kunthawat/ALwrity
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Recovered state: integrated TrendSurferAgent, restored frontend/backend files, and cleaned up recovery scripts

Browse Source
This commit is contained in:
ajaysi
2026-02-08 13:56:57 +05:30
parent 1db10ccd0f
commit e404a86502
333 changed files with 42223 additions and 10875 deletions
Download Patch File Download Diff File Expand all files Collapse all files

115
backend/services/ai_service_manager.py
View File

@@ -87,37 +87,25 @@ class AIServiceManager:
"""Load centralized AI prompts."""
return {
'content_gap_analysis': """
As an expert SEO content strategist with 15+ years of experience in content marketing and competitive analysis, analyze this comprehensive content gap analysis data and provide actionable strategic insights:
As an expert SEO content strategist, analyze the provided client profile and competitive landscape to find specific content gaps.
TARGET ANALYSIS:
- Website: {target_url}
- Industry: {industry}
- SERP Opportunities: {serp_opportunities} keywords not ranking
- Keyword Expansion: {expanded_keywords_count} additional keywords identified
- Competitors Analyzed: {competitors_analyzed} websites
- Content Quality Score: {content_quality_score}/10
- Market Competition Level: {competition_level}
CLIENT PROFILE & COMPETITIVE DATA:
{analysis_data}
DOMINANT CONTENT THEMES:
{dominant_themes}
CRITICAL INSTRUCTIONS:
1. **HYPER-RELEVANCE**: Recommendations must be strictly about the client's specific niche (e.g., if "Vegan Cooking", don't suggest "Steak recipes" or "Cloud Hosting").
2. **LOW-HANGING FRUIT**: Identify topics competitors are covering but the client is missing, or topics where competitors have weak content.
3. **SPECIFIC TITLES**: Suggest actual blog post titles or keywords, not generic categories (e.g., suggest "Best Vegan Cheese for Pizza 2024" instead of "Cheese reviews").
COMPETITIVE LANDSCAPE:
{competitive_landscape}
PROVIDE CONTENT GAPS (JSON Format):
1. **Low Hanging Fruit (Content Recommendations)**:
- recommendation: A specific, high-potential content topic or title.
- priority: High/Medium/Low.
- estimated_traffic: A realistic estimate (e.g., "Medium", "High", or numeric range).
- roi_estimate: Why this brings value (e.g., "High conversion intent").
- implementation_time: e.g., "2-4 hours".
PROVIDE COMPREHENSIVE ANALYSIS:
1. Strategic Content Gap Analysis (identify 3-5 major gaps with impact assessment)
2. Priority Content Recommendations (top 5 with ROI estimates)
3. Keyword Strategy Insights (trending, seasonal, long-tail opportunities)
4. Competitive Positioning Advice (differentiation strategies)
5. Content Format Recommendations (video, interactive, comprehensive guides)
6. Technical SEO Opportunities (structured data, schema markup)
7. Implementation Timeline (30/60/90 days with milestones)
8. Risk Assessment and Mitigation Strategies
9. Success Metrics and KPIs
10. Resource Allocation Recommendations
Consider user intent, search behavior patterns, and content consumption trends in your analysis.
Format as structured JSON with clear, actionable recommendations and confidence scores.
Format as structured JSON matching the schema exactly.
""",
'market_position_analysis': """
@@ -203,30 +191,24 @@ Format as structured JSON with detailed predictions and actionable insights.
""",
'strategic_intelligence': """
As a senior content strategy consultant with expertise in digital marketing, competitive intelligence, and strategic planning, generate comprehensive strategic insights:
As a senior content strategy consultant with expertise in digital marketing, competitive intelligence, and strategic planning, generate comprehensive strategic insights.
ANALYSIS DATA:
ANALYSIS DATA (Includes Advertools site hierarchy and word frequency themes):
{analysis_data}
STRATEGIC CONTEXT:
- Business Objectives: {business_objectives}
- Target Audience: {target_audience}
- Competitive Landscape: {competitive_landscape}
- Market Opportunities: {market_opportunities}
CRITICAL INSTRUCTIONS:
1. **DATA-DRIVEN PRECISION**: Use the `augmented_themes` and `competitor_content_themes` to identify specific topic authority shifts.
2. **STRICT NICHE RELEVANCE**: Only suggest actions relevant to the user's specific industry and topics. Avoid generic tech/cloud storage jargon unless that is the user's niche.
3. **SITE HIERARCHY INSIGHTS**: Analyze the `competitor_hierarchies` to suggest structural improvements to the user's website.
4. **STALE CONTENT STRATEGY**: If stale content is detected in market intelligence, suggest a "Refresh & Relaunch" strategy.
PROVIDE STRATEGIC INTELLIGENCE:
1. Content Strategy Recommendations (pillar content, topic clusters)
2. Competitive Positioning Advice (differentiation strategies)
1. Content Strategy Recommendations (pillar content, topic clusters based on themes)
2. Competitive Positioning Advice (differentiation strategies using site hierarchy)
3. Content Optimization Suggestions (quality, format, frequency)
4. Innovation Opportunities (emerging trends, new formats)
5. Risk Mitigation Strategies (competitive threats, algorithm changes)
6. Resource Allocation (budget, team, timeline)
7. Performance Optimization (KPIs, metrics, tracking)
8. Market Expansion Opportunities (new audiences, verticals)
9. Technology Integration (AI, automation, tools)
10. Long-term Strategic Vision (3-5 year roadmap)
4. Innovation Opportunities (emerging trends from competitor word frequency)
5. Risk Mitigation Strategies (competitive threats, cadence shifts)
Consider market dynamics, user behavior trends, and competitive landscape in your analysis.
Format as structured JSON with strategic insights and implementation guidance.
""",
@@ -618,12 +600,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
raise RuntimeError("user_id is required for subscription checking. All AI calls must be authenticated.")
return await self._execute_ai_call(service_type, prompt, schema, user_id=user_id)
async def generate_content_gap_analysis(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_content_gap_analysis(self, analysis_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate content gap analysis using centralized AI service.
Args:
analysis_data: Analysis data
user_id: User ID for subscription checking
Returns:
Content gap analysis results
@@ -646,7 +629,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.CONTENT_GAP_ANALYSIS,
prompt,
self.schemas['content_gap_analysis']
self.schemas['content_gap_analysis'],
user_id=user_id
)
return result if result else {}
@@ -655,12 +639,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in content gap analysis: {str(e)}")
raise Exception(f"Failed to generate content gap analysis: {str(e)}")
async def generate_market_position_analysis(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_market_position_analysis(self, market_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate market position analysis using centralized AI service.
Args:
market_data: Market analysis data
user_id: User ID for subscription checking
Returns:
Market position analysis results
@@ -679,7 +664,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.MARKET_POSITION_ANALYSIS,
prompt,
self.schemas['market_position_analysis']
self.schemas['market_position_analysis'],
user_id=user_id
)
return result if result else {}
@@ -688,12 +674,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in market position analysis: {str(e)}")
raise Exception(f"Failed to generate market position analysis: {str(e)}")
async def generate_keyword_analysis(self, keyword_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_keyword_analysis(self, keyword_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate keyword analysis using centralized AI service.
Args:
keyword_data: Keyword analysis data
user_id: User ID for subscription checking
Returns:
Keyword analysis results
@@ -712,7 +699,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.KEYWORD_ANALYSIS,
prompt,
self.schemas['keyword_analysis']
self.schemas['keyword_analysis'],
user_id=user_id
)
return result if result else {}
@@ -721,12 +709,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in keyword analysis: {str(e)}")
raise Exception(f"Failed to generate keyword analysis: {str(e)}")
async def generate_performance_prediction(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_performance_prediction(self, content_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate performance prediction using centralized AI service.
Args:
content_data: Content data for prediction
user_id: User ID for subscription checking
Returns:
Performance prediction results
@@ -744,7 +733,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.PERFORMANCE_PREDICTION,
prompt,
self.schemas['performance_prediction']
self.schemas['performance_prediction'],
user_id=user_id
)
return result if result else {}
@@ -753,12 +743,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in performance prediction: {str(e)}")
raise Exception(f"Failed to generate performance prediction: {str(e)}")
async def generate_strategic_intelligence(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_strategic_intelligence(self, analysis_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate strategic intelligence using centralized AI service.
Args:
analysis_data: Analysis data for strategic insights
user_id: User ID for subscription checking
Returns:
Strategic intelligence results
@@ -777,7 +768,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.STRATEGIC_INTELLIGENCE,
prompt,
self.schemas['strategic_intelligence']
self.schemas['strategic_intelligence'],
user_id=user_id
)
return result if result else {}
@@ -786,12 +778,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in strategic intelligence: {str(e)}")
raise Exception(f"Failed to generate strategic intelligence: {str(e)}")
async def generate_content_quality_assessment(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
async def generate_content_quality_assessment(self, content_data: Dict[str, Any], user_id: str) -> Dict[str, Any]:
"""
Generate content quality assessment using centralized AI service.
Args:
content_data: Content data for assessment
user_id: User ID for subscription checking
Returns:
Content quality assessment results
@@ -810,7 +803,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
result = await self._execute_ai_call(
AIServiceType.CONTENT_QUALITY_ASSESSMENT,
prompt,
self.schemas['content_quality_assessment']
self.schemas['content_quality_assessment'],
user_id=user_id
)
return result if result else {}
@@ -819,9 +813,13 @@ Format as structured JSON with detailed assessment and optimization guidance.
logger.error(f"Error in content quality assessment: {str(e)}")
raise Exception(f"Failed to generate content quality assessment: {str(e)}")
async def generate_content_schedule(self, prompt: str) -> Dict[str, Any]:
async def generate_content_schedule(self, prompt: str, user_id: str) -> Dict[str, Any]:
"""
Generate content schedule using AI.
Args:
prompt: Prompt for schedule generation
user_id: User ID for subscription checking
"""
try:
logger.info("Generating content schedule using AI")
@@ -852,7 +850,8 @@ Format as structured JSON with detailed assessment and optimization guidance.
response = await self._execute_ai_call(
AIServiceType.CONTENT_SCHEDULE_GENERATION,
enhanced_prompt,
self.schemas.get('content_schedule_generation', {})
self.schemas.get('content_schedule_generation', {}),
user_id=user_id
)
logger.info("Content schedule generated successfully")