from __future__ import annotations

import os
from dataclasses import dataclass
from pathlib import Path


@dataclass(slots=True)
class RuntimeConfig:
    """Runtime settings for the OpenAI-compatible vLLM endpoint."""

    base_url: str = "http://localhost:8000/v1"
    api_key: str = "abc-123"
    model: str = "iac-secfix"
    temperature: float = 0.0
    top_p: float = 1.0
    max_tokens: int = 2048
    work_dir: Path = Path("runs")

    @classmethod
    def from_env(cls) -> "RuntimeConfig":
        return cls(
            base_url=os.getenv("IAC_SECFIX_BASE_URL", cls.base_url),
            api_key=os.getenv("IAC_SECFIX_API_KEY", cls.api_key),
            model=os.getenv("IAC_SECFIX_MODEL", cls.model),
            temperature=float(os.getenv("IAC_SECFIX_TEMPERATURE", str(cls.temperature))),
            top_p=float(os.getenv("IAC_SECFIX_TOP_P", str(cls.top_p))),
            max_tokens=int(os.getenv("IAC_SECFIX_MAX_TOKENS", str(cls.max_tokens))),
            work_dir=Path(os.getenv("IAC_SECFIX_WORK_DIR", str(cls.work_dir))),
        )


def vllm_extra_body() -> dict[str, object]:
    """Small vLLM compatibility body.

    vLLM accepts these fields on OpenAI-compatible chat completions. Keeping this
    helper tiny avoids depending on notebook globals.
    """

    return {
        "guided_decoding_backend": "outlines",
    }