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

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

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

The following connection properties are required in order to connect to data.

Server: The host name or IP of the server hosting the EnterpriseDB database.
Port: The port of the server hosting the EnterpriseDB database.

You can also optionally set the following:

Database: The default database to connect to when connecting to the EnterpriseDB Server. If this is not set, the user's default database will be used.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

User: The user which will be used to authenticate with the EnterpriseDB server.
Password: The password which will be used to authenticate with the EnterpriseDB server.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to EnterpriseDB data via a secure session. Configure the following connection properties to connect to data:

SSLClientCert: Set this to the name of the certificate store for the client certificate. Used in the case of 2-way SSL, where truststore and keystore are kept on both the client and server machines.
SSLClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
SSLClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
SSLClientCertType: The certificate type of the client store.
SSLServerCert: The certificate to be accepted from the server.

Connecting to EnterpriseDB


# Create a database engine using the CData Python Connector for EnterpriseDB
engine = create_engine("cdata_enterprisedb_2:///?User=User=postgres;Password=admin;Database=postgres;Server=127.0.0.1;Port=5444")

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 EnterpriseDB 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 EnterpriseDB Connector to get started:

Download Now

Learn more:

EnterpriseDB Python Connector

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

CData Software is a leading provider of data access and connectivity solutions. Our standards-based connectors streamline data access and insulate customers from the complexities of integrating with on-premise or cloud databases, SaaS, APIs, NoSQL, and Big Data.

Connect With Us

Get Started

Data Connectors

ETL/ ELT Solutions

Cloud & API Connectivity

OEM & Custom Drivers