How to Query Live HCL Domino Data in Natural Language in Python using LlamaIndex

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

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

Connecting to Domino

To connect to Domino data, set the following properties:

URL: The host name or IP of the server hosting the Domino database. Include the port of the server hosting the Domino database. For example: http://sampleserver:1234/
DatabaseScope: The name of a scope in the Domino Web UI. The driver exposes forms and views for the schema governed by the specified scope. In the Domino Admin UI, select the Scopes menu in the sidebar. Set this property to the name of an existing scope.

Authenticating with Domino

Domino supports authenticating via login credentials or an Azure Active Directory OAuth application:

Login Credentials

To authenticate with login credentials, set the following properties:

AuthScheme: Set this to "OAuthPassword"
User: The username of the authenticating Domino user
Password: The password associated with the authenticating Domino user

The driver uses the login credentials to automatically perform an OAuth token exchange.

AzureAD

This authentication method uses Azure Active Directory as an IdP to obtain a JWT token. You need to create a custom OAuth application in Azure Active Directory and configure it as an IdP. To do so, follow the instructions in the Help documentation. Then set the following properties:

AuthScheme: Set this to "AzureAD"
InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
OAuthClientId: The Client ID obtained when setting up the custom OAuth application.
OAuthClientSecret: The Client secret obtained when setting up the custom OAuth application.
CallbackURL: The redirect URI defined when you registered your app. For example: https://localhost:33333
AzureTenant: The Microsoft Online tenant being used to access data. Supply either a value in the form companyname.microsoft.com or the tenant ID.

The tenant ID is the same as the directory ID shown in the Azure Portal's Azure Active Directory > Properties page.

Connecting to HCL Domino


# Create a database engine using the CData Python Connector for HCL Domino
engine = create_engine("cdata_domino_2:///?User=Server=https://domino.corp.com;AuthScheme=OAuthPassword;User=my_domino_user;Password=my_domino_password;")

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 HCL Domino 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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