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

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

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

Zuora uses the OAuth standard to authenticate users. See the online Help documentation for a full OAuth authentication guide.

Configuring Tenant property

In order to create a valid connection with the provider you need to choose one of the Tenant values (USProduction by default) which matches your account configuration. The following is a list with the available options:

USProduction: Requests sent to https://rest.zuora.com.
USAPISandbox: Requests sent to https://rest.apisandbox.zuora.com"
USPerformanceTest: Requests sent to https://rest.pt1.zuora.com"
EUProduction: Requests sent to https://rest.eu.zuora.com"
EUSandbox: Requests sent to https://rest.sandbox.eu.zuora.com"

Selecting a Zuora Service

Two Zuora services are available: Data Query and AQuA API. By default ZuoraService is set to AQuADataExport.

DataQuery

The Data Query feature enables you to export data from your Zuora tenant by performing asynchronous, read-only SQL queries. We recommend to use this service for quick lightweight SQL queries.

Limitations

The maximum number of input records per table after filters have been applied: 1,000,000
The maximum number of output records: 100,000
The maximum number of simultaneous queries submitted for execution per tenant: 5
The maximum number of queued queries submitted for execution after reaching the limitation of simultaneous queries per tenant: 10
The maximum processing time for each query in hours: 1
The maximum size of memory allocated to each query in GB: 2
The maximum number of indices when using Index Join, in other words, the maximum number of records being returned by the left table based on the unique value used in the WHERE clause when using Index Join: 20,000

AQuADataExport

AQuA API export is designed to export all the records for all the objects ( tables ). AQuA query jobs have the following limitations:

Limitations

If a query in an AQuA job is executed longer than 8 hours, this job will be killed automatically.
The killed AQuA job can be retried three times before returned as failed.

Connecting to Zuora


# Create a database engine using the CData Python Connector for Zuora
engine = create_engine("cdata_zuora_2:///?User=OAuthClientID=MyOAuthClientId;OAuthClientSecret=MyOAuthClientSecret;Tenant=USProduction;ZuoraService=DataQuery;")

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

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