122 lines
5.0 KiB
Python
122 lines
5.0 KiB
Python
"""
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Outline Optimizer - AI-powered outline optimization and rebalancing.
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Optimizes outlines for better flow, SEO, and engagement.
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"""
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from typing import List
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from loguru import logger
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from models.blog_models import BlogOutlineSection
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class OutlineOptimizer:
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"""Optimizes outlines for better flow, SEO, and engagement."""
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async def optimize(self, outline: List[BlogOutlineSection], focus: str = "general optimization") -> List[BlogOutlineSection]:
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"""Optimize entire outline for better flow, SEO, and engagement."""
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outline_text = "\n".join([f"{i+1}. {s.heading}" for i, s in enumerate(outline)])
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optimization_prompt = f"""Optimize this blog outline for better flow, engagement, and SEO:
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Current Outline:
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{outline_text}
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Optimization Focus: {focus}
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Goals: Improve narrative flow, enhance SEO, increase engagement, ensure comprehensive coverage.
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Return JSON format:
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{{
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"outline": [
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{{
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"heading": "Optimized heading",
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"subheadings": ["subheading 1", "subheading 2"],
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"key_points": ["point 1", "point 2"],
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"target_words": 300,
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"keywords": ["keyword1", "keyword2"]
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}}
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]
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}}"""
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try:
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from services.llm_providers.main_text_generation import llm_text_gen
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optimization_schema = {
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"type": "object",
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"properties": {
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"outline": {
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"type": "array",
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"items": {
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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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}
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},
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"required": ["outline"],
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"propertyOrdering": ["outline"]
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}
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optimized_data = llm_text_gen(
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prompt=optimization_prompt,
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json_struct=optimization_schema,
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system_prompt=None
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)
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# Handle the new schema format with "outline" wrapper
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if isinstance(optimized_data, dict) and 'outline' in optimized_data:
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optimized_sections = []
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for i, section_data in enumerate(optimized_data['outline']):
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section = BlogOutlineSection(
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id=f"s{i+1}",
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heading=section_data.get('heading', f'Section {i+1}'),
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subheadings=section_data.get('subheadings', []),
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key_points=section_data.get('key_points', []),
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references=outline[i].references if i < len(outline) else [],
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target_words=section_data.get('target_words', 300),
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keywords=section_data.get('keywords', [])
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)
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optimized_sections.append(section)
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logger.info(f"✅ Outline optimization completed: {len(optimized_sections)} sections optimized")
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return optimized_sections
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else:
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logger.warning(f"Invalid optimization response format: {type(optimized_data)}")
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except Exception as e:
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logger.warning(f"AI outline optimization failed: {e}")
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logger.info("Returning original outline without optimization")
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return outline
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def rebalance_word_counts(self, outline: List[BlogOutlineSection], target_words: int) -> List[BlogOutlineSection]:
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"""Rebalance word count distribution across sections."""
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total_sections = len(outline)
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if total_sections == 0:
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return outline
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# Calculate target distribution
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intro_words = int(target_words * 0.12) # 12% for intro
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conclusion_words = int(target_words * 0.12) # 12% for conclusion
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main_content_words = target_words - intro_words - conclusion_words
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# Distribute main content words across sections
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words_per_section = main_content_words // total_sections
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remainder = main_content_words % total_sections
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for i, section in enumerate(outline):
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if i == 0: # First section (intro)
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section.target_words = intro_words
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elif i == total_sections - 1: # Last section (conclusion)
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section.target_words = conclusion_words
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else: # Main content sections
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section.target_words = words_per_section + (1 if i < remainder else 0)
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return outline
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