Join GitHub today
GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.Sign up
Fetching latest commit…
Cannot retrieve the latest commit at this time.
|Failed to load latest commit information.|
Saplo provides services that extract and refine valuable information from large quantities of text. We offer a range of cost efficient text analysis services through an API (Application programming interface). Services can be used either stand alone or combined into new and exiting concepts and technologies. Documentation and guides for Saplo API is found on the developer page: http://developer.saplo.com. Getting Started Install pip install git+git://github.com/rogeliorv/pysaplo.git Or download https://github.com/rogeliorv/pysaplo/zipball/master and run python setup.py install Get your API key http://saplo.com/api # Import from pysaplo.saploapi import * # Authentication # To authenticate with the API you run the method auth.accessToken. If your API key and your Secret key (your key pair) # is valid a token is created which you will use for further API calls. # When creating the SaploJSONClient object you will automatically be authenticated. client = SaploJSONClient("YOUR API KEY", "YOUR SECRET KEY") # You can also explicitly get your Access Token print client.token # To be able to extract information (Tags, Related Texts, Related Contexts etc.) from the API you first need to add some # texts to the system. Texts are stored in text collections that you create. # A collection can only contain text for a single language (e.g. only English texts). collection_id = client.collection.create( name = "My Collection", description = "This is my first collection for english texts.", language = "en" ) print "New collection created with Id:", collection_id # Add a text to a collection # After creating a collection you can start adding texts to it. # Add a text using the method text.add. When the text has been added it will get a unique id inside of that collection. text_id = client.text.create( collection_id=678, headline='Example Headline', body='This is a short example text. Insert a longer one for better results' ) print "New text created with Id:", text_id # Get Entity Tags # Now that we have a text in our collection we can get entity tags from that text. # Get tags using the method text.tags tags = client.text.tags( collection_id=678, text_id=2, wait=10 ) print "Retrieved these tags from the text:", tags # Get Related Texts # To be able to get related texts we need to add additional texts to the collection. When fetching related texts the API # searches through the collection to find the texts that are most similar to each other. # To get related texts we run the method text.relatedTexts and specify the text id and collection id we want results for. # The system will now index all texts and give return a list with results. In the result list you will find the related # text id and the relevance which is a value between 0.0 and 1.0, a higher number indicating more relevance. related_texts = client.text.related_texts( collection_id=678, text_id=1, wait=30 ) print "Related texts:", related_texts # Reset a Collection reset_collection = client.collection.reset( collection_id=678, ) print "Successfully reseted corpus with properties: ", reset_collection