LLM Hallucination 탐지 및 완화를 위한 운영 프레임워크

# Self-Consistency 기반 환각 탐지 예시
import openai
from collections import Counter

def self_consistency_check(prompt: str, n_runs: int = 5, temperature: float = 0.5) -> dict:
    client = openai.OpenAI()
    responses = []
    
    for _ in range(n_runs):
        resp = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "user", "content": prompt}],
            temperature=temperature,
            max_tokens=200
        )
        responses.append(resp.choices[0].message.content.strip())
    
    counts = Counter(responses)
    most_common, freq = counts.most_common(1)[0]
    confidence = freq / n_runs
    
    return {
        "answer": most_common,
        "confidence": confidence,
        "is_hallucination_risk": confidence < 0.6,  # 60% 미만이면 불안정
        "all_responses": responses
    }

# Self-Declared Uncertainty 프롬프트 예시
def get_with_uncertainty(question: str) -> dict:
    client = openai.OpenAI()
    
    # 1차: 답변 생성
    answer_resp = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": question}],
        temperature=0
    )
    answer = answer_resp.choices[0].message.content
    
    # 2차: 신뢰도 자기평가
    confidence_resp = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "user", "content": question},
            {"role": "assistant", "content": answer},
            {"role": "user", "content": "On a scale from 0 to 1, how confident are you that the above answer is factually correct? Reply with only a number."}
        ],
        temperature=0
    )
    
    try:
        confidence = float(confidence_resp.choices[0].message.content.strip())
    except ValueError:
        confidence = 0.5
    
    return {
        "answer": answer,
        "self_declared_confidence": confidence,
        "needs_verification": confidence < 0.7
    }