consultoria-as
b3fab9f8df
- Configure Alembic for database migrations with initial schema - Fix base.py to use lazy loading for async engine (avoids import-time issues) - Change AI model from sonnet to haiku (claude-3-haiku-20240307) - Fix pytest version compatibility with pytest-asyncio - Add frontend package-lock.json Phase 1 tasks completed: - F1.1: Development environment (Docker, Node.js 20, Python 3.12, venv) - F1.2: PostgreSQL with 8 categories seeded - F1.3: Redis connection verified - F1.4: Anthropic API configured and tested - F1.5: Backend server + WebSocket verified Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
import json
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from anthropic import Anthropic
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from app.config import get_settings
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settings = get_settings()
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class AIGenerator:
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def __init__(self):
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self.client = Anthropic(api_key=settings.anthropic_api_key)
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async def generate_questions(
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self,
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category_name: str,
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difficulty: int,
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count: int = 5
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) -> list[dict]:
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"""
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Generate trivia questions using Claude AI.
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Args:
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category_name: Name of the category (e.g., "Nintendo", "Anime")
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difficulty: 1-5 (1=very easy, 5=very hard)
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count: Number of questions to generate
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Returns:
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list[dict]: List of question objects
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"""
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difficulty_descriptions = {
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1: "muy fácil - conocimiento básico que la mayoría conoce",
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2: "fácil - conocimiento común entre fans casuales",
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3: "medio - requiere ser fan de la categoría",
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4: "difícil - conocimiento profundo del tema",
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5: "muy difícil - solo expertos conocerían esto"
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}
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prompt = f"""Genera {count} preguntas de trivia para la categoría "{category_name}".
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Dificultad: {difficulty} ({difficulty_descriptions.get(difficulty, 'medio')})
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Requisitos:
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- Las preguntas deben ser verificables y precisas
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- Evitar ambigüedades
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- Las respuestas deben ser específicas y concisas
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- Incluir variaciones comunes de la respuesta
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- Para gaming: referencias a juegos, personajes, mecánicas, fechas de lanzamiento
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- Para anime: personajes, series, estudios, seiyuus
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- Para música: artistas, canciones, álbumes, letras famosas
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- Para películas: actores, directores, frases icónicas, premios
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- Para libros: autores, obras, personajes literarios
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- Para historia-cultura: eventos, fechas, personajes históricos, arte
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Formato JSON (array de objetos):
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[
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{{
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"question": "texto de la pregunta",
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"correct_answer": "respuesta principal",
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"alt_answers": ["variación1", "variación2"],
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"fun_fact": "dato curioso opcional sobre la respuesta"
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}}
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]
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Responde SOLO con el JSON, sin texto adicional."""
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try:
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message = self.client.messages.create(
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model="claude-3-haiku-20240307",
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max_tokens=2000,
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messages=[
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{"role": "user", "content": prompt}
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]
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)
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response_text = message.content[0].text.strip()
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# Parse JSON response
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questions = json.loads(response_text)
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# Add metadata to each question
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for q in questions:
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q["difficulty"] = difficulty
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q["points"] = settings.default_points.get(difficulty, 300)
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q["time_seconds"] = settings.default_times.get(difficulty, 25)
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return questions
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except json.JSONDecodeError as e:
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print(f"Error parsing AI response: {e}")
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print(f"Response was: {response_text}")
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return []
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except Exception as e:
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print(f"Error generating questions: {e}")
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return []
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# Singleton instance
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ai_generator = AIGenerator()
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