How to use SQLAlchemy ORM to access Browse AI Data in Python

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData API Driver for Python and the SQLAlchemy toolkit, you can build Browse AI-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Browse AI data to query Browse AI data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Browse AI data in Python. When you issue complex SQL queries from Browse AI, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Browse AI and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Browse AI Data

Connecting to Browse AI data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

Start by setting the Profile connection property to the location of the BrowseAI Profile on disk (e.g. C:\profiles\BrowseAI.apip). Next, set the ProfileSettings connection property to the connection string for BrowseAI (see below).

BrowseAI API Profile Settings

BrowseAI is a web scraping and automation platform that allows you to train robots to extract and monitor data from websites. Authentication is performed using an API key that is sent as a Bearer token in the Authorization header.

The BrowseAI API key has the format {userId}:{apiKey}, where both parts are UUIDs separated by a colon. This full string is provided as a single value in the BrowseAI dashboard.

To obtain your API key:

1. Log in to your BrowseAI account at https://app.browse.ai
2. Navigate to Settings in the dashboard
3. Locate the API section
4. Copy the full API key string displayed (format: userId:apiKey)

After obtaining your API key, set the following connection properties:

• AuthScheme: Set this to APIKey.

Set the following in the ProfileSettings connection property:

• APIKey: Set this to your BrowseAI API key (format: userId:apiKey).

Follow the procedure below to install SQLAlchemy and start accessing Browse AI through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy
pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker

Model Browse AI Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Browse AI data.

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("api:///?Profile=C:\profiles\BrowseAI.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_user_id:your_api_key'")

Declare a Mapping Class for Browse AI Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the RobotTasks table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class RobotTasks(base):
	__tablename__ = "RobotTasks"
	Id = Column(String,primary_key=True)
	Status = Column(String)
	...

Query Browse AI Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("api:///?Profile=C:\profiles\BrowseAI.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_user_id:your_api_key'")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(RobotTasks).filter_by(RobotId="your-robot-id"):
	print("Id: ", instance.Id)
	print("Status: ", instance.Status)
	print("---------")

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

RobotTasks_table = RobotTasks.metadata.tables["RobotTasks"]
for instance in session.execute(RobotTasks_table.select().where(RobotTasks_table.c.RobotId == "your-robot-id")):
	print("Id: ", instance.Id)
	print("Status: ", instance.Status)
	print("---------")

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Browse AI data. Reach out to our Support Team if you have any questions.