add main.py

main.py

@@ -0,0 +1,60 @@
+"""
+Simple hierarchical LangChain agent for shopping list.
+"""
+
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from langchain.tools import tool
+from langchain.agents import create_agent, AgentExecutor
+import json
+
+# LLM configuration – replace <model_name> with your LM Studio model name
+llm = ChatOpenAI(
+    model="<model_name>",
+    base_url="http://localhost:1234/v1",
+    api_key=SecretStr("fake"),
+    temperature=0.7,
+)
+
+# Sub‑agent that generates a price table for one product in a city
+@tool(name="get_price", description="Return realistic price for a product in a city as a markdown table.")
+def get_price(product: str, city: str) -> str:
+    """
+    Generates a markdown table with columns: Product | Price (руб.) | Store.
+    The sub‑agent uses the same LLM to produce realistic values.
+    """
+    # Create a tiny agent that only returns the price table
+    prompt = (
+        f"You are a local market assistant. Provide a markdown table with columns:\n"
+        f"| Продукт | Цена (руб.) | Магазин |\n"
+        f"For product '{product}' in city '{city}'. Use realistic Russian prices and store names.")
+    sub_agent = create_agent(
+        model=llm,
+        tools=[],
+        system_prompt=prompt,
+    )
+    result = sub_agent.invoke({"messages": [{"role": "human", "content": "Generate table"}]})
+    return result["messages"][-1]["content"]
+
+# Main agent with get_price tool
+main_agent = create_agent(
+    model=llm,
+    tools=[get_price],
+    system_prompt="Ты помощник по планированию покупок.",
+)
+
+if __name__ == "__main__":
+    user_query = (
+        "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+    )
+    response = main_agent.invoke({"messages": [{"role": "human", "content": user_query}]})
+    # Print all messages
+    for msg in response["messages"]:
+        if msg.get("content"):
+            print(msg["content"])
+        elif msg.get("tool_calls"):
+            call = msg["tool_calls"][0]
+            print(f"{call['name']}({json.dumps(call['args'])})")
+    # Final answer
+    final = response["messages"][-1]["content"]
+    print("\n---\n", final)