4.7. Enabling Open edX Search

You can add a search feature to your Open edX site so that prospective learners can find courses more easily. When a learner searches for a key word or words, the search feature returns a list of the courses that are currently open for enrollment and that match the entered key words.

This section describes how to enable search in your instance of Open edX.

4.7.1. Overview

EdX Search is a Django application that provides access to search services from within edX Platform applications. Searching is accomplished by creating an index of documents, and then searching within that index for matching information.

When you install the Open edX devstack, edX search is enabled by default. You must enable this feature to use it with Open edX fullstack.

Note

Before proceeding, review Guidelines for Updating the Open edX Platform.

4.7.2. Search Engines and edX Search

By default, edX Search uses MockSearchEngine for testing and ElasticSearch Engine for production. You can configure edX Search to use a different search engine.

4.7.2.1. MockSearchEngine

MockSearchEngine is a simple implementation using a JSON file for index storage. It has no specific requirements, but it does not scale well and should only be used for testing.

4.7.2.2. ElasticSearchEngine

ElasticSearchEngine is a ElasticSearch back-end implementation. It uses same ElasticSearch version that is already part of Open edX Platform. The current version is v1.5.2. The ElasticSearch Python client is the latest 1.x version.

4.7.3. EdX Search Requirements

EdX Search requires the following applications.

• Django (edX Platform version)

• pyMongo (edX Platform version)

• pytz

• Django elasticsearch (0.4.5)

4.7.4. Install edX Search

EdX Search is included in Open edX Platform GitHub requirements and is installed automatically when you install the Open edX Platform.

4.7.5. Enable Indexing

You enable course indexing by setting the ENABLE_COURSEWARE_INDEX flag.

You enable library indexing by setting the ENABLE_LIBRARY_INDEX flag.

Indexing is done from Studio as a Celery task. Every publish event triggers the reindex procedure.

You can also reindex the course manually through the Reindex button in the Course Overview page.

Note

The search feature only returns results for courses that are currently open for enrollment. Course teams can set the enrollment start and end dates for their courses in Studio. For more information, see Guidelines for Start and End Dates in the Building and Running an Open edX Course guide.

4.7.5.1. Which Data Gets Indexed

Which data gets indexed is determined by the module index_dictionary() function implementation. Modules supporting this method are Sequence, Vertical, Video, and HTML Block. You can add support to any module type.

Course metadata, including the name, description, and start and end dates are also indexed.

4.7.6. Supported Flags

The following flags are supported in the CMS and LMS applications.

4.7.6.1. CMS

• ENABLE_COURSEWARE_INDEX: Enables and disables courseware content and course info indexing.

• ENABLE_LIBRARY_INDEX: Enables and disables library content indexing.

• SEARCH_ENGINE: Sets the search engine to use. There are two predefined values.

  • "search.elastic.ElasticSearchEngine"

  • "search.tests.mock_search_engine.MockSearchEngine"

• ELASTIC_FIELD_MAPPINGS: Sets any additional field mappings that elastic search should be aware of. For example, the following code includes the course start date.

  ELASTIC_FIELD_MAPPINGS = {
    "start_date": {
      "type": "date"
    }
  }
  

4.7.6.2. LMS

• ENABLE_COURSEWARE_SEARCH: Enables and disables Courseware Search feature (in course searching).

• ENABLE_DASHBOARD_SEARCH: Enables and disables Dashboard Search feature (in enrolled courses searching).

• ENABLE_COURSE_DISCOVERY: Enables and disables Course Discovery feature (over courses searching and facet filtering).

• COURSE_DISCOVERY_FILTERS: If provided, overrides the list of facets that are used in the Course Discovery feature to filter the results. By default, all facets will be displayed. The list of available facets includes:

  • Course organization: "org"

  • Course type: "modes"

  • Course language: "language"

• SEARCH_ENGINE: Sets the search engine to use. The following values are predefined.

  • "search.elastic.ElasticSearchEngine"

  • "search.tests.mock_search_engine.MockSearchEngine"

• SEARCH_INITIALIZER: Used to set custom SearchInitializer. SearchInitializer provides an extension to achieve masquerade and other presearch environmental settings.

  • default: SearchInitializer

  • LMS implementation: lms.lib.courseware_search.lms_search_initializer.LmsSearchInitializer

• SEARCH_RESULT_PROCESSOR: Used to set custom SearchResultProcessor. SearchResultProcessor does post processing and data manipulation on a result set returned by SearchEngine.

  • default: SearchResultProcessor

  • LMS implementation: lms.lib.courseware_search.lms_result_processor.LmsSearchResultProcessor

• SEARCH_FILTER_GENERATOR: Used to set custom SearchFilterGenerator. SearchFilterGenerator sets filters defined by current active user. Basic implementation sets only course start date filter.

  • default: SearchFilterGenerator

  • LMS implementation: lms.lib.courseware_search.lms_filter_generator.LmsSearchFilterGenerator