d90d441019
- Blog writer enhancements and bug fixes - Wix integration improvements - Frontend UI updates - GSC dashboard docs cleanup - Image studio assets - LinkedIn requirements file - Various dependency updates
104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
"""
|
|
Content Angle Generator - AI-powered content angle discovery.
|
|
|
|
Generates strategic content angles from research content for blog posts.
|
|
"""
|
|
|
|
from typing import List
|
|
from loguru import logger
|
|
|
|
|
|
class ContentAngleGenerator:
|
|
"""Generates strategic content angles from research content."""
|
|
|
|
def generate(self, content: str, topic: str, industry: str, user_id: str = None) -> List[str]:
|
|
"""Parse strategic content angles from the research content using AI."""
|
|
angles_prompt = f"""
|
|
Analyze the following research content and create strategic content angles for: {topic} in {industry}
|
|
|
|
Research Content:
|
|
{content[:8000]}
|
|
|
|
Create 7 compelling content angles that:
|
|
1. Leverage current trends and data from the research
|
|
2. Address content gaps and opportunities
|
|
3. Appeal to different audience segments
|
|
4. Include unique perspectives not covered by competitors
|
|
5. Incorporate specific statistics, case studies, or expert insights
|
|
6. Create emotional connection and urgency
|
|
7. Provide actionable value to readers
|
|
|
|
Each angle should be:
|
|
- Specific and data-driven
|
|
- Unique and differentiated
|
|
- Compelling and click-worthy
|
|
- Actionable for readers
|
|
|
|
Respond with JSON:
|
|
{{
|
|
"content_angles": [
|
|
"Specific angle 1 with data/trends",
|
|
"Specific angle 2 with unique perspective",
|
|
"Specific angle 3 with actionable insights",
|
|
"Specific angle 4 with case study focus",
|
|
"Specific angle 5 with future outlook",
|
|
"Specific angle 6 with problem-solving focus",
|
|
"Specific angle 7 with industry insights"
|
|
]
|
|
}}
|
|
"""
|
|
|
|
from services.llm_providers.main_text_generation import llm_text_gen
|
|
|
|
angles_schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"content_angles": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"minItems": 5,
|
|
"maxItems": 7
|
|
}
|
|
},
|
|
"required": ["content_angles"]
|
|
}
|
|
|
|
raw = llm_text_gen(
|
|
prompt=angles_prompt,
|
|
user_id=user_id
|
|
)
|
|
|
|
# Parse JSON from LLM response (works with both string and dict return types)
|
|
import json, re
|
|
if isinstance(raw, str):
|
|
cleaned = raw.strip()
|
|
if cleaned.startswith('```json'):
|
|
cleaned = cleaned[7:]
|
|
if cleaned.startswith('```'):
|
|
cleaned = cleaned[3:]
|
|
if cleaned.endswith('```'):
|
|
cleaned = cleaned[:-3]
|
|
cleaned = cleaned.strip()
|
|
try:
|
|
angles_result = json.loads(cleaned)
|
|
except json.JSONDecodeError:
|
|
json_match = re.search(r'\{.*\}', cleaned, re.DOTALL)
|
|
if json_match:
|
|
angles_result = json.loads(json_match.group(0))
|
|
else:
|
|
raise ValueError(f"Content angles returned non-JSON string: {cleaned[:200]}")
|
|
elif isinstance(raw, dict):
|
|
angles_result = raw
|
|
else:
|
|
raise ValueError(f"Unexpected LLM response type: {type(raw)}")
|
|
|
|
if 'error' in angles_result:
|
|
raise ValueError(f"Content angles generation failed: {angles_result.get('error', 'Unknown error')}")
|
|
|
|
if 'content_angles' not in angles_result:
|
|
raise ValueError(f"Content angles missing from response")
|
|
|
|
logger.info("✅ AI content angles generation completed successfully")
|
|
return angles_result['content_angles'][:7]
|
|
|