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


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

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

Set the Server and Port connection properties to connect, in addition to any authentication properties that may be required.

To enable TLS/SSL, set UseSSL to true.

Authenticating with LDAP

In order to authenticate with LDAP, set the following connection properties:

  • AuthScheme: Set this to LDAP.
  • User: The username being authenticated with in LDAP.
  • Password: The password associated with the User you are authenticating against LDAP with.

Authenticating with Kerberos

In order to authenticate with KERBEROS, set the following connection properties:

  • AuthScheme: Set this to KERBEROS.
  • KerberosKDC: The Kerberos Key Distribution Center (KDC) service used to authenticate the user.
  • KerberosRealm: The Kerberos Realm used to authenticate the user with.
  • KerberosSPN: The Service Principal Name for the Kerberos Domain Controller.
  • KerberosKeytabFile: The Keytab file containing your pairs of Kerberos principals and encrypted keys.
  • User: The user who is authenticating to Kerberos.
  • Password: The password used to authenticate to Kerberos.

Connecting to Presto

# Create a database engine using the CData Python Connector for Presto engine = create_engine("cdata_presto_2:///?User=Server=127.0.0.1;Port=8080;")

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 Presto 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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