Update utils/optimizer.py

utils/optimizer.py

@@ -100,6 +100,18 @@ class ContentOptimizer:
             "}}\n"
         )
     
+        # Dedicated prompt for rewriting/optimizing content
+        self.optimization_rewrite_prompt = (
+            "You are an expert AI content optimizer. Rewrite the provided text to maximize clarity, logical structure, and suitability for LLM-based search and conversational AI. "
+            "Your rewritten version should be more precise, well-organized, and easier for AI systems to extract answers from. "
+            "Return your output in the following JSON format:\n"
+            "```json\n"
+            "{{\n"
+            "  \"optimized_text\": \"...your rewritten content here...\"\n"
+            "}}\n"
+            "```"
+        )
+
     def optimize_content(self, content: str, analyze_only: bool = False, 
                         include_keywords: bool = True, optimization_type: str = "seo") -> Dict[str, Any]:
         """
@@ -127,10 +139,8 @@ class ContentOptimizer:
     def _standard_optimization(self, content: str, analyze_only: bool, include_keywords: bool) -> Dict[str, Any]:
         """Standard content optimization using enhancement prompt"""
         try:
-            # Modify prompt based on options
-            prompt_text = self.enhancement_prompt
-            
             if analyze_only:
+                prompt_text = self.enhancement_prompt
                 prompt_text = prompt_text.replace(
                     "Rewrite the text to improve:",
                     "Analyze the text for potential improvements in:"
@@ -138,48 +148,44 @@ class ContentOptimizer:
                     '"optimized_text": "..."',
                     '"optimization_suggestions": ["suggestion 1", "suggestion 2"]'
                 )
-            
-            if not include_keywords:
-                prompt_text = prompt_text.replace(
-                    '"keywords": ["example", "installation", "setup"],',
-                    ''
-                )
-            
-            # Create and run chain
-            prompt_template = ChatPromptTemplate.from_messages([
-                 SystemMessagePromptTemplate.from_template(prompt_text),
-                HumanMessagePromptTemplate.from_template(content[:6000])  # Limit content length
-            ])
-                # ("system", prompt_text),
-                # ("user", content[:6000])  # Limit content length
-            
-            chain = prompt_template | self.llm
-            result = chain.invoke({})
-            
-            # Parse result
-            result_content = result.content if hasattr(result, 'content') else str(result)
-            parsed_result = self._parse_optimization_result(result_content)
-            
-            # Add metadata
-            parsed_result.update({
-                'optimization_type': 'standard',
-                'analyze_only': analyze_only,
-                'original_length': len(content),
-                'original_word_count': len(content.split())
-            })
-            
-            return parsed_result
-            
-        except Exception as e:
-            return {'error': f"Standard optimization failed: {str(e)}"}
-    
+                if not include_keywords:
+                    prompt_text = prompt_text.replace(
+                        '"keywords": ["example", "installation", "setup"],',
+                        ''
+                    )
+            else:
+                # Use dedicated rewrite prompt for optimization
+                prompt_text = self.optimization_rewrite_prompt
+
+        prompt_template = ChatPromptTemplate.from_messages([
+            SystemMessagePromptTemplate.from_template(prompt_text),
+            HumanMessagePromptTemplate.from_template(content[:6000])
+        ])
+
+        chain = prompt_template | self.llm
+        result = chain.invoke({})
+
+        result_content = result.content if hasattr(result, 'content') else str(result)
+        parsed_result = self._parse_optimization_result(result_content)
+
+        parsed_result.update({
+            'optimization_type': 'standard',
+            'analyze_only': analyze_only,
+            'original_length': len(content),
+            'original_word_count': len(content.split())
+        })
+
+        return parsed_result
+
     def _seo_style_optimization(self, content: str, analyze_only: bool) -> Dict[str, Any]:
         """SEO-focused optimization for AI search engines"""
         try:
             prompt_template = ChatPromptTemplate.from_messages([
-                ("system", self.seo_style_prompt),
-                ("user", f"Optimize this content for AI search engines:\n\n{content[:6000]}")
+                 SystemMessagePromptTemplate.from_template(self.seo_style_prompt),
+                 HumanMessagePromptTemplate.from_template(f"Optimize this content for AI search engines:\n\n{content[:6000]}")
             ])
+                # ("system", self.seo_style_prompt),
+                # ("user", f"Optimize this content for AI search engines:\n\n{content[:6000]}")
             
             chain = prompt_template | self.llm
             result = chain.invoke({})
@@ -205,9 +211,11 @@ class ContentOptimizer:
             formatted_prompt = self.competitive_analysis_prompt.format(content=content[:5000])
             
             prompt_template = ChatPromptTemplate.from_messages([
-                ("system", formatted_prompt),
-                ("user", "Perform the competitive analysis and provide optimization recommendations.")
+                 SystemMessagePromptTemplate.from_template(formatted_prompt),
+                 HumanMessagePromptTemplate.from_template("Perform the competitive analysis and provide optimization recommendations.")
             ])
+                # ("system", formatted_prompt),
+                # ("user", "Perform the competitive analysis and provide optimization recommendations.")
             
             chain = prompt_template | self.llm
             result = chain.invoke({})
@@ -278,22 +286,25 @@ class ContentOptimizer:
             try:
                 custom_prompt = f"""You are optimizing content for AI systems. {variation_prompts[i]}.
 
-Original content: {content[:4000]}
+                                    Original content: {content[:4000]}
 
-Provide the optimized variation in JSON format:
-```json
-{{
-"variation_type": "conversational/authoritative/structured",
-"optimized_content": "the rewritten content...",
-"key_changes": ["change 1", "change 2"],
-"target_use_case": "description of ideal use case"
-}}
-```"""
+                                    Provide the optimized variation in JSON format:
+                                    ```json
+                                    {{
+                                    "variation_type": "conversational/authoritative/structured",
+                                    "optimized_content": "the rewritten content...",
+                                    "key_changes": ["change 1", "change 2"],
+                                    "target_use_case": "description of ideal use case"
+                                    }}
+                                    ```
+                                """
                 
                 prompt_template = ChatPromptTemplate.from_messages([
-                    ("system", custom_prompt),
-                    ("user", "Generate the variation.")
+                    SystemMessagePromptTemplate.from_template(custom_prompt),
+                    HumanMessagePromptTemplate.from_template("Generate the variation.")
                 ])
+                    # ("system", custom_prompt),
+                    # ("user", "Generate the variation.")
                 
                 chain = prompt_template | self.llm
                 result = chain.invoke({})
@@ -388,31 +399,34 @@ Provide the optimized variation in JSON format:
         try:
             entity_prompt = """Extract key entities, topics, and concepts from this content for AI optimization.
 
-Content: {content}
+                                Content: {content}
 
-Identify:
-1. Named entities (people, places, organizations)
-2. Key concepts and topics
-3. Technical terms and jargon
-4. Potential semantic keywords
-5. Question-answer opportunities
+                                Identify:
+                                1. Named entities (people, places, organizations)
+                                2. Key concepts and topics
+                                3. Technical terms and jargon
+                                4. Potential semantic keywords
+                                5. Question-answer opportunities
 
-Format as JSON:
-```json
-{{
-"named_entities": ["entity1", "entity2"],
-"key_topics": ["topic1", "topic2"],
-"technical_terms": ["term1", "term2"],
-"semantic_keywords": ["keyword1", "keyword2"],
-"question_opportunities": ["What is...", "How does..."],
-"entity_relationships": ["relationship descriptions"]
-}}
-```"""
+                                Format as JSON:
+                                ```json
+                                {{
+                                "named_entities": ["entity1", "entity2"],
+                                "key_topics": ["topic1", "topic2"],
+                                "technical_terms": ["term1", "term2"],
+                                "semantic_keywords": ["keyword1", "keyword2"],
+                                "question_opportunities": ["What is...", "How does..."],
+                                "entity_relationships": ["relationship descriptions"]
+                                }}
+                                ```
+                            """
             
             prompt_template = ChatPromptTemplate.from_messages([
-                ("system", entity_prompt.format(content=content[:5000])),
-                ("user", "Extract the entities and topics.")
+                SystemMessagePromptTemplate.from_template(entity_prompt.format(content=content[:5000])),
+                HumanMessagePromptTemplate.from_template("Extract the entities and topics.")
             ])
+                # ("system", entity_prompt.format(content=content[:5000])),
+                # ("user", "Extract the entities and topics.")
             
             chain = prompt_template | self.llm
             result = chain.invoke({})
@@ -436,33 +450,36 @@ Format as JSON:
         try:
             voice_prompt = """Optimize this content for voice search and conversational AI systems.
 
-Focus on:
-1. Natural language patterns
-2. Question-based structure
-3. Conversational tone
-4. Clear, direct answers
-5. Featured snippet optimization
+                                Focus on:
+                                1. Natural language patterns
+                                2. Question-based structure
+                                3. Conversational tone
+                                4. Clear, direct answers
+                                5. Featured snippet optimization
 
-Original content: {content}
+                                Original content: {content}
 
-Provide optimization in JSON:
-```json
-{{
-"voice_optimized_content": "conversational version...",
-"question_answer_pairs": [
-  {{"question": "What is...", "answer": "Direct answer..."}},
-  {{"question": "How does...", "answer": "Step by step..."}}
-],
-"featured_snippet_candidates": ["snippet 1", "snippet 2"],
-"natural_language_improvements": ["improvement 1", "improvement 2"],
-"conversational_score": 8.5
-}}
-```"""
+                                Provide optimization in JSON:
+                                ```json
+                                {{
+                                "voice_optimized_content": "conversational version...",
+                                "question_answer_pairs": [
+                                  {{"question": "What is...", "answer": "Direct answer..."}},
+                                  {{"question": "How does...", "answer": "Step by step..."}}
+                                ],
+                                "featured_snippet_candidates": ["snippet 1", "snippet 2"],
+                                "natural_language_improvements": ["improvement 1", "improvement 2"],
+                                "conversational_score": 8.5
+                                }}
+                                ```
+                            """
             
             prompt_template = ChatPromptTemplate.from_messages([
-                ("system", voice_prompt.format(content=content[:4000])),
-                ("user", "Optimize for voice search.")
+                SystemMessagePromptTemplate.from_template(voice_prompt.format(content=content[:4000])),
+                HumanMessagePromptTemplate.from_template("Optimize for voice search.")
             ])
+                # ("system", voice_prompt.format(content=content[:4000])),
+                # ("user", "Optimize for voice search.")
             
             chain = prompt_template | self.llm
             result = chain.invoke({})