Learn more
 

How to Query Live Sage 300 Data in Natural Language in Python using LlamaIndex


Use LlamaIndex to query live Sage 300 data data in natural language using Python.

Start querying live data from Sage 300 using the CData Python Connector for Sage 300. 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 Sage 300 data in Python. When you issue complex SQL queries from Python, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 300 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 Sage 300 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 Sage 300 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.sage300 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 Sage 300 using the CData connector using a connection string with the required connection properties.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

  • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
  • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
  • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

  • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
  • User: Set this to the username of your account.
  • Password: Set this to the password of your account.

Connecting to Sage 300

# Create a database engine using the CData Python Connector for Sage 300 engine = create_engine("cdata_sage300_2:///?User=User=SAMPLE;Password=password;URL=http://127.0.0.1/Sage300WebApi/v1/-/;Company=SAMINC;")

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 Sage 300 and start querying your live data seamlessly. Experience the power of natural language processing and unlock valuable insights from your data today.

Ready to get started?

Download a free trial of the Sage 300 Connector to get started:

 Download Now

Learn more:

Sage 300 Icon Sage 300 Python Connector

Python Connector Libraries for Sage 300 Data Connectivity. Integrate Sage 300 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

In this article

Related articles