82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
"""
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Section Enhancer - AI-powered section enhancement and improvement.
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Enhances individual outline sections for better engagement and value.
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"""
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from loguru import logger
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from models.blog_models import BlogOutlineSection
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class SectionEnhancer:
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"""Enhances individual outline sections using AI."""
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async def enhance(self, section: BlogOutlineSection, focus: str = "general improvement") -> BlogOutlineSection:
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"""Enhance a section using AI with research context."""
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enhancement_prompt = f"""
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Enhance the following blog section to make it more engaging, comprehensive, and valuable:
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Current Section:
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Heading: {section.heading}
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Subheadings: {', '.join(section.subheadings)}
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Key Points: {', '.join(section.key_points)}
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Target Words: {section.target_words}
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Keywords: {', '.join(section.keywords)}
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Enhancement Focus: {focus}
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Improve:
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1. Make subheadings more specific and actionable
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2. Add more comprehensive key points with data/insights
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3. Include practical examples and case studies
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4. Address common questions and objections
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5. Optimize for SEO with better keyword integration
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Respond with JSON:
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{{
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"heading": "Enhanced heading",
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"subheadings": ["enhanced subheading 1", "enhanced subheading 2"],
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"key_points": ["enhanced point 1", "enhanced point 2"],
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"target_words": 400,
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"keywords": ["keyword1", "keyword2"]
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}}
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"""
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try:
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from services.llm_providers.gemini_provider import gemini_structured_json_response
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enhancement_schema = {
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"type": "object",
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"properties": {
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"heading": {"type": "string"},
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"subheadings": {"type": "array", "items": {"type": "string"}},
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"key_points": {"type": "array", "items": {"type": "string"}},
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"target_words": {"type": "integer"},
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"keywords": {"type": "array", "items": {"type": "string"}}
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},
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"required": ["heading", "subheadings", "key_points", "target_words", "keywords"]
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}
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enhanced_data = gemini_structured_json_response(
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prompt=enhancement_prompt,
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schema=enhancement_schema,
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temperature=0.4,
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max_tokens=1000
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)
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if isinstance(enhanced_data, dict) and 'error' not in enhanced_data:
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return BlogOutlineSection(
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id=section.id,
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heading=enhanced_data.get('heading', section.heading),
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subheadings=enhanced_data.get('subheadings', section.subheadings),
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key_points=enhanced_data.get('key_points', section.key_points),
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references=section.references,
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target_words=enhanced_data.get('target_words', section.target_words),
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keywords=enhanced_data.get('keywords', section.keywords)
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)
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except Exception as e:
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logger.warning(f"AI section enhancement failed: {e}")
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return section
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