328 lines
15 KiB
Python
328 lines
15 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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WP-25b: Implementierung der Lazy-Prompt-Orchestration (Modell-spezifisch).
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VERSION: 3.5.5 (WP-25b: Prompt Orchestration & Full Resilience)
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STATUS: Active
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FIX:
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- WP-25b: get_prompt() unterstützt Hierarchie: Model-ID -> Provider -> Default.
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- WP-25b: generate_raw_response() unterstützt prompt_key + variables für Lazy-Formatting.
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- WP-25a: Voller Erhalt der rekursiven Fallback-Kaskade und visited_profiles Schutz.
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- WP-20: Restaurierung des internen Ollama-Retry-Loops für Hardware-Stabilität.
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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
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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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# Import der neutralen Bereinigungs-Logik
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from app.core.registry import clean_llm_text
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logger = logging.getLogger(__name__)
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class LLMService:
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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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self.profiles = self._load_llm_profiles()
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self._decision_engine = None
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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
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self.google_client = None
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if self.settings.GOOGLE_API_KEY:
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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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timeout=45.0
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)
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logger.info("🛰️ LLMService: OpenRouter Integration active.")
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@property
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def decision_engine(self):
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if self._decision_engine is None:
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from app.core.retrieval.decision_engine import DecisionEngine
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self._decision_engine = DecisionEngine()
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return self._decision_engine
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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():
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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 _load_llm_profiles(self) -> dict:
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"""WP-25a: Lädt die zentralen MoE-Profile aus der llm_profiles.yaml."""
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path_str = getattr(self.settings, "LLM_PROFILES_PATH", "config/llm_profiles.yaml")
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path = Path(path_str)
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if not path.exists():
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logger.warning(f"⚠️ LLM Profiles 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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data = yaml.safe_load(f) or {}
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return data.get("profiles", {})
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except Exception as e:
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logger.error(f"❌ Failed to load llm_profiles.yaml: {e}")
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return {}
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def get_prompt(self, key: str, model_id: str = None, provider: str = None) -> str:
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"""
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WP-25b: Hochpräziser Prompt-Lookup mit detailliertem Trace-Logging.
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"""
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data = self.prompts.get(key, "")
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if not isinstance(data, dict):
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return str(data)
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# 1. Spezifischstes Match: Exakte Modell-ID
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if model_id and model_id in data:
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logger.info(f"🎯 [PROMPT-TRACE] Level 1 Match: Model-specific ('{model_id}') for key '{key}'")
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return str(data[model_id])
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# 2. Mittlere Ebene: Provider
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if provider and provider in data:
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logger.info(f"📡 [PROMPT-TRACE] Level 2 Match: Provider-fallback ('{provider}') for key '{key}'")
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return str(data[provider])
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# 3. Globaler Fallback
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default_val = data.get("default", data.get("gemini", data.get("ollama", "")))
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logger.info(f"⚓ [PROMPT-TRACE] Level 3 Match: Global Default for key '{key}'")
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return str(default_val)
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async def generate_raw_response(
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self,
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prompt: str = None,
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prompt_key: str = None, # WP-25b: Lazy Loading Key
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variables: dict = None, # WP-25b: Daten für Formatierung
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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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profile_name: Optional[str] = None,
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visited_profiles: Optional[list] = None
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) -> str:
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"""Haupteinstiegspunkt für LLM-Anfragen mit Lazy-Prompt Orchestrierung."""
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visited_profiles = visited_profiles or []
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target_provider = provider
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target_model = model_override
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target_temp = None
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fallback_profile = None
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# 1. Profil-Auflösung (Mixture of Experts)
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if profile_name and self.profiles:
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profile = self.profiles.get(profile_name)
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if profile:
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target_provider = profile.get("provider", target_provider)
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target_model = profile.get("model", target_model)
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target_temp = profile.get("temperature")
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fallback_profile = profile.get("fallback_profile")
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visited_profiles.append(profile_name)
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logger.info(f"🎭 MoE Dispatch: Profil='{profile_name}' -> Provider='{target_provider}' | Model='{target_model}'")
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else:
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logger.warning(f"⚠️ Profil '{profile_name}' nicht in llm_profiles.yaml gefunden!")
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if not target_provider:
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target_provider = self.settings.MINDNET_LLM_PROVIDER
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# 2. WP-25b: Lazy Prompt Resolving
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# Wir laden den Prompt erst JETZT, basierend auf dem gerade aktiven Modell.
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current_prompt = prompt
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if prompt_key:
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template = self.get_prompt(prompt_key, model_id=target_model, provider=target_provider)
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try:
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# Formatierung mit den übergebenen Variablen
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current_prompt = template.format(**(variables or {}))
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except Exception as e:
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logger.error(f"❌ Prompt formatting failed for key '{prompt_key}': {e}")
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current_prompt = template # Sicherheits-Fallback
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# 3. Ausführung mit Fehler-Handling für Kaskade
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try:
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if priority == "background":
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async with LLMService._background_semaphore:
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res = await self._dispatch(
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target_provider, current_prompt, system, force_json,
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max_retries, base_delay, target_model,
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json_schema, json_schema_name, strict_json_schema, target_temp
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)
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else:
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res = await self._dispatch(
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target_provider, current_prompt, system, force_json,
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max_retries, base_delay, target_model,
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json_schema, json_schema_name, strict_json_schema, target_temp
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)
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# Check auf leere Cloud-Antworten (WP-25 Stability)
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if not res and target_provider != "ollama":
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logger.warning(f"⚠️ Empty response from {target_provider}. Triggering fallback.")
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raise ValueError(f"Empty response from {target_provider}")
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return clean_llm_text(res) if not force_json else res
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except Exception as e:
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logger.error(f"❌ Error during execution of profile '{profile_name}' ({target_provider}): {e}")
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# 4. WP-25b Kaskaden-Logik (Rekursiv mit Modell-spezifischem Re-Loading)
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if fallback_profile and fallback_profile not in visited_profiles:
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logger.info(f"🔄 Switching to fallback profile: '{fallback_profile}'")
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return await self.generate_raw_response(
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prompt=prompt,
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prompt_key=prompt_key,
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variables=variables, # Ermöglicht neues Formatting für Fallback-Modell
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system=system, force_json=force_json,
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max_retries=max_retries, base_delay=base_delay,
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priority=priority, provider=None, model_override=None,
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json_schema=json_schema, json_schema_name=json_schema_name,
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strict_json_schema=strict_json_schema,
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profile_name=fallback_profile,
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visited_profiles=visited_profiles
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)
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# 5. Ultimativer Notanker: Falls alles fehlschlägt, direkt zu Ollama
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if target_provider != "ollama" and self.settings.LLM_FALLBACK_ENABLED:
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logger.warning(f"🚨 Kaskade erschöpft. Nutze finalen Ollama-Notanker.")
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res = await self._execute_ollama(current_prompt, system, force_json, max_retries, base_delay, target_temp, target_model)
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return clean_llm_text(res) if not force_json else res
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raise e
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async def _dispatch(
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self, provider, prompt, system, force_json, max_retries, base_delay,
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model_override, json_schema, json_schema_name, strict_json_schema, temperature
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) -> str:
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"""Routet die Anfrage an den spezifischen Provider-Executor."""
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rate_limit_attempts = 0
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max_rate_retries = min(max_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, system=system, force_json=force_json,
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model_override=model_override, json_schema=json_schema,
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json_schema_name=json_schema_name, strict_json_schema=strict_json_schema,
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temperature=temperature
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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, temperature)
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return await self._execute_ollama(prompt, system, force_json, max_retries, base_delay, temperature, model_override)
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except Exception as e:
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err_str = str(e)
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if any(x in err_str for x in ["429", "RESOURCE_EXHAUSTED", "rate_limited"]):
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rate_limit_attempts += 1
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logger.warning(f"⏳ Rate Limit {provider}. Attempt {rate_limit_attempts}. Wait {wait_time}s.")
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await asyncio.sleep(wait_time)
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continue
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raise e
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async def _execute_google(self, prompt, system, force_json, model_override, temperature):
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model = (model_override or self.settings.GEMINI_MODEL).replace("models/", "")
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config_kwargs = {
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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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if temperature is not None:
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config_kwargs["temperature"] = temperature
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config = types.GenerateContentConfig(**config_kwargs)
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response = await asyncio.wait_for(
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asyncio.to_thread(self.google_client.models.generate_content, model=model, contents=prompt, config=config),
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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(self, prompt, system, force_json, model_override, json_schema, json_schema_name, strict_json_schema, temperature) -> str:
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model = model_override or self.settings.OPENROUTER_MODEL
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logger.info(f"🛰️ OpenRouter Call: Model='{model}' | Temp={temperature}")
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messages = []
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if system: 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 temperature is not None: kwargs["temperature"] = temperature
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if force_json:
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if json_schema:
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kwargs["response_format"] = {"type": "json_schema", "json_schema": {"name": json_schema_name, "strict": strict_json_schema, "schema": json_schema}}
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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(model=model, messages=messages, **kwargs)
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if not response.choices: return ""
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return response.choices[0].message.content.strip() if response.choices[0].message.content else ""
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async def _execute_ollama(self, prompt, system, force_json, max_retries, base_delay, temperature=None, model_override=None):
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# WP-20: Restaurierter Retry-Loop für lokale Hardware-Resilienz
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effective_model = model_override or self.settings.LLM_MODEL
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effective_temp = temperature if temperature is not None else (0.1 if force_json else 0.7)
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payload = {
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"model": effective_model,
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"prompt": prompt, "stream": False,
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"options": {"temperature": effective_temp, "num_ctx": 8192}
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}
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if force_json: payload["format"] = "json"
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if system: 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 failure after {attempt} attempts: {e}")
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raise e
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await asyncio.sleep(base_delay * (2 ** (attempt - 1)))
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async def generate_rag_response(self, query: str, context_str: Optional[str] = None) -> str:
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return await self.decision_engine.ask(query)
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async def close(self):
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if self.ollama_client:
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await self.ollama_client.aclose() |