How to Connect to Live Google Cloud Storage Data from OpenAI Python Applications (via CData Connect AI)

Jerod Johnson
Director, Technology Evangelism

Leverage the CData Connect AI Remote MCP Server to enable OpenAI-powered Python applications to securely read and take actions on your Google Cloud Storage data through natural language.

OpenAI's Python SDK provides powerful capabilities for building AI applications that can interact with various data sources. When combined with CData Connect AI Remote MCP, you can build intelligent chat applications that interact with your Google Cloud Storage data in real-time through natural language queries. This article outlines the process of connecting to Google Cloud Storage using Connect AI Remote MCP and configuring an OpenAI-powered Python application to interact with your Google Cloud Storage data through conversational AI.

CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Google Cloud Storage data. The CData Connect AI Remote MCP Server enables secure communication between OpenAI applications and Google Cloud Storage. This allows your AI assistants to read from and take actions on your live Google Cloud Storage data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Google Cloud Storage. This leverages server-side processing to swiftly deliver the requested Google Cloud Storage data.

In this article, we show how to configure an OpenAI-powered Python application to conversationally explore (or Vibe Query) your data using natural language. With Connect AI you can build AI assistants with access to live Google Cloud Storage data, plus hundreds of other sources.

Step 1: Configure Google Cloud Storage Connectivity for OpenAI Applications

Connectivity to Google Cloud Storage from OpenAI applications is made possible through CData Connect AI Remote MCP. To interact with Google Cloud Storage data from your OpenAI assistant, we start by creating and configuring a Google Cloud Storage connection in CData Connect AI.

Log into Connect AI, click Sources, and then click Add Connection

Select "Google Cloud Storage" from the Add Connection panel

Enter the necessary authentication properties to connect to Google Cloud Storage.
Authenticate with a User Account

You can connect without setting any connection properties for your user credentials. After setting InitiateOAuth to GETANDREFRESH, you are ready to connect.

When you connect, the Google Cloud Storage OAuth endpoint opens in your default browser. Log in and grant permissions, then the OAuth process completes

Authenticate with a Service Account

Service accounts have silent authentication, without user authentication in the browser. You can also use a service account to delegate enterprise-wide access scopes.
You need to create an OAuth application in this flow. See the Help documentation for more information. After setting the following connection properties, you are ready to connect:
- InitiateOAuth: Set this to GETANDREFRESH.
- OAuthJWTCertType: Set this to "PFXFILE".
- OAuthJWTCert: Set this to the path to the .p12 file you generated.
- OAuthJWTCertPassword: Set this to the password of the .p12 file.
- OAuthJWTCertSubject: Set this to "*" to pick the first certificate in the certificate store.
- OAuthJWTIssuer: In the service accounts section, click Manage Service Accounts and set this field to the email address displayed in the service account Id field.
- OAuthJWTSubject: Set this to your enterprise Id if your subject type is set to "enterprise" or your app user Id if your subject type is set to "user".
- ProjectId: Set this to the Id of the project you want to connect to.
The OAuth flow for a service account then completes.
Click Save & Test
Navigate to the Permissions tab in the Add Google Cloud Storage Connection page and update the User-based permissions.

Add a Personal Access Token

A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from your OpenAI application. It is best practice to create a separate PAT for each service to maintain granularity of access.

Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
On the Settings page, go to the Access Tokens section and click Create PAT.
Give the PAT a name and click Create.
The personal access token is only visible at creation, so be sure to copy it and store it securely for future use.

With the connection configured and a PAT generated, we are ready to connect to Google Cloud Storage data from your OpenAI application.

Step 2: Configure Your OpenAI Python Application for CData Connect AI

Follow these steps to configure your OpenAI Python application to connect to CData Connect AI. You can use our pre-built client as a starting point, available at https://github.com/CDataSoftware/openai-mcp-client, or follow the instructions below to create your own.

Ensure you have Python 3.8+ installed and install the required dependencies:
```
pip install openai python-dotenv httpx
```

Clone or download the OpenAI MCP client from GitHub:

git clone https://github.com/CDataSoftware/openai-mcp-client.git
cd openai-mcp-client

Set up your environment variables. Create a .env file in your project root with the following variables:
```
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
MCP_SERVER_URL=https://mcp.cloud.cdata.com/mcp
MCP_USERNAME=YOUR_EMAIL
MCP_PASSWORD=YOUR_PAT
OPENAI_MODEL=gpt-4
    
```
Replace YOUR_OPENAI_API_KEY with your OpenAI API key, YOUR_EMAIL with your Connect AI email address, and YOUR_PAT with the Personal Access Token created in Step 1.

If creating your own application, here's the core implementation for connecting to CData Connect AI MCP Server:

import os
import asyncio
import base64
from dotenv import load_dotenv
from mcp_client import MCPServerStreamableHttp, MCPAgent

# Load environment variables
load_dotenv()

async def main():
    """Main chat loop for interacting with Google Cloud Storage data."""
    # Get configuration
    api_key = os.getenv('OPENAI_API_KEY')
    mcp_url = os.getenv('MCP_SERVER_URL', 'https://mcp.cloud.cdata.com/mcp')
    username = os.getenv('MCP_USERNAME', '')
    password = os.getenv('MCP_PASSWORD', '')
    model = os.getenv('OPENAI_MODEL', 'gpt-4')

    # Create auth header for MCP server
    headers = {}
    if username and password:
        auth = base64.b64encode(f"{username}:{password}".encode()).decode()
        headers = {"Authorization": f"Basic {auth}"}

    # Connect to CData MCP Server
    async with MCPServerStreamableHttp(
        name="CData MCP Server",
        params={
            "url": mcp_url,
            "headers": headers,
            "timeout": 30,
            "verify_ssl": True
        }
    ) as mcp_server:

        # Create AI agent with access to Google Cloud Storage data
        agent = MCPAgent(
            name="data_assistant",
            model=model,
            mcp_servers=[mcp_server],
            instructions="""You are a data query assistant with access to Google Cloud Storage data through CData Connect AI.

            You can help users explore and query their Google Cloud Storage data in real-time.
            Use the available MCP tools to:
            - List available databases and schemas
            - Explore table structures
            - Execute SQL queries
            - Provide insights about the data

            Always explain what you're doing and format results clearly.""",
            api_key=api_key
        )

        await agent.initialize()
        print(f"Connected! {len(agent._tools_cache)} tools available.")
        print("
Chat with your Google Cloud Storage data (type 'exit' to quit):
")

        # Interactive chat loop
        conversation = []
        while True:
            user_input = input("You: ")
            if user_input.lower() in ['exit', 'quit']:
                break

            conversation.append({"role": "user", "content": user_input})

            print("Assistant: ", end="", flush=True)
            response = await agent.run(conversation)
            print(response["content"])

            conversation.append({"role": "assistant", "content": response["content"]})

if __name__ == "__main__":
    asyncio.run(main())

Run your OpenAI application:
```
python client.py
```
Start interacting with your Google Cloud Storage data through natural language queries. Your OpenAI assistant now has access to your Google Cloud Storage data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Applications with Live Google Cloud Storage Data Access

With your OpenAI Python application configured and connected to CData Connect AI, you can now build sophisticated AI assistants that interact with your Google Cloud Storage data using natural language. The MCP integration provides your applications with powerful data access capabilities through OpenAI's advanced language models.

Available MCP Tools for Your Assistant

Your OpenAI assistant has access to the following CData Connect AI MCP tools:

queryData: Execute SQL queries against connected data sources and retrieve results
getCatalogs: Retrieve a list of available connections from CData Connect AI
getSchemas: Retrieve database schemas for a specific catalog
getTables: Retrieve database tables for a specific catalog and schema
getColumns: Retrieve column metadata for a specific table
getProcedures: Retrieve stored procedures for a specific catalog and schema
getProcedureParameters: Retrieve parameter metadata for stored procedures
executeProcedure: Execute stored procedures with parameters

Example Use Cases

Here are some examples of what your OpenAI-powered applications can do with live Google Cloud Storage data access:

Conversational Analytics: Build chat interfaces that answer complex business questions using natural language
Automated Reporting: Generate dynamic reports and summaries based on real-time data queries
Data Discovery Assistant: Help users explore and understand their data structure without SQL knowledge
Intelligent Data Monitor: Create AI assistants that proactively identify trends and anomalies
Custom Query Builder: Enable users to create complex queries through conversational interactions

Interacting with Your Assistant

Once running, you can interact with your OpenAI assistant through natural language. Example queries include:

"Show me all available databases"
"What tables are in the sales database?"
"List the top 10 customers by revenue"
"Find all orders from the last month"
"Analyze the trend in sales over the past quarter"
"What's the structure of the customer table?"

Your OpenAI assistant will automatically translate these natural language queries into appropriate SQL queries and execute them against your Google Cloud Storage data through the CData Connect AI MCP Server, providing intelligent insights without requiring users to write complex SQL or understand the underlying data structure.

Advanced Features

The OpenAI MCP integration supports advanced capabilities:

Context Awareness: The assistant maintains conversation context for follow-up questions
Multi-turn Conversations: Build complex queries through iterative dialogue
Intelligent Error Handling: Get helpful suggestions when queries encounter issues
Data Insights: Leverage GPT's analytical capabilities to identify patterns and trends
Format Flexibility: Request results in various formats (tables, summaries, JSON, etc.)

Get CData Connect AI

To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your OpenAI applications, try CData Connect AI today!

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