breakpilot-compliance/breakpilot-compliance-sdk/services/rag-service/rag/assistant.py

"""
Assistant Service for RAG

Handles Q&A using LLM with retrieved context.
"""

import httpx
from typing import List, Optional, Dict, Any
import structlog

from .search import SearchService

logger = structlog.get_logger()


SYSTEM_PROMPT = """Du bist ein Experte für Datenschutz- und Compliance-Recht.
Beantworte Fragen basierend auf den bereitgestellten Rechtstexten.
Zitiere immer die relevanten Artikel und Paragraphen.
Antworte auf Deutsch.
Wenn du dir nicht sicher bist, sage das klar.
"""


class AssistantService:
    """Service for legal Q&A using RAG."""

    def __init__(self, settings):
        self.settings = settings
        self.search_service = SearchService(settings)

    async def ask(
        self,
        question: str,
        context: Optional[str] = None,
        regulation_codes: Optional[List[str]] = None,
        include_citations: bool = True
    ) -> Dict[str, Any]:
        """Answer a legal question using RAG."""

        # Search for relevant context
        search_results = await self.search_service.search(
            query=question,
            regulation_codes=regulation_codes,
            limit=5,
            min_score=0.6
        )

        # Build context from search results
        retrieved_context = "\n\n".join([
            f"[{r['regulation_code']} Art. {r['article']}]: {r['content']}"
            for r in search_results
        ])

        # Add user-provided context if any
        if context:
            retrieved_context = f"{context}\n\n{retrieved_context}"

        # Build prompt
        prompt = f"""Kontext aus Rechtstexten:
{retrieved_context}

Frage: {question}

Beantworte die Frage basierend auf dem Kontext. Zitiere relevante Artikel."""

        # Generate answer
        answer = await self._generate_response(prompt)

        # Extract citations
        citations = []
        if include_citations:
            for result in search_results:
                citations.append({
                    "regulation_code": result["regulation_code"],
                    "article": result.get("article", ""),
                    "text": result["content"][:200] + "...",
                    "relevance": result["score"]
                })

        return {
            "answer": answer,
            "citations": citations,
            "confidence": self._calculate_confidence(search_results)
        }

    async def _generate_response(self, prompt: str) -> str:
        """Generate response using Ollama."""
        try:
            async with httpx.AsyncClient() as client:
                response = await client.post(
                    f"{self.settings.ollama_url}/api/generate",
                    json={
                        "model": self.settings.llm_model,
                        "prompt": prompt,
                        "system": SYSTEM_PROMPT,
                        "stream": False,
                        "options": {
                            "temperature": 0.3,
                            "top_p": 0.9
                        }
                    },
                    timeout=120.0
                )
                response.raise_for_status()
                return response.json()["response"]
        except httpx.TimeoutException:
            logger.error("LLM request timed out")
            return "Die Anfrage hat zu lange gedauert. Bitte versuchen Sie es erneut."
        except Exception as e:
            logger.error("LLM generation failed", error=str(e))
            # Return fallback response
            return self._generate_fallback_response(prompt)

    def _generate_fallback_response(self, prompt: str) -> str:
        """Generate a fallback response without LLM."""
        return """Basierend auf den verfügbaren Rechtstexten:

Die relevanten Regelungen finden sich in den zitierten Artikeln.
Für eine detaillierte rechtliche Bewertung empfehle ich die Konsultation
der vollständigen Gesetzestexte oder eines Rechtsbeistands.

Hinweis: Dies ist eine automatisch generierte Antwort.
Der LLM-Dienst war nicht verfügbar."""

    def _calculate_confidence(self, search_results: List[Dict]) -> float:
        """Calculate confidence score based on search results."""
        if not search_results:
            return 0.3

        # Average relevance score
        avg_score = sum(r["score"] for r in search_results) / len(search_results)

        # Adjust based on number of results
        if len(search_results) >= 3:
            confidence = avg_score * 1.1
        else:
            confidence = avg_score * 0.9

        return min(confidence, 1.0)