LangChain

LangChain is a framework for developing applications powered by large language models (LLMs).

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Prerequisites

Before you can configure and use LangChain with Connect AI, you must first do the following:

• OAuth JWT bearer token �𐶐����܂��BPAT ���R�s�[���A�F�؎��Ƀp�X���[�h�Ƃ��Ďg�p���܂��B
• Obtain an OpenAI API key: https://platform.openai.com/.
• Make sure you have Python >= 3.10 in order to install the LangChain and LangGraph packages.

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Create the Python Files

1. Create a folder for LangChain MCP.

2. Create two Python files within the folder: config.py and langchain.py.

3. In config.py, create a class Config to define your MCP server authentication and URL. MCP_AUTH should be set to "OAUTH_JWT_TOKEN", not EMAIL:PAT:

   class Config:
       MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL
       MCP_AUTH = "base64encoded(EMAIL:PAT)"   #Base64 encoded Connect AI Email:PAT
   
4. In langchain.py, set up your MCP server and MCP client to call the tools and prompts:

   """
   Integrates a LangChain ReAct agent with CData Connect AI MCP server.
   The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning.
   """
   import asyncio
   from langchain_mcp_adapters.client import MultiServerMCPClient
   from langchain_openai import ChatOpenAI
   from langgraph.prebuilt import create_react_agent
   from config import Config
   
   async def main():
       # Initialize MCP client with one or more server URLs    
       mcp_client = MultiServerMCPClient(
           connections={
               "default": {  # you can name this anything
               "transport": "streamable_http",
               "url": Config.MCP_BASE_URL,
               "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"},
           }
       }
       )
       # Load remote MCP tools exposed by the server
       all_mcp_tools = await mcp_client.get_tools()
       print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools])
       # Create and run the ReAct style agent
       llm = ChatOpenAI(
           model="gpt-4o", 
           temperature=0.2,
           api_key="YOUR_OPEN_API_KEY"   #Use your OpenAPI Key here. This can be found here: https://platform.openai.com/.
       )
       agent = create_react_agent(llm, all_mcp_tools)
       user_prompt = "Tell me how many sales I had in Q1 for the current fiscal year."   #Change prompts as per need
       print(f"\nUser prompt: {user_prompt}")
       # Send a prompt asking the agent to use the MCP tools
       response = await agent.ainvoke(
           { "messages": [{ "role": "user", "content": (user_prompt),}]}
       )
       # Print out the agent's final response
       final_msg = response["messages"][-1].content
       print("Agent final response:", final_msg)
   
   if __name__ == "__main__":
       asyncio.run(main())
   

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Install the LangChain and LangGraph Packages

Run the following command in your project terminal:

pip install langchain-mcp-adapters langchain-openai langgraph

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Run the Python Script

1. When the installation finishes, run the following command to execute the script:

   python langchain.py
   
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.