"""LLM factory — centralised model instantiation via LiteLLM.

Identical to services/chat/app/llm.py. Uses shared.config.settings.
"""

from __future__ import annotations

import os
import warnings

from openai import AsyncOpenAI
import litellm

from langchain_openai import ChatOpenAI
from langchain_litellm import ChatLiteLLM

from shared.config import settings

litellm.drop_params = True

warnings.filterwarnings(
    "ignore",
    message=r"PydanticSerializationUnexpectedValue\(Expected `ResponseAPIUsage`",
    category=UserWarning,
)


def _api_key_for_model(model: str) -> str | None:
    if model.startswith("anthropic/"):
        return settings.ANTHROPIC_API_KEY or None
    if model.startswith("gemini/") or model.startswith("google/"):
        return settings.GOOGLE_API_KEY or None
    if model.startswith("cerebras/"):
        return settings.CEREBRAS_API_KEY or None
    if model.startswith("github_copilot/"):
        return None
    return settings.OPENAI_API_KEY or None


def get_llm(
    *,
    model: str | None = None,
    temperature: float = 0,
    callbacks: list | None = None,
) -> ChatOpenAI | ChatLiteLLM:
    model = model or settings.LLM_MODEL

    if settings.GITHUB_COPILOT_TOKEN_DIR:
        os.environ.setdefault("GITHUB_COPILOT_TOKEN_DIR", settings.GITHUB_COPILOT_TOKEN_DIR)

    if "/" in model:
        return ChatLiteLLM(model=model, temperature=temperature, callbacks=callbacks)

    return ChatOpenAI(
        model=model,
        temperature=temperature,
        api_key=_api_key_for_model(model),
        callbacks=callbacks,
    )


def get_router_llm(
    *,
    temperature: float = 0,
) -> ChatOpenAI | ChatLiteLLM:
    return get_llm(model=settings.LLM_ROUTER_MODEL, temperature=temperature)


async def embed(text: str) -> list[float]:
    model = settings.LLM_EMBED_MODEL

    if model.startswith("github_copilot/") or "/" in model:
        response = await litellm.aembedding(model=model, input=[text])
        return response.data[0]["embedding"]

    client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY)
    response = await client.embeddings.create(model=model, input=text)
    return response.data[0].embedding