Learn more
 

How to Query Live Okta Data in Natural Language in Python using LlamaIndex


Use LlamaIndex to query live Okta data data in natural language using Python.

Start querying live data from Okta using the CData Python Connector for Okta. Leverage the power of AI with LlamaIndex and retrieve insights using simple English, eliminating the need for complex SQL queries. Benefit from real-time data access that enhances your decision-making process, while easily integrating with your existing Python applications.

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

Whether you're analyzing trends, generating reports, or visualizing data, our Python connectors enable you to harness the full potential of your live data source with ease.

Overview

Here's how to query live data with CData's Python connector for Okta data using LlamaIndex:

  • Import required Python, CData, and LlamaIndex modules for logging, database connectivity, and NLP.
  • Retrieve your OpenAI API key for authenticating API requests from your application.
  • Connect to live Okta data using the CData Python Connector.
  • Initialize OpenAI and create instances of SQLDatabase and NLSQLTableQueryEngine for handling natural language queries.
  • Create the query engine and specific database instance.
  • Execute natural language queries (e.g., "Who are the top-earning employees?") to get structured responses from the database.
  • Analyze retrieved data to gain insights and inform data-driven decisions.

Import Required Modules

Import the necessary modules CData, database connections, and natural language querying.

import os import logging import sys # Configure logging logging.basicConfig(stream=sys.stdout, level=logging.INFO, force=True) logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) # Import required modules for CData and LlamaIndex import cdata.okta as mod from sqlalchemy import create_engine from llama_index.core.query_engine import NLSQLTableQueryEngine from llama_index.core import SQLDatabase from llama_index.llms.openai import OpenAI

Set Your OpenAI API Key

To use OpenAI's language model, you need to set your API key as an environment variable. Make sure you have your OpenAI API key available in your system's environment variables.

# Retrieve the OpenAI API key from the environment variables OPENAI_API_KEY = os.environ["OPENAI_API_KEY"] ''as an alternative, you can also add your API key directly within your code (though this method is not recommended for production environments due to security risks):'' # Directly set the API key (not recommended for production use) OPENAI_API_KEY = "your-api-key-here"

Create a Database Connection

Next, establish a connection to Okta using the CData connector using a connection string with the required connection properties.

To connect to Okta, set the Domain connection string property to your Okta domain.

You will use OAuth to authenticate with Okta, so you need to create a custom OAuth application.

Creating a Custom OAuth Application

From your Okta account:

  1. Sign in to your Okta developer edition organization with your administrator account.
  2. In the Admin Console, go to Applications > Applications.
  3. Click Create App Integration.
  4. For the Sign-in method, select OIDC - OpenID Connect.
  5. For Application type, choose Web Application.
  6. Enter a name for your custom application.
  7. Set the Grant Type to Authorization Code. If you want the token to be automatically refreshed, also check Refresh Token.
  8. Set the callback URL:
    • For desktop applications and headless machines, use http://localhost:33333 or another port number of your choice. The URI you set here becomes the CallbackURL property.
    • For web applications, set the callback URL to a trusted redirect URL. This URL is the web location the user returns to with the token that verifies that your application has been granted access.
  9. In the Assignments section, either select Limit access to selected groups and add a group, or skip group assignment for now.
  10. Save the OAuth application.
  11. The application's Client Id and Client Secret are displayed on the application's General tab. Record these for future use. You will use the Client Id to set the OAuthClientId and the Client Secret to set the OAuthClientSecret.
  12. Check the Assignments tab to confirm that all users who must access the application are assigned to the application.
  13. On the Okta API Scopes tab, select the scopes you wish to grant to the OAuth application. These scopes determine the data that the app has permission to read, so a scope for a particular view must be granted for the driver to have permission to query that view. To confirm the scopes required for each view, see the view-specific pages in Data Model < Views in the Help documentation.

Connecting to Okta

# Create a database engine using the CData Python Connector for Okta engine = create_engine("cdata_okta_2:///?User=Domain=dev-44876464.okta.com;")

Initialize the OpenAI Instance

Create an instance of the OpenAI language model. Here, you can specify parameters like temperature and the model version.

# Initialize the OpenAI language model instance llm = OpenAI(temperature=0.0, model="gpt-3.5-turbo")

Set Up the Database and Query Engine

Now, set up the SQL database and the query engine. The NLSQLTableQueryEngine allows you to perform natural language queries against your SQL database.

# Create a SQL database instance sql_db = SQLDatabase(engine) # This includes all tables # Initialize the query engine for natural language SQL queries query_engine = NLSQLTableQueryEngine(sql_database=sql_db)

Execute a Query

Now, you can execute a natural language query against your live data source. In this example, we will query for the top two earning employees.

# Define your query string query_str = "Who are the top earning employees?" # Get the response from the query engine response = query_engine.query(query_str) # Print the response print(response)

Download a free, 30-day trial of the CData Python Connector for Okta and start querying your live data seamlessly. Experience the power of natural language processing and unlock valuable insights from your data today.

Ready to get started?

Download a free trial of the Okta Connector to get started:

 Download Now

Learn more:

Okta Icon Okta Python Connector

Python Connector Libraries for Okta Data Connectivity. Integrate Okta with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

In this article

Related articles