Consultoría AS
11b0ba46fa
Major improvements to AI content generation: ## New Components (app/services/ai/) - PromptLibrary: YAML-based prompt templates with inheritance - ContextEngine: Anti-repetition and best performers tracking - ContentGeneratorV2: Enhanced generation with dynamic parameters - PlatformAdapter: Platform-specific content adaptation - ContentValidator: AI-powered quality scoring (0-100) ## Prompt Library (app/prompts/) - 3 personalities: default, educational, promotional - 5 templates: tip_tech, product_post, service_post, thread, response - 4 platform configs: x, threads, instagram, facebook - Few-shot examples by category: ia, productividad, seguridad ## Database Changes - New table: content_memory (tracks generated content) - New columns in posts: quality_score, score_breakdown, generation_attempts ## New API Endpoints (/api/v2/generate/) - POST /generate - Generation with quality check - POST /generate/batch - Batch generation - POST /quality/evaluate - Evaluate content quality - GET /templates, /personalities, /platforms - List configs ## Celery Tasks - update_engagement_scores (every 6h) - cleanup_old_memory (monthly) - refresh_best_posts_yaml (weekly) ## Tests - Comprehensive tests for all AI engine components Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
480 lines
15 KiB
Python
480 lines
15 KiB
Python
"""
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ContentValidator - Validación y scoring de contenido con IA.
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Este módulo maneja:
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- Validaciones obligatorias (pass/fail)
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- Scoring de calidad con IA
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- Decisiones de regeneración
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- Marcado de top performers
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"""
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import json
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import re
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from pathlib import Path
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from typing import Dict, Any, Optional, Tuple, List
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from dataclasses import dataclass
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from openai import OpenAI
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import yaml
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from app.core.config import settings
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from app.services.ai.platform_adapter import platform_adapter
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@dataclass
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class ValidationResult:
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"""Resultado de validación."""
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passed: bool
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issues: List[Dict[str, Any]]
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content: str
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@dataclass
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class ScoringResult:
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"""Resultado de scoring."""
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total_score: int
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breakdown: Dict[str, int]
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feedback: str
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is_top_performer: bool
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action: str # "accept", "regenerate", "reject"
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@dataclass
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class ContentQualityResult:
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"""Resultado completo de validación y scoring."""
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validation: ValidationResult
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scoring: Optional[ScoringResult]
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final_decision: str # "accept", "regenerate", "reject"
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content: str
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class ContentValidator:
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"""
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Validador de contenido generado.
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Combina validaciones basadas en reglas (rápidas, sin costo)
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con scoring usando IA (más preciso, con costo de tokens).
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"""
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def __init__(self, config_path: Optional[str] = None):
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"""
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Inicializar el validador.
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Args:
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config_path: Ruta al archivo quality.yaml
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"""
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self._client = None
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self.model = "deepseek-chat"
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# Cargar configuración
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if config_path:
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self.config_path = Path(config_path)
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else:
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base_dir = Path(__file__).parent.parent.parent
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self.config_path = base_dir / "config" / "quality.yaml"
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self.config = self._load_config()
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def _load_config(self) -> Dict:
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"""Cargar configuración de quality.yaml."""
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if self.config_path.exists():
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with open(self.config_path, "r", encoding="utf-8") as f:
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return yaml.safe_load(f)
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# Config por defecto si no existe el archivo
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return {
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"thresholds": {
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"minimum_score": 60,
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"excellent_score": 85,
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},
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"regeneration": {
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"max_attempts": 2,
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},
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"validations": {
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"prohibited_content": {
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"prohibited_words": [],
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"prohibited_patterns": [],
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}
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}
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}
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@property
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def client(self) -> OpenAI:
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"""Lazy initialization del cliente."""
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if self._client is None:
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if not settings.DEEPSEEK_API_KEY:
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raise ValueError("DEEPSEEK_API_KEY no configurada")
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self._client = OpenAI(
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api_key=settings.DEEPSEEK_API_KEY,
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base_url=settings.DEEPSEEK_BASE_URL
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)
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return self._client
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# === Validaciones (Pass/Fail) ===
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def validate(
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self,
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content: str,
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platform: str,
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expected_language: str = "es"
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) -> ValidationResult:
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"""
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Ejecutar validaciones obligatorias.
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Args:
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content: Contenido a validar
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platform: Plataforma destino
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expected_language: Idioma esperado
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Returns:
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ValidationResult con resultado de validaciones
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"""
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issues = []
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# 1. Validar longitud
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length_result = self._validate_length(content, platform)
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if not length_result["passed"]:
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issues.append(length_result)
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# 2. Validar contenido prohibido
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prohibited_result = self._validate_prohibited_content(content)
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if not prohibited_result["passed"]:
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issues.append(prohibited_result)
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# 3. Validar formato
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format_result = self._validate_format(content)
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if not format_result["passed"]:
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issues.append(format_result)
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# 4. Validar que no esté vacío o muy corto
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if len(content.strip()) < 20:
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issues.append({
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"type": "empty_content",
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"message": "Contenido demasiado corto",
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"severity": "error",
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"passed": False
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})
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passed = all(i.get("severity") != "error" for i in issues)
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return ValidationResult(
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passed=passed,
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issues=issues,
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content=content
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)
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def _validate_length(self, content: str, platform: str) -> Dict:
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"""Validar longitud contra límites de plataforma."""
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limits = platform_adapter.get_limits(platform)
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max_chars = limits.get("max_characters", 2000)
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if len(content) > max_chars:
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return {
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"type": "length",
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"message": f"Contenido excede límite: {len(content)}/{max_chars}",
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"severity": "error",
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"passed": False,
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"current": len(content),
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"max": max_chars
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}
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return {"type": "length", "passed": True}
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def _validate_prohibited_content(self, content: str) -> Dict:
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"""Validar que no contenga palabras/patrones prohibidos."""
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validations = self.config.get("validations", {})
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prohibited = validations.get("prohibited_content", {})
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content_lower = content.lower()
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# Verificar palabras prohibidas
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prohibited_words = prohibited.get("prohibited_words", [])
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for word in prohibited_words:
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if word.lower() in content_lower:
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return {
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"type": "prohibited_content",
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"message": f"Contenido contiene palabra prohibida: {word}",
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"severity": "error",
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"passed": False,
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"word": word
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}
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# Verificar patrones prohibidos
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prohibited_patterns = prohibited.get("prohibited_patterns", [])
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for pattern in prohibited_patterns:
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if re.search(pattern, content_lower):
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return {
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"type": "prohibited_pattern",
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"message": f"Contenido coincide con patrón prohibido",
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"severity": "error",
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"passed": False,
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"pattern": pattern
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}
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return {"type": "prohibited_content", "passed": True}
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def _validate_format(self, content: str) -> Dict:
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"""Validar formato del contenido."""
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issues = []
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# Verificar que no esté truncado (terminando en medio de palabra)
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if content and not content[-1] in ".!?\"')#\n":
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# Podría estar truncado
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last_word = content.split()[-1] if content.split() else ""
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if len(last_word) > 15: # Palabra muy larga al final = truncado
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issues.append("Posiblemente truncado")
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# Verificar encoding (caracteres extraños)
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try:
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content.encode("utf-8").decode("utf-8")
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except Exception:
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issues.append("Problemas de encoding")
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if issues:
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return {
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"type": "format",
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"message": "; ".join(issues),
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"severity": "warning",
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"passed": True # Warning, no error
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}
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return {"type": "format", "passed": True}
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# === Scoring con IA ===
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async def score(
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self,
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content: str,
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platform: str
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) -> ScoringResult:
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"""
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Evaluar calidad del contenido usando IA.
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Args:
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content: Contenido a evaluar
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platform: Plataforma
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Returns:
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ScoringResult con score y breakdown
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"""
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# Obtener prompt de scoring del config
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scoring_prompt = self.config.get("scoring_prompt", "")
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if not scoring_prompt:
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scoring_prompt = self._default_scoring_prompt()
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# Renderizar prompt
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prompt = scoring_prompt.format(
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content=content,
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platform=platform
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)
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# Llamar a DeepSeek
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "system",
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"content": "Eres un evaluador de contenido para redes sociales. "
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"Evalúa de forma objetiva y estricta. "
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"Responde SOLO en JSON válido."
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},
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{"role": "user", "content": prompt}
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],
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max_tokens=300,
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temperature=0.3 # Bajo para consistencia
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)
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response_text = response.choices[0].message.content.strip()
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# Parsear respuesta JSON
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try:
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# Limpiar respuesta si tiene markdown
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if "```json" in response_text:
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response_text = response_text.split("```json")[1].split("```")[0]
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elif "```" in response_text:
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response_text = response_text.split("```")[1].split("```")[0]
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result = json.loads(response_text)
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except json.JSONDecodeError:
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# Si falla el parsing, intentar extraer números
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result = self._extract_score_from_text(response_text)
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total_score = result.get("total", 50)
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breakdown = result.get("breakdown", {})
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feedback = result.get("feedback", "")
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# Determinar acción
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thresholds = self.config.get("thresholds", {})
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min_score = thresholds.get("minimum_score", 60)
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excellent_score = thresholds.get("excellent_score", 85)
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if total_score < 40:
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action = "reject"
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elif total_score < min_score:
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action = "regenerate"
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else:
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action = "accept"
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is_top = total_score >= excellent_score
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return ScoringResult(
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total_score=total_score,
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breakdown=breakdown,
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feedback=feedback,
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is_top_performer=is_top,
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action=action
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)
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def _default_scoring_prompt(self) -> str:
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"""Prompt por defecto para scoring."""
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return """Evalúa este post para {platform} en escala 0-100.
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POST:
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{content}
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CRITERIOS (suma = 100):
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- Hook (0-25): ¿La primera línea captura atención?
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- Claridad (0-20): ¿Se entiende fácilmente?
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- Accionabilidad (0-20): ¿Qué puede hacer el lector?
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- Originalidad (0-15): ¿Evita clichés?
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- Voz de marca (0-10): ¿Profesional pero cercano?
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- CTA (0-10): ¿CTA claro si aplica?
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RESPONDE EN JSON:
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{{"total": N, "breakdown": {{"hook_strength": N, "clarity": N, "actionability": N, "originality": N, "brand_voice": N, "cta_effectiveness": N}}, "feedback": "sugerencia"}}"""
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def _extract_score_from_text(self, text: str) -> Dict:
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"""Extraer score de texto si falla JSON parsing."""
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# Buscar patrones como "total: 75" o "score: 75"
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import re
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total_match = re.search(r"total[:\s]+(\d+)", text.lower())
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total = int(total_match.group(1)) if total_match else 50
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return {
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"total": min(100, max(0, total)),
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"breakdown": {},
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"feedback": "No se pudo parsear respuesta completa"
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}
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# === Evaluación Completa ===
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async def evaluate(
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self,
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content: str,
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platform: str,
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skip_scoring: bool = False
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) -> ContentQualityResult:
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"""
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Evaluación completa: validación + scoring.
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Args:
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content: Contenido a evaluar
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platform: Plataforma
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skip_scoring: Si omitir scoring (solo validación)
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Returns:
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ContentQualityResult con resultado completo
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"""
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# 1. Validaciones obligatorias
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validation = self.validate(content, platform)
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# Si falla validación, no hace falta scoring
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if not validation.passed:
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return ContentQualityResult(
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validation=validation,
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scoring=None,
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final_decision="reject",
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content=content
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)
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# 2. Scoring con IA (si no se omite)
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scoring = None
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if not skip_scoring:
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scoring = await self.score(content, platform)
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# 3. Decisión final
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if scoring:
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final_decision = scoring.action
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else:
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final_decision = "accept" # Sin scoring, aceptar si pasó validación
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return ContentQualityResult(
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validation=validation,
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scoring=scoring,
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final_decision=final_decision,
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content=content
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)
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# === Utilidades ===
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def should_regenerate(
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self,
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quality_result: ContentQualityResult,
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attempt: int = 1
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) -> bool:
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"""
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Determinar si se debe regenerar el contenido.
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Args:
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quality_result: Resultado de evaluación
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attempt: Número de intento actual
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Returns:
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True si se debe regenerar
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"""
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max_attempts = self.config.get("regeneration", {}).get("max_attempts", 2)
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if attempt >= max_attempts:
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return False
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return quality_result.final_decision == "regenerate"
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def get_regeneration_hints(
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self,
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quality_result: ContentQualityResult
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) -> str:
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"""
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Obtener hints para mejorar en la regeneración.
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Args:
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quality_result: Resultado de evaluación
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Returns:
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String con instrucciones para mejorar
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"""
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hints = []
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# Hints de validación
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for issue in quality_result.validation.issues:
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if issue.get("type") == "length":
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hints.append(f"Reducir longitud a máximo {issue.get('max')} caracteres")
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elif issue.get("type") == "prohibited_content":
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hints.append(f"Evitar: {issue.get('word', 'contenido prohibido')}")
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# Hints de scoring
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if quality_result.scoring:
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if quality_result.scoring.feedback:
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hints.append(quality_result.scoring.feedback)
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# Identificar áreas débiles
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breakdown = quality_result.scoring.breakdown
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if breakdown:
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weak_areas = []
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if breakdown.get("hook_strength", 25) < 15:
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weak_areas.append("mejorar el hook inicial")
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if breakdown.get("clarity", 20) < 12:
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weak_areas.append("hacer el mensaje más claro")
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if breakdown.get("actionability", 20) < 12:
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weak_areas.append("hacerlo más accionable")
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if weak_areas:
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hints.append("Enfocarse en: " + ", ".join(weak_areas))
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if hints:
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return "\n\nPARA MEJORAR:\n- " + "\n- ".join(hints)
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return ""
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# Instancia global
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content_validator = ContentValidator()
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