feat: voice-service von lehrer nach core verschoben, Pipeline erweitert (voice, BQAS, embedding, night-scheduler)
This commit is contained in:
Benjamin Boenisch
248
voice-service/services/fallback_llm_client.py
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248
voice-service/services/fallback_llm_client.py
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"""
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Fallback LLM Client - Ollama Integration
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Text-only fallback when PersonaPlex is not available
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Used in development on Mac Mini with:
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- qwen2.5:32b for conversation
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- Local processing (DSGVO-konform)
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"""
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import structlog
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import httpx
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from typing import Optional, List, Dict, Any
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from config import settings
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logger = structlog.get_logger(__name__)
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class FallbackLLMClient:
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"""
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Ollama LLM client for text-only processing.
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When PersonaPlex is not available (development mode),
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this client provides:
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- Intent detection (text-based)
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- Response generation
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- Task execution assistance
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Note: Audio transcription requires a separate ASR service
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(e.g., Whisper) when using this fallback.
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"""
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def __init__(self):
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self._base_url = settings.ollama_base_url
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self._model = settings.ollama_voice_model
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self._timeout = settings.ollama_timeout
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self._client: Optional[httpx.AsyncClient] = None
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async def _get_client(self) -> httpx.AsyncClient:
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"""Get or create HTTP client."""
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if self._client is None:
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self._client = httpx.AsyncClient(timeout=self._timeout)
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return self._client
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async def generate(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: int = 500,
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) -> str:
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"""
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Generate text completion.
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Args:
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prompt: User prompt
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system_prompt: Optional system instructions
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temperature: Sampling temperature
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max_tokens: Maximum tokens to generate
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Returns:
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Generated text
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"""
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if settings.fallback_llm_provider == "none":
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logger.warning("No LLM provider configured")
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return "LLM nicht verfügbar"
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client = await self._get_client()
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# Build messages
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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try:
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response = await client.post(
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f"{self._base_url}/api/chat",
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json={
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"model": self._model,
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"messages": messages,
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"options": {
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"temperature": temperature,
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"num_predict": max_tokens,
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},
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"stream": False,
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},
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)
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response.raise_for_status()
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data = response.json()
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return data.get("message", {}).get("content", "")
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except httpx.HTTPError as e:
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logger.error("Ollama request failed", error=str(e))
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return "Fehler bei der Verarbeitung"
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except Exception as e:
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logger.error("Unexpected error", error=str(e))
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return "Unerwarteter Fehler"
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async def detect_intent(self, text: str) -> Dict[str, Any]:
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"""
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Detect intent from text using LLM.
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Returns:
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{
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"type": "student_observation" | "reminder" | ...,
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"confidence": 0.0-1.0,
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"parameters": {...},
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"is_actionable": bool
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}
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"""
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system_prompt = """Du bist ein Intent-Detektor für Lehrer-Sprachbefehle.
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Analysiere den Text und bestimme die Absicht.
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Mögliche Intents:
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- student_observation: Beobachtung zu einem Schüler
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- reminder: Erinnerung an etwas
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- homework_check: Hausaufgaben kontrollieren
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- conference_topic: Thema für Konferenz
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- correction_note: Notiz zur Korrektur
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- worksheet_generate: Arbeitsblatt erstellen
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- worksheet_differentiate: Differenzierung
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- quick_activity: Schnelle Aktivität
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- quiz_generate: Quiz erstellen
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- parent_letter: Elternbrief
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- class_message: Nachricht an Klasse
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- canvas_edit: Canvas bearbeiten
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- canvas_layout: Layout ändern
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- operator_checklist: Operatoren-Checkliste
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- eh_passage: EH-Passage suchen
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- feedback_suggest: Feedback vorschlagen
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- reminder_schedule: Erinnerung planen
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- task_summary: Aufgaben zusammenfassen
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- unknown: Unbekannt
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Antworte NUR mit JSON:
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{"type": "intent_name", "confidence": 0.0-1.0, "parameters": {...}, "is_actionable": true/false}"""
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result = await self.generate(
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prompt=f"Text: {text}",
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system_prompt=system_prompt,
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temperature=0.1,
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max_tokens=200,
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)
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try:
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# Parse JSON from response
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import json
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# Find JSON in response
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start = result.find("{")
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end = result.rfind("}") + 1
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if start >= 0 and end > start:
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return json.loads(result[start:end])
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except Exception as e:
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logger.warning("Intent parsing failed", error=str(e))
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return {
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"type": "unknown",
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"confidence": 0.0,
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"parameters": {},
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"is_actionable": False,
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}
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async def process_audio_description(self, audio_data: bytes) -> str:
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"""
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Process audio by describing it (placeholder for ASR).
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In production, this would use Whisper or similar.
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For MVP, this returns a placeholder.
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"""
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# Calculate audio duration
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samples = len(audio_data) // 2 # 16-bit = 2 bytes
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duration_sec = samples / settings.audio_sample_rate
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logger.debug(
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"Audio received (no ASR in fallback mode)",
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duration_sec=duration_sec,
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bytes=len(audio_data),
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)
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# Placeholder - in production, integrate with Whisper
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return ""
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async def chat(
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self,
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messages: List[Dict[str, str]],
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temperature: float = 0.7,
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) -> str:
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"""
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Multi-turn conversation.
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Args:
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messages: List of {"role": "user"|"assistant", "content": "..."}
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temperature: Sampling temperature
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Returns:
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Assistant response
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"""
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if settings.fallback_llm_provider == "none":
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return "LLM nicht verfügbar"
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client = await self._get_client()
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# Add system prompt
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system_prompt = """Du bist Breakpilot, ein hilfreicher Assistent für Lehrer.
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Du hilfst bei:
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- Notizen und Beobachtungen
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- Unterrichtsvorbereitung
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- Elternkommunikation
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- Korrekturunterstützung
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Antworte kurz und präzise. Halte Antworten unter 100 Wörtern."""
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full_messages = [{"role": "system", "content": system_prompt}] + messages
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try:
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response = await client.post(
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f"{self._base_url}/api/chat",
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json={
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"model": self._model,
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"messages": full_messages,
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"options": {
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"temperature": temperature,
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"num_predict": 300,
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},
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"stream": False,
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},
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)
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response.raise_for_status()
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data = response.json()
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return data.get("message", {}).get("content", "")
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except Exception as e:
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logger.error("Chat failed", error=str(e))
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return "Entschuldigung, ein Fehler ist aufgetreten."
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async def health_check(self) -> bool:
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"""Check if Ollama is available."""
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if settings.fallback_llm_provider == "none":
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return False
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try:
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client = await self._get_client()
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response = await client.get(f"{self._base_url}/api/tags")
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return response.status_code == 200
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except Exception:
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return False
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