from langchain.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.language_models.fake_chat_models import FakeListChatModel


def print_history(label: str, messages: list) -> None:
    print(label)
    for position, message in enumerate(messages, start=1):
        if isinstance(message, dict):
            role = message["role"]
            content = message["content"]
        else:
            role = message.type
            content = message.content
        print(f"{position}. {role}: {content}")


object_messages = [
    SystemMessage("You answer support questions with concise status updates."),
    HumanMessage("Check order A1001 and answer with the shipping status."),
    AIMessage(
        content="I will look up the order before answering.",
        tool_calls=[
            {
                "name": "lookup_order",
                "args": {"order_id": "A1001"},
                "id": "call_lookup_order",
            }
        ],
    ),
    ToolMessage(
        content='{"order_id": "A1001", "status": "shipped"}',
        tool_call_id="call_lookup_order",
    ),
    HumanMessage("Reply to the customer in one sentence."),
]

dict_messages = [
    {"role": "system", "content": "You classify support requests."},
    {"role": "user", "content": "The customer says they cannot reset a password."},
    {"role": "assistant", "content": "Ask for the account email before changing credentials."},
    {"role": "user", "content": "Account email confirmed."},
]

model = FakeListChatModel(
    responses=[
        "Order A1001 shipped and is ready for delivery tracking.",
        "The account email is confirmed for support replies.",
    ]
)

print_history("message objects", object_messages)
print()
print_history("dictionary messages", dict_messages)

object_response = model.invoke(object_messages)
dict_response = model.invoke(dict_messages)

print()
print(f"object response: {type(object_response).__name__}: {object_response.content}")
print(f"dict response: {type(dict_response).__name__}: {dict_response.content}")