chore: import upstream snapshot with attribution

docs/en/documentation/getting-started/quickstart/python/langchain/quickstart.py

@@ -0,0 +1,47 @@
+import asyncio
+
+from langgraph.prebuilt import create_react_agent
+
+# TODO(developer): replace this with another import if needed
+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 = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
+    # 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)]}
+            print(f"\n[INPUT] User: {query}")
+            response = agent.invoke(inputs, stream_mode="values", config=config)
+            print(f"[OUTPUT] AI: {response['messages'][-1].content}")
+
+asyncio.run(main())