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Algolia Search Engine Documentation

website·Indexed Apr 16, 2026

https://www.algolia.com/doc/

Official documentation for Algolia, a search-as-a-service platform. Covers core concepts including the Algolia engine, API clients, and ecosystem integrations. Includes guides on getting started, dashboard usage, and managing results. Detailed tutorials address custom ranking strategies, searchable attributes, faceting, filtering with boolean operators, and geolocation search capabilities such as filtering by polygon or rectangle areas.

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What's inside Algolia Documentation

  1. Overview of Algolia for Flutter and Flutter Helper

    Algolia for Flutter is an open-source library designed to help developers quickly build search and discovery interfaces in cross-platform Flutter applications. It is part of the InstantSearch family but focuses on providing basic functionalities and business-logic components to assemble a unique search interface.

    Key capabilities include:

    • Autocomplete search experience
    • Faceting
    • Sorting
    • Search hit highlighting
    • Filtering

    The library is maintained by a community of global developers and is supported by Google. It allows you to build beautiful, natively compiled applications for multiple platforms from a single codebase.

  2. Introduction to Scout Extended for Laravel

    Scout Extended is a Laravel package that simplifies integrating Algolia into your application. It is built on top of the official Laravel Scout package, allowing you to use all Laravel Scout features while leveraging the full power of Algolia. It is 100% open source and available on GitHub.

    Before using Scout Extended, you should read the official Laravel Scout documentation to understand the base interface. Scout Extended adds specific functionalities on top of Scout, such as aggregators (for multiple models in one index) and zero-downtime reimports for production environments.

  3. Overview of Algolia Connectors for zero-code indexing

    Algolia Connectors allow you to index data from external sources into Algolia without writing code. They function by pulling data from a source, optionally transforming it, and pushing it to a destination (like an Algolia index). Connectors are built on the Ingestion API and consist of three main components: Sources (where data is pulled from, e.g., MySQL, JSON files), Destinations (where data is pushed, e.g., Algolia index), and Tasks (which define the sync schedule and logic). Transformations can be added to destinations to modify data before indexing.

  4. Overview of Algolia for Flutter components

    Algolia for Flutter provides a set of widgets to build search interfaces. The core components are organized by functionality: search handling, results display, refinements, metadata, and sorting. To get started with building a search UI, refer to the official guide on building a search UI with InstantSearch for Flutter.

  5. Overview of Autocomplete JS and its role in the stack

    Autocomplete JS is a virtual DOM-based renderer that creates an autocomplete experience. It is agnostic and can be used in JavaScript, Preact, React, or Vue projects. It works in conjunction with autocomplete-core (the headless logic) to provide a UI. It is designed to be used out-of-the-box; custom renderers are an advanced pattern only needed when hitting the limits of the provided renderer (e.g., non-virtual DOM environments or highly constrained layouts).

  6. Overview of merchandising solutions in Algolia for Magento

    The Algolia extension for Adobe Commerce and Magento Open Source provides three main merchandising solutions to promote, upsell, and customize search results:

    1. Query Merchandising: Promotes or demotes products for specific search queries and allows visual customizations like adding banners.
    2. Category Merchandising: Manages the display order of products on category pages using a drag-and-drop system to pin, move up, or move down products.
    3. Landing Page Builder: Creates dynamic, unique landing pages with custom URLs, CMS content, and merchandised product listings powered by Algolia.

    Important Note: Starting with version 3.11 of the Algolia extension, Merchandising Studio is available for users on a Premium plan. While legacy features remain, it is recommended to migrate to Merchandising Studio for full capabilities.

  7. Overview of Algolia Recommend models and algorithms

    Algolia Recommend uses supervised machine learning models trained on your product data and user interactions to display recommendations. It employs two algorithm types: collaborative filtering (analyzing user events) and content-based filtering (analyzing item attributes). Models generate up to 30 recommendations and re-train once daily. Minimum data requirements vary by model, with some extending data collection periods from 30 to 90 days if initial data is insufficient.

  8. Overview of InstantSearch.js and its three customization levels

    InstantSearch.js is an open-source UI library for vanilla JavaScript that enables you to build search interfaces by combining widgets. It offers three levels of control over your UI:

    1. Predefined widgets: Use ready-made widgets (like searchBox or refinementList) that you can configure and style with CSS.
    2. Customize predefined widgets: Use connectors to redefine the behavior and DOM output of existing widgets (e.g., rendering a list as a <select> element).
    3. Custom widgets: Create entirely new widgets from scratch using the connector and widget APIs for full control over render and behavior.

  9. Overview of Algolia Crawler capabilities and workflow

    The Algolia Crawler is a tool that extracts content from your website and makes it searchable within Algolia. It automates the process of discovering pages, extracting relevant data, and sending that data to your Algolia indices.

    Workflow:

    1. Start URLs: You provide a set of starting URLs.
    2. Discovery: The crawler visits these pages, extracts data, and discovers other pages through internal links.
    3. Indexing: Extracted data is sent to your Algolia indices. You can configure the crawler to run on a schedule to keep indices up-to-date.

    Benefits over manual API integration:

    • No code maintenance: Eliminates the need to write and maintain custom code for content extraction, transformation, and scheduling.
    • Unstructured data support: Capable of extracting data from unstructured formats like HTML and PDF files.
    • Access flexibility: Can index web pages even when direct access to the source is restricted or when resources are managed by different teams using different tools.

  10. Overview of Algolia Salesforce B2C Commerce product indexing jobs

    The int_algolia cartridge synchronizes Salesforce B2C Commerce data with Algolia using specific indexing jobs. Understanding these jobs is essential for managing catalog updates, price changes, and inventory levels.

    The cartridge distinguishes between B2C business objects (like Product, Pricebook) and the resulting JSON product records stored in Algolia. The JSON structure is configurable and may differ from the source B2C object structure.

    Key indexing jobs include:

    • AlgoliaProductIndex_v2: Performs a full catalog update, re-indexing the entire product catalog.
    • AlgoliaProductDeltaIndex_v2: Performs delta updates using the Salesforce B2C Delta Export feature to sync only changed products.
    • AlgoliaProductPriceIndex_v2: Updates price information for all products in the index.
    • AlgoliaProductInventoryIndex_v2: Updates inventory information for all products in the index.
    • AlgoliaCategoryIndex_v2: Updates category indices.

  11. Overview of Algolia Insights event types and feature mapping

    The Algolia Insights API accepts three types of events: click, conversion, and view. Sending these events enables features like Search (NeuralSearch, Dynamic Re-Ranking, Query Categorization), Recommend, and Personalization.

    Key event methods include:

    • clickedObjectIDsAfterSearch, convertedObjectIDsAfterSearch, addedToCartObjectIDsAfterSearch, purchasedObjectIDsAfterSearch: Used for Search, Recommend, and Personalization.
    • clickedObjectIDs, convertedObjectIDs, addedToCartObjectIDs, purchasedObjectIDs: Used for Recommend and Personalization (not Search).
    • viewedObjectIDs, clickedFilters, convertedFilters, viewedFilters: Used for Advanced and Classic Personalization only.
    // Example event payload structure (conceptual)
{
  "eventType": "click",
  "objectIDs": ["product_123"],
  "queryID": "query_abc",
  "userToken": "user_xyz"
}

  12. Overview of InstantSearch.js navigation and utility widgets

    InstantSearch.js provides several widgets for navigation, analytics, and metadata display. These widgets help users understand their position in the search hierarchy, view search statistics, and enable analytics tracking without rendering visible UI elements.
    // Common navigation and utility widgets:
// - breadcrumb: Shows hierarchy navigation
// - stats: Displays hit count and search time
// - poweredBy: Shows Algolia logo link
// - analytics: Pushes search state to analytics platform
// - queryRuleCustomData: Displays custom data from Rules
// - queryRuleContext: Sets Rule contexts silently