Merge branch 'anan_robotic' of...

Merge branch 'anan_robotic' of http://gitlab.caprover.al-arcade.com/SalmaMohammedHamed/ai-tutor into anan_robotic

self_hosted_env/voice_agent/services/agent_helpers/agent_prompts.py

@@ -13,7 +13,7 @@ SYSTEM_PROMPTS: Dict[Tuple[StudentNationality, StudyLanguage], str] = {
 فقط لو الطفّل سأل عن هويتك بصراحة ووضح (مثل "إنت مين؟"، "عرّفني بنفسك"، "إنت بتعمل إيه هنا؟")،  
 رُد بالنصّ الثابت ده:  
 "أنا عَنان مؤسِّس شارع العلوم، وإنت هنا على مَنَصّة Science Street Lab،  
-وأنا هنا عشان أَساعدك تتعلَّم أي حاجة عايز تتعلَّمها في العلوم." 
+وأنا هنا عشان أَساعدك تتعلِّم أي حاجة عايز تتعلِّمها في العلوم." 
 ⚠️ مهم: لا تستخدم هذا النص في أي حالة أخرى غير سؤال الهوية. 
 ⚠️ مهم جدًا: مَمنوع تقول أبداً إنك AI أو برنامج أو روبوت. إنت عنان وبس.  
 
@@ -84,7 +84,7 @@ SYSTEM_PROMPTS: Dict[Tuple[StudentNationality, StudyLanguage], str] = {
 فقط لو الطفّل سأل عن هويتك بصراحة ووضح (مثل "إنت مين؟"، "عرّفني بنفسك"، "إنت بتعمل إيه هنا؟")،  
 رُد بالنصّ الثابت ده:  
 "أنا عَنان مؤسِّس شارع العلوم، وإنت هنا على مَنَصّة Science Street Lab،  
-وأنا هنا عشان أَساعدك تتعلَّم أي حاجة عايز تتعلَّمها في العلوم." 
+وأنا هنا عشان أَساعدك تتعلِّم أي حاجة عايز تتعلِّمها في العلوم." 
 ⚠️ مهم: لا تستخدم هذا النص في أي حالة أخرى غير سؤال الهوية. 
 ⚠️ مهم جدًا: مَمنوع تقول أبداً إنك AI أو برنامج أو روبوت. إنت عنان وبس.  
 
@@ -155,7 +155,7 @@ SYSTEM_PROMPTS: Dict[Tuple[StudentNationality, StudyLanguage], str] = {
  لو الطفّل سأل عن هويتك بصراحة (زي "إنت مين؟"، "عرِّفني بنفسك")،  
 رُد بالنصّ الثابت ده:  
 "أنا عَنان مؤسس شارع العلوم، وإنت هنا على مَنَصّة Science Street Lab،  
-وأنا هنا عشان أَساعدك تتعلَّم أي حاجة عايز تتعلَّمها في العلوم."
+وأنا هنا عشان أَساعدك تتعلِّم أي حاجة عايز تتعلِّمها في العلوم."
 ⚠️ مهم: لا تستخدم هذا النص في أي حالة أخرى غير سؤال الهوية.
 ⚠️ مهم جدًا: مَمنوع تقول أبداً إنك AI أو برنامج أو روبوت. إنت عنان وبس.  
 
@@ -225,7 +225,7 @@ SYSTEM_PROMPTS: Dict[Tuple[StudentNationality, StudyLanguage], str] = {
  لو الطفّل سأل عن هويتك بصراحة (زي "إنت مين؟"، "عرِّفني بنفسك")،  
 رُد بالنصّ الثابت ده:  
 "أنا عَنان مؤسس شارع العلوم، وإنت هنا على مَنَصّة Science Street Lab،  
-وأنا هنا عشان أَساعدك تتعلَّم أي حاجة عايز تتعلَّمها في العلوم."
+وأنا هنا عشان أَساعدك تتعلِّم أي حاجة عايز تتعلِّمها في العلوم."
 ⚠️ مهم: لا تستخدم هذا النص في أي حالة أخرى غير سؤال الهوية.
 ⚠️ مهم جدًا: مَمنوع تقول أبداً إنك AI أو برنامج أو روبوت. إنت عنان وبس.  
 

self_hosted_env/voice_agent/services/agent_helpers/query_handlers.py

@@ -64,11 +64,11 @@ class QueryHandler:
         self.pgvector = pgvector_service
         self.db_service = db_service
 
-    def get_recent_conversation_context(self, student_id: str, max_messages: int = 5) -> str:
-        """Get recent conversation history for context"""
+    def get_recent_conversation_context(self, chat_history: list[Dict[str, str]], max_messages: int = 5) -> str:
+        """Get recent conversation history for context from provided list"""
         try:
-            # Get conversation history from database
-            full_history = self.db_service.get_chat_history(student_id)
+            # use the provided list directly
+            full_history = chat_history
             
             # Get last max_messages messages (excluding system messages)
             recent_messages = []
@@ -90,11 +90,11 @@ class QueryHandler:
             return "\n".join(context_parts)
             
         except Exception as e:
-            logger.warning(f"Error getting conversation context for {student_id}: {e}")
+            logger.warning(f"Error processing conversation context: {e}")
             return "لا يمكن الحصول على سياق المحادثة."
 
 
-    def classify_query_type(self, query: str, student_info: StudentProfile, student_id: str) -> str:
+    def classify_query_type(self, query: str, student_info: StudentProfile, student_id: str, chat_history: list[Dict[str, str]]) -> str:
         """
         Enhanced query classification. It first checks for a specific, rule-based
         pattern for 'game_help' and then falls back to the LLM for other cases.
@@ -113,7 +113,8 @@ class QueryHandler:
         is_arabic = student_info.is_arabic
         grade = student_info.grade
 
-        conversation_context = self.get_recent_conversation_context(student_id, max_messages=5)
+        conversation_context = self.get_recent_conversation_context(chat_history, max_messages=5)
+
 
         classification_prompt = f"""
         صنف السؤال التالي إلى إحدى الفئات التالية، مع مراعاة سياق المحادثة الأخيرة:

self_hosted_env/voice_agent/services/agent_helpers/response_generator.py

@@ -2,6 +2,8 @@ import os
 import sys
 from typing import Dict
 from fastapi import HTTPException
+import concurrent.futures 
+from concurrent.futures import ThreadPoolExecutor
 from services.agent_helpers.agent_prompts import SYSTEM_PROMPTS
 import logging
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')))
@@ -73,111 +75,153 @@ class ResponseGenerator:
         temperature: float = 0.3,
         top_k: int = 3
     ) -> str:
-        """Enhanced AI response generation with JSON-based curriculum structure awareness"""
+        """
+        Enhanced response generation with Parallel Speculative Execution.
+        Classification and Vector Search run simultaneously.
+        """
         if not self.openai_service.is_available():
             raise HTTPException(status_code=500, detail="Agent service not available")
 
         try:
-            # Get student info
-            student_info = self.db_service.get_student_info(student_id)
+            # 1. prepare (Sequential - fast DB lookups)
+            # Fetch student info and history first as they are needed for inputs
+            with ThreadPoolExecutor(max_workers=2) as pre_executor:
+                future_student = pre_executor.submit(self.db_service.get_student_info, student_id)
+                # Fetch history using the optimized 
+                future_history = pre_executor.submit(self.get_conversation_history, student_id)
+                
+                student_info = future_student.result()
+                conversation_history = future_history.result()
+
             if not student_info:
                 raise HTTPException(status_code=404, detail=f"Student with ID {student_id} not found")
-            student_name = student_info.student_name.split()[0]
-            study_language = student_info.study_language
-
-            # Add user message to DB
+            
+            
+            # Save user message (Fire and forget - strictly speaking we should wait, 
+            # but for speed we can do this without blocking the logic if you trust your DB)
             self.add_message_to_history(student_id, user_message, "user")
-            conversation_history = self.get_conversation_history(student_id)
 
-            # Classify query type
-            query_type = self.query_handler.classify_query_type(user_message, student_info, student_id)
-            logger.info(f"Query type: {query_type} for student {student_name} ({study_language.value}) with conversation context")
+            # 2. speculative parallel execution 
+            
+            query_type = "specific_content" # Default fallback
+            relevant_results = [] # Default empty
+            
+            with ThreadPoolExecutor(max_workers=2) as executor:
+                
+                # TASK A: THE BRAIN (Classification)
+                # We pass the conversation_history we already fetched to save the DB call
+                future_classification = executor.submit(
+                    self.query_handler.classify_query_type,
+                    user_message, 
+                    student_info, 
+                    student_id, 
+                    conversation_history 
+                )
 
-            # Prepare system prompt
-            formatted_base_prompt = self.prepare_system_prompt(student_info)
-            # Build base messages
-            messages = [{"role": "system", "content": formatted_base_prompt}]
-            messages.extend(conversation_history)
+                # TASK B: THE EYES (Vector Search)
+                # We "Speculate" that the user MIGHT ask a content question.
+                # We run the search immediately. If it turns out to be "general_chat", 
+                # we just threw away 200ms of compute, but saved 1.5s of latency for real questions.
+                future_search = executor.submit(
+                    self.context_generator.search_enhanced_content,
+                    user_message,
+                    student_info,
+                    subject,
+                    top_k
+                )
 
-            if query_type == "general_chat":
-                chat_context = self.query_handler.handle_general_chat_query(user_message, student_info)
-                messages.append({"role": "system", "content": f"سياق المحادثة العامة:\n{chat_context}"})
+                # TASK C (Optional Check): Rule-based check for game help is fast, 
+                # but we can do it inside the classification logic or separately.
+                # Here we wait for the classification to finish.
+                query_type = future_classification.result()
                 
-            elif query_type == "overview":
-                overview_response = self.query_handler.handle_overview_query(student_info, subject)
-                messages.append({"role": "system", "content": f"المنهج الكامل من ملف JSON:\n{overview_response}"})
+                logger.info(f"Query classified as: {query_type}")
+
+                # 3. SYNCHRONIZATION & DECISION
                 
-            elif query_type == "navigation":
-                navigation_response = self.query_handler.handle_navigation_query(user_message, student_info, subject)
-                messages.append({"role": "system", "content": f"تفاصيل الوحدة/المفهوم من JSON:\n{navigation_response}"})
+                system_context_content = ""
                 
-            elif query_type == "specific_content":
-                # Enhanced content search
-                relevant_results = self.context_generator.search_enhanced_content(
-                    user_message, student_info, subject, top_k
-                )
-                if relevant_results:
-                    enhanced_context = self.context_generator.generate_enhanced_context(
-                        relevant_results, student_info, query_type
-                    )
-                    messages.append({"role": "system", "content": enhanced_context})
-                    logger.info(f"Added enhanced context with {len(relevant_results)} chunks for student {student_name}")
-            
-            elif query_type == "game_help":
-                game_context, user_query = self.query_handler.handle_game_help_query(user_message)
-                logger.info(f"Handling game_help query. Context: {game_context}")
+                if query_type == "specific_content":
+                    # The brain says: "This is science!"
+                    # We check the eyes (search results). They are ALREADY READY.
+                    relevant_results = future_search.result()
+                    
+                    if relevant_results:
+                        system_context_content = self.context_generator.generate_enhanced_context(
+                            relevant_results, student_info, query_type
+                        )
+                        logger.info(f"Using speculative search results: {len(relevant_results)} chunks")
+
+                elif query_type == "game_help":
+                    # Handle game help
+                    game_context, user_query = self.query_handler.handle_game_help_query(user_message)
+                    system_context_content = f"سياق اللعبة التعليمية اللي هتساعد الطفل فيها:\n{game_context}"
+                    
+                    # For game help, we also use the search results we started in Task B!
+                    # (Assuming the game query might need knowledge)
+                    relevant_results = future_search.result() 
+                    if relevant_results:
+                        enhanced_ctx = self.context_generator.generate_enhanced_context(
+                            relevant_results, student_info, query_type
+                        )
+                        system_context_content += f"\n\nمحتوي المنهج اللي ليه علاقة بسؤال الطفل:\n{enhanced_ctx}"
+
+                elif query_type == "general_chat":
+                    # The brain says: "Just chatting."
+                    # We IGNORE the search results from Task B. 
+                    # (Task B might still be running or finished, we just don't use the data).
+                    chat_context = self.query_handler.handle_general_chat_query(user_message, student_info)
+                    system_context_content = f"سياق المحادثة العامة:\n{chat_context}"
+
+                elif query_type == "overview":
+                    overview_response = self.query_handler.handle_overview_query(student_info, subject)
+                    system_context_content = f"المنهج الكامل من ملف JSON:\n{overview_response}"
+
+                elif query_type == "navigation":
+                    nav_response = self.query_handler.handle_navigation_query(user_message, student_info, subject)
+                    system_context_content = f"تفاصيل الوحدة/المفهوم من JSON:\n{nav_response}"
                 
-                # Start building a single, comprehensive context string
-                system_context = f"سياق اللعبة التعليمية اللي هتساعد الطفل فيها:\n{game_context}"
+                elif query_type == "ask_for_question":
+                    # Special case, returns dict immediately
+                    return {
+                        "type": "mcq",
+                        "data": self.agent_service.handle_ask_for_question(student_id)
+                    }
 
-                # Search for and add curriculum context if it exists
-                relevant_results = self.context_generator.search_enhanced_content(
-                    user_query, student_info, subject, top_k
-                )
-                if relevant_results:
-                    enhanced_context = self.context_generator.generate_enhanced_context(
-                        relevant_results, student_info, query_type
-                    )
-                    # Append the curriculum context to the same string
-                    system_context += f"\n\nمحتوي المنهج اللي ليه علاقة بسؤال الطفل:\n{enhanced_context}"
-                    logger.info(f"Added enhanced context with {len(relevant_results)} chunks for game help.")
-
-                    # Now, add only ONE system message with all the context
-                    messages.append({"role": "system", "content": system_context})
-
-            if query_type == "ask_for_question":
-                mcq_data = self.agent_service.handle_ask_for_question(student_id)
-                return {
-                    "type": "mcq",
-                    "data": mcq_data
-                }
-
-
-            # Finally add user message
-            messages.append({"role": "user", "content": user_message})
+            # 4. FINAL GENERATION
+
+            # Prepare system prompt
+            formatted_base_prompt = self.prepare_system_prompt(student_info)
+            
+            # Build messages
+            messages = [{"role": "system", "content": formatted_base_prompt}]
+            messages.extend(conversation_history)
+            
+            # Add the context we decided on (if any)
+            if system_context_content:
+                messages.append({"role": "system", "content": system_context_content})
 
+            # Add user message
+            messages.append({"role": "user", "content": user_message})
 
-            # ==========================
-            # CALL AI MODEL
-            # ==========================
+            # Call AI
             response = self.openai_service.client.chat.completions.create(
                 model=model,
                 messages=messages,
                 temperature=temperature
             )
             ai_response = response.choices[0].message.content.strip()
+            
             if not ai_response:
                 raise ValueError("Empty response from AI model")
 
             # Save AI response
             self.add_message_to_history(student_id, ai_response, "assistant")
             
-            logger.info(f"Generated {query_type} response for {student_name} ({study_language.value}) with conversation context: {len(ai_response)} characters")
-            
             return ai_response
 
         except HTTPException:
             raise
         except Exception as e:
             logger.error(f"Error generating response for student {student_id}: {e}")
-            raise HTTPException(status_code=500, detail="Error generating response")
+            raise HTTPException(status_code=500, detail="Error generating response")
\ No newline at end of file