Trixy7/plugins/nlp_llm/main.py

# -*- coding: utf-8 -*-
"""
LLM-basiertes NLP Plugin für Trixy.

Verantwortlich für:
1. Intent-Erkennung: speech_recognized → intent_received
2. Antwort-Generierung: create_output_text → output_text_created

Event-Flow:
  speech_recognized
       ↓
  [NLP Plugin] Intent-Erkennung
       ↓
  intent_received
       ↓
  [Intent Dispatcher] Handler aufrufen
       ↓
  intent_handled
       ↓
  [NLP Plugin] Antwort generieren (falls nötig)
       ↓
  output_text_created
       ↓
  [TTS Plugin] Sprachausgabe
"""

import asyncio
import json
import time
import uuid
from typing import Any

from trixy_core.plugins.trixy_plugin import TrixyPlugin
from trixy_core.plugins.extensions.points.conversation import NLPExtension
from trixy_core.events.decorators import TrixyEvent
from trixy_core.events.event_data.basic import (
    SpeechRecognized,
    IntentReceived,
    IntentHandled,
    CreateOutputText,
    OutputTextCreated,
    PluginLoaded,
    PluginUnloaded,
)
from trixy_core.nlp import (
    NLPConfig,
    NLPContext,
    NLPResult,
    IntentRegistry,
    get_system_intents_for_context,
    is_admin_intent,
)
from trixy_core.nlp.keyword_matcher import KeywordIntentMatcher
from trixy_core.nlp.entities import EntityRegistry
from trixy_core.utils.debug import pinfo, pdebug, perror, pwarn


# Cache-Refresh-Intervall in Sekunden
INTENT_CACHE_REFRESH_INTERVAL = 600  # 10 Minuten


class LLMNLPExtension(NLPExtension):
    """NLP Extension für LLM-basierte Verarbeitung."""

    def __init__(self, plugin: "LLMNLPPlugin") -> None:
        super().__init__(
            extension_id="nlp_llm",
            name="LLM NLP",
            plugin_name=plugin.name,
            engine="llm",
            capabilities=["intent", "response", "entity"],
            offline=True,
            description="Lokales LLM für Intent-Erkennung und Antwortgenerierung",
        )
        self._plugin = plugin

    async def create_instance(self, config: dict[str, Any]):
        return self._plugin.provider


class IntentCache:
    """Cache für verfügbare Intents."""

    def __init__(self) -> None:
        self._plugin_intents: list[dict[str, Any]] = []
        self._last_refresh: float = 0
        self._cached_prompt_intents: dict[str, str] = {}

    def refresh_plugin_intents(self) -> None:
        """Lädt Plugin-Intents aus der Registry neu."""
        registry = IntentRegistry.get_instance()
        self._plugin_intents = registry.get_all_as_dict()
        self._cached_prompt_intents.clear()
        self._last_refresh = time.time()
        pdebug(f"Intent-Cache aktualisiert: {len(self._plugin_intents)} Plugin-Intents")

    def needs_refresh(self) -> bool:
        return (time.time() - self._last_refresh) > INTENT_CACHE_REFRESH_INTERVAL

    def get_intents_for_context(
        self,
        wakeword_type: str = "custom",
        is_authenticated: bool = False,
    ) -> list[dict[str, Any]]:
        """Gibt alle verfügbaren Intents für den Kontext zurück."""
        system_intents = get_system_intents_for_context(
            wakeword_type=wakeword_type,
            is_authenticated=is_authenticated,
        )
        return system_intents + self._plugin_intents

    @property
    def plugin_intent_count(self) -> int:
        return len(self._plugin_intents)


class LLMNLPPlugin(TrixyPlugin):
    """
    LLM-basiertes NLP Plugin.

    Aufgaben:
    1. Intent-Erkennung aus speech_recognized
    2. Antwort-Generierung für create_output_text
    """

    NAME = "nlp_llm"
    VERSION = "2.0.0"
    DESCRIPTION = "LLM-basierte NLP (Event-Driven)"
    AUTHOR = "Trixy"

    def __init__(self, application, plugin_path, config) -> None:
        super().__init__(application, plugin_path, config)
        self._provider = None
        self._extension = None
        self._intent_cache = IntentCache()
        self._keyword_matcher: KeywordIntentMatcher | None = None
        self._entity_registry: EntityRegistry | None = None
        self._refresh_task: asyncio.Task | None = None
        self._admin_sessions: dict[str, dict[str, Any]] = {}

    @property
    def provider(self):
        return self._provider

    async def on_load(self) -> None:
        """Initialisiert das Plugin."""
        pinfo(f"Lade {self.NAME} Plugin...")

        backend = self.get_config_value("backend", "llama_cpp")

        if backend == "ollama":
            from plugins.nlp_llm.backends.ollama import OllamaNLPBackend
            self._provider = OllamaNLPBackend()
        else:
            from plugins.nlp_llm.backends.llama_cpp import LlamaCppNLPBackend
            self._provider = LlamaCppNLPBackend()

        nlp_config = NLPConfig(
            backend=backend,
            model_name=self.get_config_value("model_name", "qwen2.5-1.5b-instruct"),
            model_path=self.get_config_value("model_path"),
            use_gpu=self.get_config_value("use_gpu", False),
            num_threads=self.get_config_value("num_threads", 4),
            temperature=self.get_config_value("temperature", 0.1),
            max_tokens=self.get_config_value("max_tokens", 256),
            context_window=self.get_config_value("context_window", 2048),
            extra={
                "ollama_host": self.get_config_value("ollama_host", "http://localhost:11434"),
                "response_language": self.get_config_value("response_language", "de"),
            }
        )

        success = await self._provider.initialize(nlp_config)
        if success:
            pinfo(f"[NLP] Provider initialisiert: {backend}, model={nlp_config.model_name}, ready={self._provider.is_ready}")
        else:
            perror(f"[NLP] Provider-Initialisierung fehlgeschlagen: {backend}")

        self._intent_cache.refresh_plugin_intents()

        # Entity-Registry initialisieren
        from pathlib import Path
        self._entity_registry = EntityRegistry()
        project_root = Path(__file__).resolve().parent.parent.parent
        entities_dir = project_root / "entities"
        plugins_dir = project_root / "plugins"
        language = self.get_config_value("response_language",
                   nlp_config.extra.get("response_language", "de"))
        # Globale Entities laden (entities/{lang}/*.yml|*.json)
        self._entity_registry.load(entities_dir, language)
        # Plugin-Entities laden (plugins/*/entities/{lang}/*.yml|*.json)
        self._entity_registry.load_plugin_entities(plugins_dir, language)
        pinfo(f"[NLP] Entity-Registry geladen: {len(self._entity_registry.entity_types)} Typen")

        # Keyword-Matcher initialisieren (Pre-Filter vor LLM)
        matcher_config = self.get_config_value("keyword_matcher", {})
        if matcher_config.get("enabled", True):
            self._keyword_matcher = KeywordIntentMatcher(matcher_config)
            self._keyword_matcher.set_entity_registry(self._entity_registry)
            self._refresh_keyword_matcher()
            pinfo("[NLP] Keyword-Matcher aktiviert (mit Entity-Validierung)")
        else:
            self._keyword_matcher = None

        self._refresh_task = asyncio.create_task(self._periodic_cache_refresh())

        self._extension = LLMNLPExtension(self)
        # Extension nicht registrieren - das Plugin arbeitet direkt event-driven

        pinfo(f"{self.NAME} Plugin geladen (Event-Driven Mode)")

    async def on_unload(self) -> None:
        """Fährt das Plugin herunter."""
        if self._refresh_task:
            self._refresh_task.cancel()
            try:
                await self._refresh_task
            except asyncio.CancelledError:
                pass

        if self._provider:
            await self._provider.shutdown()
            self._provider = None

        self._extension = None

    def _refresh_keyword_matcher(self) -> None:
        """Aktualisiert den Keyword-Matcher mit allen verfuegbaren Intents."""
        if not self._keyword_matcher:
            return
        all_intents = self._intent_cache.get_intents_for_context()
        self._keyword_matcher.compile_intents(all_intents)

    async def _periodic_cache_refresh(self) -> None:
        """Periodischer Cache-Refresh."""
        while True:
            await asyncio.sleep(INTENT_CACHE_REFRESH_INTERVAL)
            self._intent_cache.refresh_plugin_intents()
            self._refresh_keyword_matcher()

    # =========================================================================
    # Event Handler: Plugin Changes
    # =========================================================================

    @TrixyEvent(["plugin_loaded", "plugin_unloaded"])
    async def on_plugin_change(self, event_name: str, event_data) -> None:
        """Aktualisiert Cache und Keyword-Matcher bei Plugin-Änderungen."""
        plugin_name = getattr(event_data, "plugin_name", "")
        if plugin_name != self.NAME:
            self._intent_cache.refresh_plugin_intents()
            self._refresh_keyword_matcher()

    # =========================================================================
    # Event Handler: Intent-Erkennung (speech_recognized → intent_received)
    # =========================================================================

    @TrixyEvent(["speech_recognized"])
    async def on_speech_recognized(self, event_name: str, event_data: SpeechRecognized) -> None:
        """
        Erkennt Intent aus Spracheingabe.

        Emittiert: intent_received
        """
        pinfo(f"[NLP] speech_recognized empfangen: '{getattr(event_data, 'text', '?')}'")

        if not self._provider:
            pwarn("[NLP] Kein Provider konfiguriert")
            return

        if not self._provider.is_ready:
            pwarn(f"[NLP] Provider nicht bereit (state={getattr(self._provider, '_state', '?')})")
            return

        text = event_data.text
        if not text or not text.strip():
            pdebug("[NLP] Leerer Text, überspringe")
            return

        # STT-Korrektur: Häufige Fehlerkennungen korrigieren
        text = self._correct_stt_text(text)

        pinfo(f"[NLP] Starte Intent-Erkennung für: '{text}'")

        # Session-Info holen
        session_info = self._get_session_info(event_data.satellite_id)
        wakeword_type = session_info.get("wakeword_type", "custom")
        is_authenticated = self._is_authenticated(event_data.satellite_id)

        # Cache aktualisieren falls nötig
        if self._intent_cache.needs_refresh():
            self._intent_cache.refresh_plugin_intents()

        available_intents = self._intent_cache.get_intents_for_context(
            wakeword_type=wakeword_type,
            is_authenticated=is_authenticated,
        )

        # Keyword-Matcher Pre-Filter (vor LLM)
        import time as _time
        metadata: dict[str, Any] = {}

        if self._keyword_matcher:
            t_kw_start = _time.monotonic()
            kw_match = self._keyword_matcher.match(text)
            t_kw_elapsed = (_time.monotonic() - t_kw_start) * 1000  # ms

            if kw_match:
                pinfo(
                    f"[NLP] Keyword-Match: '{kw_match.intent}' "
                    f"(confidence={kw_match.confidence:.2f}, "
                    f"{kw_match.matched_by}, {t_kw_elapsed:.1f}ms)"
                )
                result = NLPResult(
                    intent=kw_match.intent,
                    confidence=kw_match.confidence,
                    slots=kw_match.slots,
                    success=True,
                    processing_time=t_kw_elapsed / 1000,
                )
                if kw_match.sentiment:
                    metadata["sentiment"] = kw_match.sentiment
            else:
                pdebug(f"[NLP] Kein Keyword-Match ({t_kw_elapsed:.1f}ms), Fallback auf LLM")
                kw_match = None
        else:
            kw_match = None

        # LLM-Fallback (nur wenn kein Keyword-Match)
        if kw_match is None:
            pinfo(f"[NLP] {len(available_intents)} Intents verfügbar, sende an LLM...")
            t_start = _time.monotonic()

            result = await self._recognize_intent(
                text=text,
                satellite_id=event_data.satellite_id,
                session_info=session_info,
                wakeword_type=wakeword_type,
                is_authenticated=is_authenticated,
                language=event_data.language or "de",
            )

            t_elapsed = _time.monotonic() - t_start

            if not result.success:
                perror(f"[NLP] Intent-Erkennung fehlgeschlagen nach {t_elapsed:.1f}s: {result.error}")
                return

            pinfo(f"[NLP] Intent erkannt in {t_elapsed:.1f}s: intent='{result.intent}', confidence={result.confidence:.2f}, slots={result.slots}")
        else:
            t_elapsed = result.processing_time

        # Sicherheitsprüfung
        if not self._check_authorization(result.intent, wakeword_type, is_authenticated):
            pwarn(f"[NLP] Autorisierung verweigert für Intent: {result.intent}")
            await self._emit_authorization_error(event_data.satellite_id, session_info)
            return

        # Konfidenz prüfen
        min_confidence = self.get_config_value("min_confidence", 0.3)
        intent = result.intent if result.confidence >= min_confidence else "unknown"
        if intent == "unknown" and result.intent != "unknown":
            pdebug(f"[NLP] Konfidenz zu niedrig ({result.confidence:.2f} < {min_confidence}), Intent -> 'unknown'")

        # intent_received emittieren
        intent_event = IntentReceived(
            satellite_id=event_data.satellite_id,
            intent=intent,
            confidence=result.confidence,
            slots=result.slots,
            original_text=text,
            session_id=session_info.get("session_id", ""),
            room_id=session_info.get("room_id", ""),
            wakeword_type=wakeword_type,
            is_authenticated=is_authenticated,
            language=event_data.language or "de",
            metadata=metadata,
        )

        pinfo(f"[NLP] Emittiere intent_received: '{intent}' (confidence={result.confidence:.2f})")
        await self._application.events.trigger("intent_received", intent_event)

    async def _recognize_intent(
        self,
        text: str,
        satellite_id: str,
        session_info: dict[str, Any],
        wakeword_type: str,
        is_authenticated: bool,
        language: str,
    ):
        """Führt Intent-Erkennung durch."""
        available_intents = self._intent_cache.get_intents_for_context(
            wakeword_type=wakeword_type,
            is_authenticated=is_authenticated,
        )

        context = NLPContext(
            text=text,
            satellite_id=satellite_id,
            room_id=session_info.get("room_id", ""),
            session_id=session_info.get("session_id", ""),
            available_intents=available_intents,
            language=language,
        )

        return await self._provider.process(context)

    # =========================================================================
    # Event Handler: Antwort-Generierung (create_output_text → output_text_created)
    # =========================================================================

    @TrixyEvent(["create_output_text"])
    async def on_create_output_text(self, event_name: str, event_data: CreateOutputText) -> None:
        """
        Generiert Antworttext aus Handler-Ergebnis.

        Emittiert: output_text_created
        """
        pinfo(f"[NLP] create_output_text empfangen: intent='{event_data.intent}', text='{event_data.original_text[:50]}'")

        if not self._provider or not self._provider.is_ready:
            pwarn("[NLP] Provider nicht bereit für Antwort-Generierung")
            return

        pinfo(f"[NLP] Generiere Antwort für Intent: '{event_data.intent}'...")

        # Antwort generieren
        response_text = await self._provider.generate_response(
            intent=event_data.intent,
            slots=event_data.slots,
            handler_result=event_data.handler_data,
            original_text=event_data.original_text,
            room_id=event_data.room_id,
        )

        if not response_text:
            # Fallback für unbekannte Intents
            response_text = await self._provider.generate_fallback_response(
                event_data.original_text
            )

        # output_text_created emittieren
        output_event = OutputTextCreated(
            satellite_id=event_data.satellite_id,
            session_id=event_data.session_id,
            room_id=event_data.room_id,
            text=response_text,
            intent=event_data.intent,
            is_followup=False,
            expects_response=False,
        )

        pdebug(f"Emittiere output_text_created: {response_text[:50]}...")
        await self._application.events.trigger("output_text_created", output_event)

    # =========================================================================
    # Hilfsmethoden
    # =========================================================================

    # STT-Korrekturen: Wort-Aliase für häufige Fehlerkennungen.
    # Nur angewendet wenn das Wort alleine steht (gesamter Text).
    _STT_WORD_ALIASES: dict[str, str] = {
        "shop": "stopp",
        "schop": "stopp",
    }

    def _correct_stt_text(self, text: str) -> str:
        """Korrigiert häufige STT-Fehlerkennungen."""
        stripped = text.strip().lower()

        # Einzelwort-Aliase (nur wenn das Wort alleine steht)
        if stripped in self._STT_WORD_ALIASES:
            corrected = self._STT_WORD_ALIASES[stripped]
            pdebug(f"[NLP] STT-Korrektur: '{text}' → '{corrected}'")
            return corrected

        return text

    def _get_session_info(self, satellite_id: str) -> dict[str, Any]:
        """Holt Session-Info."""
        info = {"session_id": "", "room_id": "", "wakeword_type": "custom"}

        if hasattr(self._application, "services"):
            try:
                conv_service = self._application.services.get_service("ConversationService")
                if conv_service:
                    session = conv_service.get_active_session(satellite_id)
                    if session:
                        info["session_id"] = session.session_id
                        info["room_id"] = getattr(session, "room_id", "")
                        info["wakeword_type"] = getattr(session, "wakeword_type", "custom")
            except Exception:
                pass

        return info

    def _is_authenticated(self, satellite_id: str) -> bool:
        """Prüft Admin-Authentifizierung."""
        session = self._admin_sessions.get(satellite_id)
        if not session:
            return False
        if time.time() > session.get("expires", 0):
            del self._admin_sessions[satellite_id]
            return False
        return session.get("authenticated", False)

    def _check_authorization(
        self, intent: str, wakeword_type: str, is_authenticated: bool
    ) -> bool:
        """Prüft Intent-Autorisierung."""
        if not is_admin_intent(intent):
            return True
        if wakeword_type != "system_command":
            return False
        if intent == "system_login":
            return True
        return is_authenticated

    async def _emit_authorization_error(
        self, satellite_id: str, session_info: dict[str, Any]
    ) -> None:
        """Emittiert Autorisierungs-Fehler."""
        error_event = OutputTextCreated(
            satellite_id=satellite_id,
            session_id=session_info.get("session_id", ""),
            room_id=session_info.get("room_id", ""),
            text="Dieser Befehl erfordert Administrator-Rechte. "
                 "Bitte verwende das System-Wakeword und melde dich an.",
            intent="authorization_error",
        )
        await self._application.events.trigger("output_text_created", error_event)