| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| def answer_question(question, docs): | |
| """ | |
| Basit semantic search ile en uygun dokümanı seçer | |
| """ | |
| try: | |
| vectorizer = TfidfVectorizer() | |
| X = vectorizer.fit_transform(docs + [question]) | |
| similarities = cosine_similarity(X[-1], X[:-1]) | |
| best_idx = similarities.argmax() | |
| return docs[best_idx] | |
| except Exception as e: | |
| return f"Cevap üretilemedi: {e}" |