Overview

This document highlights the Semantic Search functionality within the search module, offering a refined search experience that allows users to search for products using nuanced, natural language queries. This feature interprets the intent and contextual nuances behind text queries, delivering search results that closely align with the user's intentions and preferences, even when these are not explicitly detailed.

Use case

Imagine a user utilizing our text search functionality on a fashion eCommerce platform. They are searching for a specific type of dress, but only remember partial details or keywords such as color, fabric, or style. By inputting these details as a text query, our Text Search API analyzes the input using advanced algorithms and generates a highly relevant list of dresses that closely match the query.

Example

In the sample request outlined below, we searched for a dress, applying faceting based on categories, tags, fit, color, style, and price. We also included specific filters in the search results, ensuring that only items with the fitted fit, and vintage style are included. Additionally, we specified filters to exclude certain results like items tagged as "outlet" and "27/12/2023" or categorized as "jersey" should be excluded. Finally, the sorting is set to arrange the results in descending order based on the item's name.

Sample Request

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Sample Response

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If you want to provide auto-suggestions as a user types into the search bar, you can utilize the suggest API.