4435e7ea0a
Services: Admin-Compliance, Backend-Compliance, AI-Compliance-SDK, Consent-SDK, Developer-Portal, PCA-Platform, DSMS Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
140 lines
4.5 KiB
Python
140 lines
4.5 KiB
Python
"""
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Assistant Service for RAG
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Handles Q&A using LLM with retrieved context.
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"""
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import httpx
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from typing import List, Optional, Dict, Any
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import structlog
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from .search import SearchService
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logger = structlog.get_logger()
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SYSTEM_PROMPT = """Du bist ein Experte für Datenschutz- und Compliance-Recht.
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Beantworte Fragen basierend auf den bereitgestellten Rechtstexten.
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Zitiere immer die relevanten Artikel und Paragraphen.
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Antworte auf Deutsch.
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Wenn du dir nicht sicher bist, sage das klar.
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"""
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class AssistantService:
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"""Service for legal Q&A using RAG."""
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def __init__(self, settings):
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self.settings = settings
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self.search_service = SearchService(settings)
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async def ask(
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self,
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question: str,
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context: Optional[str] = None,
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regulation_codes: Optional[List[str]] = None,
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include_citations: bool = True
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) -> Dict[str, Any]:
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"""Answer a legal question using RAG."""
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# Search for relevant context
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search_results = await self.search_service.search(
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query=question,
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regulation_codes=regulation_codes,
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limit=5,
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min_score=0.6
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)
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# Build context from search results
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retrieved_context = "\n\n".join([
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f"[{r['regulation_code']} Art. {r['article']}]: {r['content']}"
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for r in search_results
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])
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# Add user-provided context if any
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if context:
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retrieved_context = f"{context}\n\n{retrieved_context}"
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# Build prompt
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prompt = f"""Kontext aus Rechtstexten:
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{retrieved_context}
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Frage: {question}
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Beantworte die Frage basierend auf dem Kontext. Zitiere relevante Artikel."""
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# Generate answer
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answer = await self._generate_response(prompt)
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# Extract citations
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citations = []
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if include_citations:
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for result in search_results:
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citations.append({
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"regulation_code": result["regulation_code"],
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"article": result.get("article", ""),
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"text": result["content"][:200] + "...",
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"relevance": result["score"]
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})
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return {
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"answer": answer,
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"citations": citations,
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"confidence": self._calculate_confidence(search_results)
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}
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async def _generate_response(self, prompt: str) -> str:
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"""Generate response using Ollama."""
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try:
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async with httpx.AsyncClient() as client:
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response = await client.post(
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f"{self.settings.ollama_url}/api/generate",
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json={
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"model": self.settings.llm_model,
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"prompt": prompt,
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"system": SYSTEM_PROMPT,
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"stream": False,
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"options": {
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"temperature": 0.3,
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"top_p": 0.9
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}
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},
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timeout=120.0
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)
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response.raise_for_status()
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return response.json()["response"]
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except httpx.TimeoutException:
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logger.error("LLM request timed out")
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return "Die Anfrage hat zu lange gedauert. Bitte versuchen Sie es erneut."
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except Exception as e:
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logger.error("LLM generation failed", error=str(e))
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# Return fallback response
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return self._generate_fallback_response(prompt)
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def _generate_fallback_response(self, prompt: str) -> str:
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"""Generate a fallback response without LLM."""
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return """Basierend auf den verfügbaren Rechtstexten:
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Die relevanten Regelungen finden sich in den zitierten Artikeln.
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Für eine detaillierte rechtliche Bewertung empfehle ich die Konsultation
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der vollständigen Gesetzestexte oder eines Rechtsbeistands.
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Hinweis: Dies ist eine automatisch generierte Antwort.
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Der LLM-Dienst war nicht verfügbar."""
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def _calculate_confidence(self, search_results: List[Dict]) -> float:
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"""Calculate confidence score based on search results."""
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if not search_results:
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return 0.3
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# Average relevance score
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avg_score = sum(r["score"] for r in search_results) / len(search_results)
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# Adjust based on number of results
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if len(search_results) >= 3:
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confidence = avg_score * 1.1
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else:
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confidence = avg_score * 0.9
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return min(confidence, 1.0)
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