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Prerequisites

Before you can configure and use LlamaIndex with Connect AI, you must do the following:
  • Connect a data source to your Connect AI account. See Sources for more information.
  • Generate a Personal Access Token (PAT) on the Settings page. Copy this down, as it acts as your password during authentication.
  • Obtain an OpenAI API key: https://platform.openai.com.
  • Make sure you have Python >= 3.10 in order to install the LlamaIndex packages.

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Create the Python Files

1
Create a folder for LlamaIndex MCP.
2
Create a Python file within the folder called llamaindex.py.
3
In llamaindex.py, set up your MCP server and MCP client to call the tools and prompts. For Authorization, you need to provide your Base64-encoded Connect AI username and PAT (obtained in the prerequisites) after Basic.
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, aget_tools_from_mcp_url
from llama_index.core.agent import ReActAgent
import asyncio

client = BasicMCPClient(
    "http://mcp.cloud.cdata.com/mcp",
    headers={"Authorization": "Basic Base64-encoded (CONNECTAI_USERNAME:PAT)"}
)

async def main():
    # List available tools
    tools = await aget_tools_from_mcp_url("http://mcp.cloud.cdata.com/mcp", client=client)
   
    llm = OpenAI(model="gpt-4o", api_key="YOUR_OPENAI_KEY")   
    # Create ReActAgent
    agent = ReActAgent(tools=tools, llm=llm, verbose=True)
    # # Run a query
    response = await agent.run("List all the catalogs for me please")
    print(response)

asyncio.run(main())

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Install the LlamaIndex Packages

Run pip install llama-index llama-index-llms-openai llama-index-tools-mcp in your project terminal.

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Run the Python Script

1
When the installation finishes, run python llamaindex.py to execute the script.
2
The script discovers the Connect AI MCP tools needed for the LLM to query the connected data.
3
Supply a prompt for the agent. The agent provides a response.
LlamaIndex Terminal