Add main.py

main.py

@@ -0,0 +1,58 @@
+import os
+from langchain_openai import ChatOpenAI
+from langchain.tools import tool
+from langchain.agents import create_agent
+from pydantic import SecretStr
+
+# 1. Connect to local LLM
+llm = ChatOpenAI(
+    model="<название модели в LM Studio>",
+    base_url="http://localhost:1234/v1",
+    api_key=SecretStr("fake"),
+    temperature=0.7,
+)
+
+# 2. Sub-agent tool to get price
+@tool
+def get_price(product: str, city: str) -> str:
+    """Return a realistic price table for a product in a city."""
+    # Sub-agent that generates a price table
+    sub_llm = ChatOpenAI(
+        model="<название модели в LM Studio>",
+        base_url="http://localhost:1234/v1",
+        api_key=SecretStr("fake"),
+        temperature=0.7,
+    )
+    sub_agent = create_agent(
+        model=sub_llm,
+        tools=[],
+        system_prompt=f"You are a price estimator for {city}. Provide a table with columns: Продукт, Цена (руб.), Магазин. Use realistic Russian prices.",
+    )
+    prompt = f"Generate a price table for product '{product}' in city '{city}'."
+    result = sub_agent.invoke({"messages": [{"role": "human", "content": prompt}]} )
+    # Extract the last message content
+    return result["messages"][-1]["content"]
+
+# 3. Main agent
+main_agent = create_agent(
+    model=llm,
+    tools=[get_price],
+    system_prompt="Ты помощник по планированию покупок.",
+)
+
+# 4. Query
+question = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+response = main_agent.invoke({"messages": [{"role": "human", "content": question}]})
+
+# 5. Print all messages
+for msg in response["messages"]:
+    if "content" in msg:
+        print(msg["content"])
+    elif "tool_calls" in msg:
+        for call in msg["tool_calls"]:
+            print(f"{call['name']}({call['args']})")
+    else:
+        print(msg)
+
+if __name__ == "__main__":
+    pass