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>
448 lines
16 KiB
Python
448 lines
16 KiB
Python
"""
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Tests para el Content Generation Engine v2.
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Tests para:
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- PromptLibrary: Carga de YAMLs, renderizado
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- ContextEngine: Anti-repetición, best performers
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- PlatformAdapter: Adaptación por plataforma
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- ContentValidator: Validación y scoring
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"""
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import pytest
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from pathlib import Path
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from unittest.mock import MagicMock, patch, AsyncMock
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import json
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# ============================================================
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# PromptLibrary Tests
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# ============================================================
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class TestPromptLibrary:
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"""Tests para PromptLibrary."""
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@pytest.fixture
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def prompt_lib(self):
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"""Create PromptLibrary instance."""
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from app.services.ai.prompt_library import PromptLibrary
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return PromptLibrary()
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def test_list_templates(self, prompt_lib):
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"""Test listado de templates disponibles."""
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templates = prompt_lib.list_templates()
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assert isinstance(templates, list)
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assert "tip_tech" in templates
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assert "product_post" in templates
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assert "service_post" in templates
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assert "thread" in templates
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def test_list_personalities(self, prompt_lib):
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"""Test listado de personalidades disponibles."""
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personalities = prompt_lib.list_personalities()
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assert isinstance(personalities, list)
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assert "default" in personalities
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assert "educational" in personalities
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assert "promotional" in personalities
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def test_list_platforms(self, prompt_lib):
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"""Test listado de plataformas configuradas."""
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platforms = prompt_lib.list_platforms()
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assert isinstance(platforms, list)
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assert "x" in platforms
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assert "threads" in platforms
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assert "instagram" in platforms
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assert "facebook" in platforms
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def test_get_personality_default(self, prompt_lib):
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"""Test carga de personalidad default."""
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personality = prompt_lib.get_personality("default")
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assert "name" in personality
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assert personality["name"] == "default"
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assert "voice" in personality
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assert "system_prompt" in personality
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def test_get_personality_with_inheritance(self, prompt_lib):
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"""Test que personalidades heredan correctamente."""
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educational = prompt_lib.get_personality("educational")
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# Debe tener propiedades de base (default)
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assert "system_prompt" in educational
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# Debe tener propiedades propias
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assert "teaching_techniques" in educational
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def test_get_template(self, prompt_lib):
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"""Test carga de template."""
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template = prompt_lib.get_template("tip_tech")
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assert "name" in template
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assert template["name"] == "tip_tech"
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assert "template" in template
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assert "variables" in template
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assert "parameters" in template
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def test_render_template_basic(self, prompt_lib):
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"""Test renderizado básico de template."""
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with patch('app.services.ai.prompt_library.settings') as mock_settings:
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mock_settings.BUSINESS_NAME = "Test Corp"
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mock_settings.BUSINESS_LOCATION = "Test City"
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mock_settings.BUSINESS_WEBSITE = "test.com"
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mock_settings.CONTENT_TONE = "Professional"
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rendered = prompt_lib.render_template(
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"tip_tech",
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{"category": "productividad"}
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)
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assert "system_prompt" in rendered
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assert "user_prompt" in rendered
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assert "parameters" in rendered
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assert "productividad" in rendered["user_prompt"]
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def test_render_template_with_defaults(self, prompt_lib):
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"""Test que variables con defaults funcionan."""
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with patch('app.services.ai.prompt_library.settings') as mock_settings:
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mock_settings.BUSINESS_NAME = "Test Corp"
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mock_settings.BUSINESS_LOCATION = "Test City"
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mock_settings.BUSINESS_WEBSITE = "test.com"
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mock_settings.CONTENT_TONE = "Professional"
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# Solo category es requerida, difficulty_level tiene default
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rendered = prompt_lib.render_template(
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"tip_tech",
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{"category": "ia"}
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)
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# Debe funcionar sin error y usar default
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assert "user_prompt" in rendered
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def test_get_platform_config(self, prompt_lib):
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"""Test carga de configuración de plataforma."""
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config = prompt_lib.get_platform_config("x")
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assert "platform" in config
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assert config["platform"] == "x"
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assert "limits" in config
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assert "max_characters" in config["limits"]
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assert config["limits"]["max_characters"] == 280
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def test_get_platform_limits(self, prompt_lib):
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"""Test obtención de límites de plataforma."""
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limits = prompt_lib.get_platform_limits("instagram")
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assert "max_characters" in limits
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assert limits["max_characters"] == 2200
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assert "max_hashtags" in limits
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def test_cache_works(self, prompt_lib):
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"""Test que el cache evita lecturas repetidas."""
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# Primera carga
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prompt_lib.get_template("tip_tech")
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# Debe estar en cache
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assert any("tip_tech" in key for key in prompt_lib._cache.keys())
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# Segunda carga usa cache (no debería fallar)
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template = prompt_lib.get_template("tip_tech")
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assert template["name"] == "tip_tech"
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def test_clear_cache(self, prompt_lib):
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"""Test limpieza de cache."""
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prompt_lib.get_template("tip_tech")
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assert len(prompt_lib._cache) > 0
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prompt_lib.clear_cache()
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assert len(prompt_lib._cache) == 0
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# ============================================================
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# PlatformAdapter Tests
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# ============================================================
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class TestPlatformAdapter:
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"""Tests para PlatformAdapter."""
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@pytest.fixture
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def adapter(self):
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"""Create PlatformAdapter instance."""
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from app.services.ai.platform_adapter import PlatformAdapter
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return PlatformAdapter()
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def test_get_limits(self, adapter):
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"""Test obtención de límites."""
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limits = adapter.get_limits("x")
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assert limits["max_characters"] == 280
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assert limits["max_hashtags"] == 2
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def test_adapt_short_content(self, adapter):
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"""Test adaptación de contenido corto."""
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content = "Tip corto #Tech"
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result = adapter.adapt(content, "x")
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assert result.content == content.strip()
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assert not result.truncated
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assert result.platform == "x"
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def test_adapt_long_content_truncates(self, adapter):
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"""Test que contenido largo se trunca."""
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# Contenido de 400 caracteres
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content = "Este es un contenido muy largo. " * 15 + "#Tech #AI"
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result = adapter.adapt(content, "x")
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assert len(result.content) <= 280
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assert result.truncated
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assert "Contenido truncado" in str(result.changes_made)
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def test_adapt_reduces_hashtags(self, adapter):
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"""Test que hashtags se reducen al límite."""
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content = "Tip importante #Tech #AI #Python #Tips #Code #Dev"
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result = adapter.adapt(content, "x")
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# X solo permite 2 hashtags
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hashtag_count = result.content.count("#")
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assert hashtag_count <= 2
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assert result.hashtags_adjusted
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def test_validate_for_platform_valid(self, adapter):
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"""Test validación de contenido válido."""
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content = "Tip corto #Tech"
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validation = adapter.validate_for_platform(content, "x")
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assert validation["valid"]
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assert len(validation["issues"]) == 0
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def test_validate_for_platform_too_long(self, adapter):
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"""Test validación de contenido muy largo."""
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content = "X" * 300 # Excede límite de X
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validation = adapter.validate_for_platform(content, "x")
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assert not validation["valid"]
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assert any(i["type"] == "length" for i in validation["issues"])
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def test_adapt_for_all_platforms(self, adapter):
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"""Test adaptación para múltiples plataformas."""
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content = "Contenido de prueba con mucho texto. " * 5 + "#Tech"
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results = adapter.adapt_for_all_platforms(
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content,
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["x", "threads", "instagram"]
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)
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assert "x" in results
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assert "threads" in results
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assert "instagram" in results
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# X debe ser más corto
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assert len(results["x"].content) <= len(results["instagram"].content)
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# ============================================================
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# ContextEngine Tests
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# ============================================================
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class TestContextEngine:
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"""Tests para ContextEngine."""
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@pytest.fixture
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def context_engine(self):
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"""Create ContextEngine instance."""
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from app.services.ai.context_engine import ContextEngine
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return ContextEngine()
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def test_analyze_content_detects_hook(self, context_engine):
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"""Test detección de tipo de hook."""
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# Pregunta retórica
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content1 = "¿Sabías que el 90% de developers usan IA? #Tech"
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analysis1 = context_engine.analyze_content(content1, "tip_tech", "x")
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assert analysis1["hook_type"] == "pregunta_retórica"
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# Dato impactante
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content2 = "El 73% de empresas ya usan IA. #Tech"
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analysis2 = context_engine.analyze_content(content2, "tip_tech", "x")
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assert analysis2["hook_type"] == "dato_impactante"
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# Tip directo
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content3 = "Tip: usa ChatGPT para debugging. #Tech"
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analysis3 = context_engine.analyze_content(content3, "tip_tech", "x")
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assert analysis3["hook_type"] == "tip_directo"
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def test_analyze_content_extracts_topics(self, context_engine):
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"""Test extracción de temas."""
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content = "ChatGPT y Claude son herramientas de IA geniales para productividad. #Tech"
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analysis = context_engine.analyze_content(content, "tip_tech", "x")
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assert "ia" in analysis["topics"]
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assert "productividad" in analysis["topics"]
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def test_suggest_hook_type_varies(self, context_engine):
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"""Test que sugiere hooks variados."""
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# Sin DB, debería retornar el primer preferido
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mock_db = MagicMock()
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mock_db.query.return_value.filter.return_value.order_by.return_value.limit.return_value.all.return_value = []
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suggested = context_engine.suggest_hook_type(mock_db)
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# Debería ser uno de los hooks preferidos
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assert suggested in [
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"pregunta_retórica", "dato_impactante", "tip_directo",
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"afirmación_bold", "historia_corta"
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]
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# ============================================================
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# ContentValidator Tests
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# ============================================================
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class TestContentValidator:
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"""Tests para ContentValidator."""
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@pytest.fixture
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def validator(self):
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"""Create ContentValidator instance."""
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from app.services.ai.validator import ContentValidator
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return ContentValidator()
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def test_validate_length_passes(self, validator):
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"""Test validación de longitud válida."""
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content = "Contenido corto #Tech"
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result = validator.validate(content, "x")
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assert result.passed
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assert not any(i["type"] == "length" for i in result.issues)
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def test_validate_length_fails(self, validator):
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"""Test validación de longitud excedida."""
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content = "X" * 300 # Excede 280 de X
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result = validator.validate(content, "x")
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assert not result.passed
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assert any(i["type"] == "length" and i["severity"] == "error" for i in result.issues)
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def test_validate_prohibited_content(self, validator):
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"""Test validación de contenido prohibido."""
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# Modificar config temporalmente para test
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validator.config["validations"]["prohibited_content"]["prohibited_words"] = ["test_banned"]
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content = "Este contenido tiene test_banned palabra"
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result = validator.validate(content, "x")
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assert not result.passed
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assert any(i["type"] == "prohibited_content" for i in result.issues)
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def test_validate_empty_content_fails(self, validator):
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"""Test que contenido vacío falla."""
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content = " "
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result = validator.validate(content, "x")
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assert not result.passed
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assert any(i["type"] == "empty_content" for i in result.issues)
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def test_should_regenerate_respects_max_attempts(self, validator):
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"""Test que regeneración respeta máximo de intentos."""
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from app.services.ai.validator import ContentQualityResult, ValidationResult, ScoringResult
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mock_quality = ContentQualityResult(
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validation=ValidationResult(passed=True, issues=[], content="test"),
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scoring=ScoringResult(
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total_score=55,
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breakdown={},
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feedback="Needs improvement",
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is_top_performer=False,
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action="regenerate"
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),
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final_decision="regenerate",
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content="test"
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)
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# Primer intento: debería regenerar
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assert validator.should_regenerate(mock_quality, attempt=1)
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# Segundo intento: debería regenerar
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assert validator.should_regenerate(mock_quality, attempt=2)
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# Tercer intento: NO debería regenerar (max_attempts=2)
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assert not validator.should_regenerate(mock_quality, attempt=3)
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# ============================================================
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# Integration Tests
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# ============================================================
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class TestAIEngineIntegration:
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"""Tests de integración del motor completo."""
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@pytest.mark.asyncio
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async def test_full_generation_flow_mocked(self, mock_openai_client):
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"""Test flujo completo de generación con mock."""
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with patch('app.services.ai.generator.settings') as mock_settings:
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mock_settings.DEEPSEEK_API_KEY = "test-key"
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mock_settings.DEEPSEEK_BASE_URL = "https://api.deepseek.com"
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mock_settings.BUSINESS_NAME = "Test Corp"
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mock_settings.BUSINESS_LOCATION = "Test City"
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mock_settings.BUSINESS_WEBSITE = "test.com"
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mock_settings.CONTENT_TONE = "Professional"
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from app.services.ai.generator import ContentGeneratorV2
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generator = ContentGeneratorV2()
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generator._client = mock_openai_client
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result = await generator.generate(
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template_name="tip_tech",
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variables={"category": "productividad"},
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platform="x",
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use_context=False,
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use_few_shot=False
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)
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assert "content" in result
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assert "adapted_content" in result
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assert "metadata" in result
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assert result["metadata"]["template"] == "tip_tech"
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assert result["metadata"]["platform"] == "x"
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def test_prompt_library_to_adapter_flow(self):
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"""Test flujo de PromptLibrary a PlatformAdapter."""
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from app.services.ai.prompt_library import PromptLibrary
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from app.services.ai.platform_adapter import PlatformAdapter
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lib = PromptLibrary()
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adapter = PlatformAdapter()
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# Obtener límites de plataforma desde library
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limits_from_lib = lib.get_platform_limits("x")
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# Obtener límites desde adapter
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limits_from_adapter = adapter.get_limits("x")
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# Deberían ser consistentes
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assert limits_from_lib["max_characters"] == limits_from_adapter["max_characters"]
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# ============================================================
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# Fixtures adicionales
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# ============================================================
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@pytest.fixture
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def mock_openai_client():
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"""Mock OpenAI client for DeepSeek API tests."""
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mock_client = MagicMock()
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Generated test content #TechTip #AI"
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mock_response.usage = MagicMock()
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mock_response.usage.total_tokens = 100
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mock_client.chat.completions.create.return_value = mock_response
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return mock_client
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