Description
Elasticlunr is a small, full-text search library for use in the Elixir environment. It indexes JSON documents and provides a friendly search interface to retrieve documents.
Elasticlunr alternatives and similar packages
Based on the "Search" category.
Alternatively, view ex_elasticlunr alternatives based on common mentions on social networks and blogs.
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elasticsearch_elixir_bulk_processor
Elasticsearch Elixir Bulk Processor is a configurable manager for efficiently inserting data into Elasticsearch. This processor uses GenStages (data-exchange steps) for handling backpressure, and various settings to control the bulk payloads being uploaded to Elasticsearch.
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README
Elasticlunr
Elasticlunr is a small, full-text search library for use in the Elixir environment. It indexes JSON documents and provides a friendly search interface to retrieve documents.
Why
The library is built for web applications that do not require the deployment complexities of popular search engines while taking advantage of the Beam capabilities.
Imagine how much is gained when the search functionality of your application resides in the same environment (Beam VM) as your business logic; search resolves faster, the number of services (Elasticsearch, Solr, and so on) to monitor reduces.
Installation
The library can be installed by adding elasticlunr to your list of dependencies in mix.exs:
def deps do
[
{:elasticlunr, "~> 0.5"}
]
end
Documentation can be found at hexdocs.pm. See blog post Introduction to Elasticlunr and Livebook for examples.
Features
- Query-Time Boosting, you don't need to set up boosting weight in the index building procedure, Query-Time Boosting makes it more flexible so you could try different boosting schemes
- More Rational Scoring Mechanism, Elasticlunr uses a similar scoring mechanism as Elasticsearch, and also this scoring mechanism is used by Lucene
- Field-Search, you can choose which field to index and which field to search
- Boolean Model, you can set which field to search and the boolean model for each query token, such as "OR" and "AND"
- Combined Boolean Model, TF/IDF Model, and the Vector Space Model make the results ranking more reliable.
Token Expansion
Sometimes users want to expand a query token to increase RECALL. For example, user query token is "micro", and assume "microwave" and "microscope" are in the index, if the user chooses to expand the query token "micro" to increase RECALL, both "microwave" and "microscope" will be returned and search in the index. The query results from expanded tokens are penalized because they are not the same as the query token.
Livebook
The repository includes a livebook file that you can run. You can click the button below to run it using livebook.dev!
License
Elasticlunr is released under the MIT License - see the LICENSE file.
*Note that all licence references and agreements mentioned in the Elasticlunr README section above
are relevant to that project's source code only.