Modules that provide additional or alternative search functionality.

Apachesolr GetSatisfaction

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Introduction

This module provides the bare need of indexing GetSatisfaction content along with Drupal content. It does so by hitting the API, creating a queue and working on that queue every cron run.

Entity BackReference

Entity BackReference module provides backreference indexing support for Search API.

Use this module to index backreferences (fields from other entities that reference your indexed entity).

You'll be able to select which backreference fields you want to index and index them and use them in views

Search API Synonym

INTRODUCTION

This module let editors or administrators manage synonyms for Search API
directly in Drupal.

Synonyms can be export using the build in Drupal Console command.
Drush command and automatic export using Drupal cron job is in development.

The module support the synonyms.txt format used in Apache Solr.
Other formats can be added using the Export plugin annotation.

Flag Search API

Flag Search API

Flag Search API module provides flag indexing for Search API.

Use this module to index flags (flagged content) using Search API module. Once indexed, the flags can be used elsewhere, e.g. in Views.

It also has support for reindexing flagged entity on flagging action

Search API Bundle Boost

INTRODUCTION

Search API core does not support boosting of specific entity bundles like
Apache Solr module did in Drupal 7. This module tries to solve this by allowing administrators to configure the document boost per entity bundle.

Suggestion

This module will provide autocomplete search suggestions.

The module is compatible with standard Drupal search, search views and Apache SOLR, but not reliant on any of them.

Suggestions are created from 3 sources:

  1. Titles of selected content types. This provides a starting point for suggestions and requires significant content to generate enough suggestions.
    1. Strings are tokenized, (lowercased, stopwords removed, short/long words removed and extra spaces removed).
    2. Strings are broken into different length ngrams.
    3. The score of each ngram is tallied and used for ordering.
  2. Priority suggestions added via the admin interface. These suggestions have the highest score and allow the admin to create a set of useful suggestions where the initial set created doesn't proved enough quality suggestions.
    1. Strings are tokenized, (lowercased, stopwords removed, short/long words removed and extra spaces removed).
    2. Strings are broken into different length ngrams.
    3. Priority ngrams start with the highest score.
  3. Surfer searches. These suggestions provide an organic way to create successful suggestions.
    1. Surfer searches are sourced when the search is submitted.
    2. Strings are tokenized, (lowercased, stopwords removed, short/long words removed and extra spaces removed).
    3. If the tokenized strings exists as an ngram then the score is increased.
    4. If the tokeized string doesn't exist then it is scored and generated ngrams added.

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