Open Sourcing a sleek intelligence API

Back in 2011-2012 I put a lot of time and energy into creating a simple and sleek JSON API framework for quick intelligence prototyping; an API capable of managing JSON objects, and performing a lot of smart computing tasks. Fast forward to 2016, I decided to open source the codebase, sharing it with the world because I believe this framework, although a bit outdated by now, still has the potential to help others.


SQLpie™ is an open source API framework that uses all sorts of SQL statements to creatively perform all kinds of computing tasks (thus, SQLpie). With SQLpie, developers can store JSON objects in a SQL database and run a lot of information retrieval and machine learning tasks on the data, covering areas such as: Text Classification, Text Summarization, Collaborative Filtering (item recommendation and similarity), Boolean/Vector Search, Document Matching, TagClouds, etc… The project is 100% written in Python and runs on top of a MySQL database.

The SQLpie project went after a lot of big challenges, and although I do not advocate that it includes the best implementations to handle all of those tasks, I believe the combined effort can help people quickly prototype new ideas, and hopefully, create new and awesome products.

Its API services can help developers with the following type of questions:

How can one store JSON documents? (answer: documents services)
How can one keep track of document relationships? (answer: observations services)
What documents exist for query Q? (answer: indexing and search services)
What documents are located near location L? (answer: geosearch service)
What top keyphrases and keywords relate to query Q? (answer: tagcloud search service)
What are the key sentences, entities, and terms associated with document D? (answer: summarization service)
What documents are similar (or relate) to document D? (answer: document matching service)
Will user U like document D? (answer: classification service)
How likely is user U to like document D? (answer: classification service)
What documents is user U likely to love based on user data? (answer: recommendation service)
What other users have a document taste similar to user U? (answer: similarity service)

If you’re a developer, learn more at The project is hosted on Github.

~ Andre Lessa