Add Linker and Optimizer modules with API integration and frontend components
- Added Linker and Optimizer apps to `INSTALLED_APPS` in `settings.py`. - Configured API endpoints for Linker and Optimizer in `urls.py`. - Implemented `OptimizeContentFunction` for content optimization in the AI module. - Created prompts for content optimization and site structure generation. - Updated `OptimizerService` to utilize the new AI function for content optimization. - Developed frontend components including dashboards and content lists for Linker and Optimizer. - Integrated new routes and sidebar navigation for Linker and Optimizer in the frontend. - Enhanced content management with source and sync status filters in the Writer module. - Comprehensive test coverage added for new features and components.
This commit is contained in:
alorig
167
backend/igny8_core/ai/functions/optimize_content.py
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167
backend/igny8_core/ai/functions/optimize_content.py
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"""
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Optimize Content AI Function
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Phase 4 – Linker & Optimizer
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"""
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import json
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import logging
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from typing import Any, Dict
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from igny8_core.ai.base import BaseAIFunction
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from igny8_core.ai.prompts import PromptRegistry
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from igny8_core.business.content.models import Content
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logger = logging.getLogger(__name__)
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class OptimizeContentFunction(BaseAIFunction):
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"""AI function that optimizes content for SEO, readability, and engagement."""
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def get_name(self) -> str:
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return 'optimize_content'
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def get_metadata(self) -> Dict:
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metadata = super().get_metadata()
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metadata.update({
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'display_name': 'Optimize Content',
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'description': 'Optimize content for SEO, readability, and engagement.',
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'phases': {
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'INIT': 'Validating content data…',
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'PREP': 'Preparing content context…',
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'AI_CALL': 'Optimizing content with AI…',
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'PARSE': 'Parsing optimized content…',
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'SAVE': 'Saving optimized content…',
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'DONE': 'Content optimized!'
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}
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})
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return metadata
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def validate(self, payload: dict, account=None) -> Dict[str, Any]:
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if not payload.get('ids'):
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return {'valid': False, 'error': 'Content ID is required'}
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return {'valid': True}
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def prepare(self, payload: dict, account=None) -> Dict[str, Any]:
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content_ids = payload.get('ids', [])
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queryset = Content.objects.filter(id__in=content_ids)
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if account:
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queryset = queryset.filter(account=account)
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content = queryset.select_related('account', 'site', 'sector').first()
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if not content:
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raise ValueError("Content not found")
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# Get current scores from analyzer
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from igny8_core.business.optimization.services.analyzer import ContentAnalyzer
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analyzer = ContentAnalyzer()
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scores_before = analyzer.analyze(content)
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return {
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'content': content,
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'scores_before': scores_before,
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'html_content': content.html_content or '',
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'meta_title': content.meta_title or '',
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'meta_description': content.meta_description or '',
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'primary_keyword': content.primary_keyword or '',
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}
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def build_prompt(self, data: Dict[str, Any], account=None) -> str:
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content: Content = data['content']
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scores_before = data.get('scores_before', {})
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context = {
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'CONTENT_TITLE': content.title or 'Untitled',
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'HTML_CONTENT': data.get('html_content', ''),
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'META_TITLE': data.get('meta_title', ''),
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'META_DESCRIPTION': data.get('meta_description', ''),
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'PRIMARY_KEYWORD': data.get('primary_keyword', ''),
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'WORD_COUNT': str(content.word_count or 0),
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'CURRENT_SCORES': json.dumps(scores_before, indent=2),
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'SOURCE': content.source,
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'INTERNAL_LINKS_COUNT': str(len(content.internal_links) if content.internal_links else 0),
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}
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return PromptRegistry.get_prompt(
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'optimize_content',
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account=account or content.account,
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context=context
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)
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def parse_response(self, response: str, step_tracker=None) -> Dict[str, Any]:
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if not response:
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raise ValueError("AI response is empty")
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response = response.strip()
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try:
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return self._ensure_dict(json.loads(response))
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except json.JSONDecodeError:
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logger.warning("Response not valid JSON, attempting to extract JSON object")
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cleaned = self._extract_json_object(response)
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if cleaned:
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return self._ensure_dict(json.loads(cleaned))
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raise ValueError("Unable to parse AI response into JSON")
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def save_output(
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self,
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parsed: Dict[str, Any],
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original_data: Dict[str, Any],
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account=None,
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progress_tracker=None,
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step_tracker=None
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) -> Dict[str, Any]:
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content: Content = original_data['content']
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# Extract optimized content
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optimized_html = parsed.get('html_content') or parsed.get('content') or content.html_content
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optimized_meta_title = parsed.get('meta_title') or content.meta_title
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optimized_meta_description = parsed.get('meta_description') or content.meta_description
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# Update content
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content.html_content = optimized_html
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if optimized_meta_title:
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content.meta_title = optimized_meta_title
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if optimized_meta_description:
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content.meta_description = optimized_meta_description
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# Recalculate word count
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from igny8_core.business.content.services.content_generation_service import ContentGenerationService
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content_service = ContentGenerationService()
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content.word_count = content_service._count_words(optimized_html)
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# Increment optimizer version
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content.optimizer_version += 1
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# Get scores after optimization
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from igny8_core.business.optimization.services.analyzer import ContentAnalyzer
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analyzer = ContentAnalyzer()
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scores_after = analyzer.analyze(content)
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content.optimization_scores = scores_after
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content.save(update_fields=[
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'html_content', 'meta_title', 'meta_description',
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'word_count', 'optimizer_version', 'optimization_scores', 'updated_at'
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])
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return {
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'success': True,
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'content_id': content.id,
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'scores_before': original_data.get('scores_before', {}),
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'scores_after': scores_after,
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'word_count_before': original_data.get('word_count', 0),
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'word_count_after': content.word_count,
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'html_content': optimized_html,
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'meta_title': optimized_meta_title,
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'meta_description': optimized_meta_description,
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}
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# Helper methods
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def _ensure_dict(self, data: Any) -> Dict[str, Any]:
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if isinstance(data, dict):
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return data
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raise ValueError("AI response must be a JSON object")
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def _extract_json_object(self, text: str) -> str:
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start = text.find('{')
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end = text.rfind('}')
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if start != -1 and end != -1 and end > start:
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return text[start:end + 1]
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return ''
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2
backend/igny8_core/ai/functions/tests/__init__.py
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2
backend/igny8_core/ai/functions/tests/__init__.py
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@@ -0,0 +1,2 @@
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# AI functions tests
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179
backend/igny8_core/ai/functions/tests/test_optimize_content.py
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179
backend/igny8_core/ai/functions/tests/test_optimize_content.py
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@@ -0,0 +1,179 @@
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"""
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Tests for OptimizeContentFunction
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"""
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from unittest.mock import Mock, patch, MagicMock
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from django.test import TestCase
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from igny8_core.business.content.models import Content
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from igny8_core.ai.functions.optimize_content import OptimizeContentFunction
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from igny8_core.api.tests.test_integration_base import IntegrationTestBase
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class OptimizeContentFunctionTests(IntegrationTestBase):
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"""Tests for OptimizeContentFunction"""
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def setUp(self):
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super().setUp()
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self.function = OptimizeContentFunction()
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# Create test content
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self.content = Content.objects.create(
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account=self.account,
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site=self.site,
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sector=self.sector,
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title="Test Content",
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html_content="<p>This is test content.</p>",
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meta_title="Test Title",
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meta_description="Test description",
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primary_keyword="test keyword",
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word_count=500,
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status='draft'
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)
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def test_function_validation_phase(self):
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"""Test validation phase"""
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# Valid payload
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result = self.function.validate({'ids': [self.content.id]}, self.account)
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self.assertTrue(result['valid'])
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# Invalid payload - missing ids
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result = self.function.validate({}, self.account)
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self.assertFalse(result['valid'])
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self.assertIn('error', result)
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def test_function_prep_phase(self):
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"""Test prep phase"""
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payload = {'ids': [self.content.id]}
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data = self.function.prepare(payload, self.account)
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self.assertIn('content', data)
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self.assertIn('scores_before', data)
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self.assertIn('html_content', data)
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self.assertEqual(data['content'].id, self.content.id)
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def test_function_prep_phase_content_not_found(self):
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"""Test prep phase with non-existent content"""
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payload = {'ids': [99999]}
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with self.assertRaises(ValueError):
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self.function.prepare(payload, self.account)
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@patch('igny8_core.ai.functions.optimize_content.PromptRegistry.get_prompt')
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def test_function_build_prompt(self, mock_get_prompt):
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"""Test prompt building"""
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mock_get_prompt.return_value = "Test prompt"
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data = {
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'content': self.content,
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'html_content': '<p>Test</p>',
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'meta_title': 'Title',
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'meta_description': 'Description',
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'primary_keyword': 'keyword',
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'scores_before': {'overall_score': 50.0}
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}
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prompt = self.function.build_prompt(data, self.account)
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self.assertEqual(prompt, "Test prompt")
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mock_get_prompt.assert_called_once()
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# Check that context was passed
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call_args = mock_get_prompt.call_args
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self.assertIn('context', call_args.kwargs)
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def test_function_parse_response_valid_json(self):
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"""Test parsing valid JSON response"""
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response = '{"html_content": "<p>Optimized</p>", "meta_title": "New Title"}'
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parsed = self.function.parse_response(response)
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self.assertIn('html_content', parsed)
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self.assertEqual(parsed['html_content'], "<p>Optimized</p>")
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self.assertEqual(parsed['meta_title'], "New Title")
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def test_function_parse_response_invalid_json(self):
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"""Test parsing invalid JSON response"""
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response = "This is not JSON"
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with self.assertRaises(ValueError):
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self.function.parse_response(response)
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def test_function_parse_response_extracts_json_object(self):
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"""Test that JSON object is extracted from text"""
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response = 'Some text {"html_content": "<p>Optimized</p>"} more text'
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parsed = self.function.parse_response(response)
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self.assertIn('html_content', parsed)
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self.assertEqual(parsed['html_content'], "<p>Optimized</p>")
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@patch('igny8_core.business.optimization.services.analyzer.ContentAnalyzer.analyze')
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@patch('igny8_core.business.content.services.content_generation_service.ContentGenerationService._count_words')
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def test_function_save_phase(self, mock_count_words, mock_analyze):
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"""Test save phase updates content"""
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mock_count_words.return_value = 600
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mock_analyze.return_value = {
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'seo_score': 75.0,
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'readability_score': 80.0,
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'engagement_score': 70.0,
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'overall_score': 75.0
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}
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parsed = {
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'html_content': '<p>Optimized content.</p>',
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'meta_title': 'Optimized Title',
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'meta_description': 'Optimized Description'
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}
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original_data = {
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'content': self.content,
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'scores_before': {'overall_score': 50.0},
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'word_count': 500
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}
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result = self.function.save_output(parsed, original_data, self.account)
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self.assertTrue(result['success'])
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self.assertEqual(result['content_id'], self.content.id)
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# Refresh content from DB
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self.content.refresh_from_db()
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self.assertEqual(self.content.html_content, '<p>Optimized content.</p>')
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self.assertEqual(self.content.optimizer_version, 1)
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self.assertIsNotNone(self.content.optimization_scores)
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def test_function_handles_invalid_content_id(self):
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"""Test that function handles invalid content ID"""
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payload = {'ids': [99999]}
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with self.assertRaises(ValueError):
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self.function.prepare(payload, self.account)
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def test_function_respects_account_isolation(self):
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"""Test that function respects account isolation"""
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from igny8_core.auth.models import Account
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other_account = Account.objects.create(
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name="Other Account",
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slug="other",
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plan=self.plan,
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owner=self.user
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)
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payload = {'ids': [self.content.id]}
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# Should not find content from different account
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with self.assertRaises(ValueError):
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self.function.prepare(payload, other_account)
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def test_get_name(self):
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"""Test get_name method"""
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self.assertEqual(self.function.get_name(), 'optimize_content')
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def test_get_metadata(self):
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"""Test get_metadata method"""
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metadata = self.function.get_metadata()
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self.assertIn('display_name', metadata)
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self.assertIn('description', metadata)
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self.assertIn('phases', metadata)
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self.assertEqual(metadata['display_name'], 'Optimize Content')
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@@ -332,6 +332,62 @@ Make sure each prompt is detailed enough for image generation, describing the vi
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'image_prompt_template': 'Create a high-quality {image_type} image to use as a featured photo for a blog post titled "{post_title}". The image should visually represent the theme, mood, and subject implied by the image prompt: {image_prompt}. Focus on a realistic, well-composed scene that naturally communicates the topic without text or logos. Use balanced lighting, pleasing composition, and photographic detail suitable for lifestyle or editorial web content. Avoid adding any visible or readable text, brand names, or illustrative effects. **And make sure image is not blurry.**',
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'negative_prompt': 'text, watermark, logo, overlay, title, caption, writing on walls, writing on objects, UI, infographic elements, post title',
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'optimize_content': """You are an expert content optimizer specializing in SEO, readability, and engagement.
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Your task is to optimize the provided content to improve its SEO score, readability, and engagement metrics.
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CURRENT CONTENT:
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Title: {CONTENT_TITLE}
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Word Count: {WORD_COUNT}
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Source: {SOURCE}
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Primary Keyword: {PRIMARY_KEYWORD}
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Internal Links: {INTERNAL_LINKS_COUNT}
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CURRENT META DATA:
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Meta Title: {META_TITLE}
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Meta Description: {META_DESCRIPTION}
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CURRENT SCORES:
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{CURRENT_SCORES}
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HTML CONTENT:
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{HTML_CONTENT}
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OPTIMIZATION REQUIREMENTS:
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1. SEO Optimization:
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- Ensure meta title is 30-60 characters (if provided)
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- Ensure meta description is 120-160 characters (if provided)
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- Optimize primary keyword usage (natural, not keyword stuffing)
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- Improve heading structure (H1, H2, H3 hierarchy)
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- Add internal links where relevant (maintain existing links)
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2. Readability:
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- Average sentence length: 15-20 words
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- Use clear, concise language
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- Break up long paragraphs
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- Use bullet points and lists where appropriate
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- Ensure proper paragraph structure
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3. Engagement:
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- Add compelling headings
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- Include relevant images placeholders (alt text)
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- Use engaging language
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- Create clear call-to-action sections
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- Improve content flow and structure
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OUTPUT FORMAT:
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Return ONLY a JSON object in this format:
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{{
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"html_content": "[Optimized HTML content]",
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"meta_title": "[Optimized meta title, 30-60 chars]",
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"meta_description": "[Optimized meta description, 120-160 chars]",
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"optimization_notes": "[Brief notes on what was optimized]"
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}}
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Do not include any explanations, text, or commentary outside the JSON output.
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""",
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}
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# Mapping from function names to prompt types
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@@ -343,6 +399,7 @@ Make sure each prompt is detailed enough for image generation, describing the vi
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'extract_image_prompts': 'image_prompt_extraction',
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'generate_image_prompts': 'image_prompt_extraction',
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'generate_site_structure': 'site_structure_generation',
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'optimize_content': 'optimize_content',
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}
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@classmethod
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@@ -99,10 +99,16 @@ def _load_generate_site_structure():
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from igny8_core.ai.functions.generate_site_structure import GenerateSiteStructureFunction
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return GenerateSiteStructureFunction
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def _load_optimize_content():
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"""Lazy loader for optimize_content function"""
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from igny8_core.ai.functions.optimize_content import OptimizeContentFunction
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return OptimizeContentFunction
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register_lazy_function('auto_cluster', _load_auto_cluster)
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register_lazy_function('generate_ideas', _load_generate_ideas)
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register_lazy_function('generate_content', _load_generate_content)
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register_lazy_function('generate_images', _load_generate_images)
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register_lazy_function('generate_image_prompts', _load_generate_image_prompts)
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register_lazy_function('generate_site_structure', _load_generate_site_structure)
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register_lazy_function('optimize_content', _load_optimize_content)
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