178 lines
7.2 KiB
Python
178 lines
7.2 KiB
Python
"""
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FILE: app/services/semantic_analyzer.py
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DESCRIPTION: KI-gestützte Kanten-Validierung. Nutzt LLM (Background-Priority), um Kanten präzise einem Chunk zuzuordnen.
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WP-20 Fix: Volle Kompatibilität mit der provider-basierten Routing-Logik (OpenRouter Primary).
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WP-22: Integration von valid_types zur Halluzinations-Vermeidung.
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FIX: Finale DoD-Härtung, Entfernung von Hardcoded Limits und optimiertes Error-Handling.
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VERSION: 2.2.4
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STATUS: Active
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DEPENDENCIES: app.services.llm_service, app.services.edge_registry, json, logging
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"""
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import json
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import logging
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import re
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from typing import List, Optional
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from dataclasses import dataclass
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# Importe
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from app.services.llm_service import LLMService
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# WP-22: Registry für Vokabular-Erzwingung
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from app.services.edge_registry import registry as edge_registry
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logger = logging.getLogger(__name__)
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class SemanticAnalyzer:
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def __init__(self):
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self.llm = LLMService()
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def _is_valid_edge_string(self, edge_str: str) -> bool:
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"""
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Prüft, ob ein String eine valide Kante im Format 'kind:target' ist.
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Verhindert, dass LLM-Geschwätz als Kante durchrutscht.
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"""
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if not isinstance(edge_str, str) or ":" not in edge_str:
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return False
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parts = edge_str.split(":", 1)
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kind = parts[0].strip()
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target = parts[1].strip()
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# Regel 1: Ein 'kind' (Beziehungstyp) darf keine Leerzeichen enthalten.
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if " " in kind:
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return False
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# Regel 2: Plausible Länge für den Typ (Vermeidet Sätze als Typ)
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if len(kind) > 40 or len(kind) < 2:
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return False
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# Regel 3: Target darf nicht leer sein
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if not target:
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return False
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return True
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async def assign_edges_to_chunk(self, chunk_text: str, all_edges: List[str], note_type: str) -> List[str]:
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"""
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Sendet einen Chunk und eine Liste potenzieller Kanten an das LLM.
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Das LLM filtert heraus, welche Kanten für diesen Chunk relevant sind.
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WP-20: Nutzt primär den konfigurierten Provider (z.B. OpenRouter).
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"""
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if not all_edges:
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return []
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# 1. Bestimmung des Providers und Modells (Dynamisch über Settings)
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provider = self.llm.settings.MINDNET_LLM_PROVIDER
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model = self.llm.settings.OPENROUTER_MODEL if provider == "openrouter" else self.llm.settings.GEMINI_MODEL
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# 2. Prompt laden (Provider-spezifisch)
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prompt_template = self.llm.get_prompt("edge_allocation_template", provider)
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if not prompt_template or not isinstance(prompt_template, str):
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logger.warning("⚠️ [SemanticAnalyzer] Prompt 'edge_allocation_template' ungültig. Nutze Recovery-Template.")
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prompt_template = (
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"TASK: Wähle aus den Kandidaten die relevanten Kanten für den Text.\n"
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"TEXT: {chunk_text}\n"
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"KANDIDATEN: {edge_list}\n"
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"OUTPUT: JSON Liste von Strings [\"kind:target\"]."
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)
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# 3. Daten für Template vorbereiten (Vokabular-Check)
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edge_registry.ensure_latest()
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valid_types_str = ", ".join(sorted(list(edge_registry.valid_types)))
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edges_str = "\n".join([f"- {e}" for e in all_edges])
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logger.debug(f"🔍 [SemanticAnalyzer] Request: {len(chunk_text)} chars Text, {len(all_edges)} Candidates.")
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# 4. Prompt füllen mit Format-Check (Kein Shortcut)
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try:
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# Wir begrenzen den Text auf eine vernünftige Länge für das Kontextfenster (ca. 10k Tokens max)
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final_prompt = prompt_template.format(
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chunk_text=chunk_text[:6000],
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edge_list=edges_str,
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valid_types=valid_types_str
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)
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except Exception as format_err:
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logger.error(f"❌ [SemanticAnalyzer] Prompt Formatting failed: {format_err}")
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return []
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try:
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# 5. LLM Call mit Background Priority & Semaphore Control
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response_json = await self.llm.generate_raw_response(
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prompt=final_prompt,
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force_json=True,
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max_retries=3,
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base_delay=2.0,
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priority="background",
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provider=provider,
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model_override=model
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)
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# 6. Bulletproof JSON Extraction (Analog zur Ingestion)
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# Entfernt Markdown-Code-Blöcke falls vorhanden
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match = re.search(r"```(?:json)?\s*(.*?)\s*```", response_json, re.DOTALL)
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clean_json = match.group(1) if match else response_json
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clean_json = clean_json.strip()
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if not clean_json:
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return []
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try:
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data = json.loads(clean_json)
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except json.JSONDecodeError:
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# Letzter Rettungsversuch: Suche nach dem ersten '[' und letzten ']'
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start = clean_json.find('[')
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end = clean_json.rfind(']') + 1
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if start != -1 and end != 0:
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try:
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data = json.loads(clean_json[start:end])
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except:
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logger.error("❌ [SemanticAnalyzer] JSON Recovery failed.")
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return []
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else:
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return []
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# 7. Robuste Normalisierung (List vs Dict Recovery)
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raw_candidates = []
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if isinstance(data, list):
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raw_candidates = data
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elif isinstance(data, dict):
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logger.info(f"ℹ️ [SemanticAnalyzer] LLM returned dict, trying recovery.")
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for key in ["edges", "results", "kanten", "matches"]:
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if key in data and isinstance(data[key], list):
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raw_candidates.extend(data[key])
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break
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# Falls immer noch leer, nutze Schlüssel-Wert Paare als Behelf
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if not raw_candidates:
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for k, v in data.items():
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if isinstance(v, str): raw_candidates.append(f"{k}:{v}")
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elif isinstance(v, list): [raw_candidates.append(f"{k}:{i}") for i in v if isinstance(i, str)]
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# 8. Strikte Validierung gegen Kanten-Format
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valid_edges = []
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for e in raw_candidates:
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e_str = str(e).strip()
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if self._is_valid_edge_string(e_str):
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valid_edges.append(e_str)
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else:
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logger.debug(f" [SemanticAnalyzer] Rejected invalid edge format: '{e_str}'")
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if valid_edges:
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logger.info(f"✅ [SemanticAnalyzer] Assigned {len(valid_edges)} edges to chunk.")
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return valid_edges
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except Exception as e:
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logger.error(f"💥 [SemanticAnalyzer] Critical error during analysis: {e}", exc_info=True)
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return []
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async def close(self):
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if self.llm:
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await self.llm.close()
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# Singleton Instanziierung
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_analyzer_instance = None
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def get_semantic_analyzer():
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global _analyzer_instance
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if _analyzer_instance is None:
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_analyzer_instance = SemanticAnalyzer()
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return _analyzer_instance |