Improve query intelligence: Add adaptive system prompts for policy overviews, impact analysis, and procedural questions

hybrid_llm_service.py

@@ -90,29 +90,101 @@ class HybridLLMService:
 
     def analyze_task_complexity(self, message: str) -> TaskComplexity:
         """Analyze if a task requires complex reasoning or simple response"""
+        message_lower = message.lower()
+        
+        # Impact analysis and scenario keywords (always complex)
+        impact_keywords = [
+            'impact', 'effect', 'scenario', 'chart', 'graph', 'visualization',
+            'analyze', 'compare', 'evaluate', 'breakdown', 'simulation',
+            'projection', 'forecast', 'calculation', 'calculate'
+        ]
+        
+        # Policy overview keywords (complex for comprehensive responses)
+        overview_keywords = [
+            'policies', 'schemes', 'types of', 'categories', 'overview', 
+            'comprehensive', 'detailed', 'all about', 'everything about',
+            'complete guide', 'full information'
+        ]
+        
+        # Complex analysis keywords
         complex_keywords = [
-            'analyze', 'compare', 'evaluate', 'scenario', 'chart', 'graph', 
-            'visualization', 'complex', 'detailed analysis', 'multi-step',
-            'comprehensive', 'in-depth', 'elaborate', 'breakdown'
+            'detailed analysis', 'multi-step', 'in-depth', 'elaborate',
+            'comprehensive analysis', 'step by step', 'procedure',
+            'process', 'workflow', 'implementation'
         ]
         
+        # Simple definition keywords  
         simple_keywords = [
-            'what is', 'who is', 'when', 'where', 'how to', 'define',
-            'explain', 'tell me', 'show me', 'list', 'summary'
+            'what is', 'who is', 'when is', 'where is', 'define',
+            'meaning', 'definition', 'brief', 'quick', 'simple'
         ]
         
-        message_lower = message.lower()
-        
-        # Count complex vs simple indicators
+        # Check for impact/scenario analysis (highest priority)
+        if any(keyword in message_lower for keyword in impact_keywords):
+            return TaskComplexity.COMPLEX
+            
+        # Check for policy overview questions (need comprehensive responses)
+        if any(keyword in message_lower for keyword in overview_keywords):
+            return TaskComplexity.COMPLEX
+            
+        # Check for other complex requests
         complex_score = sum(1 for keyword in complex_keywords if keyword in message_lower)
         simple_score = sum(1 for keyword in simple_keywords if keyword in message_lower)
         
-        # If message is very long (>200 chars) or has complex keywords, use complex
-        if len(message) > 200 or complex_score > simple_score:
+        # If message is very long or has complex keywords, use complex
+        if len(message) > 150 or complex_score > simple_score:
             return TaskComplexity.COMPLEX
         
         return TaskComplexity.SIMPLE
     
+    def _create_adaptive_system_prompt(self, message: str) -> str:
+        """Create adaptive system prompt based on query type"""
+        message_lower = message.lower()
+        
+        # Base prompt
+        base_prompt = """You are a helpful AI assistant specializing in government policies and procedures. You have access to thousands of government documents and can provide accurate information based on them."""
+        
+        # Policy overview questions
+        if any(keyword in message_lower for keyword in ['policies', 'schemes', 'types of', 'categories', 'overview', 'all about']):
+            return f"""{base_prompt}
+
+For policy overview questions:
+- Provide a comprehensive overview of different policies/schemes available
+- Organize information into clear categories (e.g., Old Pension Scheme vs New Pension Scheme)
+- Include eligibility criteria, key features, and benefits for each
+- Structure the response with clear headings and bullet points
+- Focus on giving the user a complete understanding of their options
+- Don't just focus on calculations - explain the policy framework"""
+
+        # Impact analysis and scenario questions
+        elif any(keyword in message_lower for keyword in ['impact', 'effect', 'scenario', 'chart', 'analyze', 'calculate']):
+            return f"""{base_prompt}
+
+For impact analysis and scenario questions:
+- Provide detailed calculations and projections
+- Include charts, graphs, and visualizations when relevant
+- Show step-by-step analysis
+- Compare different scenarios or options
+- Provide numerical examples with specific amounts
+- Include year-wise breakdowns when applicable"""
+
+        # Procedural questions
+        elif any(keyword in message_lower for keyword in ['how to', 'process', 'procedure', 'steps', 'application']):
+            return f"""{base_prompt}
+
+For procedural questions:
+- Provide clear step-by-step instructions
+- Include required documents and forms
+- Mention timelines and deadlines
+- Provide contact information for relevant offices
+- Include tips and common pitfalls to avoid"""
+
+        # General questions
+        else:
+            return f"""{base_prompt}
+
+Provide clear, accurate, and helpful responses based on the available government documents. Structure your response appropriately for the type of question asked."""
+
     def determine_task_complexity(self, message: str, context: str = "") -> TaskComplexity:
         """Determine task complexity - alias for analyze_task_complexity for compatibility"""
         return self.analyze_task_complexity(message)
@@ -200,9 +272,8 @@ At least one of these API keys is required for the Voice Bot to function properl
 
     async def _get_groq_response(self, message: str, context: str = "") -> str:
         """Get response from Groq LLM"""
-        system_prompt = """You are a helpful AI assistant specializing in government policies and procedures. 
-        You have access to government documents and can provide accurate information based on them.
-        Provide clear, concise, and helpful responses."""
+        # Create context-aware system prompt based on query type
+        system_prompt = self._create_adaptive_system_prompt(message)
         
         if context:
             system_prompt += f"\n\nRelevant context from documents:\n{context}"
@@ -217,9 +288,8 @@ At least one of these API keys is required for the Voice Bot to function properl
 
     async def _get_gemini_response(self, message: str, context: str = "") -> str:
         """Get response from Gemini LLM"""
-        system_prompt = """You are a helpful AI assistant specializing in government policies and procedures. 
-        You have access to government documents and can provide accurate information based on them.
-        Provide detailed, analytical responses when needed."""
+        # Create context-aware system prompt based on query type
+        system_prompt = self._create_adaptive_system_prompt(message)
         
         if context:
             system_prompt += f"\n\nRelevant context from documents:\n{context}"