Profile: Python Engineer
The Squirro Academy has a full course for Squirro Python Engineers.
As a Python engineer, you can use the Python programming language to expand Squirro’s capabilities by building and uploading the following:
Squirro operates under a framework of Gather -> Understand -> Act. This page details how to use Python to expand Squirro’s capabilities at each stage.
If there’s something you’d like to do that you don’t see covered on this page, ask the community for help by posting in the Squirro forum or contacting one of our solutions engineers at Squirro Support.
Data Loader Plugins#
You can load data from multiple sources into Squirro via the data loader. Out of the box, the data loader can extract data from multiple different sources, including databases, CSV files, Excel files, JSON files, and more.
To learn how to build a custom data loading plugin, see How To Write a Custom Data Loader Plugin.
Tip: Squirro offers many prebuilt data loading plugins (see Built-In Data Connectors). These include plugins for Confluence, Jira, Dropbox, Github, Gmail, MS Exchange, Salesforce, and more. Make sure to check that a plugin doesn’t already exist for your data source before building a custom one.
When ingesting data into Squirro, you must assign a pipeline workflow to your data source.
The pipeline workflow is a set of steps applied to the data you’re ingesting. In addition to out-of-the-box steps, you can add your own custom enrichment or processing steps, called pipelets.
To learn how to build, test, and upload a custom pipelet, see the Pipelets Tutorial.
Custom libNLP Query-Processing Steps#
Squirro’s NLP library (libNLP) is a general-purpose library. You can use it to perform query processing, and any other kind of data processing on items within the project.
Powered by machine learning (ML), it’s the core of Squirro’s ML applications like AI Studio, Q&A, and Query Processing.
libNLP query-processing steps help Squirro understand natural language queries better, improving the user’s overall search experience.
Squirro projects come with a preconfigured query processing workflow out of the box. The workflow is a collection of libNLP steps you can expand upon as a Python engineer.
For information on building custom steps, see How to Create Custom Query-Processing Steps.
You can connect to all Squirro APIs through the SquirroClient (Python SDK).
Using Python, you can buld custom integrations with external dashboards or the Squirro Studio plugin.
For the Studio plugin, you’ll typically work alongside frontend developers to build custom widgets or dashboard views to customize and extend Squirro’s out-of-the-box functionalities.