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

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

Start querying live data from SingleStore using the CData Python Connector for SingleStore. 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 SingleStore data in Python. When you issue complex SQL queries from Python, the driver pushes supported SQL operations, like filters and aggregations, directly to SingleStore 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 SingleStore 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 SingleStore 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.singlestore 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 SingleStore 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 SingleStore database.
Port: The port of the server hosting the SingleStore database.
Database (Optional): The default database to connect to when connecting to the SingleStore Server. If this is not set, tables from all databases will be returned.

Connect Using Standard Authentication

To authenticate using standard authentication, set the following:

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

Connect Using Integrated Security

As an alternative to providing the standard username and password, you can set IntegratedSecurity to True to authenticate trusted users to the server via Windows Authentication.

Connect Using SSL Authentication

You can leverage SSL authentication to connect to SingleStore 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.

Connect Using SSH Authentication

Using SSH, you can securely login to a remote machine. To access SingleStore data via SSH, configure the following connection properties:

SSHClientCert: Set this to the name of the certificate store for the client certificate.
SSHClientCertPassword: If a client certificate store is password-protected, set this value to the store's password.
SSHClientCertSubject: The subject of the TLS/SSL client certificate. Used to locate the certificate in the store.
SSHClientCertType: The certificate type of the client store.
SSHPassword: The password that you use to authenticate with the SSH server.
SSHPort: The port used for SSH operations.
SSHServer: The SSH authentication server you are trying to authenticate against.
SSHServerFingerPrint: The SSH Server fingerprint used for verification of the host you are connecting to.
SSHUser: Set this to the username that you use to authenticate with the SSH server.

Connecting to SingleStore


# Create a database engine using the CData Python Connector for SingleStore
engine = create_engine("cdata_singlestore_2:///?User=User=myUser;Password=myPassword;Database=NorthWind;Server=myServer;Port=3306;")

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 SingleStore 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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SingleStore Python Connector

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

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