breakpilot-pwa/voice-service/services/fallback_llm_client.py

"""
Fallback LLM Client - Ollama Integration
Text-only fallback when PersonaPlex is not available

Used in development on Mac Mini with:
- qwen2.5:32b for conversation
- Local processing (DSGVO-konform)
"""
import structlog
import httpx
from typing import Optional, List, Dict, Any

from config import settings

logger = structlog.get_logger(__name__)


class FallbackLLMClient:
    """
    Ollama LLM client for text-only processing.

    When PersonaPlex is not available (development mode),
    this client provides:
    - Intent detection (text-based)
    - Response generation
    - Task execution assistance

    Note: Audio transcription requires a separate ASR service
    (e.g., Whisper) when using this fallback.
    """

    def __init__(self):
        self._base_url = settings.ollama_base_url
        self._model = settings.ollama_voice_model
        self._timeout = settings.ollama_timeout
        self._client: Optional[httpx.AsyncClient] = None

    async def _get_client(self) -> httpx.AsyncClient:
        """Get or create HTTP client."""
        if self._client is None:
            self._client = httpx.AsyncClient(timeout=self._timeout)
        return self._client

    async def generate(
        self,
        prompt: str,
        system_prompt: Optional[str] = None,
        temperature: float = 0.7,
        max_tokens: int = 500,
    ) -> str:
        """
        Generate text completion.

        Args:
            prompt: User prompt
            system_prompt: Optional system instructions
            temperature: Sampling temperature
            max_tokens: Maximum tokens to generate

        Returns:
            Generated text
        """
        if settings.fallback_llm_provider == "none":
            logger.warning("No LLM provider configured")
            return "LLM nicht verfügbar"

        client = await self._get_client()

        # Build messages
        messages = []
        if system_prompt:
            messages.append({"role": "system", "content": system_prompt})
        messages.append({"role": "user", "content": prompt})

        try:
            response = await client.post(
                f"{self._base_url}/api/chat",
                json={
                    "model": self._model,
                    "messages": messages,
                    "options": {
                        "temperature": temperature,
                        "num_predict": max_tokens,
                    },
                    "stream": False,
                },
            )
            response.raise_for_status()

            data = response.json()
            return data.get("message", {}).get("content", "")

        except httpx.HTTPError as e:
            logger.error("Ollama request failed", error=str(e))
            return "Fehler bei der Verarbeitung"
        except Exception as e:
            logger.error("Unexpected error", error=str(e))
            return "Unerwarteter Fehler"

    async def detect_intent(self, text: str) -> Dict[str, Any]:
        """
        Detect intent from text using LLM.

        Returns:
            {
                "type": "student_observation" | "reminder" | ...,
                "confidence": 0.0-1.0,
                "parameters": {...},
                "is_actionable": bool
            }
        """
        system_prompt = """Du bist ein Intent-Detektor für Lehrer-Sprachbefehle.
Analysiere den Text und bestimme die Absicht.

Mögliche Intents:
- student_observation: Beobachtung zu einem Schüler
- reminder: Erinnerung an etwas
- homework_check: Hausaufgaben kontrollieren
- conference_topic: Thema für Konferenz
- correction_note: Notiz zur Korrektur
- worksheet_generate: Arbeitsblatt erstellen
- worksheet_differentiate: Differenzierung
- quick_activity: Schnelle Aktivität
- quiz_generate: Quiz erstellen
- parent_letter: Elternbrief
- class_message: Nachricht an Klasse
- canvas_edit: Canvas bearbeiten
- canvas_layout: Layout ändern
- operator_checklist: Operatoren-Checkliste
- eh_passage: EH-Passage suchen
- feedback_suggest: Feedback vorschlagen
- reminder_schedule: Erinnerung planen
- task_summary: Aufgaben zusammenfassen
- unknown: Unbekannt

Antworte NUR mit JSON:
{"type": "intent_name", "confidence": 0.0-1.0, "parameters": {...}, "is_actionable": true/false}"""

        result = await self.generate(
            prompt=f"Text: {text}",
            system_prompt=system_prompt,
            temperature=0.1,
            max_tokens=200,
        )

        try:
            # Parse JSON from response
            import json
            # Find JSON in response
            start = result.find("{")
            end = result.rfind("}") + 1
            if start >= 0 and end > start:
                return json.loads(result[start:end])
        except Exception as e:
            logger.warning("Intent parsing failed", error=str(e))

        return {
            "type": "unknown",
            "confidence": 0.0,
            "parameters": {},
            "is_actionable": False,
        }

    async def process_audio_description(self, audio_data: bytes) -> str:
        """
        Process audio by describing it (placeholder for ASR).

        In production, this would use Whisper or similar.
        For MVP, this returns a placeholder.
        """
        # Calculate audio duration
        samples = len(audio_data) // 2  # 16-bit = 2 bytes
        duration_sec = samples / settings.audio_sample_rate

        logger.debug(
            "Audio received (no ASR in fallback mode)",
            duration_sec=duration_sec,
            bytes=len(audio_data),
        )

        # Placeholder - in production, integrate with Whisper
        return ""

    async def chat(
        self,
        messages: List[Dict[str, str]],
        temperature: float = 0.7,
    ) -> str:
        """
        Multi-turn conversation.

        Args:
            messages: List of {"role": "user"|"assistant", "content": "..."}
            temperature: Sampling temperature

        Returns:
            Assistant response
        """
        if settings.fallback_llm_provider == "none":
            return "LLM nicht verfügbar"

        client = await self._get_client()

        # Add system prompt
        system_prompt = """Du bist Breakpilot, ein hilfreicher Assistent für Lehrer.
Du hilfst bei:
- Notizen und Beobachtungen
- Unterrichtsvorbereitung
- Elternkommunikation
- Korrekturunterstützung

Antworte kurz und präzise. Halte Antworten unter 100 Wörtern."""

        full_messages = [{"role": "system", "content": system_prompt}] + messages

        try:
            response = await client.post(
                f"{self._base_url}/api/chat",
                json={
                    "model": self._model,
                    "messages": full_messages,
                    "options": {
                        "temperature": temperature,
                        "num_predict": 300,
                    },
                    "stream": False,
                },
            )
            response.raise_for_status()

            data = response.json()
            return data.get("message", {}).get("content", "")

        except Exception as e:
            logger.error("Chat failed", error=str(e))
            return "Entschuldigung, ein Fehler ist aufgetreten."

    async def health_check(self) -> bool:
        """Check if Ollama is available."""
        if settings.fallback_llm_provider == "none":
            return False

        try:
            client = await self._get_client()
            response = await client.get(f"{self._base_url}/api/tags")
            return response.status_code == 200
        except Exception:
            return False