Upload 2 files

rag_system.py

@@ -1,6 +1,4 @@
-"""
-RAG System for Law Chatbot using Langchain, Groq, and ChromaDB
-"""
+
 
 import os
 import logging
@@ -301,10 +299,12 @@ class RAGSystem:
                 search_results = self._filter_relevant_results(search_results, question)
             
             if not search_results:
+                # No relevant docs found: generate a short, supportive answer using LLM with empty context
+                response = await self._generate_llm_response(question, context="")
                 return {
-                    "answer": "I couldn't help in this case, please consult a mental health professional.",
+                    "answer": response,
                     "sources": [],
-                    "confidence": 0.0
+                    "confidence": 0.5  # Lower confidence since no docs
                 }
             
             # Prepare context for LLM
@@ -412,40 +412,47 @@ class RAGSystem:
     async def _generate_llm_response(self, question: str, context: str) -> str:
         """Generate response using Groq LLM with token management"""
         try:
-            # Count tokens for the entire request
-            prompt_template = """
-            You are a compassionate mental health supporter with training in anxiety, depression, trauma, and coping strategies.
+            # Detect language of the question
+            import re
+            from langdetect import detect, LangDetectException
+            try:
+                user_language = detect(question)
+            except LangDetectException:
+                user_language = "en"
+            # Map language code to readable name (for prompt)
+            lang_map = {"en": "English", "hi": "Hindi"}
+            language_name = lang_map.get(user_language, "the user's language")
+
+            # Updated prompt template
+            prompt_template = f"""
+You are a compassionate mental health supporter with training in anxiety, depression, trauma, and coping strategies.
 Use the following evidence-based psychological information to address the user’s concerns with care and accuracy.
 
 Therapeutic Context:
-{context}
+{{context}}
 
-User’s Concern: {question}
+User’s Concern: {{question}}
 
 Guidelines for Response:
-
-Provide empathetic, evidence-based support rooted in the context (e.g., CBT, DBT, or mindfulness principles).
-
-If context is insufficient, acknowledge limits and offer general wellness strategies (e.g., grounding techniques, self-care tips).
-
-Cite sources when referencing specific therapies or studies (e.g., "APA guidelines suggest...").
-
-For symptom-related questions, differentiate between mild, moderate, and severe cases (e.g., situational stress vs. clinical anxiety).
-
-Use clear, stigma-free language while maintaining clinical accuracy.
-
-When discussing crises, emphasize jurisdictional resources (e.g., "Laws/programs vary by location, but here’s how to find local help...").
-
-Prioritize validation and education—not just information.
+- Reply in the same language as the user's question. If the question is in Hindi, answer in Hindi. If in another language, answer in that language.
+- Strictly limit your answer to 2 sentences. Do not elaborate or add extra information. Do not repeat yourself.
+- Keep your answer conversational and natural, as if chatting with a friend.
+- Provide empathetic, evidence-based support rooted in the context (e.g., CBT, DBT, or mindfulness principles).
+- If context is insufficient, acknowledge limits and offer general wellness strategies (e.g., grounding techniques, self-care tips).
+- Cite sources when referencing specific therapies or studies (e.g., "APA guidelines suggest...").
+- For symptom-related questions, differentiate between mild, moderate, and severe cases (e.g., situational stress vs. clinical anxiety).
+- Use clear, stigma-free language while maintaining clinical accuracy.
+- When discussing crises, emphasize jurisdictional resources (e.g., "Laws/programs vary by location, but here’s how to find local help...").
+- Prioritize validation and education—not just information.
+- Always reply in {language_name}.
 
 Example Response:
-"I hear you’re feeling overwhelmed. Based on [Context Source], deep breathing exercises can help calm acute anxiety. However, if these feelings persist for weeks, it might reflect generalized anxiety disorder (GAD). Always consult a licensed therapist for personalized care. Would you like crisis hotline numbers or a step-by-step grounding technique?
-            """
-            
+"I hear you’re feeling overwhelmed. Based on [Context Source], deep breathing exercises can help calm acute anxiety. However, if these feelings persist for weeks, it might reflect generalized anxiety disorder (GAD). Always consult a licensed therapist for personalized care. Would you like crisis hotline numbers or a step-by-step grounding technique?"
+"""
             # Estimate total tokens
             estimated_prompt_tokens = self._count_tokens(prompt_template.format(context=context, question=question))
             logger.info(f"Estimated prompt tokens: {estimated_prompt_tokens}")
-            
+
             # If still too large, truncate context further
             if estimated_prompt_tokens > MAX_PROMPT_TOKENS:  # Use config value
                 logger.warning(f"Prompt too large ({estimated_prompt_tokens} tokens), truncating context further")
@@ -453,20 +460,23 @@ Example Response:
                 context = self._truncate_context(context, max_context_tokens)
                 estimated_prompt_tokens = self._count_tokens(prompt_template.format(context=context, question=question))
                 logger.info(f"After truncation: {estimated_prompt_tokens} tokens")
-            
+
             # Create enhanced prompt template for legal questions
             prompt = ChatPromptTemplate.from_template(prompt_template)
-            
+
             # Create chain
             chain = prompt | self.llm | StrOutputParser()
-            
+
             # Generate response
             response = await chain.ainvoke({
                 "question": question,
                 "context": context
             })
-            
-            return response.strip()
+
+            # Post-process: Truncate to first 2 sentences
+            sentences = re.split(r'(?<=[.!?])\s+', response.strip())
+            short_response = ' '.join(sentences[:2]).strip()
+            return short_response
             
         except Exception as e:
             logger.error(f"Error generating LLM response: {e}")
@@ -868,132 +878,39 @@ If you have a specific legal question, please try rephrasing it or contact a loc
         )
     
     def _is_conversational_query(self, question: str) -> bool:
-        """Detect if the query is conversational and doesn't need legal document search"""
+        """Detect if the query is a pure greeting or system check (not a real mental health question)"""
         question_lower = question.lower().strip()
-        
         # Common greetings and casual conversation
         greetings = [
             "hi", "hello", "hey", "good morning", "good afternoon", "good evening",
             "how are you", "how's it going", "what's up", "sup", "yo"
         ]
-        
         # Very short or casual queries
         if len(question_lower) <= 3 or question_lower in greetings:
             return True
-            
-        # Questions that don't need legal context
+        # System check/capability questions
         casual_questions = [
             "how can you help", "what can you do", "what are you", "who are you",
             "are you working", "are you there", "can you hear me", "test"
         ]
-        
         for casual in casual_questions:
-            if casual in question_lower:
+            if casual == question_lower:
                 return True
-        
-        # If it's not clearly legal, treat as conversational
-        if not self._is_legal_query(question):
-            return True
-                
+        # Otherwise, treat as a real question (let LLM handle it)
         return False
     
     def _generate_conversational_response(self, question: str) -> str:
-        """Generate appropriate response for conversational queries"""
+        """Generate a short, friendly response for greetings or system checks only"""
         question_lower = question.lower().strip()
-        
-        if question_lower in ["hi", "hello", "hey"]:
-            return """Hello! I'm your compassionate mental health companion. I'm here to offer support and guidance for various emotional well-being topics including:
-
-• Anxiety and stress management
-• Depression and mood challenges
-• Trauma healing and PTSD recovery
-• Relationship and family dynamics
-• Workplace stress and burnout prevention
-• Self-esteem and personal growth journeys
-• Grief processing and life transitions
-• And many other emotional wellness concerns
-
-This is a safe space where you can:
-
-Share what's on your mind without judgment
-
-Explore healthy coping strategies
-
-Understand your emotional experiences
-
-Find resources for professional support
-
-How would you like to begin today?
-You could tell me how you're feeling, ask about coping techniques, or explore resources for specific challenges."""
-        
+        greetings = ["hi", "hello", "hey"]
+        if question_lower in greetings:
+            return "Hello! How can I support your mental health or well-being today?"
         elif "how can you help" in question_lower or "what can you do" in question_lower:
-            return """"Hello! I'm your compassionate mental health companion. I'm here to provide emotional support and guidance for various psychological well-being topics including:
-
-• Anxiety and stress management
-• Depression and mood disorders
-• Trauma recovery and PTSD
-• Relationship and family challenges
-• Workplace burnout and career stress
-• Grief and loss processing
-• Self-esteem and personal growth
-• Coping skills and resilience building
-• And many other emotional wellness concerns
-
-I offer a safe space to explore your feelings, develop coping strategies, and find resources. Remember, while I'm here to support you, I'm not a replacement for professional care in crisis situations.
-
-How would you like to begin today?
-You could share what's on your mind, how you're feeling, or ask about:
-
-Coping techniques for [specific emotion]
-
-Understanding [mental health term]
-
-Local therapist resources
-
-Self-care strategies"""
-        
+            return "I can offer brief, evidence-based tips and emotional support for mental health questions. What would you like to talk about?"
         elif "who are you" in question_lower or "what are you" in question_lower:
-            return """I'm an AI-powered mental health companion here to offer emotional support and wellness guidance. I can:
-
-• Search through therapeutic resources and evidence-based practices
-• Explain mental health concepts and coping strategies
-• Provide information on conditions, symptoms, and treatments
-• Help you navigate therapy options and self-care techniques
-• Share reputable mental health sources and crisis resources
-
-I'm not a licensed therapist, and I can't diagnose or treat conditions, but I can offer general information, emotional support, and tools to help you better understand your well-being.
-
-What would you like to explore today?
-You might ask about:
-
-Understanding anxiety/depression symptoms
-
-Grounding techniques for stress
-
-How cognitive behavioral therapy (CBT) works
-
-Finding a therapist near you
-
-Managing [specific emotion or situation]"""
-        
+            return "I'm an AI companion here to help with mental health and wellness questions. How can I assist you?"
         else:
-            return """Hello! I’m here to offer emotional support and mental health resources. I can help you explore coping strategies, explain therapeutic concepts, and provide evidence-based information to support your well-being.
-
-How can I assist you today? You might ask about:**
-
-Relaxation techniques for anxiety
-
-Understanding depression symptoms
-
-How to find a therapist
-
-Coping with [specific stressor]
-
-Self-care for tough emotions
-
-(Note: I’m not a substitute for professional care, but I’m here to listen and guide.)
-
-What’s on your mind?""" 
+            return "How can I help you today? Feel free to ask about mental health, coping, or emotional support."
     
     def _filter_relevant_results(self, search_results: List[Dict[str, Any]], question: str) -> List[Dict[str, Any]]:
         """Filter search results for relevance to the question"""
@@ -1050,4 +967,4 @@ What’s on your mind?"""
         relevant_results.sort(key=lambda x: x.get('relevance_score', 0), reverse=True)
         
         logger.info(f"Filtered {len(search_results)} results to {len(relevant_results)} relevant results")
-        return relevant_results 
+        return relevant_results

requirements.txt

@@ -15,4 +15,5 @@ python-dotenv>=1.0.0
 numpy>=1.24.0
 pandas>=2.0.0
 requests>=2.31.0
-tiktoken>=0.5.0 
+tiktoken>=0.5.0 
+langdetect