BentoSearch

bento_search provides an abstraction/normalization layer for querying and displaying results from external search engines, in Ruby on Rails. Works with Rails 5.2 - 6.1, ruby 2.5 through 3.0.

• Get up and running as quickly as possible with searching and displaying results from a third-party service. Simple common code API, with idiosyncracies and undocumented workarounds abstracted away.
• Let you switch out one search service for another in an already built application with as little code rewriting as possible. Avoid vendor lock-in.
• Give you the harness to write adapters for new search services, without having to rewrite common general functionality, just focus on the interface with the new API you want to support.

bento_search is focused on use cases for academic libraries; the shared model for search results includes including fields that matter in our domain (issn, vol/issue/page, etc), although they ought to have what's needed for general basic use too. There is some targetted functionality for academic library/publishing use (eg OpenURL generation).

Adapters currently included in bento_search

• Google Books (requires free api key)
• Scopus (requires license)
• Serial Solution Summon (requires license)
• Ex Libris Primo (requires license)
• EBSCO Discovery Service (requires license)
• EBSCOHost 'traditional' API (requires license)
• WorldCat Search (requires OCLC membership to get api key)
• Google Site Search (requires sign-up for more than 100 searches/day)
• JournalTOCs (limited support for fetching current articles by ISSN, free but requires registration)
• Directory of Open Access Journals (DOAJ) article search. (free, no registration required)

bento_search could be considered building blocks for a type of 'federated search' functionality, but it does not and will never support merging results from multiple engines into one result set. It is meant to support displaying the first few results from multiple engines on one page, "bento box" style (as named by Tito Sierra@NCSU), as well as more expanded single-search-on-a-page uses -- or back-end functionality supporting features that are not straight discovery.

• bento_search provides abstract functionality for pagination, sorting, and single-field-specified queries. Faceting and generalized limiting are not yet supported, but possibly will be built out in the future.

Not all search engine adapters support all features. Some engines offer engine-specific features, such as limiting. Search engine adapters can declare search fields and sort options with 'semantics', so you can for instance search or sort by 'title' across search engines without regard to internal engine-specific field names.

bento_search is designed to allow code to be written agnostic of the search provider, so you can switch out the search provider.

See code-level api documentation for more details, especially at BentoSearch::SearchEngine. http://rubydoc.info/gems/bento_search/frames/

An example app using BentoSearch and showing it's features is available at http://github.com/jrochkind/sample_megasearch There is a short screencast showing that sample app in action here: http://screencast.com/t/JLS0lclrBZU

When you instantiate an engine, you can provide configuration keys. There are a few standard keys (see BentoSearch::SearchEngine), and others that may be engine-specific. Some engine-specific keys (such as api auth keys) may be required for certain engines.

    engine = BentoSearch::GoogleBooksEngine.new(:api_key => "my_gbs_api_key")
    results = engine.search("a query")

results are a BentoSearch::Results object, which acts like an array of BentoSearch::ResultItem objects, along with some meta-information about the search itself (pagination keys, etc). BentoSearch::Results and Item fields are standardized across engines. BentoSearch::Items provide semantic values (title, author, etc.), as available from the particular engine.

To see which engines come bundled with BentoSearch, and any special engine-specific instructions, look at BentoSearch source in ./app/search_engines/bento_search

It can be convenient to register an engine in global configuration, and is required for certain functionality (like out-of-the-box AJAX loading).

In an initializer in your app, like say ./config/initializers/bento_search.rb:

    BentoSearch.register_engine("gbs") do |conf|
       conf.engine = "BentoSearch::GoogleBooksEngine"
       conf.api_key = "my_google_api_key"
       # any other configuration
    end

Then you can refer to it, for instance in a controller, by the id you registered:

    @results = BentoSearch.get_engine("gbs").search("my query")

You can of course write your own code to display a BentoSearch::Results object however you like. But BentoSearch comes with a helper method for displaying a list of BentoSearch::Results in a standard way, using the bento_search helper method.

    <%= bento_search @results %>

See also the Customizing Results Display wiki page.

You can search by an internal engine-specific field name:

    google_books_engine.search("smith", :search_field => "inauthor")

Or, if the engine provides it, you can search by normalized semantic search field type names:

    google_books_engine.search("smith", :semantic_search_field => :title)

You can find out what fields a particular engine supports.

    google_books_engine.search_keys # => internal keys
    google_books_engine.semantic_search_keys

A helper method for generating an html select of search field options is available in bento_field_hash_for, check it out.

You can also provide all arguments in a single hash when it's convenient to do so:

    google_books_engine.search(:query => "smith", :search_field => "inauthor")

Search fields that are not recognized (semantic or internal) will normally be ignored, but set :unrecognized_search_field => :raise in configuration or search arg to get an ArgumentError instead.

An engine advertises what sort types it supports:

    google_books_engine.sort_keys

An array of sort identifiers, where possible chosen from a standard list of semantics. (See list in ./config/i18n/en.yml, bento_search.sort_keys).

    google_books_engine.search("my query", :sort => "date_desc")

For help creating your UI, you can use built-in helper method, perhaps with Rails helper options_for_select:

    <%= options_for_select( bento_sort_hash_for(engine), params[:sort] ) %>

You can tell the search engine how many items you want per-page, and use either :start (0-based item offset) or :page (1-based page offset) keys to paginate into the results.

    results = google_books_engine.search("my query", :per_page => 20, :start => 40)
    results = google_books_engine.search("my query", :per_page => 20, :page => 2) # means same as above

An engine instance advertises it's maximum per-page values.

    google_books_engine.max_per_page

bento_search fixes the default per_page at 10.

For help creating your UI, you can ask a BentoSearch::Results for results.pagination, which returns a BentoSearch::Results::Pagination object which should be suitable for passing to kaminari paginate, or else have convenient methods for roll your own pagination UI. Kaminari's paginate method:

    <%= paginate results.pagination %>

Some search engines support-multi field searching, an engine advertises if it does:

engine_instance.multi_field_searching? # => `true` or `false`

The bento_search multi-field search feature always combines multiple fields with boolean 'and' (intersection). You call a multi-field search with a :query hash argument whose value is a hash of search-fields and queries:

engine.search(:query => {
  :title  => '"Reflections on the History of Debt Resistance"',
  :author => 'Caffentzis'
})

The search field keys can be either semantic_search_field names, or internal engine search fields, or a combination. If the key matches a semantic search field declared for the engine, that will be preferred.

This can be used to expose a multi-field search to users, and the bento_field_hash_for helper method might be helpful in creating your UI. But this is also useful for looking up known-item citations -- either by author/title, or issn/volume/issue/page, or doi, or anything else -- as back-end support for various possible functions.

If you're going to search 2 or more search engines at once, you'll want to execute those searches concurrently. For instance, if GoogleBooks results take 2 second to come in, and Scopus results take 3 seconds -- you don't want to first wait the 2 second then wait the 3 seconds for a total of 5 -- you instead want to execute concurrently in seperate threads, so the total wait time is the slowest engine, not the sum of the engines.

You can write your own logic using ruby threads to do this, but BentoSearch provides a multi-searching helper using concurrent-ruby to help you do this easily. Say, in a controller:

    # constructor takes id's registered with BentoSearch.register_engine
    searcher = BentoSearch::ConcurentSearcher.new(:gbs, :scopus, :summon)

    # Call 'search' with any parameters you would give to an_engine.search
    searcher.search("my query", :semantic_search_field => :author, :sort => "title")

    # At this point, all searches are executing asynchronously in seperate threads.
    # To get the results, blocking until all complete:
    @results = searcher.results

    # @results will be a hash, keyed by registered engine id, values
    # are BentoSearch::Results

Even if you are only searching one engine, this may be useful to have the search execute in a seperate thread, so you can continue doing other work in the main thread (like search a local store of some kind outside of bento_search)

If you are using a Rails previous to 5.x, you will have to add the concurrent-ruby gem to your Gemfile (It's already a dependency of Rails5).

If you are using Rails5, ConcurrentSearcher uses new Rails API that should make development-mode class reloading work fine even with the ConcurrentSearcher's concurrency.

For more info, see BentoSearch::ConcurrentSearcher.

BentoSearch provides some basic support for initially displaying a placeholder progress spinner, and having Javascript call back to get the actual results.

It's not a panacea for pathologically slow search results, and can be tricky for results that need access controls. But it can be useful in some situations, both for automatic on-page-load ajax loading, and triggered ajax loading.

See the wiki page for more info.

You can configure Decorators, in the form of plain old ruby modules, to be applied to BentoSearch::Items, on an engine-by-engine basis. These can modify, add, or remove Item data, as well as over-ride some presentational methods.

One common use for these Decorators is changing, adding, or removing links associated with an item. For instance, to link to your local OpenURL link resolver.

BentoSearch::Items can have a main link associated with them (generally hyperlinked from title), as well as a list of additional links. Most engines do not provide additional links by default, custom local Decorators would be used to add them. See wiki on display cusotmization for more info on decorators, and BentoSearch::Link for fields.

Academic library uses often need openurl links from scholarly citations. One of the design goals of bento_search is to produce standardized normalized BentoSearch::ResultItem models, with sufficient semantics for translation to other formats.

See ResultItem#to_openurl_kev (string URL query encoding of OpenURL), and ResultItem#to_openurl (a ruby OpenURL gem object).

Quality may vary, depending on how well the particular engine adapter captures semantics, especially the format/type of results (See bento_search's internal format/type vocabulary documented at ResultItem#format). As well as how well the #to_openurl routine handles all edge cases (OpenURL can be weird). As edge cases are discovered, they can be solved.

See ./app/item_decorators/bento_search/openurl_add_other_link.rb for an example of using item decorators to add a link to your openurl resover to an item when displayed.

A class is included to convert an individual BentoSearch::ResultItem to the RIS format, suitable for import into EndNote, Refworks, etc.

    ris_data = RISCreator.new( bento_item ).export

Accomodating actual exports into the transactional flow of a web app can be tricky, and often requires use of the result_item#unique_id and engine.get( unique_id ) features. See the wiki on exports and #unique_id

Translation of any BentoSearch::Results to an Atom response that is enhanced to include nearly all the elements of each BentoSearch::ResultItem, so can serves well as machine-readable api response in general, not just for Atom feed readers.

You can use the bento_search/atom_results view template, perhaps in your action method like so:

# ...
respond_to do |format|
   format.html # default view
   format.atom do
      render( :template => "bento_search/atom_results",
              :locals   => {
                 :atom_results     => @results,
                 :feed_name        => "Acme results",
                 :feed_author_name => "MyCorp"
              }
      )
end

There are additional details that might matter to you, for more info see the wiki page

You can serialize BentoSearch::Results to a simple straightforward JSON structure, and de-serialize them back into BentoSearch::Results.

json_str          = results.dump_to_json
copy_of_results   = BentoSearch::Results.load_json(json_str)

Search context (query, start, per_page) are not serialized, and will be lost on de-serialization.

Unlike the Atom serialization, the JSON serialization is of internal data state, without decoration. Configuration context is not serialized.

However, the engine_id is included in serialization if present, and configuration from the specified engine will be re-assigned on de-serialization. This means if the configuration changed between serialization and de-serialization, you get the new stuff assigned on de-serialization.

The use case guiding JSON serialization is storage somewhere, and round-trip de-serialization in the current app context.

If you want to take de-serialized results that did not have an engine_id, or set configuration on them to a different engine (registered or not) you can:

  restored = BentoSearch::Results.load_json(json_str)
  some_engine.fill_in_search_metadata_for(restored)

  # restored Results will have configuration (engine_id, decorators, etc)
  # set to those configured on some_engine

If you want a serialization to be consumed by something other than an app using the bento_search gem, as an API, we recommend the Atom serialization instead.

I am trying to keep BentoSearch as simple as it can be to conveniently meet actual use cases. Trying to avoid premature over-engineering, and pave the cowpaths as needed.

Probably:

• Support for display facets for engines that support such, as well as search with limits from controlled vocabulary (ie, selected facet, but also may be supported by some engines that do not support facetting).

Other needs or suggestions?

We are going to try to be strictly backwards compatible with all post 1.0 releases that do not increment the major version number (semantic versioning).

As a general rule, we're going to let our tests enforce this -- if a test has to be changed to pass with new code, that's a very strong sign that it is not a backwards-compat change, and you should think very carefully to be sure it is an exception to this rule before changing any existing tests for new functionality.

BentoSearch is fairly well covered by automated tests. We simply use Test::Unit. Run tests with rake test.

The testing environment was generated with rails plugin new, and includes a dummy app used when testing at ./test/dummy.

For integration tests against live external search API's, we use the awesome VCR gem to cache responses. To write your own Test::Unit tests using VCR, take note of the test_with_cassette method provided in ./test/support/test_with_cassette.rb.

Also note use of VCR.filter_sensitive_data to make sure your API keys do not get saved in cached response in the repo, while still allowing tests to be run against cached responses even for engines that require auth.

To re-generate cached responses, delete the relevant files in ./test/vcr_cassettes and re-run tests. You may have to set an ENV variable with your own API keys to re-run tests without cached response like this.

Also note BentoSearch::MockEngine, a simple mock/dummy SearchEngine implementation that can be used in other tests, including in client software where convenient.

Pull requests welcome. Pull requests with additional search engine implementations welcome. See more info on writing a BentoSearch::SearchEngine in the inline docs in that file.