling mistakes.
Negative Tone
If you find that users are using negatives in their searches (“Dress that is not white”), you’ll find that traditional search methodology doesn’t understand how to process this, and will ignore the negative term (“not”). Turning this feature on will use AI to adjust the query automatically and help the Engine respect the negative and provide more relevant results in these use cases.
Explicit Search
If your use case includes the possibility of searching by SKU or exact product name, Enhanced Explicit Search will do a literal lookup of the query against SKU and Product Name before running the search through DeepSearch or GenSearch, ensuring that these specific use cases are properly handled 100% of the time. Note: This will only work for exact matches, this feature will not respect deviation.
Query Guard
Due to the open and interpretative nature of GenSearch, it is technically possible to return search results for inappropriate queries (political topics, illegal topics, and other potentially controversial subjects). To solve for this, Query Guard can be enabled to restrict or adjust any such queries.
Query Guard operates at either the subject or keyword level, and you have the ability to:
Add specific keywords to redact
Select from a list of pre-defined subjects to avoid
Create your own subject to avoid
Add keywords to never-alter (if there is a word that is in a product title or IS relevant to your product line, this allows you to bypass Query Guard for any related terms)
Once you have configured what to avoid, you can then determine what is done with queries that are identified:
Return zero search results
Zero search results PLUS a text response
Edit the query to remove the flagged topic
Hybrid Parallel Settings
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Product Price Enhancement
Brand, color, and gender are automatically included within model configurations, but price is optional. If you want to enable queries that are directly correlated to the price data point in your product catalog, such as “Jeans under $100”, then you can activate the result enhancement switch here.
Hybrid Model Relevancy Thresholds
At its most fundamental level, GenSearch ranks every product in the catalog with a relevancy score against the query, with “1” being a perfect match, and “0.01” being totally irrelevant. The Relevancy Threshold allows you configure how “open-ended” you want the search results to be, with a high value being restrictive and a low-value allowing it to return products that might be less relevant.
Explicit Fuzzinesss
Fuzziness allows your search engine to look for other similar terms or words you are asking for instead of rigid keyword to keyword exact match.
Response Settings
Response Settings allow you to configure what happens after a query has been responded to.
Disable Cache
Enable Cache
Caching is an important part of maintaining speed and performance, and you have the ability to fully control how caching is set up at the Engine level.
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It is important to understand that the cache exists at the query level, meaning that each individual query is itself cached until either the TTL is hit (from 6 hours up to 30 days, based on configuration) or there is a change in the product catalog from an update.
Sorting
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Sorting allows you to determine how the results will be sorted, after the Engine has isolated the relevant products using all previous configuration. The default is by relevancy to the query itself, however this can be overridden with the following algorithms:
Sort Types
Algorithm | Description | Options |
|---|---|---|
Search Engine Relevance | This is the default sorting, and will have all results sorted by relevance to the query itself. | None |
Current Recommendation Engine | This will sort the results against relevancy to the individual, based on the current XRecommend model that has been deployed |
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Product Filter | Sorting by a Product Filter allows you to make sure that products that meet the condition of the filter show up first. |
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Bestsellers | This will sort the results by arranging them in sequence of bestseller rank, with the top sellers first. |
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Most Viewed | This will sort the results by arranging them in sequence of most viewed, with the most viewed products first. |
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