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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 Presto data in real-time through natural language queries. This article outlines the process of connecting to Presto using Connect AI Remote MCP and configuring an OpenAI-powered Python application to interact with your Presto data through conversational AI.
CData Connect AI offers a dedicated cloud-to-cloud interface for connecting to Presto data. The CData Connect AI Remote MCP Server enables secure communication between OpenAI applications and Presto. This allows your AI assistants to read from and take actions on your live Presto data. With its inherent optimized data processing capabilities, CData Connect AI efficiently channels all supported SQL operations, including filters and JOINs, directly to Presto. This leverages server-side processing to swiftly deliver the requested Presto 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 Presto data, plus hundreds of other sources.
Accessing and integrating live data from Trino and Presto SQL engines has never been easier with CData. Customers rely on CData connectivity to:
Presto and Trino allow users to access a variety of underlying data sources through a single endpoint. When paired with CData connectivity, users get pure, SQL-92 access to their instances, allowing them to integrate business data with a data warehouse or easily access live data directly from their preferred tools, like Power BI and Tableau.
In many cases, CData's live connectivity surpasses the native import functionality available in tools. One customer was unable to effectively use Power BI due to the size of the datasets needed for reporting. When the company implemented the CData Power BI Connector for Presto they were able to generate reports in real-time using the DirectQuery connection mode.
Connectivity to Presto from OpenAI applications is made possible through CData Connect AI Remote MCP. To interact with Presto data from your OpenAI assistant, we start by creating and configuring a Presto connection in CData Connect AI.
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.
In order to authenticate with LDAP, set the following connection properties:
In order to authenticate with KERBEROS, set the following connection properties:
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.
With the connection configured and a PAT generated, we are ready to connect to Presto data from your OpenAI application.
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.
pip install openai python-dotenv httpx
git clone https://github.com/CDataSoftware/openai-mcp-client.git cd openai-mcp-client
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-4Replace 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.
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 Presto 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 Presto data
agent = MCPAgent(
name="data_assistant",
model=model,
mcp_servers=[mcp_server],
instructions="""You are a data query assistant with access to Presto data through CData Connect AI.
You can help users explore and query their Presto 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 Presto 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())
python client.py
With your OpenAI Python application configured and connected to CData Connect AI, you can now build sophisticated AI assistants that interact with your Presto data using natural language. The MCP integration provides your applications with powerful data access capabilities through OpenAI's advanced language models.
Your OpenAI assistant has access to the following CData Connect AI MCP tools:
Here are some examples of what your OpenAI-powered applications can do with live Presto data access:
Once running, you can interact with your OpenAI assistant through natural language. Example queries include:
Your OpenAI assistant will automatically translate these natural language queries into appropriate SQL queries and execute them against your Presto data through the CData Connect AI MCP Server, providing intelligent insights without requiring users to write complex SQL or understand the underlying data structure.
The OpenAI MCP integration supports advanced capabilities:
To get live data access to hundreds of SaaS, Big Data, and NoSQL sources directly from your OpenAI applications, try CData Connect AI today!
Learn more about CData Connect AI or sign up for free trial access:
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