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

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

Connecting to Local or Cloud-Stored (Box, Google Drive, Amazon S3, SharePoint) Excel Files

CData Drivers let you work with Excel files stored locally and stored in cloud storage services like Box, Amazon S3, Google Drive, or SharePoint, right where they are.

Setting connection properties for local files

Set the URI property to local folder path.

Setting connection properties for files stored in Amazon S3

To connect to Excel file(s) within Amazon S3, set the URI property to the URI of the Bucket and Folder where the intended Excel files exist. In addition, at least set these properties:

• AWSAccessKey: AWS Access Key (username)
• AWSSecretKey: AWS Secret Key

Setting connection properties for files stored in Box

To connect to Excel file(s) within Box, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Box.

Dropbox

To connect to Excel file(s) within Dropbox, set the URI proprerty to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect to Dropbox. Either User Account or Service Account can be used to authenticate.

SharePoint Online (SOAP)

To connect to Excel file(s) within SharePoint with SOAP Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. Set User, Password, and StorageBaseURL.

SharePoint Online REST

To connect to Excel file(s) within SharePoint with REST Schema, set the URI proprerty to the URI of the document library that includes the intended Excel file. StorageBaseURL is optional. If not set, the driver will use the root drive. OAuth is used to authenticate.

Google Drive

To connect to Excel file(s) within Google Drive, set the URI property to the URI of the folder that includes the intended Excel file(s). Use the OAuth authentication method to connect and set InitiateOAuth to GETANDREFRESH.

Connecting to Excel

# Create a database engine using the CData Python Connector for Microsoft Excel
engine = create_engine("cdata_excel_2:///?User=URI='C:/MyExcelWorkbooks/SampleWorkbook.xlsx';")

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