mindnet/app/services/semantic_analyzer.py

"""
app/services/semantic_analyzer.py — Edge Validation & Filtering
Version: 2.0 (Update: Background Priority for Batch Jobs)
"""

import json
import logging
from typing import List, Optional
from dataclasses import dataclass

# Importe
from app.services.llm_service import LLMService

logger = logging.getLogger(__name__)

class SemanticAnalyzer:
    def __init__(self):
        self.llm = LLMService()

    async def assign_edges_to_chunk(self, chunk_text: str, all_edges: List[str], note_type: str) -> List[str]:
        """
        Sendet einen Chunk und eine Liste potenzieller Kanten an das LLM.
        Das LLM filtert heraus, welche Kanten für diesen Chunk relevant sind.
        
        Features:
        - Retry Strategy: Wartet bei Überlastung (max_retries=5).
        - Priority Queue: Läuft als "background" Task, um den Chat nicht zu blockieren.
        - Observability: Loggt Input-Größe, Raw-Response und Parsing-Details.
        """
        if not all_edges:
            return []

        # 1. Prompt laden
        prompt_template = self.llm.prompts.get("edge_allocation_template")
        
        if not prompt_template:
            logger.warning("⚠️ [SemanticAnalyzer] Prompt 'edge_allocation_template' fehlt. Nutze Fallback.")
            prompt_template = (
                "TASK: Wähle aus den Kandidaten die relevanten Kanten für den Text.\n"
                "TEXT: {chunk_text}\n"
                "KANDIDATEN: {edge_list}\n"
                "OUTPUT: JSON Liste von Strings [\"kind:target\"]."
            )

        # 2. Kandidaten-Liste formatieren
        edges_str = "\n".join([f"- {e}" for e in all_edges])
        
        # LOG: Request Info
        logger.debug(f"🔍 [SemanticAnalyzer] Request: {len(chunk_text)} chars Text, {len(all_edges)} Candidates.")

        # 3. Prompt füllen
        final_prompt = prompt_template.format(
            chunk_text=chunk_text[:3500], 
            edge_list=edges_str
        )

        try:
            # 4. LLM Call mit Traffic Control (NEU: priority="background")
            # Wir nutzen die "Slow Lane", damit der User im Chat nicht warten muss.
            response_json = await self.llm.generate_raw_response(
                prompt=final_prompt,
                force_json=True,
                max_retries=5, 
                base_delay=5.0,
                priority="background"  # <--- WICHTIG: Drosselung aktivieren
            )

            # LOG: Raw Response Preview
            logger.debug(f"📥 [SemanticAnalyzer] Raw Response (Preview): {response_json[:200]}...")

            # 5. Parsing & Cleaning
            clean_json = response_json.replace("```json", "").replace("```", "").strip()
            
            if not clean_json: 
                logger.warning("⚠️ [SemanticAnalyzer] Leere Antwort vom LLM erhalten. Trigger Fallback.")
                return []

            try:
                data = json.loads(clean_json)
            except json.JSONDecodeError as json_err:
                logger.error(f"❌ [SemanticAnalyzer] JSON Decode Error.")
                logger.error(f"   Grund: {json_err}")
                logger.error(f"   Empfangener String: {clean_json[:500]}")
                logger.info("   -> Workaround: Fallback auf 'Alle Kanten' (durch Chunker).")
                return []

            valid_edges = []

            # 6. Robuste Validierung (List vs Dict)
            if isinstance(data, list):
                # Standardfall: ["kind:target", ...]
                valid_edges = [str(e) for e in data if isinstance(e, str) and ":" in e]
            
            elif isinstance(data, dict):
                # Abweichende Formate behandeln
                logger.info(f"ℹ️ [SemanticAnalyzer] LLM lieferte Dict statt Liste. Versuche Reparatur. Keys: {list(data.keys())}")
                
                for key, val in data.items():
                    # Fall A: {"edges": ["kind:target"]}
                    if key.lower() in ["edges", "results", "kanten", "matches"] and isinstance(val, list):
                         valid_edges.extend([str(e) for e in val if isinstance(e, str) and ":" in e])
                    
                    # Fall B: {"kind": "target"}
                    elif isinstance(val, str):
                        valid_edges.append(f"{key}:{val}")
                    
                    # Fall C: {"kind": ["target1", "target2"]}
                    elif isinstance(val, list):
                        for target in val:
                            if isinstance(target, str):
                                valid_edges.append(f"{key}:{target}")

            # Safety: Filtere nur Kanten, die halbwegs valide aussehen
            final_result = [e for e in valid_edges if ":" in e]
            
            # LOG: Ergebnis
            if final_result:
                logger.info(f"✅ [SemanticAnalyzer] Success. {len(final_result)} Kanten zugewiesen.")
            else:
                logger.debug("   [SemanticAnalyzer] Keine spezifischen Kanten erkannt (Empty Result).")

            return final_result

        except Exception as e:
            logger.error(f"💥 [SemanticAnalyzer] Kritischer Fehler: {e}", exc_info=True)
            return []

    async def close(self):
        if self.llm:
            await self.llm.close()

# Singleton Helper
_analyzer_instance = None
def get_semantic_analyzer():
    global _analyzer_instance
    if _analyzer_instance is None:
        _analyzer_instance = SemanticAnalyzer()
    return _analyzer_instance