312 lines
12 KiB
Python
312 lines
12 KiB
Python
"""
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FILE: app/services/llm_service.py
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DESCRIPTION: Hybrid-Client für Ollama, Google GenAI (Gemini) und OpenRouter.
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Verwaltet provider-spezifische Prompts und Background-Last.
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WP-20: Optimiertes Fallback-Management zum Schutz von Cloud-Quoten.
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WP-20 Fix: Bulletproof Prompt-Auflösung für format() Aufrufe.
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WP-22/JSON: Optionales JSON-Schema + strict (für OpenRouter structured outputs).
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FIX: Intelligente Rate-Limit Erkennung (429 Handling), v1-API Sync & Timeouts.
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VERSION: 3.3.6
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STATUS: Active
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DEPENDENCIES: httpx, yaml, logging, asyncio, json, google-genai, openai, app.config
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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 asyncio
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import json
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from google import genai
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from google.genai import types
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from openai import AsyncOpenAI # Für OpenRouter (OpenAI-kompatibel)
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from pathlib import Path
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from typing import Optional, Dict, Any, Literal
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from app.config import get_settings
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logger = logging.getLogger(__name__)
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class LLMService:
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# GLOBALER SEMAPHOR für Hintergrund-Last Steuerung (WP-06)
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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
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if LLMService._background_semaphore is None:
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limit = getattr(self.settings, "BACKGROUND_LIMIT", 2)
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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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# 1. Lokaler Ollama Client
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self.ollama_client = httpx.AsyncClient(
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base_url=self.settings.OLLAMA_URL,
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timeout=httpx.Timeout(self.settings.LLM_TIMEOUT)
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)
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# 2. Google GenAI Client (Modern SDK)
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self.google_client = None
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if self.settings.GOOGLE_API_KEY:
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# FIX: Wir erzwingen api_version 'v1' für höhere Stabilität bei 2.5er Modellen.
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self.google_client = genai.Client(
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api_key=self.settings.GOOGLE_API_KEY,
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http_options={'api_version': 'v1'}
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)
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logger.info("✨ LLMService: Google GenAI (Gemini) active.")
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# 3. OpenRouter Client
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self.openrouter_client = None
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if self.settings.OPENROUTER_API_KEY:
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self.openrouter_client = AsyncOpenAI(
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base_url="https://openrouter.ai/api/v1",
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api_key=self.settings.OPENROUTER_API_KEY,
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# Strikter Timeout für OpenRouter Free-Tier zur Vermeidung von Hangs.
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timeout=45.0
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)
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logger.info("🛰️ LLMService: OpenRouter Integration active.")
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def _load_prompts(self) -> dict:
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"""Lädt die Prompt-Konfiguration aus der YAML-Datei."""
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path = Path(self.settings.PROMPTS_PATH)
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if not path.exists():
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logger.error(f"❌ Prompts file not found at {path}")
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return {}
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try:
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with open(path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f) or {}
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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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def get_prompt(self, key: str, provider: str = None) -> str:
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"""
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Hole provider-spezifisches Template mit intelligenter Text-Kaskade.
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HINWEIS: Dies ist nur ein Text-Lookup und verbraucht kein API-Kontingent.
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Kaskade: Gewählter Provider -> Gemini (Cloud-Stil) -> Ollama (Basis-Stil).
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WP-20 Fix: Garantiert die Rückgabe eines Strings, um AttributeError zu vermeiden.
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"""
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active_provider = provider or self.settings.MINDNET_LLM_PROVIDER
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data = self.prompts.get(key, "")
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if isinstance(data, dict):
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# Wir versuchen erst den Provider, dann Gemini, dann Ollama
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val = data.get(active_provider, data.get("gemini", data.get("ollama", "")))
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# Falls val durch YAML-Fehler immer noch ein Dict ist, extrahiere ersten String
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if isinstance(val, dict):
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logger.warning(f"⚠️ [LLMService] Nested dictionary detected for key '{key}'. Using first entry.")
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val = next(iter(val.values()), "") if val else ""
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return str(val)
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return str(data)
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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 = 2,
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base_delay: float = 2.0,
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priority: Literal["realtime", "background"] = "realtime",
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provider: Optional[str] = None,
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model_override: Optional[str] = None,
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json_schema: Optional[Dict[str, Any]] = None,
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json_schema_name: str = "mindnet_json",
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strict_json_schema: bool = True
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) -> str:
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"""
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Haupteinstiegspunkt für LLM-Anfragen mit Priorisierung.
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force_json:
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- Ollama: nutzt payload["format"]="json"
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- Gemini: nutzt response_mime_type="application/json"
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- OpenRouter: nutzt response_format=json_object (Fallback) oder json_schema
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"""
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target_provider = provider or self.settings.MINDNET_LLM_PROVIDER
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if priority == "background":
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async with LLMService._background_semaphore:
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return await self._dispatch(
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target_provider, prompt, system, force_json,
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max_retries, base_delay, model_override,
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json_schema, json_schema_name, strict_json_schema
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)
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return await self._dispatch(
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target_provider, prompt, system, force_json,
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max_retries, base_delay, model_override,
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json_schema, json_schema_name, strict_json_schema
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)
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async def _dispatch(
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self,
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provider: str,
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prompt: str,
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system: Optional[str],
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force_json: bool,
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max_retries: int,
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base_delay: float,
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model_override: Optional[str],
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json_schema: Optional[Dict[str, Any]],
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json_schema_name: str,
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strict_json_schema: bool
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) -> str:
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"""
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Routet die Anfrage mit intelligenter Rate-Limit Erkennung (WP-20 + WP-76).
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Schleife läuft über MINDNET_LLM_RATE_LIMIT_RETRIES.
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"""
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rate_limit_attempts = 0
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max_rate_retries = getattr(self.settings, "LLM_RATE_LIMIT_RETRIES", 3)
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wait_time = getattr(self.settings, "LLM_RATE_LIMIT_WAIT", 60.0)
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while rate_limit_attempts <= max_rate_retries:
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try:
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if provider == "openrouter" and self.openrouter_client:
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return await self._execute_openrouter(
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prompt=prompt,
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system=system,
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force_json=force_json,
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model_override=model_override,
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json_schema=json_schema,
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json_schema_name=json_schema_name,
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strict_json_schema=strict_json_schema
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)
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if provider == "gemini" and self.google_client:
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return await self._execute_google(prompt, system, force_json, model_override)
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# Default/Fallback zu Ollama
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return await self._execute_ollama(prompt, system, force_json, max_retries, base_delay)
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except Exception as e:
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err_str = str(e)
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# Intelligente 429 Erkennung für alle Cloud-Provider
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is_rate_limit = any(x in err_str for x in ["429", "RESOURCE_EXHAUSTED", "rate_limited", "Too Many Requests"])
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if is_rate_limit and rate_limit_attempts < max_rate_retries:
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rate_limit_attempts += 1
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logger.warning(
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f"⏳ [LLMService] Rate Limit (429) detected from {provider}. "
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f"Attempt {rate_limit_attempts}/{max_rate_retries}. "
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f"Waiting {wait_time}s before cloud retry..."
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)
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await asyncio.sleep(wait_time)
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continue # Nächster Versuch in der Cloud-Schleife
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# Wenn kein Rate-Limit oder Retries erschöpft -> Fallback zu Ollama (falls aktiviert)
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if self.settings.LLM_FALLBACK_ENABLED and provider != "ollama":
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logger.warning(
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f"🔄 Provider {provider} failed ({err_str}). Falling back to LOCAL OLLAMA."
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)
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return await self._execute_ollama(prompt, system, force_json, max_retries, base_delay)
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raise e
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async def _execute_google(self, prompt, system, force_json, model_override):
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"""Native Google SDK Integration (Gemini) mit v1 Fix."""
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model = model_override or self.settings.GEMINI_MODEL
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# Fix: Bereinige Modellnamen (Entfernung von 'models/' Präfix)
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clean_model = model.replace("models/", "")
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config = types.GenerateContentConfig(
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system_instruction=system,
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response_mime_type="application/json" if force_json else "text/plain"
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)
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# Thread-Offloading mit striktem Timeout gegen "Hangs"
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response = await asyncio.wait_for(
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asyncio.to_thread(
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self.google_client.models.generate_content,
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model=clean_model, contents=prompt, config=config
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),
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timeout=45.0
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)
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return response.text.strip()
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async def _execute_openrouter(
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self,
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prompt: str,
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system: Optional[str],
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force_json: bool,
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model_override: Optional[str],
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json_schema: Optional[Dict[str, Any]] = None,
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json_schema_name: str = "mindnet_json",
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strict_json_schema: bool = True
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) -> str:
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"""OpenRouter API Integration (OpenAI-kompatibel) mit Schema-Support."""
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model = model_override or self.settings.OPENROUTER_MODEL
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messages = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": prompt})
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kwargs: Dict[str, Any] = {}
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if force_json:
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if json_schema:
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kwargs["response_format"] = {
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"type": "json_schema",
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"json_schema": {
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"name": json_schema_name,
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"strict": strict_json_schema,
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"schema": json_schema
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}
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}
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else:
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kwargs["response_format"] = {"type": "json_object"}
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response = await self.openrouter_client.chat.completions.create(
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model=model,
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messages=messages,
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**kwargs
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)
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return response.choices[0].message.content.strip()
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async def _execute_ollama(self, prompt, system, force_json, max_retries, base_delay):
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"""Lokaler Ollama Call mit exponentiellem Backoff."""
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payload = {
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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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res = await self.ollama_client.post("/api/generate", json=payload)
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res.raise_for_status()
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return res.json().get("response", "").strip()
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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"❌ Ollama Error after {attempt} retries: {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"⚠️ Ollama attempt {attempt} failed. Retrying in {wait_time}s...")
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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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"""Vollständiges RAG Chat-Interface."""
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provider = self.settings.MINDNET_LLM_PROVIDER
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system_prompt = self.get_prompt("system_prompt", provider)
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rag_template = self.get_prompt("rag_template", provider)
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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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priority="realtime"
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)
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async def close(self):
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"""Schließt die HTTP-Verbindungen."""
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if self.ollama_client:
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await self.ollama_client.aclose() |