"""
Shopping‑list AI assistant.

The script demonstrates a hierarchical LangChain agent that can estimate prices for
products in a city.  The top‑level agent uses the ``get_price`` tool, which itself
creates a short‑lived sub‑agent to generate a markdown table row with the price.

Three example calls are executed when the module is run as a script:
1. Milk, bread, apples – Kazan
2. Eggs, cheese – Moscow
3. Coffee – Saint‑Petersburg

Each example prints the full message chain (tool calls and final answer).
"""

from __future__ import annotations

import os
from typing import List

# Local LLM configuration – environment variables allow CI to override.
LOCAL_LLM_MODEL = os.getenv("LOCAL_LLM_MODEL", "gpt-3.5-turbo")
LOCAL_LLM_BASE_URL = os.getenv("LOCAL_LLM_BASE_URL", "http://localhost:1234/v1")
LOCAL_LLM_API_KEY = os.getenv("LOCAL_LLM_API_KEY", "fake")  # LM Studio dummy key

from langchain_openai import ChatOpenAI
from langchain.tools import tool
from langchain.agents import create_agent
from langchain_core.messages import HumanMessage, SystemMessage

# Import the price‑tool from the helper module.
from agent import get_price

# Base LLM used by all agents.
_base_llm = ChatOpenAI(
    model=LOCAL_LLM_MODEL,
    base_url=LOCAL_LLM_BASE_URL,
    api_key=LOCAL_LLM_API_KEY,
    temperature=0.2,
)

# Main agent – it only has the ``get_price`` tool.
shopping_agent = create_agent(
    llm=_base_llm,
    tools=[get_price],
    system_prompt="You are a helpful assistant for planning shopping lists.",
)

def run_example(products: List[str], city: str) -> None:
    """Run the agent on *products* in *city* and print the full chain."""
    # Build the user message.
    product_list = ", ".join(products)
    prompt = f"Help me plan a shopping list: {product_list}. I am in {city}."

    # Invoke the agent.
    result = shopping_agent.invoke({"messages": [HumanMessage(content=prompt)]})

    # Pretty‑print the chain of messages.
    print("\n=== Example: " + prompt + " ===")
    for msg in result["messages"]:
        if hasattr(msg, "content") and msg.content:
            print(f"Assistant: {msg.content}")
        elif hasattr(msg, "tool_calls") and msg.tool_calls:
            call = msg.tool_calls[0]
            print(f"Tool call – {call['name']}({call['args']})")
    print("\n--- End of example ---\n")

if __name__ == "__main__":
    # Three distinct examples.
    run_example(["milk", "bread", "apples"], "Kazan")
    run_example(["eggs", "cheese"], "Moscow")
    run_example(["coffee"], "Saint‑Petersburg")

# End of script.