MCP Toolbox for Databases

import asyncio from langgraph.prebuilt import create_react_agent # TODO(developer): replace this with another import if needed from langchain_google_vertexai import ChatVertexAI # from langchain_google_genai import ChatGoogleGenerativeAI # from langchain_anthropic import ChatAnthropic from langgraph.checkpoint.memory import MemorySaver from toolbox_langchain import ToolboxClient prompt = """ You're a helpful hotel assistant. You handle hotel searching, booking and cancellations. When the user searches for a hotel, mention it's name, id, location and price tier. Always mention hotel ids while performing any searches. This is very important for any operations. For any bookings or cancellations, please provide the appropriate confirmation. Be sure to update checkin or checkout dates if mentioned by the user. Don't ask for confirmations from the user. """ queries = [ "Find hotels in Basel with Basel in its name.", "Can you book the Hilton Basel for me?", "Oh wait, this is too expensive. Please cancel it and book the Hyatt Regency instead.", "My check in dates would be from April 10, 2024 to April 19, 2024.", ] async def main(): # TODO(developer): replace this with another model if needed model = ChatVertexAI(model_name="gemini-2.0-flash-001") # model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-001") # model = ChatAnthropic(model="claude-3-5-sonnet-20240620") # Load the tools from the Toolbox server async with ToolboxClient("http://127.0.0.1:5000") as client: tools = await client.aload_toolset() agent = create_react_agent(model, tools, checkpointer=MemorySaver()) config = {"configurable": {"thread_id": "thread-1"}} for query in queries: inputs = {"messages": [("user", prompt + query)]} response = agent.invoke(inputs, stream_mode="values", config=config) print(response["messages"][-1].content) asyncio.run(main())