Pure Ruby wrapper library for Cortical.io's Retina API. Register for a free API key and include retina-sdk.rb to add language intelligence to your application.
Cortical.io's Retina API allows the user to perform semantic operations on text. One can for example:
- measure the semantic similarity between two written entities
- create a semantic classifier based on positive and negative example texts
- extract keywords from a text
- divide a text into sub-sections corresponding to semantic changes
- extract terms from a text based on part of speech tags
The meaning of terms and texts is stored in a sparse binary representation that allows the user to apply logical operators to refine the semantic representation of a concept.
You can read more about the technology at the documentation page.
To access the API, you will need to register for an API key.
The client is available as a regular Ruby gem and can be installed as follows:
Add this line to your application's Gemfile:
gem 'retina-sdk'
And then execute:
$ bundle
Or install it yourself as:
$ gem install retina-sdk
This gem will not pull in any other library.
IMPORTANT NOTE This client is compatible with Ruby 2.0 and above. It won't run with Ruby 1.8 or 1.9. It is compatible with 2.x.x versions of cortical.io's api.
retina-sdk.rb offers two abstractions of the Cortical.io Retina API, a lightweight module that offers simplified access to the most common and useful API functions available and a full version module that gives the user complete control over various parameter settings and complete access to all API endpoints.
The LiteClient module is sufficient for most applications and offers the ability to quickly and easily compute keywords for a text, semantically compare two texts, retrieve similar terms, create category filters for semantic filtering and generate semantic fingerprints of a given text. To get started, create an instance of the lightweight client by passing your API key as follows:
require 'retina-sdk'
lite_client = RetinaSDK::LiteClient.new("your_api_key")
The lite_client
object has 5 methods for accessing the API. You can:
- Convert text into a semantic fingerprint, which is a list of integers (here shortened):
> lite_client.get_fingerprint("Ruby is a widely used general-purpose, high-level programming language.")
[1, 3, 7, 8, 33, 35, 65, 66, 68, 69, 70, 71, 72, 77, 79, 89, 117, ..., 16380, 16381, 16382]
- Retrieve similar terms for a text (or a fingerprint):
> lite_client.get_similar_terms("ruby")
["ruby", "implementations", "perl", "javascript", "scripting", "feels", "programmers", "lets", "python", "plugins", "html", "thinks", "compiler", "files", "api", "max", "bugs", "gets", "adds", "lucas"]
- Get the keywords of a text:
> lite_client.get_keywords("Vienna is the capital and largest city of Austria, and one of the nine states of Austria")
['austria', 'vienna']
- Compute the similarity (in the range [0;1]) between two texts (here just terms to keep the example short), or any combination of texts and fingerprints:
> lite_client.compare("apple", "microsoft")
0.4024390243902438
> lite_client.compare(lite_client.get_fingerprint("apple"), lite_client.get_fingerprint("microsoft"))
0.4024390243902438
> lite_client.compare(lite_client.get_fingerprint("apple"), "microsoft")
0.4024390243902438
- Construct a composite Fingerprint from a list of texts (here just terms to keep the example short), and use the filter to compare and classify new texts:
neurology_filter = lite_client.create_category_filter(["neuron", "synapse", "neocortex"])
> lite_client.compare(neurology_filter, "cortical column")
0.35455851788907006 # high semantic similarity -> poitive classification
> lite_client.compare(neurology_filter, "skylab")
0.056544622895956895 # low semantic similarity -> negative classification
Using this client, you will have access to the full functionality of Cortical.io's Retina API.
As with the LiteClient, the FullClient must be instantiated with a valid Cortical.io API key, but you can also change the default host address (in case you have your own AWS instance), as well as which retina to use.
require 'retina-sdk'
full_client = RetinaSDK::FullClient.new("your_api_key", api_server: "http://api.cortical.io/rest", retina_name: "en_associative")```
On the full client, you get responses back in accordance with the response objects delivered from the
[REST api](http://api.cortical.io/). For
example calling `get_terms` will return a list of `Term` objects, and `compare` will return a `Metric` object:
```ruby
> full_client.get_terms(term="ruby")
[Term(@fingerprint=Fingerprint(@positions=[]), @term="ruby", @df=0.00031801664007613897, @pos_types=["NOUN"], @score=0.0)]
> require 'json'
> full_client.compare(JSON.dump([{"term" => "synapse"}, {"term" => "skylab"}]))
Metric(@cosine_similarity=0.032885723350542136, @jaccard_distance=0.9836956521739131, @overlapping_all=6, @overlapping_left_right=0.02631578947368421, @overlapping_right_left=0.0410958904109589, @size_left=228, @size_right=146, @weighted_scoring=0.6719223186964691, @euclidean_distance=0.9679144385026738)
All return types are defined in modules in the source folder retina-sdk/model/
. Please visit the
REST api for more details about return types, or consult the documentation of the methods
of the full_client
.
The semantic fingerprint is the basic unit within the Retina API. A text or a term can be resolved into a fingerprint using the API. Fingerprints can also be combined in expressions, and a number of methods expect input in our expression language. This is explained in more detail here.
Expressions are essentially json
strings with reserved keys: term
, text
, and positions
.
In the previous example, we note that the compare
function takes as argument a list of two such expressions.
In Ruby we can create an array of two hashes with (in this case) term
elements and use
the json
module to convert it to a valid json
string (JSON.dump()
). You can however also create the string yourself:
> full_client.compare('[{"term": "synapse"}, {"term": "skylab"}]')
Metric(@cosine_similarity=0.032885723350542136, @jaccard_distance=0.9836956521739131, @overlapping_all=6, @overlapping_left_right=0.02631578947368421, @overlapping_right_left=0.0410958904109589, @size_left=228, @size_right=146, @weighted_scoring=0.6719223186964691, @euclidean_distance=0.9679144385026738)
Please note that in the Ruby version, the client uses the conventional snake case notation for method names. The required parameters in method calls are passed as regular parameters whereas the optional uses named parameters (see examples below).
require 'json'
# Retrieve a list of all available Retinas
full_client.get_retinas()
# Retrieve information about a specific term
full_client.get_terms(term="ruby")
# Get contexts for a given term
full_client.get_contexts_for_term("ruby", max_results: 3)
# Get similar terms and their Fingerprints for a given term
full_client.get_similar_terms_for_term("ruby", get_fingerprint: true)
# Encode a text into a Semantic Fingerprint
full_client.get_fingerprint_for_text("Ruby is a widely used general-purpose, high-level programming language.")
# Return keywords from a text
full_client.get_keywords_for_text("Ruby is a widely used general-purpose, high-level programming language.")
# Returns tokens from an input text (NOTE: the pos_tags parameter is called POStag in the rest API
# but method parameters in Ruby can start with a capital letter)
full_client.get_tokens_for_text("Ruby is a widely used general-purpose, high-level programming language.", pos_tags: "NN,NNP")
# Slice the input text according to semantic changes (works best on larger texts of at least several sentences)
full_client.get_slices_for_text("longer text with several sentences...")
# Return Semantic Fingerprints for numerous texts in a single call
full_client.get_fingerprints_for_texts(["first text", "second text"])
# Detect the language for an input text
full_client.get_language_for_text("Dieser Text ist auf Deutsch")
# Return the Fingerprint for an input expression
full_client.get_fingerprint_for_expression(JSON.dump({"text" => "Ruby is a widely used general-purpose, high-level programming language."}))
# Return contexts for an input expression
full_client.get_contexts_for_expression(JSON.dump({"text" => "Ruby is a widely used general-purpose, high-level programming language."}))
# Return similar terms for an input expression
full_client.get_similar_terms_for_expression(JSON.dump({"text" => "Ruby is a widely used general-purpose, high-level programming language."}))
# Return Fingerprints for multiple semantic expressions
full_client.get_fingerprints_for_expressions(JSON.dump([{"text" => "first text"}, {"text" => "second text"}]))
# Return contexts for multiple semantic expressions
full_client.get_contexts_for_expressions(JSON.dump([{"text" => "first text"}, {"text" => "second text"}]))
# Return similar terms for multiple semantic expressions
full_client.get_similar_terms_for_expressions(JSON.dump([{"text" => "first text"}, {"text" => "second text"}]))
# Compute the semantic similarity of two input expressions
full_client.compare(JSON.dump([{"term" => "synapse"}, {"term" => "skylab"}]))
# Make multiple comparisons in a single call
comparison1 = [{"term" => "synapse"}, {"term" => "skylab"}]
comparison2 = [{"term" => "mir"}, {"text" => "skylab was a space station"}]
full_client.compare_bulk(JSON.dump([comparison1, comparison2]))
# Create an image from an expression
image_data = full_client.get_image(JSON.dump({"term" => "ruby"}), plot_shape: "square")
# Create multiple images from multiple expressions in a single call
full_client.get_images(JSON.dump([{"text" => "first text"}, {"text" => "second text"}]))
# Create a composite image showing the visual overlap between two expressions
full_client.compare_image(JSON.dump([{"text" => "first text"}, {"text" => "second text"}]))
# Create a filter Fingerprint from positive (related) and negative (unrelated) example texts.
full_client.create_category_filter("test", ["Ruby is a widely used general-purpose, high-level programming language."], negative_examples: ["A ruby is a pink to blood-red colored gemstone, a variety of the mineral corundum. Large rubies can fetch higher prices than equivalently sized diamonds."])
For further documentation about the Retina-API and information on cortical.io's 'Retina' technology please see our
Knowledge Base. Also the tests
folder contains more examples of how to use the
clients.
If you have any questions or problems please visit our forum.
v 1.0.0
- Initial release.