How to Connect to Live Elasticsearch Data from OpenAI Python Applications (via CData Connect AI)

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 Elasticsearch data in real-time through natural language queries. This article outlines the process of connecting to Elasticsearch using Connect AI Remote MCP and configuring an OpenAI-powered Python application to interact with your Elasticsearch data through conversational AI.

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

Getting Started

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Step 1: Configure Elasticsearch Connectivity for OpenAI Applications

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

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

2. Select "Elasticsearch" from the Add Connection panel

3. Enter the necessary authentication properties to connect to Elasticsearch.

   Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

   The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

   Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

   
4. Click Save & Test
5. Navigate to the Permissions tab in the Add Elasticsearch 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.

1. Click on the Gear icon () at the top right of the Connect AI app to open the settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. 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 Elasticsearch 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.

1. Ensure you have Python 3.8+ installed and install the required dependencies:

   pip install openai python-dotenv httpx

2. Clone or download the OpenAI MCP client from GitHub:

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

3. 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.
4. 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 Elasticsearch 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 Elasticsearch data
           agent = MCPAgent(
               name="data_assistant",
               model=model,
               mcp_servers=[mcp_server],
               instructions="""You are a data query assistant with access to Elasticsearch data through CData Connect AI.
   
               You can help users explore and query their Elasticsearch 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 Elasticsearch 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())
       

5. Run your OpenAI application:

   python client.py

6. Start interacting with your Elasticsearch data through natural language queries. Your OpenAI assistant now has access to your Elasticsearch data through the CData Connect AI MCP Server.

Step 3: Build Intelligent Applications with Live Elasticsearch 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 Elasticsearch 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 Elasticsearch 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 Elasticsearch 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 300+ SaaS, Big Data, and NoSQL sources directly from your OpenAI applications, try CData Connect AI today!