Search Index

The Search Index is essential to your search functionality. It is here that you define what fields should be searchable for a given entity type (products, product variants, product categories, content, content categories), as well as defining the weight of these searchable fields. Without a search index, your search functionality will not function as intended, as the Relewise search index by default only looks at ID and DisplayName, and nothing else. As such, Relewise highly recommends taking the time to going through the search index and configuring it according to your needs.

Different entities come with certain fields as standard (most commonly DisplayName and ID), but you should add whatever data keys that are relevant for your entities into the index, so that you can assign weight to them. To enter a data key into the index, simply enter the name into the available field, and assign it a weight and prediction source type (explained below).

On Having More Than One Search Index

By default, Relewise provides you with one search index for you to configure. In the vast majority of cases, this is sufficient to give you the control you need, even with multiple languages to configure. If, however, you find that you require more than one search index, we urge you to reach out to us and discuss the possibilities of having another index added to your administration.

Search Index Weight

Field Weighting in Search

Weight is a score assigned to a data field to determine its influence on search relevance. It is a numeric value (byte) ranging from 1 to 255, where higher numbers indicate greater importance. These weights are relative—both to each other and to the maximum assigned value.

🔢 Example

• Two fields with weights 1 and 2:
  → The second field is 100% more influential than the first.
• Three fields with weights 1, 2, and 3:
  → Field 2 is 33% stronger than field 1, and
  → Field 3 is 66% stronger than field 1.

📊 Impact on Search Ranking

• Large differences in weights lead to a strong impact on result ordering.
• Smaller differences allow other ranking factors to take over, such as:
  • Personalization
  • Popularity
  • Relevance modifiers
  • Merchandising rules

💡 Tip: Use higher weight differences when you want to ensure a specific field dominates search relevance. Use similar weights when you'd like other dynamic signals to play a bigger role.

Weight affects a search term only once. A search term will not gain additional weight from being present in multiple fields, and only gains the weight of the most prominent field it is associated with. In a search with multiple terms, each unique term can gain its own weight, which can help increase search accuracy.

When weighting fields, it can be useful to set some fields at the same weight value. Weighting works in tandem with the Relewise personalization engine, and this method allows the personalization to push the more relevant results to the top of the search. This is also a useful tool if you want to test which of two fields might be more useful to your users; set them as the same weighting, and compare the results of searches after a few weeks.

To avoid cluttering search results, it is recommended to set the weight of any field containing a lot of text (such as product descriptions or the body of a content page) to 1.

Include Field in Predictions

This feature toggles whether the particular data field should be used for the purpose of Search Term Prediction. Toggling it on means that the data contained within the specific field will be used to attempt to predict what a user is typing. As such, it is best used with data fields that contain brief texts, such as displayNames.

Search Match Types

The index can be configured with five different match type settings. Match Types define how a particular field is treated by the search engine, and helps shape the behavior of your Relewise search.

Match Types: Compound

The Compound match type allows the search to match on indexed data, where the queried term is found to be part of a compound word within the indexed data; this returns matches for the compounded word.

Use case example

User searches for dog house. The Compound setting identifies the word doghouse in the index, and returns search results for the compounded word.

Match Types: Exact Match

The Exact Match match type allows the search to match on indexed data, where the queried term matches exactly with the datakey value of the entity.

Use case example

The user searches for dog house, which matches on the DisplayName for the entity Dog House.

Match Types: Starts With

The Starts With match type allows the search to match on indexed data, where the queried term starts with the datakey value of the entity.

Use case example

The user searches for dog, which matches on the start of the DisplayName for the entity doghouse.

Match Types: Ends With

The Ends With match type allows the search to match on indexed data, where the queried term ends with the datakey value of the entity.

Use case example

The user searches for house, which matches on the end of the DisplayName for the entity doghouse.

Match Types: Fuzzy

The Fuzzy match type allows the search to match on indexed data, where the queried term is spelled incorrectly.

Use case example

The user searches for doghose, which matches closely to the DisplayName for the entity doghouse.