Base code
This commit is contained in:
Kunthawat Greethong
562
backend/services/ai_prompt_optimizer.py
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562
backend/services/ai_prompt_optimizer.py
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"""
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AI Prompt Optimizer Service
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Advanced AI prompt optimization and management for content planning system.
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"""
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from typing import Dict, Any, List, Optional
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from loguru import logger
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from datetime import datetime
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import json
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import re
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# Import AI providers
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from services.llm_providers.main_text_generation import llm_text_gen
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from services.llm_providers.gemini_provider import gemini_structured_json_response
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class AIPromptOptimizer:
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"""Advanced AI prompt optimization and management service."""
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def __init__(self):
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"""Initialize the AI prompt optimizer."""
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self.logger = logger
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self.prompts = self._load_advanced_prompts()
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self.schemas = self._load_advanced_schemas()
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logger.info("AIPromptOptimizer initialized")
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def _load_advanced_prompts(self) -> Dict[str, str]:
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"""Load advanced AI prompts from deep dive analysis."""
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return {
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# Strategic Content Gap Analysis Prompt
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'strategic_content_gap_analysis': """
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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:
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TARGET ANALYSIS:
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- Website: {target_url}
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- Industry: {industry}
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- SERP Opportunities: {serp_opportunities} keywords not ranking
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- Keyword Expansion: {expanded_keywords_count} additional keywords identified
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- Competitors Analyzed: {competitors_analyzed} websites
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- Content Quality Score: {content_quality_score}/10
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- Market Competition Level: {competition_level}
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DOMINANT CONTENT THEMES:
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{dominant_themes}
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COMPETITIVE LANDSCAPE:
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{competitive_landscape}
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PROVIDE COMPREHENSIVE ANALYSIS:
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1. Strategic Content Gap Analysis (identify 3-5 major gaps with impact assessment)
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2. Priority Content Recommendations (top 5 with ROI estimates)
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3. Keyword Strategy Insights (trending, seasonal, long-tail opportunities)
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4. Competitive Positioning Advice (differentiation strategies)
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5. Content Format Recommendations (video, interactive, comprehensive guides)
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6. Technical SEO Opportunities (structured data, schema markup)
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7. Implementation Timeline (30/60/90 days with milestones)
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8. Risk Assessment and Mitigation Strategies
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9. Success Metrics and KPIs
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10. Resource Allocation Recommendations
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Consider user intent, search behavior patterns, and content consumption trends in your analysis.
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Format as structured JSON with clear, actionable recommendations and confidence scores.
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""",
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# Market Position Analysis Prompt
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'market_position_analysis': """
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As a senior competitive intelligence analyst specializing in digital marketing and content strategy, analyze the market position of competitors in the {industry} industry:
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COMPETITOR ANALYSES:
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{competitor_analyses}
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MARKET CONTEXT:
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- Industry: {industry}
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- Market Size: {market_size}
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- Growth Rate: {growth_rate}
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- Key Trends: {key_trends}
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PROVIDE COMPREHENSIVE MARKET ANALYSIS:
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1. Market Leader Identification (with reasoning)
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2. Content Leader Analysis (content strategy assessment)
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3. Quality Leader Assessment (content quality metrics)
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4. Market Gaps Identification (3-5 major gaps)
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5. Opportunities Analysis (high-impact opportunities)
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6. Competitive Advantages (unique positioning)
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7. Strategic Positioning Recommendations (differentiation)
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8. Content Strategy Insights (format, frequency, quality)
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9. Innovation Opportunities (emerging trends)
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10. Risk Assessment (competitive threats)
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Include market share estimates, competitive positioning matrix, and strategic recommendations with implementation timeline.
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Format as structured JSON with detailed analysis and confidence levels.
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""",
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# Advanced Keyword Analysis Prompt
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'advanced_keyword_analysis': """
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As an expert keyword research specialist with deep understanding of search algorithms and user behavior, analyze keyword opportunities for {industry} industry:
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KEYWORD DATA:
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- Target Keywords: {target_keywords}
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- Industry Context: {industry}
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- Search Volume Data: {search_volume_data}
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- Competition Analysis: {competition_analysis}
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- Trend Analysis: {trend_analysis}
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PROVIDE COMPREHENSIVE KEYWORD ANALYSIS:
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1. Search Volume Estimates (with confidence intervals)
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2. Competition Level Assessment (difficulty scoring)
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3. Trend Analysis (seasonal, cyclical, emerging)
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4. Opportunity Scoring (ROI potential)
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5. Content Format Recommendations (based on intent)
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6. Keyword Clustering (semantic relationships)
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7. Long-tail Opportunities (specific, low-competition)
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8. Seasonal Variations (trending patterns)
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9. Search Intent Classification (informational, commercial, navigational, transactional)
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10. Implementation Priority (quick wins vs long-term)
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Consider search intent, user journey stages, and conversion potential in your analysis.
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Format as structured JSON with detailed metrics and strategic recommendations.
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"""
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}
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def _load_advanced_schemas(self) -> Dict[str, Dict[str, Any]]:
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"""Load advanced JSON schemas for structured responses."""
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return {
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'strategic_content_gap_analysis': {
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"type": "object",
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"properties": {
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"strategic_insights": {
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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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"type": {"type": "string"},
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"insight": {"type": "string"},
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"confidence": {"type": "number"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"},
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"risk_level": {"type": "string"}
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}
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}
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},
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"content_recommendations": {
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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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"type": {"type": "string"},
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"recommendation": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_traffic": {"type": "string"},
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"implementation_time": {"type": "string"},
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"roi_estimate": {"type": "string"},
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"success_metrics": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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}
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},
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"keyword_strategy": {
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"type": "object",
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"properties": {
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"trending_keywords": {
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"type": "array",
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"items": {"type": "string"}
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},
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"seasonal_opportunities": {
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"type": "array",
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"items": {"type": "string"}
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},
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"long_tail_opportunities": {
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"type": "array",
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"items": {"type": "string"}
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},
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"intent_classification": {
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"type": "object",
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"properties": {
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"informational": {"type": "number"},
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"commercial": {"type": "number"},
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"navigational": {"type": "number"},
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"transactional": {"type": "number"}
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}
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}
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}
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}
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}
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},
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'market_position_analysis': {
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"type": "object",
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"properties": {
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"market_leader": {"type": "string"},
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"content_leader": {"type": "string"},
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"quality_leader": {"type": "string"},
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"market_gaps": {
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"type": "array",
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"items": {"type": "string"}
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},
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"opportunities": {
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"type": "array",
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"items": {"type": "string"}
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},
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"competitive_advantages": {
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"type": "array",
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"items": {"type": "string"}
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},
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"strategic_recommendations": {
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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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"type": {"type": "string"},
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"recommendation": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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}
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}
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}
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},
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'advanced_keyword_analysis': {
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"type": "object",
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"properties": {
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"keyword_opportunities": {
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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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"keyword": {"type": "string"},
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"search_volume": {"type": "number"},
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"competition_level": {"type": "string"},
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"difficulty_score": {"type": "number"},
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"trend": {"type": "string"},
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"intent": {"type": "string"},
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"opportunity_score": {"type": "number"},
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"recommended_format": {"type": "string"},
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"estimated_traffic": {"type": "string"},
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"implementation_priority": {"type": "string"}
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}
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}
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},
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"keyword_clusters": {
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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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"cluster_name": {"type": "string"},
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"main_keyword": {"type": "string"},
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"related_keywords": {
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"type": "array",
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"items": {"type": "string"}
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},
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"search_volume": {"type": "number"},
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"competition_level": {"type": "string"},
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"content_suggestions": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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}
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}
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}
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}
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}
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async def generate_strategic_content_gap_analysis(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate strategic content gap analysis using advanced AI prompts.
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Args:
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analysis_data: Comprehensive analysis data
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Returns:
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Strategic content gap analysis results
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"""
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try:
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logger.info("🤖 Generating strategic content gap analysis using advanced AI")
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# Format the advanced prompt
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prompt = self.prompts['strategic_content_gap_analysis'].format(
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target_url=analysis_data.get('target_url', 'N/A'),
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industry=analysis_data.get('industry', 'N/A'),
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serp_opportunities=analysis_data.get('serp_opportunities', 0),
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expanded_keywords_count=analysis_data.get('expanded_keywords_count', 0),
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competitors_analyzed=analysis_data.get('competitors_analyzed', 0),
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content_quality_score=analysis_data.get('content_quality_score', 7.0),
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competition_level=analysis_data.get('competition_level', 'medium'),
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dominant_themes=json.dumps(analysis_data.get('dominant_themes', {}), indent=2),
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competitive_landscape=json.dumps(analysis_data.get('competitive_landscape', {}), indent=2)
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)
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# Use advanced schema for structured response
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=self.schemas['strategic_content_gap_analysis']
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)
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# Handle response - gemini_structured_json_response returns dict directly
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if isinstance(response, dict):
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result = response
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elif isinstance(response, str):
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# If it's a string, try to parse as JSON
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try:
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result = json.loads(response)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse AI response as JSON: {e}")
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raise Exception(f"Invalid AI response format: {str(e)}")
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else:
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logger.error(f"Unexpected response type from AI service: {type(response)}")
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raise Exception(f"Unexpected response type from AI service: {type(response)}")
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logger.info("✅ Advanced strategic content gap analysis completed")
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return result
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except Exception as e:
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logger.error(f"Error generating strategic content gap analysis: {str(e)}")
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return self._get_fallback_content_gap_analysis()
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async def generate_advanced_market_position_analysis(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate advanced market position analysis using optimized AI prompts.
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Args:
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market_data: Market analysis data
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Returns:
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Advanced market position analysis results
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"""
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try:
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logger.info("🤖 Generating advanced market position analysis using optimized AI")
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# Format the advanced prompt
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prompt = self.prompts['market_position_analysis'].format(
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industry=market_data.get('industry', 'N/A'),
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competitor_analyses=json.dumps(market_data.get('competitors', []), indent=2),
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market_size=market_data.get('market_size', 'N/A'),
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growth_rate=market_data.get('growth_rate', 'N/A'),
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key_trends=json.dumps(market_data.get('key_trends', []), indent=2)
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)
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# Use advanced schema for structured response
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=self.schemas['market_position_analysis']
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)
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# Handle response - gemini_structured_json_response returns dict directly
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if isinstance(response, dict):
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result = response
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elif isinstance(response, str):
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# If it's a string, try to parse as JSON
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try:
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result = json.loads(response)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse AI response as JSON: {e}")
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raise Exception(f"Invalid AI response format: {str(e)}")
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else:
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logger.error(f"Unexpected response type from AI service: {type(response)}")
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raise Exception(f"Unexpected response type from AI service: {type(response)}")
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logger.info("✅ Advanced market position analysis completed")
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return result
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except Exception as e:
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logger.error(f"Error generating advanced market position analysis: {str(e)}")
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return self._get_fallback_market_position_analysis()
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async def generate_advanced_keyword_analysis(self, keyword_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Generate advanced keyword analysis using optimized AI prompts.
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Args:
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keyword_data: Keyword analysis data
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Returns:
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Advanced keyword analysis results
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"""
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try:
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logger.info("🤖 Generating advanced keyword analysis using optimized AI")
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# Format the advanced prompt
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prompt = self.prompts['advanced_keyword_analysis'].format(
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industry=keyword_data.get('industry', 'N/A'),
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target_keywords=json.dumps(keyword_data.get('target_keywords', []), indent=2),
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search_volume_data=json.dumps(keyword_data.get('search_volume_data', {}), indent=2),
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competition_analysis=json.dumps(keyword_data.get('competition_analysis', {}), indent=2),
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trend_analysis=json.dumps(keyword_data.get('trend_analysis', {}), indent=2)
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)
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# Use advanced schema for structured response
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=self.schemas['advanced_keyword_analysis']
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)
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# Handle response - gemini_structured_json_response returns dict directly
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if isinstance(response, dict):
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result = response
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elif isinstance(response, str):
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# If it's a string, try to parse as JSON
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try:
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result = json.loads(response)
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except json.JSONDecodeError as e:
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logger.error(f"Failed to parse AI response as JSON: {e}")
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raise Exception(f"Invalid AI response format: {str(e)}")
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else:
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logger.error(f"Unexpected response type from AI service: {type(response)}")
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raise Exception(f"Unexpected response type from AI service: {type(response)}")
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logger.info("✅ Advanced keyword analysis completed")
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return result
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except Exception as e:
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logger.error(f"Error generating advanced keyword analysis: {str(e)}")
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return self._get_fallback_keyword_analysis()
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# Fallback methods for error handling
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def _get_fallback_content_gap_analysis(self) -> Dict[str, Any]:
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"""Fallback content gap analysis when AI fails."""
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return {
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'strategic_insights': [
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{
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'type': 'content_strategy',
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'insight': 'Focus on educational content to build authority',
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'confidence': 0.85,
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'priority': 'high',
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'estimated_impact': 'Authority building',
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'implementation_time': '3-6 months',
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'risk_level': 'low'
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}
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],
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'content_recommendations': [
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{
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'type': 'content_creation',
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'recommendation': 'Create comprehensive guides for high-opportunity keywords',
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'priority': 'high',
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'estimated_traffic': '5K+ monthly',
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'implementation_time': '2-3 weeks',
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'roi_estimate': 'High ROI potential',
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'success_metrics': ['Traffic increase', 'Authority building', 'Lead generation']
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}
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],
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'keyword_strategy': {
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'trending_keywords': ['industry trends', 'best practices'],
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'seasonal_opportunities': ['holiday content', 'seasonal guides'],
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'long_tail_opportunities': ['specific tutorials', 'detailed guides'],
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'intent_classification': {
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'informational': 0.6,
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'commercial': 0.2,
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'navigational': 0.1,
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'transactional': 0.1
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}
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}
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}
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def _get_fallback_market_position_analysis(self) -> Dict[str, Any]:
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"""Fallback market position analysis when AI fails."""
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return {
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'market_leader': 'competitor1.com',
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'content_leader': 'competitor2.com',
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'quality_leader': 'competitor3.com',
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'market_gaps': [
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'Video content',
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'Interactive content',
|
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'Expert interviews'
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],
|
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'opportunities': [
|
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'Niche content development',
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'Expert interviews',
|
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'Industry reports'
|
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],
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'competitive_advantages': [
|
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'Technical expertise',
|
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'Comprehensive guides',
|
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'Industry insights'
|
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],
|
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'strategic_recommendations': [
|
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{
|
||||
'type': 'differentiation',
|
||||
'recommendation': 'Focus on unique content angles',
|
||||
'priority': 'high',
|
||||
'estimated_impact': 'Brand differentiation',
|
||||
'implementation_time': '2-4 months',
|
||||
'confidence_level': '85%'
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
def _get_fallback_keyword_analysis(self) -> Dict[str, Any]:
|
||||
"""Fallback keyword analysis when AI fails."""
|
||||
return {
|
||||
'keyword_opportunities': [
|
||||
{
|
||||
'keyword': 'industry best practices',
|
||||
'search_volume': 3000,
|
||||
'competition_level': 'low',
|
||||
'difficulty_score': 35,
|
||||
'trend': 'rising',
|
||||
'intent': 'informational',
|
||||
'opportunity_score': 85,
|
||||
'recommended_format': 'comprehensive_guide',
|
||||
'estimated_traffic': '2K+ monthly',
|
||||
'implementation_priority': 'high'
|
||||
}
|
||||
],
|
||||
'keyword_clusters': [
|
||||
{
|
||||
'cluster_name': 'Industry Fundamentals',
|
||||
'main_keyword': 'industry basics',
|
||||
'related_keywords': ['fundamentals', 'introduction', 'basics'],
|
||||
'search_volume': 5000,
|
||||
'competition_level': 'medium',
|
||||
'content_suggestions': ['Beginner guide', 'Overview article']
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
async def health_check(self) -> Dict[str, Any]:
|
||||
"""
|
||||
Health check for the AI prompt optimizer service.
|
||||
|
||||
Returns:
|
||||
Health status information
|
||||
"""
|
||||
try:
|
||||
logger.info("Performing health check for AIPromptOptimizer")
|
||||
|
||||
# Test AI functionality with a simple prompt
|
||||
test_prompt = "Hello, this is a health check test."
|
||||
try:
|
||||
test_response = llm_text_gen(test_prompt)
|
||||
ai_status = "operational" if test_response else "degraded"
|
||||
except Exception as e:
|
||||
ai_status = "error"
|
||||
logger.warning(f"AI health check failed: {str(e)}")
|
||||
|
||||
health_status = {
|
||||
'service': 'AIPromptOptimizer',
|
||||
'status': 'healthy',
|
||||
'capabilities': {
|
||||
'strategic_content_gap_analysis': 'operational',
|
||||
'advanced_market_position_analysis': 'operational',
|
||||
'advanced_keyword_analysis': 'operational',
|
||||
'ai_integration': ai_status
|
||||
},
|
||||
'prompts_loaded': len(self.prompts),
|
||||
'schemas_loaded': len(self.schemas),
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
logger.info("AIPromptOptimizer health check passed")
|
||||
return health_status
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"AIPromptOptimizer health check failed: {str(e)}")
|
||||
return {
|
||||
'service': 'AIPromptOptimizer',
|
||||
'status': 'unhealthy',
|
||||
'error': str(e),
|
||||
'timestamp': datetime.utcnow().isoformat()
|
||||
}
|
||||
Reference in New Issue
Block a user