feat: solution for 'Практическое задание: AI-агент на LangChain'

main.py

@@ -0,0 +1,70 @@
+import json
+from langchain_openai import ChatOpenAI
+from pydantic import SecretStr
+from langchain.tools import tool
+from langchain.agents import create_agent
+
+# Configure the local LLM
+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,
+)
+
+@tool
+def get_price(product: str, city: str) -> str:
+    """Get realistic price for a product in a city. Returns a markdown table."""
+    # Create a sub-agent that generates a price table
+    sub_agent = create_agent(
+        model=llm,
+        tools=[],
+        system_prompt=f"You are a price estimator. Provide a realistic price for {product} in {city}. Return a markdown table with columns: Product, Price (rub.), Store.",
+    )
+    # Invoke the sub-agent
+    result = sub_agent.invoke(
+        {
+            "messages": [
+                {"role": "user", "content": f"Provide price for {product} in {city}."}
+            ]
+        }
+    )
+    # Extract the assistant message content
+    messages = result.get("messages", [])
+    for msg in messages:
+        if msg.get("role") == "assistant" and msg.get("content"):
+            return msg["content"]
+    return "No price data available."
+
+def main():
+    # Main agent that uses the get_price tool
+    agent = create_agent(
+        model=llm,
+        tools=[get_price],
+        system_prompt="You are a shopping list planner. Use the get_price tool to find prices for items.",
+    )
+    # Sample user query
+    user_query = "Помоги составить список покупок: молоко, хлеб, яблоки. Я нахожусь в Казани."
+    # Invoke the agent
+    result = agent.invoke(
+        {
+            "messages": [
+                {"role": "human", "content": user_query}
+            ]
+        }
+    )
+    # Print all messages, including tool calls and final answer
+    for msg in result.get("messages", []):
+        role = msg.get("role")
+        content = msg.get("content")
+        tool_calls = msg.get("tool_calls")
+        if content:
+            print(f"{role}: {content}")
+        elif tool_calls:
+            for call in tool_calls:
+                print(f"{role} calls {call.get('name')} with args {call.get('arguments')}")
+        else:
+            print(f"{role}: (no content)")
+
+if __name__ == "__main__":
+    main()