mindnet/app/services/llm_service.py

"""
app/services/llm_service.py — LLM Client (Ollama)
Version: 0.5.2 (Fix: Removed strict limits, increased Context)
"""

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

logger = logging.getLogger(__name__)

class Settings:
    OLLAMA_URL = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
    # Timeout für die Generierung (lang)
    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")
    
def get_settings():
    return Settings()

class LLMService:
    def __init__(self):
        self.settings = get_settings()
        self.prompts = self._load_prompts()
        
        # FIX 1: Keine künstlichen Limits mehr. httpx defaults (100) sind besser.
        # Wir wollen nicht, dass der Chat wartet, nur weil im Hintergrund Embeddings laufen.
        
        # Timeout-Konfiguration:
        # connect=10.0: Wenn Ollama nicht da ist, failen wir schnell.
        # read=LLM_TIMEOUT: Wenn Ollama denkt, geben wir ihm Zeit.
        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   
    ) -> str:
        """
        Führt einen LLM Call aus. 
        """
        payload: Dict[str, Any] = {
            "model": self.settings.LLM_MODEL,
            "prompt": prompt,
            "stream": False,
            "options": {
                "temperature": 0.1 if force_json else 0.7, 
                # FIX 2: Kontext auf 8192 erhöht. 
                # Wichtig für komplexe Schemas und JSON-Stabilität.
                "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}")
                    # Wir werfen den Fehler weiter, damit der Router nicht "Interner Fehler" als Typ interpretiert
                    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:
        """
        WICHTIG FÜR CHAT: 
        Kein JSON, keine Retries (User-Latency).
        """
        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
        )

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