How to Query Live Adobe Target Data in Natural Language in Python using LlamaIndex

Start querying live data from Adobe Target using the CData Python Connector for Adobe Target. 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 Adobe Target data in Python. When you issue complex SQL queries from Python, the driver pushes supported SQL operations, like filters and aggregations, directly to Adobe Target 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 Adobe Target 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 Adobe Target 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.adobetarget 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 Adobe Target using the CData connector using a connection string with the required connection properties.

To connect to Adobe Target, you must provide the Tenant property along with OAuth connection properties mentioned below. Note that while other connection properties can influence processing behavior, they do not affect the ability to connect.

To determine your Tenant name:

1. Log in to Adobe Experience. The URL will look similar to: "https://experience.adobe.com/#/@mycompanyname/preferences/general-section".
2. Extract the value after the "/#/@". In this example, it is "mycompanyname".
3. Set the Tenant connection property to that value.

User Accounts (OAuth)

You must set AuthScheme to OAuthClient for all user account flows.

Note: Adobe authentication via OAuth requires updating your token every two weeks.

All Applications

CData provides an embedded OAuth application that simplifies OAuth authentication. Alternatively, you can create a custom OAuth application. Review Creating a Custom OAuth App in the Help documentation for more information.

Obtaining the OAuth Access Token

Set the following properties to connect:

• InitiateOAuth: Set to GETANDREFRESH to automatically perform the OAuth exchange and refresh the OAuthAccessToken as needed.
• OAuthClientId : Set to the client Id assigned when you registered your app.
• OAuthClientSecret : Set to the client secret assigned when you registered your app.
• CallbackURL : Set to the redirect URI defined when you registered your app. For example: https://localhost:3333

With these settings, the provider obtains an access token from Adobe Target, which it uses to request data. The OAuth values are stored in the location specified by OAuthSettingsLocation, ensuring they persist across connections.

Connecting to Adobe Target

# Create a database engine using the CData Python Connector for Adobe Target
engine = create_engine("cdata_adobetarget_2:///?User=Tenant=mycompanyname;InitiateOAuth=GETANDREFRESH;")

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 Adobe Target and start querying your live data seamlessly. Experience the power of natural language processing and unlock valuable insights from your data today.