How to Query Live SQL Server Data in Natural Language in Python using LlamaIndex

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

Connecting to Microsoft SQL Server

Connect to Microsoft SQL Server using the following properties:

• Server: The name of the server running SQL Server.
• User: The username provided for authentication with SQL Server.
• Password: The password associated with the authenticating user.
• Database: The name of the SQL Server database.

Connecting to Azure SQL Server and Azure Data Warehouse

You can authenticate to Azure SQL Server or Azure Data Warehouse by setting the following connection properties:

• Server: The server running Azure. You can find this by logging into the Azure portal and navigating to "SQL databases" (or "SQL data warehouses") -> "Select your database" -> "Overview" -> "Server name."
• User: The name of the user authenticating to Azure.
• Password: The password associated with the authenticating user.
• Database: The name of the database, as seen in the Azure portal on the SQL databases (or SQL warehouses) page.

SSH Connectivity for SQL Server

You can use SSH (Secure Shell) to authenticate with SQL Server, whether the instance is hosted on-premises or in supported cloud environments. SSH authentication ensures that access is encrypted (as compared to direct network connections).

SSH Connections to SQL Server in Password Auth Mode

To connect to SQL Server via SSH in Password Auth mode, set the following connection properties:

• User: SQL Server User name
• Password: SQL Server Password
• Database: SQL Server database name
• Server: SQL Server Server name
• Port: SQL Server port number like 3306
• UserSSH: "true"
• SSHAuthMode: "Password"
• SSHPort: SSH Port number
• SSHServer: SSH Server name
• SSHUser: SSH User name
• SSHPassword: SSH Password

SSH Connections to SQL Server in Public Key Auth Mode

To connect to SQL Server via SSH in Password Auth mode, set the following connection properties:

• User: SQL Server User name
• Password: SQL Server Password
• Database: SQL Server database name
• Server: SQL Server Server name
• Port: SQL Server port number like 3306
• UserSSH: "true"
• SSHAuthMode: "Public_Key"
• SSHPort: SSH Port number
• SSHServer: SSH Server name
• SSHUser: SSH User name
• SSHClientCret: the path for the public key certificate file

Connecting to SQL Server

# Create a database engine using the CData Python Connector for SQL Server
engine = create_engine("cdata_sql_2:///?User=User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=1433;")

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