"""
app/services/llm_service.py — LLM Client
Version: 2.8.0 (Configurable Concurrency Limit)
"""

import httpx
import yaml
import logging
import os
import asyncio
from pathlib import Path
from typing import Optional, Dict, Any, Literal

logger = logging.getLogger(__name__)

class Settings:
    OLLAMA_URL = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
    LLM_TIMEOUT = float(os.getenv("MINDNET_LLM_TIMEOUT", 300.0))
    LLM_MODEL = os.getenv("MINDNET_LLM_MODEL", "phi3:mini")
    PROMPTS_PATH = os.getenv("MINDNET_PROMPTS_PATH", "./config/prompts.yaml")
    
    # NEU: Konfigurierbares Limit für Hintergrund-Last
    # Default auf 2 (konservativ), kann in .env erhöht werden.
    BACKGROUND_LIMIT = int(os.getenv("MINDNET_LLM_BACKGROUND_LIMIT", "2"))
    
def get_settings():
    return Settings()

class LLMService:
    # GLOBALER SEMAPHOR (Lazy Initialization)
    # Wir initialisieren ihn erst, wenn wir die Settings kennen.
    _background_semaphore = None

    def __init__(self):
        self.settings = get_settings()
        self.prompts = self._load_prompts()
        
        # Initialisiere Semaphore einmalig auf Klassen-Ebene basierend auf Config
        if LLMService._background_semaphore is None:
            limit = self.settings.BACKGROUND_LIMIT
            logger.info(f"🚦 LLMService: Initializing Background Semaphore with limit: {limit}")
            LLMService._background_semaphore = asyncio.Semaphore(limit)
        
        self.timeout = httpx.Timeout(self.settings.LLM_TIMEOUT, connect=10.0)
        
        self.client = httpx.AsyncClient(
            base_url=self.settings.OLLAMA_URL, 
            timeout=self.timeout
        )

    def _load_prompts(self) -> dict:
        path = Path(self.settings.PROMPTS_PATH)
        if not path.exists(): return {}
        try:
            with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f)
        except Exception as e:
            logger.error(f"Failed to load prompts: {e}")
            return {}

    async def generate_raw_response(
        self, 
        prompt: str, 
        system: str = None, 
        force_json: bool = False,
        max_retries: int = 0,     
        base_delay: float = 2.0,
        priority: Literal["realtime", "background"] = "realtime"
    ) -> str:
        """
        Führt einen LLM Call aus.
        priority="realtime": Chat (Sofort, keine Bremse).
        priority="background": Import/Analyse (Gedrosselt durch Semaphore).
        """
        
        use_semaphore = (priority == "background")
        
        if use_semaphore and LLMService._background_semaphore:
            async with LLMService._background_semaphore:
                return await self._execute_request(prompt, system, force_json, max_retries, base_delay)
        else:
            # Realtime oder Fallback (falls Semaphore Init fehlschlug)
            return await self._execute_request(prompt, system, force_json, max_retries, base_delay)

    async def _execute_request(self, prompt, system, force_json, max_retries, base_delay):
        payload: Dict[str, Any] = {
            "model": self.settings.LLM_MODEL,
            "prompt": prompt,
            "stream": False,
            "options": {
                "temperature": 0.1 if force_json else 0.7, 
                "num_ctx": 8192     
            }
        }

        if force_json:
             payload["format"] = "json"

        if system:
            payload["system"] = system

        attempt = 0
        
        while True:
            try:
                response = await self.client.post("/api/generate", json=payload)
                
                if response.status_code == 200:
                    data = response.json()
                    return data.get("response", "").strip()
                else:
                    response.raise_for_status()

            except Exception as e:
                attempt += 1
                if attempt > max_retries:
                    logger.error(f"LLM Final Error (Versuch {attempt}): {e}")
                    raise e 
                
                wait_time = base_delay * (2 ** (attempt - 1))
                logger.warning(f"⚠️ LLM Retry ({attempt}/{max_retries}) in {wait_time}s: {e}")
                await asyncio.sleep(wait_time)

    async def generate_rag_response(self, query: str, context_str: str) -> str:
        """
        Chat-Wrapper: Immer Realtime.
        """
        system_prompt = self.prompts.get("system_prompt", "")
        rag_template = self.prompts.get("rag_template", "{context_str}\n\n{query}")
        
        final_prompt = rag_template.format(context_str=context_str, query=query)
        
        return await self.generate_raw_response(
            final_prompt, 
            system=system_prompt, 
            max_retries=0,
            force_json=False,
            priority="realtime"
        )

    async def close(self):
        if self.client:
            await self.client.aclose()