- Search profiles
HCL Commerce Search uses search profiles to control the storefront search experience at a page-level. Search profiles group sets of search runtime parameters, such as search index name, search index fields, expression providers, paging, and sorting.
- Configuring Query services in ZooKeeper
You can build profiles for Ingest service customizations in ZooKeeper. Endpoints for configuring both development and production environment customizations are available. Note that these endpoints are available from within the Data environment and Development environment only. They are not available for the shopper to use through the Query service in either the Commerce Authoring or Live environment.
- Configuring Query xC custom extension
The /configuration endpoint is used to add the customized configuration details in ZooKeeper node. There is a root node in ZooKeeper with the name /configuration, and inside the root node are child nodes where you can add the configuration.
- Configuring Query runtime custom configuration
To customize the ZooKeeper node, you can also add a
wc-component.json file to override or to add new configuration properties.
To know more about component configuration properties refer to Component configuration properties - Remotely debugging the Query service
You can remote debug extensions to your Query service. This remote facility enables your developers to see the current state of variables, and leverage code hot swapping to see your changes reflected in the server as they develop code.
- Working with Search Term Associations (STAs)
Search Term Associations (STAs) enhances, diversifies, or replaces existing search results. Additionally, search phrases can be associated with particular landing pages in the store. A product recommendation strategy using STA is used to increase store sales by modifying search submissions to target or increase search results.
- Adding custom configurations to the Color Matchmaker
In the HCL Commerce Search you can run natural-language searches using colors in product queries. In ZooKeeper, colors are registered as a root node, and en_US is registered as a child of colors. Using this hierarchy you can maintain colors based on locale.
- Adding custom range filter to Matchmaker
Inside ZooKeeper is a node with the name filter, which is used to parse or ignore redundant words from the search term during Natural Language Processing. At present the operations supported with filter are update, delete, and retrieve. The add operation is currently not supported. New records are added using the POST request method. The existing records are updated (add, update, delete record) using the PATCH request method.
- Adding custom configuration to Measurement Matchmaker
The Natural Language Processing feature enables you to use units of measure. This feature is available to both the query service, for analyzing the search query string, and NiFi for analysing the attributes of products. Units of measure are updated and deleted using the PATCH request method. To delete a unit of measure, pass an empty value as the key. At present the operations supported with units of measure are update, delete, and retrieve. The add operation is currently not supported. New records are added using the POST request method. The existing records are updated (add, update, delete record) using the PATCH request method.
- Adding custom configuration to Dimension Matchmaker
In the HCL Commerce Search, you can run natural-language searches using product dimensions (Length, Width, Height) in product search queries. The product dimension pattern can be configured in the zookeeper dimensions node. If no pattern is specified, then the default WLH pattern is used.The product depth dimension can also be configured in the zookeeper dimensions node by setting up the potential value (Width, Length, Height) for the key. This value of the key is used as a replacement of D (deep/depth/thick/thickness) while indexing (for example, 10 m L x 20 m H x 30 " D ) and while searching (for example, find 10 m L x 20 m H having depth 20 " ) products at the storefront. New product dimensions are added using the POST request method. The existing product dimensions are updated using the PATCH request method.
- Adding custom configuration to part number pattern
In the
HCL Commerce Search, you can run natural-language searches using product part number. Apart from the default part number patterns, you can also add the new part number patterns to run the search using the product part number. For more information on the search based on product part number, refer to
Product part number. The part number pattern can be configured in the zookeeper
wc-component node. You can add comma-separated pattern in this node. New part number patterns are added using the POST request method. The existing part number patterns are updated using the PATCH request method.
- Adding custom nouns and classifications to NLP Name-Entity-Recognition (NER)
Name Entity Recognition (NER) is one of the most common text pre-processing techniques used in Natural Language Processing (NLP). NER is used in many fields in Artificial Intelligence (AI) including Natural Language Processing (NLP) and Machine Learning. Apart from the default entities for NER in the Stanford CoreNLP Natural Language Processor, you can also add custom language specific nouns and classifications for NER. The custom noun and classification for NER can be configured in the zookeeper filter node. New nouns and classifications are added using the POST request method. The existing nouns and classifications are updated using the PATCH request method.
- Extending Natural Language Processor profiles
A Natural Language Processor (NLP) profile is used to control the preprocessing flow of search terms before executing an Elasticsearch query. The profile is a .json file and stored in your query runtime container.
- Tuning index field boost relevancy
You can use the node index_field_relevancy to add the boost value for an index field at query time. This is used to boost the result based on the field. You can add or update using the PATCH method; to delete the existing value use the PATCH method for the endpoint by passing an empty value as its key.
- Enabling deep search sequencing
Deep search sequencing refers to the process of ordering products within a category and all its subcategories. This process is used to enhance the storefront by enabling category-based browsing flows for your customers.
- Setting up a Hero product image (Product grouping)
Set up a Hero product image to increase the relevancy of products and search results in the storefront.
- Customizing spell check correction suggestions
Customize spell check correction suggestions to improve search accuracy and help shoppers find products despite typos. By adjusting parameters in the wc-component.json file and search profiles, you can control the similarity, sorting, and frequency of suggested terms. Use the configuration REST API to manage these settings across environments, ensuring you use the PATCH method for updates to avoid overwriting existing configurations.