147 lines
5.0 KiB
Python
147 lines
5.0 KiB
Python
"""
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FILE: app/services/llm_service.py
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DESCRIPTION: Asynchroner Client für Ollama. Verwaltet Prompts und Background-Last (Semaphore).
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VERSION: 2.8.0
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STATUS: Active
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DEPENDENCIES: httpx, yaml, asyncio, app.config
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EXTERNAL_CONFIG: config/prompts.yaml
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LAST_ANALYSIS: 2025-12-15
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"""
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import httpx
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import yaml
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import logging
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import os
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import asyncio
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from pathlib import Path
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from typing import Optional, Dict, Any, Literal
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logger = logging.getLogger(__name__)
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class Settings:
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OLLAMA_URL = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
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LLM_TIMEOUT = float(os.getenv("MINDNET_LLM_TIMEOUT", 300.0))
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LLM_MODEL = os.getenv("MINDNET_LLM_MODEL", "phi3:mini")
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PROMPTS_PATH = os.getenv("MINDNET_PROMPTS_PATH", "./config/prompts.yaml")
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# NEU: Konfigurierbares Limit für Hintergrund-Last
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# Default auf 2 (konservativ), kann in .env erhöht werden.
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BACKGROUND_LIMIT = int(os.getenv("MINDNET_LLM_BACKGROUND_LIMIT", "2"))
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def get_settings():
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return Settings()
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class LLMService:
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# GLOBALER SEMAPHOR (Lazy Initialization)
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# Wir initialisieren ihn erst, wenn wir die Settings kennen.
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_background_semaphore = None
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def __init__(self):
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self.settings = get_settings()
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self.prompts = self._load_prompts()
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# Initialisiere Semaphore einmalig auf Klassen-Ebene basierend auf Config
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if LLMService._background_semaphore is None:
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limit = self.settings.BACKGROUND_LIMIT
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logger.info(f"🚦 LLMService: Initializing Background Semaphore with limit: {limit}")
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LLMService._background_semaphore = asyncio.Semaphore(limit)
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self.timeout = httpx.Timeout(self.settings.LLM_TIMEOUT, connect=10.0)
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self.client = httpx.AsyncClient(
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base_url=self.settings.OLLAMA_URL,
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timeout=self.timeout
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)
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def _load_prompts(self) -> dict:
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path = Path(self.settings.PROMPTS_PATH)
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if not path.exists(): return {}
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try:
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with open(path, "r", encoding="utf-8") as f: return yaml.safe_load(f)
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except Exception as e:
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logger.error(f"Failed to load prompts: {e}")
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return {}
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async def generate_raw_response(
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self,
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prompt: str,
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system: str = None,
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force_json: bool = False,
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max_retries: int = 0,
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base_delay: float = 2.0,
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priority: Literal["realtime", "background"] = "realtime"
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) -> str:
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"""
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Führt einen LLM Call aus.
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priority="realtime": Chat (Sofort, keine Bremse).
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priority="background": Import/Analyse (Gedrosselt durch Semaphore).
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"""
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use_semaphore = (priority == "background")
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if use_semaphore and LLMService._background_semaphore:
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async with LLMService._background_semaphore:
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return await self._execute_request(prompt, system, force_json, max_retries, base_delay)
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else:
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# Realtime oder Fallback (falls Semaphore Init fehlschlug)
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return await self._execute_request(prompt, system, force_json, max_retries, base_delay)
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async def _execute_request(self, prompt, system, force_json, max_retries, base_delay):
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payload: Dict[str, Any] = {
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"model": self.settings.LLM_MODEL,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": 0.1 if force_json else 0.7,
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"num_ctx": 8192
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}
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}
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if force_json:
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payload["format"] = "json"
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if system:
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payload["system"] = system
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attempt = 0
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while True:
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try:
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response = await self.client.post("/api/generate", json=payload)
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if response.status_code == 200:
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data = response.json()
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return data.get("response", "").strip()
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else:
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response.raise_for_status()
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except Exception as e:
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attempt += 1
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if attempt > max_retries:
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logger.error(f"LLM Final Error (Versuch {attempt}): {e}")
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raise e
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wait_time = base_delay * (2 ** (attempt - 1))
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logger.warning(f"⚠️ LLM Retry ({attempt}/{max_retries}) in {wait_time}s: {e}")
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await asyncio.sleep(wait_time)
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async def generate_rag_response(self, query: str, context_str: str) -> str:
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"""
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Chat-Wrapper: Immer Realtime.
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"""
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system_prompt = self.prompts.get("system_prompt", "")
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rag_template = self.prompts.get("rag_template", "{context_str}\n\n{query}")
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final_prompt = rag_template.format(context_str=context_str, query=query)
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return await self.generate_raw_response(
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final_prompt,
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system=system_prompt,
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max_retries=0,
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force_json=False,
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priority="realtime"
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)
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async def close(self):
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if self.client:
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await self.client.aclose() |