add main.py

main.py

@@ -0,0 +1,57 @@
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from langchain.tools import tool
+from langchain.agents import create_agent
+import json
+
+# 1. LLM connection
+llm = ChatOpenAI(
+    model="gpt-4o-mini",  # replace with your LM Studio model name
+    base_url="http://localhost:1234/v1",
+    api_key=SecretStr("fake"),
+    temperature=0.7,
+)
+
+# 2. Sub‑agent that generates a price table
+@tool
+def get_price(product: str, city: str) -> str:
+    """Return a realistic price for the product in the given city.
+    The function internally creates a small agent that asks the LLM to produce a markdown table.
+    """
+    # Sub‑agent prompt – keep it short and deterministic
+    sub_prompt = (
+        f"You are a market analyst. Provide a realistic price for {product} in {city}. "
+        "Return a markdown table with columns: Продукт, Цена (руб.), Магазин."
+    )
+    # Create the sub‑agent
+    sub_agent = create_agent(
+        model=llm,
+        tools=[],  # no external tools needed for this simple query
+        system_prompt=sub_prompt,
+    )
+    # Ask the sub‑agent and get its response
+    result = sub_agent.invoke({"messages": [{"role": "human", "content": "Generate table"}]})
+    return result["messages"][-1]["content"]
+
+# 3. Main agent with get_price tool
+main_agent = create_agent(
+    model=llm,
+    tools=[get_price],
+    system_prompt="Ты помощник по планированию покупок.",
+)
+
+# 4. Run the main agent on a sample query
+query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+response = main_agent.invoke({"messages": [{"role": "human", "content": query}]})
+# Pretty‑print all messages (including tool calls)
+for msg in response["messages"]:
+    if msg.get("content"):
+        print(msg["content"])
+    elif msg.get("tool_calls"):
+        for call in msg["tool_calls"]:
+            name = call["name"]
+            args = json.dumps(call["args"], ensure_ascii=False)
+            print(f"{name}({args})")
+
+# Final answer (last message content)
+print("\n---\nAnswer:\n", response["messages"][-1]["content"])