"""Use-case oriented LLM gateway.

This is a small compatibility layer over the existing provider and JSON helper
functions. It gives new code a stable place to request model capabilities while
older agents continue to use LangChain directly during the transition.
"""

from __future__ import annotations

from dataclasses import dataclass
from typing import Any, Callable, TypeVar

from pydantic import BaseModel

from app.core.dependencies import get_llm_provider, get_llm_provider_fast
from app.core.llm_call import allm_json_call, llm_json_call
from app.core.llm_telemetry import langchain_invoke_span

T = TypeVar("T", bound=BaseModel)


@dataclass(frozen=True)
class LlmUseCase:
    name: str
    fast: bool = False
    max_tokens: int | None = None
    temperature: float | None = None
    model: str | None = None


class LlmGateway:
    """Facade for text and JSON LLM calls."""

    def provider_for(self, use_case: LlmUseCase | None = None):
        if use_case and use_case.fast:
            return get_llm_provider_fast()
        return get_llm_provider()

    def langchain_llm_for(self, use_case: LlmUseCase | None = None) -> Any | None:
        provider = self.provider_for(use_case)
        return getattr(provider, "langchain_llm", None)

    async def chat_text(
        self,
        messages: list[dict],
        *,
        use_case: LlmUseCase | None = None,
        temperature: float | None = None,
        model: str | None = None,
        max_tokens: int | None = None,
    ) -> str:
        provider = self.provider_for(use_case)
        resolved_temperature = (
            temperature
            if temperature is not None
            else (
                use_case.temperature
                if use_case and use_case.temperature is not None
                else 0.7
            )
        )
        resolved_model = model if model is not None else (use_case.model if use_case else None)
        agent_name = use_case.name if use_case else "llm_gateway.chat"
        kwargs = dict(
            messages=messages,
            temperature=resolved_temperature,
            model=resolved_model,
            max_tokens=(
                max_tokens
                if max_tokens is not None
                else (use_case.max_tokens if use_case else None)
            ),
        )
        # DeepSeekProvider.complete 已包 langchain_invoke_span，避免双层 span
        from app.adapters.llm.deepseek import DeepSeekLLMProvider

        if isinstance(provider, DeepSeekLLMProvider):
            return await provider.complete(**kwargs)

        provider_label = type(provider).__name__.replace("Provider", "").lower() or "unknown"
        with langchain_invoke_span(
            agent=agent_name,
            provider=provider_label,
            model=resolved_model or "unknown",
            call_type="chat",
        ):
            return await provider.complete(**kwargs)

    async def json_object(
        self,
        prompt: str,
        schema: type[T],
        *,
        use_case: LlmUseCase,
        fallback_factory: Callable[[], T] | None = None,
    ) -> T:
        return await allm_json_call(
            self.langchain_llm_for(use_case),
            prompt,
            schema,
            max_tokens=use_case.max_tokens or 1024,
            agent=use_case.name,
            fallback_factory=fallback_factory,
        )

    def sync_json_object(
        self,
        prompt: str,
        schema: type[T],
        *,
        use_case: LlmUseCase,
        fallback_factory: Callable[[], T] | None = None,
    ) -> T:
        return llm_json_call(
            self.langchain_llm_for(use_case),
            prompt,
            schema,
            max_tokens=use_case.max_tokens or 1024,
            agent=use_case.name,
            fallback_factory=fallback_factory,
        )


__all__ = ["LlmGateway", "LlmUseCase"]