928c2f20aa
- Fix text selection menu not showing: wire contentRef via inputRef on multiline TextField - Fix blog title not truncating: add min-w-0 for flex item overflow - Fix outline generation 500: escape curly braces in f-string prompt template - Fix content generation 'NoneType not callable': replace SessionLocal() with get_session_for_user(), add db param to MediumBlogGenerator, fix signature mismatch in database_task_manager - Fix writing assistant suggest 500: add auth + user_id to API endpoint and service, replace sync requests with httpx.AsyncClient - Fix hallucination detector 404: explicitly include router in main.py and app.py - Fix missing error_data in task failure responses - Hide CopilotKit web inspector button - Remove hardcoded fallback suggestions from SmartTypingAssist - Fix stale closure refs in SmartTypingAssist handleTypingChange - Add two-column editor layout, stats bar, section hover menu - Various subscription, billing, and research module improvements
128 lines
5.1 KiB
Python
128 lines
5.1 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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import json
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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, user_id: str) -> BlogOutlineSection:
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"""Enhance a section using AI with research context.
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Args:
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section: Outline section to enhance
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focus: Enhancement focus (e.g., "general improvement")
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user_id: User ID (required for subscription checks and usage tracking)
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Returns:
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Enhanced outline section
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Raises:
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ValueError: If user_id is not provided
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"""
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if not user_id:
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raise ValueError("user_id is required for section enhancement (subscription checks and usage tracking)")
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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.main_text_generation import llm_text_gen
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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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raw = llm_text_gen(
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prompt=enhancement_prompt,
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system_prompt=None,
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user_id=user_id
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)
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# Parse JSON from LLM response (works with both string and dict return types)
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import re
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if isinstance(raw, str):
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cleaned = raw.strip()
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if cleaned.startswith('```json'):
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cleaned = cleaned[7:]
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if cleaned.startswith('```'):
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cleaned = cleaned[3:]
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if cleaned.endswith('```'):
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cleaned = cleaned[:-3]
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cleaned = cleaned.strip()
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try:
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enhanced_data = json.loads(cleaned)
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except json.JSONDecodeError:
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json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
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if json_match:
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try:
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enhanced_data = json.loads(json_match.group(0))
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except json.JSONDecodeError as e:
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logger.warning(f"Section enhancement returned invalid JSON: {e}")
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return section
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else:
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logger.warning(f"Section enhancement returned non-JSON string: {cleaned[:200]}")
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return section
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elif isinstance(raw, dict):
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enhanced_data = raw
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else:
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logger.warning(f"Unexpected LLM response type: {type(raw)}")
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return section
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if 'error' in enhanced_data:
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logger.warning(f"AI section enhancement failed: {enhanced_data.get('error', 'Unknown error')}")
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else:
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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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