Update enhanced_prompt_builder.py

enhanced_prompt_builder.py

@@ -1,131 +1,115 @@
-from enhanced_retriever import EnhancedRetriever
-from enhanced_knowledge_graph import EnhancedKnowledgeGraph
-from feedback_analyzer import FeedbackAnalyzer
-from typing import List, Dict
-
-class EnhancedPromptBuilder:
-    """Enhanced prompt builder with all advanced features integrated"""
-    
-    def __init__(self):
-        self.retriever = EnhancedRetriever()
-        self.knowledge_graph = EnhancedKnowledgeGraph()
-        self.feedback_analyzer = FeedbackAnalyzer()
-        
-    def build_adaptive_prompt(self, ad_text: str, tone: str, platforms: List[str]) -> str:
-        """Build an adaptive prompt using all enhancement layers"""
-        
-        # 1. Get enhanced RAG results with relevance scores
-        rag_results = self.retriever.retrieve_with_relevance(tone, platforms)
-        formatted_guidance = self.retriever.format_guidance_with_scores(rag_results)
-        
-        # 2. Get knowledge graph insights with traversal
-        kg_insights = []
-        
-        # Get recommendations for each platform
-        for platform in platforms:
-            recommendations = self.knowledge_graph.get_recommendations(tone, platform)
-            kg_insights.append(f"\n{platform} Insights:")
-            kg_insights.append(f"  - Compatibility Score: {recommendations['compatibility_score']:.2f}")
-            
-            if recommendations['suggested_elements']:
-                kg_insights.append("  - Suggestions: " + ", ".join(recommendations['suggested_elements']))
-            
-            if recommendations['warnings']:
-                kg_insights.append("  - ⚠️ Warnings: " + ", ".join(recommendations['warnings']))
-                
-            if recommendations['creative_types']:
-                kg_insights.append("  - Recommended Creative Types: " + ", ".join(recommendations['creative_types']))
-            
-            # Add relationship explanations
-            relationship = self.knowledge_graph.explain_relationship(tone, platform)
-            kg_insights.append(f"  - Relationship: {relationship}")
-        
-        kg_insights_str = "\n".join(kg_insights)
-        
-        # 3. Get adaptive weights from feedback analysis
-        weights = self.feedback_analyzer.get_adaptive_weights()
-        
-        # 4. Add performance insights if available
-        analysis = self.feedback_analyzer.analyze_patterns()
-        performance_notes = []
-        
-        if analysis.get("recommendations"):
-            relevant_recs = [rec for rec in analysis["recommendations"] 
-                           if any(p.lower() in rec.lower() for p in platforms) or tone.lower() in rec.lower()]
-            if relevant_recs:
-                performance_notes.append("\nHistorical Performance Notes:")
-                performance_notes.extend([f"  - {rec}" for rec in relevant_recs[:3]])
-        
-        performance_str = "\n".join(performance_notes) if performance_notes else ""
-        
-        # 5. Build the enhanced prompt
-        platform_str = ", ".join(platforms)
-        
-        # Apply adaptive weights to emphasize better-performing combinations
-        weight_notes = []
-        for platform in platforms:
-            combo_key = f"{tone}_{platform}"
-            weight = weights.get(combo_key, 1.0)
-            if weight > 0.8:
-                weight_notes.append(f"  - {platform}: High confidence (historical success)")
-            elif weight < 0.6:
-                weight_notes.append(f"  - {platform}: Needs improvement (based on feedback)")
-        
-        weight_str = "\n".join(weight_notes) if weight_notes else ""
-        
-        prompt = f"""
-You are an expert ad copywriter with access to advanced AI assistance.
-
-TASK: Rewrite the following ad text in a {tone} tone and optimize it individually for: {platform_str}
-
-ORIGINAL AD TEXT: "{ad_text}"
-
-=== ENHANCED GUIDANCE (with Relevance Scores) ===
-{formatted_guidance}
-
-=== KNOWLEDGE GRAPH INSIGHTS ===
-{kg_insights_str}
-
-=== ADAPTIVE LEARNING INSIGHTS ===
-{weight_str}
-{performance_str}
-
-=== INSTRUCTIONS ===
-1. Maintain the core message and key information from the original ad
-2. Adapt the tone and style according to the guidelines above
-3. Consider the compatibility scores and warnings for each platform
-4. Use suggested elements where appropriate
-5. Avoid any warned elements or approaches
-6. Apply lessons from historical performance data
-
-OUTPUT FORMAT:
-Provide a separate, optimized version for each platform:
-
-{''.join([f'{p}:\n<Your rewritten ad text here>\n\n' for p in platforms])}
-
-Remember: Each platform version should be uniquely tailored while maintaining brand consistency.
-"""
-        
-        return prompt
-    
-    def get_improvement_suggestions(self) -> List[str]:
-        """Get suggestions for improving the system based on feedback"""
-        analysis = self.feedback_analyzer.analyze_patterns()
-        suggestions = []
-        
-        # Check overall performance
-        avg_rating = analysis.get("average_rating", 0)
-        if avg_rating < 3.5:
-            suggestions.append("Consider updating tone guidelines based on feedback patterns")
-            
-        # Check for problematic combinations
-        for pattern in analysis.get("low_performing_patterns", []):
-            tone, platform = pattern["pattern"].split("_")
-            suggestions.append(f"Review and update guidelines for {tone} tone on {platform}")
-            
-        # Suggest new relationships for KG
-        high_performers = analysis.get("high_performing_patterns", [])
-        if high_performers:
-            suggestions.append("Consider strengthening KG relationships for high-performing combinations")
-            
+from enhanced_retriever import EnhancedRetriever
+from enhanced_knowledge_graph import EnhancedKnowledgeGraph
+from feedback_analyzer import FeedbackAnalyzer
+from typing import List, Dict
+
+class EnhancedPromptBuilder:
+    """Enhanced prompt builder with all advanced features integrated"""
+    
+    def __init__(self):
+        self.retriever = EnhancedRetriever()
+        self.knowledge_graph = EnhancedKnowledgeGraph()
+        self.feedback_analyzer = FeedbackAnalyzer()
+        
+    def build_adaptive_prompt(self, ad_text: str, tone: str, platforms: List[str]) -> str:
+        """Build an adaptive prompt using all enhancement layers"""
+        
+        # 1. Get enhanced RAG results with relevance scores
+        rag_results = self.retriever.retrieve_with_relevance(tone, platforms)
+        formatted_guidance = self.retriever.format_guidance_with_scores(rag_results)
+        
+        # 2. Get knowledge graph insights with traversal
+        kg_insights = []
+        
+        # Get recommendations for each platform
+        for platform in platforms:
+            recommendations = self.knowledge_graph.get_recommendations(tone, platform)
+            kg_insights.append(f"\n{platform} Insights:")
+            kg_insights.append(f"  - Compatibility Score: {recommendations['compatibility_score']:.2f}")
+            
+            if recommendations['suggested_elements']:
+                kg_insights.append("  - Suggestions: " + ", ".join(recommendations['suggested_elements']))
+            
+            if recommendations['warnings']:
+                kg_insights.append("  - ⚠️ Warnings: " + ", ".join(recommendations['warnings']))
+                
+            if recommendations['creative_types']:
+                kg_insights.append("  - Recommended Creative Types: " + ", ".join(recommendations['creative_types']))
+            
+            # Add relationship explanations
+            relationship = self.knowledge_graph.explain_relationship(tone, platform)
+            kg_insights.append(f"  - Relationship: {relationship}")
+        
+        kg_insights_str = "\n".join(kg_insights)
+        
+        # 3. Get adaptive weights from feedback analysis
+        weights = self.feedback_analyzer.get_adaptive_weights()
+        
+        # 4. Add performance insights if available
+        analysis = self.feedback_analyzer.analyze_patterns()
+        performance_notes = []
+        
+        if analysis.get("recommendations"):
+            relevant_recs = [rec for rec in analysis["recommendations"] 
+                           if any(p.lower() in rec.lower() for p in platforms) or tone.lower() in rec.lower()]
+            if relevant_recs:
+                performance_notes.append("\nHistorical Performance Notes:")
+                performance_notes.extend([f"  - {rec}" for rec in relevant_recs[:3]])
+        
+        performance_str = "\n".join(performance_notes) if performance_notes else ""
+        
+        # 5. Build the enhanced prompt
+        platform_str = ", ".join(platforms)
+        
+        # Apply adaptive weights to emphasize better-performing combinations
+        weight_notes = []
+        for platform in platforms:
+            combo_key = f"{tone}_{platform}"
+            weight = weights.get(combo_key, 1.0)
+            if weight > 0.8:
+                weight_notes.append(f"  - {platform}: High confidence (historical success)")
+            elif weight < 0.6:
+                weight_notes.append(f"  - {platform}: Needs improvement (based on feedback)")
+        
+        weight_str = "\n".join(weight_notes) if weight_notes else ""
+        
+        prompt = f"""
+You are an expert ad copywriter with access to advanced AI assistance.
+
+TASK: Rewrite the following ad text in a {tone} tone and optimize it individually for: {platform_str}
+
+ORIGINAL AD TEXT: "{ad_text}"
+
+=== ENHANCED GUIDANCE (with Relevance Scores) ===
+{formatted_guidance}
+
+=== KNOWLEDGE GRAPH INSIGHTS ===
+{kg_insights_str}
+
+=== ADAPTIVE LEARNING INSIGHTS ===
+{weight_str}
+{performance_str}
+        
+        return prompt
+    
+    def get_improvement_suggestions(self) -> List[str]:
+        """Get suggestions for improving the system based on feedback"""
+        analysis = self.feedback_analyzer.analyze_patterns()
+        suggestions = []
+        
+        # Check overall performance
+        avg_rating = analysis.get("average_rating", 0)
+        if avg_rating < 3.5:
+            suggestions.append("Consider updating tone guidelines based on feedback patterns")
+            
+        # Check for problematic combinations
+        for pattern in analysis.get("low_performing_patterns", []):
+            tone, platform = pattern["pattern"].split("_")
+            suggestions.append(f"Review and update guidelines for {tone} tone on {platform}")
+            
+        # Suggest new relationships for KG
+        high_performers = analysis.get("high_performing_patterns", [])
+        if high_performers:
+            suggestions.append("Consider strengthening KG relationships for high-performing combinations")
+            
         return suggestions