"""LangChain chat model pointed at the local oMLX server (Apple MLX, OpenAI-compatible).

oMLX parses the model's native tool-call format into standard OpenAI `tool_calls`, so we
rely on that rather than doing our own parsing. `resolve_model_id` matches the active
profile against whatever id oMLX serves, so the MODEL_PROFILE toggle survives id changes.
oMLX enforces an API key (see settings.llm_api_key, auto-discovered from ~/.omlx).
"""

from __future__ import annotations

import httpx
from langchain_openai import ChatOpenAI

from config.settings import get_settings


def _auth_header() -> dict[str, str]:
    return {"Authorization": f"Bearer {get_settings().llm_api_key}"}


def list_loaded_models() -> list[str]:
    """Model ids currently served by oMLX (GET /v1/models). Empty if unreachable."""
    s = get_settings()
    try:
        r = httpx.get(f"{s.llm_base_url}/models", headers=_auth_header(), timeout=10)
        r.raise_for_status()
        return [m["id"] for m in r.json().get("data", [])]
    except Exception:
        return []


def resolve_model_id() -> str:
    """Pick the served model id for the active profile.

    Priority: exact match of the configured id → keyword match from the profile →
    first served model → the configured id as-is (lets errors surface at call time).
    """
    s = get_settings()
    served = list_loaded_models()
    target = s.effective_model_id
    if target in served:
        return target
    for mid in served:
        low = mid.lower()
        if any(kw in low for kw in s.active_profile.match_keywords):
            return mid
    return served[0] if served else target


def build_llm(
    tools: list | None = None,
    temperature: float | None = None,
    json_mode: bool = False,
    max_tokens: int | None = None,
) -> ChatOpenAI:
    """Build a ChatOpenAI bound to oMLX.

    json_mode forces a JSON object response (used for the finalize step so structured picks
    don't drift into prose). max_tokens overrides the configured default when a step needs
    more room. Don't combine json_mode with tools.
    """
    s = get_settings()
    model_kwargs: dict = {}
    if json_mode:
        model_kwargs["response_format"] = {"type": "json_object"}
    llm = ChatOpenAI(
        base_url=s.llm_base_url,
        api_key=s.llm_api_key,
        model=resolve_model_id(),
        temperature=s.llm_temperature if temperature is None else temperature,
        max_tokens=max_tokens or s.llm_max_tokens,
        timeout=180,
        model_kwargs=model_kwargs,
    )
    if tools:
        llm = llm.bind_tools(tools)
    return llm