"""
Deep Agents contextual memory smoke test.

Uses OCI Generative AI through LangChain.
Does not modify the AgentVoice application.
"""

import asyncio
import os

from deepagents import create_deep_agent
from langchain_oci import ChatOCIGenAI
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver


async def main():
    compartment_id = os.environ.get("OCI_COMPARTMENT_ID")

    if not compartment_id:
        raise RuntimeError("OCI_COMPARTMENT_ID is required.")

    model = ChatOCIGenAI(
        model_id="openai.gpt-4o-mini",
        compartment_id=compartment_id,
        service_endpoint=(
            "https://inference.generativeai."
            "sa-vinhedo-1.oci.oraclecloud.com"
        ),
        auth_type="INSTANCE_PRINCIPAL",
        is_stream=True,
        model_kwargs={
            "temperature": 0.3,
            "max_tokens": 256,
        },
    )

    try:
        agent = create_deep_agent(
            model=model,
            tools=[],
            system_prompt=(
                "Você é o AgentVoice, um assistente por voz. "
                "Responda em português brasileiro, de forma "
                "natural, breve e contextual. "
                "Considere as mensagens anteriores da conversa. "
                "Quando o usuário corrigir uma pergunta, "
                "interprete a correção usando o histórico."
            ),
            checkpointer=MemorySaver(),
        )

        config = {
            "configurable": {
                "thread_id": "agentvoice-context-test",
            }
        }

        questions = [
            (
                "Estou planejando visitar o Japão. "
                "Guarde esse destino no contexto da conversa."
            ),
            (
                "Qual foi o país que mencionei anteriormente? "
                "Responda somente com o nome do país."
            ),
        ]

        for number, question in enumerate(questions, 1):
            print(f"\n--- Turn {number} ---")
            print("User:", question)

            result = await agent.ainvoke(
                {
                    "messages": [
                        HumanMessage(content=question)
                    ]
                },
                config=config,
            )

            messages = result.get("messages", [])

            response = next(
                (
                    message.content
                    for message in reversed(messages)
                    if message.type == "ai"
                    and isinstance(message.content, str)
                    and message.content.strip()
                ),
                None,
            )

            if not response:
                raise RuntimeError(
                    f"Turn {number} returned no AI response."
                )

            print("Agent:", response)

    finally:
        await model.aclose()


if __name__ == "__main__":
    asyncio.run(main())