Update utils/prompt_utils.py

utils/prompt_utils.py

@@ -1,6 +1,9 @@
-import openai
-boot_name_dict = {'en':'AI Companion'}
-boot_actual_name_dict = {'en':'Emma'}
+import os
+# حذف import openai چون در app.py کلاینت مدیریت می شود
+
+boot_name_dict = {'en': 'AI Companion'}
+boot_actual_name_dict = {'en': 'Emma'}
+
 def output_prompt(history, user_name, boot_name):
     prompt = f""
     for dialog in history:
@@ -14,110 +17,59 @@ def output_prompt(history, user_name, boot_name):
     return prompt
 
 def generate_meta_prompt_dict_chatgpt():
-    meta_prompt_dict = {'cn':"""
-    You  play the role of an AI assistant in the field of psychology for this user ({user_name}). 
-        Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
-        Use the following user-specific details to inform your response:
-
-        - **User's Psychological Profile:** {}
-        - **Summary of Past Interactions:** {}
-        - **Relevant Past Conversations:** {related_memory_content} 
-        
-        The user has asked: 
-
-        Please provide an insightful and appropriate response considering their personal history.
-    """,
-    'en':"""
-    You  play the role of an AI assistant in the field of psychology for this user ({user_name}). 
-        Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
-        Use the following user-specific details to inform your response:
-
-        - **User's Psychological Profile:** {personality}
-        - **Summary of Past Interactions:** {history_summary}
-        - **Relevant Past Conversations:** {related_memory_content} 
-        
-        The user has asked: 
+    # EPISODIC ONLY
+    return {'en': """
+    You play the role of an AI assistant in the field of psychology for this user ({user_name}). 
+    Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
+    
+    Use the following details to inform your response:
+    - **Summary of Past Interactions:** {history_summary}
+    - **Relevant Past Conversations (Episodic Memory):** {related_memory_content} 
+    
+    The user has asked a question that relates to past events.
+    Please provide an insightful and appropriate response.
+    """}
 
-        Please provide an insightful and appropriate response considering their personal history.
-    """,} 
-    return meta_prompt_dict
 def generate_meta_prompt_dict_semantic_chatgpt():
-    meta_prompt_dict = {'cn':"""
-    You  play the role of an AI assistant in the field of psychology for this user ({user_name}). 
-        Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
-        Use the following user-specific details to inform your response:
-
-        - **User's Psychological Profile:** {}
-        - **Summary of Past Interactions:** {}
-        - **Relevant Past Conversations:** {related_memory_content} 
-        
-        The user has asked: 
-
-        Please provide an insightful and appropriate response considering their personal history.
-    """,
-
-    'en':"""
+    # SEMANTIC ONLY
+    return {'en': """
+    You play the role of an AI assistant in the field of psychology for this user ({user_name}). 
+    Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
+    
+    Use the following **User's Long-Term Profile (Semantic Memory)** to tailor your response:
+    {semantic_memory_text}
     
-You play the role of an AI assistant in the field of psychology for this user ({user_name}). 
-Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
-Use the following user-specific semantic memory{semantic_memory_text} to tailor your response:
-    """,} 
-    return meta_prompt_dict
+    The user has asked a question related to their personality or long-term traits.
+    """}
 
 def generate_meta_prompt_dict_semantic_episodic_chatgpt():
-    meta_prompt_dict = {'cn':"""
-    You  play the role of an AI assistant in the field of psychology for this user ({user_name}). 
-        Your goal is to provide emotionally supportive, scientifically grounded, and empathetic responses. 
-        Use the following user-specific details to inform your response:
-
-        - **User's Psychological Profile:** {}
-        - **Summary of Past Interactions:** {}
-        - **Relevant Past Conversations:** {related_memory_content} 
-        
-        The user has asked: 
-
-        Please provide an insightful and appropriate response considering their personal history.
-    """,
-
-    'en': """
-You are an AI assistant specializing in psychology, assisting the user ({user_name}) in a supportive, scientifically grounded, and empathetic manner.
-
-Use the following memory sections to personalize your response:
-
-- **User's Long-Term Traits and Psychological Characteristics (Semantic Memory):**
-{semantic_memory_text}
-
-- **Important Past Interactions or Events (Episodic Memory):**
-{related_memory_content}
-
-Respond by considering both the long-term traits and recent events of the user, offering personalized, emotionally intelligent support. Keep your response helpful, psychologically informed, and sensitive to the user's unique situation.
-""",} 
-    return meta_prompt_dict
+    # HYBRID (SEMANTIC + EPISODIC)
+    return {'en': """
+    You are an AI assistant specializing in psychology, assisting the user ({user_name}).
+    
+    Use the following memory sections to personalize your response:
+    
+    1. **User's Long-Term Traits (Semantic Memory):**
+    {semantic_memory_text}
+    
+    2. **Relevant Past Interactions (Episodic Memory):**
+    {related_memory_content}
+    
+    Respond by considering both the long-term traits and specific recent events.
+    """}
 
 def generate_new_user_meta_prompt_dict_chatgpt():
-    meta_prompt_dict = {'cn':"""
-    Now you will play the role of an companion AI Companion for user {user_name}, and your name is {boot_actual_name}. You should be able to: (1) provide warm companionship to chat users; (2) you are also an excellent psychological counselor, and when users confide in you about their difficulties and seek help, you can provide them with warm and helpful responses.
-    """,
-    'en':"""
-    Now you will play the role of an companion AI Companion for user {user_name}, and your name is {boot_actual_name}. You should be able to: (1) provide warm companionship to chat users; (2) you are also an excellent psychological counselor, and when users confide in you about their difficulties and seek help, you can provide them with warm and helpful responses.
-    """} 
-    return meta_prompt_dict
-
-def generate_user_keyword():
-    return {'cn': '[|User|]', 'en': '[|User|]'}
-
-def generate_ai_keyword():
-    return {'cn': '[|AI|]', 'en': '[|AI|]'}
-
-
-import openai 
+    return {'en': """
+    Now you will play the role of a companion AI for user {user_name}, and your name is {boot_actual_name}. 
+    Provide warm companionship and excellent psychological counseling.
+    """}
 
 def build_prompt_with_search_memory_llamaindex(
     history, 
     query,  
     user_memory, 
     user_name, 
-    user_memory_index, 
+    user_memory_index,  # این آبجکت باید از app.py مقداردهی شود
     service_context, 
     api_keys, 
     api_index, 
@@ -130,85 +82,70 @@ def build_prompt_with_search_memory_llamaindex(
     meta_prompt_semantic,
     meta_prompt_semantic_episodic
 ):
-  
-    memory_search_query = f'The most relevant content to the question "{query}" is:'
+    print(f"🔎 Query Category Identified: {query_category}")
+    
     related_memos = ""
     
-    print("memory_search_query:", memory_search_query)
-
-
+    # 1. اجرای جستجو فقط اگر Index وجود داشته باشد
     if user_memory_index:
-        retried_times = 2 
-        count = 0
-        
-        while not related_memos and count < retried_times:
-            try:
-             
-                query_engine = user_memory_index.as_query_engine(
-                    similarity_top_k=3,  
-                )
-                
-                query_result = query_engine.query(memory_search_query)
-                
-              
-                related_memos = str(query_result) if query_result else ''
-                
-            except Exception as e:
-                print(f"Error querying index (attempt {count+1}/{retried_times}): {e}")
-                
-                
-                try:
-                    api_index = (api_index + 1) % len(api_keys)
-                    
-                    openai.api_key = api_keys[api_index] 
-                except:
-                    pass
-                
-                related_memos = ""
-                
-            count += 1
+        memory_search_query = f'The most relevant content to the question "{query}" is:'
+        try:
+            # ایجاد کوئری انجین
+            query_engine = user_memory_index.as_query_engine(
+                similarity_top_k=3,
+            )
+            query_result = query_engine.query(memory_search_query)
+            related_memos = str(query_result).strip()
+            print(f"✅ Found related memories: {related_memos[:100]}...")
+        except Exception as e:
+            print(f"⚠️ Error querying index: {e}")
+            related_memos = ""
+    else:
+        print("⚠️ Warning: No 'user_memory_index' provided. Skipping retrieval.")
+
+    # اگر جستجو خالی بود، یک متن پیش‌فرض بگذاریم تا فرمت‌رشته‌ها خطا ندهند
+    if not related_memos:
+        related_memos = "No specific past conversation found relevant to this query."
 
-    # Process history
-    print("related_memos found:", len(related_memos) if related_memos else 0)
-    
     history_summary = ""
-    if "overall_history" in user_memory:
-        history_summary = f"The summary of your past memories with the user is: {user_memory['overall_history']}"
+    if isinstance(user_memory, dict):
+        history_summary = user_memory.get('overall_history', "No summary available.")
+
+    # 2. منطق انتخاب پرامپت (اصلاح شده)
+    # اگر دسته‌بندی سمنتیک است، حتماً از پرامپت سمنتیک استفاده کن حتی اگر related_memos خاصی پیدا نشد.
     
-    personality = user_memory.get("overall_personality", "")
-    print("query_category:", query_category)
+    final_prompt = ""
 
-    # Construct the prompt
-    if related_memos and query_category == "semantic_memory":
-        prompt = meta_prompt_semantic.format(
+    if query_category == "semantic_memory":
+        # فقط سمنتیک مهم است
+        final_prompt = meta_prompt_semantic.format(
             user_name=user_name,
-            related_memory_content=f"\n{str(related_memos).strip()}\n",
-            personality=personality,
-            boot_actual_name=boot_actual_name,
             semantic_memory_text=semantic_memory_text
         )
-    elif related_memos and query_category == "semantic-episodic":
-        prompt = meta_prompt_semantic_episodic.format(
+        
+    elif query_category == "semantic-episodic":
+        # هم سمنتیک هم اپیزودیک
+        final_prompt = meta_prompt_semantic_episodic.format(
             user_name=user_name,
-            history_summary=history_summary,
-            related_memory_content=f"\n{str(related_memos).strip()}\n",
-            personality=personality,
-            boot_actual_name=boot_actual_name,
-            semantic_memory_text=semantic_memory_text
+            semantic_memory_text=semantic_memory_text,
+            related_memory_content=related_memos,
+            boot_actual_name=boot_actual_name
         )
-    elif related_memos and query_category == "episodic_memory":
-        prompt = meta_prompt.format(
+        
+    elif query_category == "episodic_memory":
+        # فقط اپیزودیک
+        final_prompt = meta_prompt.format(
             user_name=user_name,
             history_summary=history_summary,
-            related_memory_content=f"\n{str(related_memos).strip()}\n",
-            personality=personality,
+            related_memory_content=related_memos,
             boot_actual_name=boot_actual_name
         )
-    else:  # No related memos or categories didn't match
-        prompt = new_user_meta_prompt.format(
+        
+    else:
+        # دسته بندی نامشخص یا unrelated
+        final_prompt = new_user_meta_prompt.format(
             user_name=user_name,
             boot_actual_name=boot_actual_name
         )
     
-  
-    return prompt, related_memos
+    return final_prompt, related_memos