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Algolia Documentation

website·Indexed 18 days ago

https://algolia.com/doc

Developer guides, API references, and SDK documentation for building search and AI experiences. Covers core concepts like indices and records, managing results through custom ranking, searchable attributes, faceting, and filtering, as well as implementing geographical search and AI agents.

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What's inside Algolia

  1. Overview of Algolia Advanced Personalization

    Advanced Personalization is an optional Algolia feature that generates user profiles based on individual behaviors within a website or app. When integrated with keyword search or NeuralSearch, it allows the search engine to customize results based on user preferences (e.g., distinguishing between a user interested in technology vs. cooking when they search for 'apple'). This feature is subject to specific pricing plans; check your Algolia pricing plan to verify availability.

  2. Overview of the Algolia implementation workflow

    Implementing a search or conversational experience with Algolia typically follows these four primary steps:

    1. Index your data: Prepare your records and upload them to an Algolia index.
    2. Build the experience: Create a search UI or a conversational experience using tools like Agent Studio.
    3. Send events: Implement click and conversion event tracking from the UI to enable analytics and personalization features.
    4. Improve relevance: Fine-tune matching, ranking, and filtering to optimize search results.

  3. Overview of the Push to Algolia connector

    The Push to Algolia connector allows an application to send records to an Algolia-managed transformation before they are written to a destination index. This is the ideal choice when the application controls which records to index and when to send them. If records do not require a transformation, they should be sent directly via an API client. If records are available as hosted JSON files, the JSON connector is preferred.

  4. Overview of Algolia Merchandising

    Merchandising in Algolia allows you to promote specific products or categories to improve conversion rates, highlight best products, and follow trends. This is achieved through rules that influence the ranking of individual records or entire categories. Rules can be configured in the Algolia dashboard using two methods:

    • Visual Editor: A no-code interface to set search results and promotions for specific queries.
    • Manual Editor: A more powerful interface where rules are defined as JSON.

  5. Overview of Algolia Smart Groups

    Smart Groups enable advanced results curation by organizing and placing groups of items at specific positions within search results based on custom logic. Unlike static pinning, Smart Groups use dynamic criteria to populate results, automatically handling pagination and faceting. You can define up to three groups per composition and mix different group types within a single composition.

  6. Overview of Algolia Agent Studio client-side tools

    Client-side tools allow an Algolia Agent to run custom functions within your own application (frontend or backend). This enables the agent to access real-time user context (like shopping carts or preferences), execute actions (like adding items to a cart), and trigger UI updates while remaining within the user's security context. These tools follow the OpenAI Function Calling specification.

  7. Overview of Algolia Agent Studio

    Agent Studio is a framework for building generative AI experiences—such as shopping assistants, content summarizers, and conversational search—by connecting a chosen LLM (e.g., OpenAI, Gemini, Azure) to Algolia search indices and tools. It manages the end-to-end workflow, grounding LLM responses in live data from Algolia indices to ensure accuracy and relevance.

  9. Overview of Algolia for Salesforce B2C Commerce integration

    Algolia provides a cartridge for Salesforce B2C Commerce that synchronizes product data (including price and inventory), category, and content data. It supports both SFRA (Storefront Reference Architecture) and SiteGenesis. The integration can be used to power search and discovery via InstantSearch.js-based experiences or as a backend synchronization tool for headless and composable storefronts using React InstantSearch, Autocomplete, and Recommend UI libraries. It is compatible with Algolia's Merchandising Studio, search analytics, NeuralSearch, Personalization, and Recommend features.

  10. Overview of Algolia Search API Parameters

    Algolia provides a wide array of search API parameters that can be used to refine queries, filter results, and control response formatting. These parameters are compatible with several API operations, including searching a single index, searching multiple indices, searching for facet values, browsing, and adding or updating API key restrictions.

    Certain parameters can be set globally as index settings rather than being passed with every API request. These are typically marked as index-configurable in the documentation.

  11. Overview of Algolia Query Suggestions

    Query Suggestions provide a list of popular searches to users in real-time (typically via an autocomplete menu) to reduce typing and prevent zero-result searches. Algolia generates these by analyzing search analytics from source indices, normalizing the terms (removing duplicates/banned expressions), and ranking them by popularity over the last 30 days. Each suggestion record contains an objectID, the query string, and a popularity score.

  12. Overview of the autocomplete-js library

    The autocomplete-js package is an agnostic virtual DOM renderer used to create autocomplete experiences. It is compatible with JavaScript, Preact, React, and Vue projects. It serves as the UI layer for headless autocomplete logic built with autocomplete-core. While autocomplete-js provides a standard layout, developers can create custom renderers if they are using a library without a virtual DOM or if the default layout structure is too constraining.