Solution ready: update agent.py

agent.py

@@ -1,45 +1,117 @@
 """
-Simple LangChain agent demonstrating Human‑in‑the‑Loop middleware.
+Human‑in‑the‑Loop agent with tool calling.
 
-The chain:
-1. User prompt is passed to a PromptTemplate.
-2. The template is processed by an LLM (OpenAI).
-3. The output passes through the HIL middleware which prints the assistant’s answer and asks the user to confirm or modify it before returning.
+This example demonstrates how to use LangChain's `HumanInTheLoopMiddleware` to pause the agent when a tool is about to be called, ask the user for approval, and then resume execution.
 
-Run with:
+The agent uses a simple `get_weather` tool that returns a hard‑coded weather string. In a real project you would replace it with an API call.
+
+Run:
     python agent.py
-Make sure you have OPENAI_API_KEY set in your environment.
+Make sure you have `OPENAI_API_KEY` set in your environment.
 """
 
 import os
-from langchain.prompts import PromptTemplate
-from langchain.schema import RunnableSequence
-from langchain_openai import ChatOpenAI
-from langchain.middleware.hil import HumanInTheLoopMiddleware
+from typing import List, Dict
 
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent
+from langchain.agents.middleware import HumanInTheLoopMiddleware
+from langchain.tools import BaseTool
+from langgraph.checkpoint.memory import InMemorySaver
+from langgraph.types import Command
+
+# --- Tool ---------------------------------------------------------------
+
+class GetWeatherTool(BaseTool):
+    name: str = "get_weather"
+    description: str = "Get the weather for a city on a given date."
+    def _run(self, city: str, date: str) -> str:
+        return f"The weather in {city} on {date} is sunny with a high of 25°C."
+
+# --- LLM ---------------------------------------------------------------
 api_key = os.getenv("OPENAI_API_KEY")
 if not api_key:
     raise RuntimeError("OPENAI_API_KEY environment variable is required.")
 
-llm = OpenAI(api_key=api_key, temperature=0.7)
+llm = ChatOpenAI(api_key=api_key, temperature=0.7)
 
-prompt_template = PromptTemplate(
-    input_variables=["question"],
-    template="You are a helpful assistant. Answer the following question clearly and concisely: {question}"
+# --- Agent --------------------------------------------------------------
+memory = InMemorySaver()
+
+agent = create_agent(
+    model=llm,
+    tools=[GetWeatherTool()],
+    system_prompt="You are a helpful assistant.",
+    middleware=[
+        HumanInTheLoopMiddleware(
+            interrupt_on={
+                "get_weather": True,  # allow approve, edit, reject
+            },
+            description_prefix="Подтвердите вызов инструмента",
+        ),
+    ],
+    checkpointer=memory,
 )
 
-chain = RunnableSequence([prompt_template, llm])
-hil_chain = HumanInTheLoopMiddleware(chain)
-
+# --- Interactive loop -----------------------------------------------
 if __name__ == "__main__":
+    config = {"configurable": {"thread_id": "session-1"}}
     while True:
+        try:
             try:
                 user_input = input("\nUser: ")
+            except EOFError:
+                print("\nNo input provided. Exiting.")
+                break
             if user_input.lower() in {"exit", "quit", "q"}:
                 print("Goodbye!")
                 break
-            result = hil_chain.invoke({"question": user_input})
-            print(f"\nAssistant (confirmed): {result}\n")
+
+            # First invocation
+            result = agent.invoke({"messages": [{"role": "human", "content": user_input}]}, config=config)
+
+            # Process interrupts until finished
+            while "__interrupt__" in result:
+                interrupt = result["__interrupt__"][0].value
+                action_requests = interrupt.get("action_requests", [])
+                review_configs = interrupt.get("review_configs", [])
+
+                decisions: List[Dict] = []
+                print("\n--- Подтверждение ---")
+                for idx, action in enumerate(action_requests):
+                    name = action.get("name")
+                    args = action.get("args", {})
+                    description = action.get("description", "")
+                    print(f"Инструмент: {name}")
+                    print(f"Аргументы: {args}")
+                    if description:
+                        print(f"Описание: {description}")
+                    # Determine allowed decisions
+                    allowed = ["approve", "reject"]
+                    # Check if edit is allowed
+                    for cfg in review_configs:
+                        if cfg.get("name") == name:
+                            allowed = cfg.get("allowed_decisions", allowed)
+                            break
+                    # Prompt user
+                    while True:
+                        choice = input("a = approve, r = reject: ").strip().lower()
+                        if choice == "a" and "approve" in allowed:
+                            decisions.append({"type": "approve"})
+                            break
+                        if choice == "r" and "reject" in allowed:
+                            msg = input("Сообщение для агента (причина отказа): ")
+                            decisions.append({"type": "reject", "message": msg})
+                            break
+                        print("Неверный ввод. Попробуйте снова.")
+
+                # Resume
+                result = agent.invoke(Command(resume={"decisions": decisions}), config=config)  # Command(resume={"decisions":[…]})
+
+            # Finished
+            final_message = result.get("messages", [])[-1].get("content", "")
+            print(f"\nAssistant: {final_message}\n")
+
         except KeyboardInterrupt:
             print("\nInterrupted. Exiting.")
             break