neue Version mit Wartezeit bei externen LLM Fehler
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@ -2,8 +2,8 @@
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FILE: app/config.py
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DESCRIPTION: Zentrale Pydantic-Konfiguration.
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WP-20: Hybrid-Cloud Modus Support (OpenRouter/Gemini/Ollama).
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FIX: Update auf Gemini 2.5 Serie & Optimierung für Gemma 2 Durchsatz.
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VERSION: 0.6.6
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FIX: Einführung von Parametern zur intelligenten Rate-Limit Steuerung (429 Handling).
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VERSION: 0.6.7
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STATUS: Active
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DEPENDENCIES: os, functools, pathlib, python-dotenv
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"""
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@ -27,32 +27,36 @@ class Settings:
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VECTOR_SIZE: int = int(os.getenv("VECTOR_DIM", "768"))
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DISTANCE: str = os.getenv("MINDNET_DISTANCE", "Cosine")
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# --- Lokale Embeddings ---
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# --- Lokale Embeddings (Ollama & Sentence-Transformers) ---
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EMBEDDING_MODEL: str = os.getenv("MINDNET_EMBEDDING_MODEL", "nomic-embed-text")
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MODEL_NAME: str = os.getenv("MINDNET_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
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# --- WP-20 Hybrid LLM Provider ---
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# "openrouter" ist primär für den Ingest-Turbo mit Gemma 2 empfohlen.
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# Erlaubt: "ollama" | "gemini" | "openrouter"
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MINDNET_LLM_PROVIDER: str = os.getenv("MINDNET_LLM_PROVIDER", "openrouter").lower()
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# Google AI Studio (Fallback auf 2.5-Serie)
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# Google AI Studio (2025er Lite-Modell für höhere Kapazität)
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GOOGLE_API_KEY: str | None = os.getenv("GOOGLE_API_KEY")
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# "gemini-2.5-flash-lite" ist die skalierbare 2025-Alternative für hohe Last.
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GEMINI_MODEL: str = os.getenv("MINDNET_GEMINI_MODEL", "gemini-2.5-flash-lite")
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# OpenRouter Integration (openai/gpt-oss-20b:free oder gemma-2)
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# OpenRouter Integration (Verfügbares Free-Modell 2025)
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OPENROUTER_API_KEY: str | None = os.getenv("OPENROUTER_API_KEY")
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# "google/gemma-2-9b-it:free" bietet hohe Kapazität bei Kostenfreiheit.
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OPENROUTER_MODEL: str = os.getenv("OPENROUTER_MODEL", "google/gemma-2-9b-it:free")
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OPENROUTER_MODEL: str = os.getenv("OPENROUTER_MODEL", "mistralai/mistral-7b-instruct:free")
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LLM_FALLBACK_ENABLED: bool = os.getenv("MINDNET_LLM_FALLBACK", "true").lower() == "true"
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# --- NEU: Intelligente Rate-Limit Steuerung ---
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# Dauer der Wartezeit in Sekunden, wenn ein HTTP 429 (Rate Limit) auftritt
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LLM_RATE_LIMIT_WAIT: float = float(os.getenv("MINDNET_LLM_RATE_LIMIT_WAIT", "60.0"))
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# Anzahl der Cloud-Retries bei 429, bevor Ollama-Fallback greift
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LLM_RATE_LIMIT_RETRIES: int = int(os.getenv("MINDNET_LLM_RATE_LIMIT_RETRIES", "3"))
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# --- WP-05 Lokales LLM (Ollama) ---
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OLLAMA_URL: str = os.getenv("MINDNET_OLLAMA_URL", "http://127.0.0.1:11434")
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LLM_MODEL: str = os.getenv("MINDNET_LLM_MODEL", "phi3:mini")
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PROMPTS_PATH: str = os.getenv("MINDNET_PROMPTS_PATH", "config/prompts.yaml")
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# --- Performance & Last-Steuerung ---
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# --- WP-06 / WP-14 Performance & Last-Steuerung ---
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LLM_TIMEOUT: float = float(os.getenv("MINDNET_LLM_TIMEOUT", "300.0"))
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DECISION_CONFIG_PATH: str = os.getenv("MINDNET_DECISION_CONFIG", "config/decision_engine.yaml")
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BACKGROUND_LIMIT: int = int(os.getenv("MINDNET_LLM_BACKGROUND_LIMIT", "2"))
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@ -62,8 +66,6 @@ class Settings:
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MINDNET_VAULT_ROOT: str = os.getenv("MINDNET_VAULT_ROOT", "./vault_master")
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MINDNET_TYPES_FILE: str = os.getenv("MINDNET_TYPES_FILE", "config/types.yaml")
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MINDNET_VOCAB_PATH: str = os.getenv("MINDNET_VOCAB_PATH", "/mindnet/vault/mindnet/_system/dictionary/edge_vocabulary.md")
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# WP-22: 'full' für Multi-Hash Change Detection
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CHANGE_DETECTION_MODE: str = os.getenv("MINDNET_CHANGE_DETECTION_MODE", "full")
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# --- WP-04 Retriever Gewichte ---
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@ -4,10 +4,11 @@ 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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OHNE Breaking Changes (neue Parameter nur am Ende).
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VERSION: 3.3.3
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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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@ -47,7 +48,11 @@ class LLMService:
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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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self.google_client = genai.Client(api_key=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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@ -55,7 +60,9 @@ class LLMService:
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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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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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@ -84,7 +91,7 @@ class LLMService:
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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 (weil ähnlich leistungsfähig), dann Ollama
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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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@ -105,51 +112,32 @@ class LLMService:
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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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# --- NEW (am Ende => rückwärtskompatibel!) ---
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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 (structured outputs)
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json_schema + strict_json_schema (nur OpenRouter relevant):
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- Wenn json_schema gesetzt ist UND force_json=True -> response_format.type="json_schema"
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- strict_json_schema wird an OpenRouter/Provider weitergereicht (best effort je nach Provider)
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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,
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prompt,
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system,
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force_json,
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max_retries,
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base_delay,
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model_override,
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json_schema,
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json_schema_name,
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strict_json_schema
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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,
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prompt,
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system,
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force_json,
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max_retries,
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base_delay,
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model_override,
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json_schema,
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json_schema_name,
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strict_json_schema
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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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@ -165,47 +153,73 @@ class LLMService:
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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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"""Routet die Anfrage an den physikalischen API-Provider."""
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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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"""
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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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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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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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# 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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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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except Exception as e:
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# QUOTEN-SCHUTZ: Wenn Cloud (OpenRouter/Gemini) fehlschlägt,
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# gehen wir IMMER zu Ollama, niemals von OpenRouter zu Gemini.
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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: {e}. Falling back to LOCAL OLLAMA to protect cloud quotas."
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)
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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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raise e
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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)."""
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# Nutzt GEMINI_MODEL aus config.py falls kein override übergeben wurde
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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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# SDK Call in Thread auslagern, da die Google API blocking sein kann
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response = await asyncio.to_thread(
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self.google_client.models.generate_content,
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model=model, contents=prompt, config=config
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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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@ -215,21 +229,11 @@ class LLMService:
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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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# --- NEW (optional) ---
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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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OpenRouter API Integration (OpenAI-kompatibel).
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force_json=True:
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- Ohne json_schema -> response_format={"type":"json_object"}
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- Mit json_schema -> response_format={"type":"json_schema", "json_schema": {..., "strict": True}}
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Wichtig: response_format NICHT als None senden (robuster gegenüber SDK/Provider).
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"""
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# Nutzt OPENROUTER_MODEL aus config.py
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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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@ -237,7 +241,6 @@ class LLMService:
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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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@ -306,4 +309,4 @@ class LLMService:
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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()
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await self.ollama_client.aclose()
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