182 lines
8.4 KiB
Python
182 lines
8.4 KiB
Python
"""
|
|
Prompt Builder - Handles building of AI prompts for outline generation.
|
|
|
|
Constructs comprehensive prompts using curated keyword payloads,
|
|
research data, and strategic requirements.
|
|
"""
|
|
|
|
from typing import Dict, Any, List
|
|
from datetime import datetime
|
|
|
|
|
|
class PromptBuilder:
|
|
"""Handles building of comprehensive AI prompts for outline generation."""
|
|
|
|
def __init__(self):
|
|
"""Initialize the prompt builder."""
|
|
pass
|
|
|
|
def build_outline_prompt(self, curated_keywords: Dict[str, Any],
|
|
content_angles: List[str], sources: List, search_intent: str,
|
|
request, custom_instructions: str = None,
|
|
selected_content_angle: str = None,
|
|
selected_competitive_advantage: str = None) -> str:
|
|
"""Build the comprehensive outline generation prompt using curated keyword payload."""
|
|
|
|
research = request.research
|
|
|
|
primary_kw_text = ', '.join(curated_keywords.get('primary', [])) if curated_keywords.get('primary') else (request.topic or ', '.join(getattr(request.research, 'original_keywords', []) or ['the target topic']))
|
|
secondary_kw_text = ', '.join(curated_keywords.get('secondary', [])) if curated_keywords.get('secondary') else "None provided"
|
|
long_tail_text = ', '.join(curated_keywords.get('long_tail', [])) if curated_keywords.get('long_tail') else "None discovered"
|
|
semantic_text = ', '.join(curated_keywords.get('semantic', [])) if curated_keywords.get('semantic') else "None discovered"
|
|
trending_text = ', '.join(curated_keywords.get('trending', [])) if curated_keywords.get('trending') else "None discovered"
|
|
content_gap_text = ', '.join(curated_keywords.get('content_gap', [])) if curated_keywords.get('content_gap') else "None identified"
|
|
|
|
content_angle_text = ', '.join(content_angles) if content_angles else "No explicit angles provided; infer compelling angles from research insights."
|
|
competitor_text = ', '.join(research.competitor_analysis.get('top_competitors', [])) if research and research.competitor_analysis else "Not available"
|
|
opportunity_text = ', '.join(research.competitor_analysis.get('opportunities', [])) if research and research.competitor_analysis else "Not available"
|
|
advantages_text = ', '.join(research.competitor_analysis.get('competitive_advantages', [])) if research and research.competitor_analysis else "Not available"
|
|
|
|
# Extract additional UI-mapped context fields
|
|
analysis_insights_text = (research.keyword_analysis.get('analysis_insights', '') or '') if research and research.keyword_analysis else ''
|
|
market_positioning_text = (research.competitor_analysis.get('market_positioning', '') or '') if research and research.competitor_analysis else ''
|
|
difficulty_score = research.keyword_analysis.get('difficulty', None) if research and research.keyword_analysis else None
|
|
|
|
# Build selected angle prominence section
|
|
if selected_content_angle and selected_content_angle.strip():
|
|
selected_angle_section = f"""
|
|
PRIORITY CONTENT ANGLE (MUST PRIORITIZE):
|
|
- This outline MUST be built around the following selected content angle as its primary lens and narrative framework:
|
|
"{selected_content_angle}"
|
|
- Every major section should connect back to this angle
|
|
- Title options should reflect this angle
|
|
- The overall narrative arc should follow this angle's implied storyline
|
|
"""
|
|
else:
|
|
selected_angle_section = ""
|
|
|
|
# Build selected competitive advantage prominence section
|
|
if selected_competitive_advantage and selected_competitive_advantage.strip():
|
|
selected_advantage_section = f"""
|
|
PRIORITY COMPETITIVE ADVANTAGE (MUST LEVERAGE):
|
|
- This outline MUST prominently feature and leverage the following competitive advantage throughout the content:
|
|
"{selected_competitive_advantage}"
|
|
- Weave this advantage into key sections as a differentiator
|
|
- Frame the solutions and recommendations around this advantage
|
|
- Use this advantage to counter competitor weaknesses mentioned in research
|
|
"""
|
|
else:
|
|
selected_advantage_section = ""
|
|
|
|
# Import and use the KeywordCurator for the directive section
|
|
from .keyword_curator import KeywordCurator
|
|
keyword_directives = KeywordCurator().format_for_prompt(curated_keywords)
|
|
|
|
current_date = datetime.now().strftime("%B %d, %Y")
|
|
current_year = datetime.now().year
|
|
|
|
return f"""Create a comprehensive blog outline for: {primary_kw_text}
|
|
|
|
CONTEXT:
|
|
Current Date: {current_date}
|
|
Search Intent: {search_intent}
|
|
{f"Keyword Difficulty: {difficulty_score}/10" if difficulty_score is not None else ""}
|
|
Target: {request.word_count or 1500} words
|
|
Industry: {getattr(request.persona, 'industry', 'General') if request.persona else 'General'}
|
|
Audience: {getattr(request.persona, 'target_audience', 'General') if request.persona else 'General'}
|
|
|
|
OVERVIEW KEYWORD SUMMARY:
|
|
- Primary: {primary_kw_text}
|
|
- Secondary: {secondary_kw_text}
|
|
- Long-tail: {long_tail_text}
|
|
- Semantic: {semantic_text}
|
|
- Trending: {trending_text}
|
|
- Content Gap: {content_gap_text}
|
|
|
|
{keyword_directives}
|
|
|
|
RESEARCH INSIGHTS SYNTHESIS:
|
|
{analysis_insights_text}
|
|
|
|
CONTENT ANGLES / STORYLINES: {content_angle_text}
|
|
{selected_angle_section}
|
|
{selected_advantage_section}
|
|
COMPETITIVE INTELLIGENCE:
|
|
Top Competitors: {competitor_text}
|
|
Market Opportunities: {opportunity_text}
|
|
Competitive Advantages: {advantages_text}
|
|
{f"Market Positioning: {market_positioning_text}" if market_positioning_text else ""}
|
|
|
|
RESEARCH SOURCES: {len(sources)} authoritative sources available
|
|
|
|
{f"CUSTOM INSTRUCTIONS: {custom_instructions}" if custom_instructions else ""}
|
|
|
|
STRATEGIC REQUIREMENTS:
|
|
- MUST prioritize and anchor the outline around the selected content angle above all others
|
|
- MUST highlight and leverage the selected competitive advantage as a key differentiator
|
|
- Follow the KEYWORD PLACEMENT DIRECTIVES — treat the locked keywords as the minimum anchor set; you MAY include closely related intent-matching variations where natural
|
|
- Create SEO-optimized headings with natural keyword integration
|
|
- Surface the strongest research-backed angles within the outline
|
|
- Build logical narrative flow from problem to solution
|
|
- Include data-driven insights from research sources
|
|
- Address content gaps and market opportunities
|
|
- Optimize for search intent and user questions
|
|
- Ensure engaging, actionable content throughout
|
|
|
|
Return JSON format:
|
|
{{
|
|
"title_options": [
|
|
"Title option 1",
|
|
"Title option 2",
|
|
"Title option 3"
|
|
],
|
|
"outline": [
|
|
{{
|
|
"heading": "Section heading",
|
|
"subheadings": ["Subheading 1", "Subheading 2", "Subheading 3"],
|
|
"key_points": ["Key point 1", "Key point 2", "Key point 3"],
|
|
"target_words": 300,
|
|
"keywords": ["keyword 1", "keyword 2"]
|
|
}}
|
|
]
|
|
}}"""
|
|
|
|
def get_outline_schema(self) -> Dict[str, Any]:
|
|
"""Get the structured JSON schema for outline generation."""
|
|
return {
|
|
"type": "object",
|
|
"properties": {
|
|
"title_options": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "string"
|
|
}
|
|
},
|
|
"outline": {
|
|
"type": "array",
|
|
"items": {
|
|
"type": "object",
|
|
"properties": {
|
|
"heading": {"type": "string"},
|
|
"subheadings": {
|
|
"type": "array",
|
|
"items": {"type": "string"}
|
|
},
|
|
"key_points": {
|
|
"type": "array",
|
|
"items": {"type": "string"}
|
|
},
|
|
"target_words": {"type": "integer"},
|
|
"keywords": {
|
|
"type": "array",
|
|
"items": {"type": "string"}
|
|
}
|
|
},
|
|
"required": ["heading", "subheadings", "key_points", "target_words", "keywords"]
|
|
}
|
|
}
|
|
},
|
|
"required": ["title_options", "outline"],
|
|
"propertyOrdering": ["title_options", "outline"]
|
|
}
|