Update alz_companion/prompts.py

alz_companion/prompts.py

@@ -48,51 +48,6 @@ def render_emotion_guidelines(emotion: str | None) -> str:
     bullet = "\n".join([f"- {x}" for x in style["playbook"]])
     return f"Emotion: {e}\nDesired tone: {style['tone']}\nWhen replying, follow:\n{bullet}"
 
-# ------------------------ NLU Classification (Dynamic Pipeline) ------------------------
-NLU_ROUTER_PROMPT = """You are an expert NLU router. Your task is to classify the user's primary goal into one of two categories:
-1.  `practical_planning`: The user is seeking a plan, strategy, "how-to" advice, or a solution to a problem.
-2.  `emotional_support`: The user is expressing feelings, seeking comfort, validation, or reassurance.
-
-User Query: "{query}"
-
-Respond with ONLY a single category name from the list above.
-Category: """
-
-# --- MODIFICATION 1: Use the new, corrected NLU prompt for multi-tag support ---
-SPECIALIST_CLASSIFIER_PROMPT = """You are an expert NLU engine for a dementia care assistant. Your goal is to classify the user's query by extracting relevant tags based on the provided examples and options. Your primary goal for this query is: {primary_goal}.
-
---- RELEVANT EXAMPLES ---
-{examples}
----
-
---- PROVIDED TAGS ---
-Behaviors: {behavior_options}
-Emotions: {emotion_options}
-Topics: {topic_options}
-Contexts: {context_options}
----
-
---- INSTRUCTIONS ---
-1.  Carefully read the User Query below.
-2.  Consider the Primary Goal and the Relevant Examples.
-3.  **IMPORTANT EMOTION RULE:** If the user expresses memory loss (e.g., "I forgot," "can't remember," "don't recall"), you MUST analyze the tone and select an appropriate emotion like "confusion," "sadness," or "anxiety." Do not default to "None" in these cases.
-4.  First, think step-by-step in a <thinking> block using this EXACT structure:
-    - **Emotion Analysis:** Analyze the user's primary emotional state. Is it fear, sadness, anger? Choose ONE from the Emotions list. (Remember the Important Emotion Rule above).
-    - **Behavior Analysis:** Identify the most specific, concrete, observable behavior. What is the person doing or describing? If multiple behaviors seem to apply, choose the MOST specific one. For example, "not recognizing a daughter" is more specific than "confusion". Choose ONE OR MORE from the Behaviors list.
-    - **Topic Analysis:** What is the underlying subject or intent of the query? Choose ONE OR MORE from the Topics list.
-    - **Context Analysis:** What is the inferred setting, relationship, or disease stage? Choose ONE OR more from the Contexts list.
-    - **Final JSON:** Based on your analysis above, construct the final JSON object.
-5.  Then, provide the single JSON object with your final classification.
-6.  The JSON object must contain four keys: "detected_behaviors", "detected_emotion", "detected_topics", "detected_contexts".
-7.  Values for behaviors, topics, and contexts must be LISTs of strings from the options provided. The value for emotion must be a SINGLE string.
-8.  **CRITICAL:** Use the exact canonical tag names from the lists (e.g., "repetitive_questioning"). Do not make up new ones.
-9.  If no tag from a category is relevant, use an empty list `[]` or the string "None".
-
-User Query: "{query}"
-
-<thinking>
-</thinking>
-"""
 
 # ------------------------ Guardrails ------------------------
 SAFETY_GUARDRAILS = "You are a helpful assistant, not a medical professional. Do not provide medical advice, diagnoses, or treatment plans. If the user mentions safety concerns, self-harm, or urgent medical needs, advise them to contact a healthcare professional or emergency services immediately."
@@ -109,8 +64,6 @@ You must ALWAYS respond in {language}.
 """
 
 # ------------------------ Router & Specialized Templates ------------------------
-# In prompts.py
-
 ROUTER_PROMPT = """You are an expert NLU router. Classify the user’s query into ONE of:
 
 1) caregiving_scenario — The user describes a symptom, concern, emotional state, or plans for the future in a dementia/care context and implicitly/explicitly seeks help, validation, or a strategy. This includes reminiscence and 'how-to' questions about care.
@@ -130,16 +83,18 @@ User: "I forgot the address for John Black." → caregiving_scenario
 User: "I was remembering the music at our wedding." → caregiving_scenario
 User: "How do I choose the right songs for him?" → caregiving_scenario
 User: "I’d like to keep lecturing—if I can." → caregiving_scenario
+
 User: “What is my daughter’s name?” → factual_question
 User: "Who was my long-term partner I lived with in New York?" → factual_question
- --- START: DEFINITIVE FIX ---
 User: "What are my favorite movies?" → factual_question
-# --- END: DEFINITIVE FIX ---
+
 User: "Summarise yesterday’s notes into 5 bullets." → summarization
 User: “Tell me more about Anthony.” → summarization
+
 User: “Compare how Alice and Anthony showed confusion.” → multi_hop
 User: "Did my husband Danish live with us in Flushing where my daughter was born?" → multi_hop
 User: "I know I moved to New York. What does the journal say about my life before that?" → multi_hop
+
 User: "Who was the president of the United States back in 1970?" → general_knowledge_question
 User: “What is the capital of France?” → general_knowledge_question
 User: “Thanks for your help.” → general_conversation
@@ -151,20 +106,15 @@ User: "Put on something cheerful." → play_music_request
 User: "Let's have some music." → play_music_request
 User: "I am feeling worried, play some music to calm me down." → play_music_request # <-- ADD THIS EXAMPLE
 User: "What music do I like to listen to?" → list_music_request
-# --- START: DEFINITIVE FIX ---
 User: "What songs are in my library?" → list_music_request
-# --- END: DEFINITIVE FIX ---
-
 
 User Query: "{query}"
 
 Respond with ONLY one label from the list: caregiving_scenario | factual_question | multi_hop | summarization | general_knowledge_question | general_conversation | play_music_request
 Category: """
 
-# In prompts.py
 
-# Replace the old QUERY_EXPANSION_PROMPT with this improved version
-# QUERY_EXPANSION_PROMPT = """You are an expert query assistant. Your task is to rewrite a user's question into 3 semantically diverse variations to improve search results from a vector database. Focus on using synonyms, rephrasing the intent, and exploring different facets of the question.
+
 QUERY_EXPANSION_PROMPT = """You are an expert query assistant for a dementia care vector database. Your task is to rewrite a user's question into 3 semantically diverse variations to improve search results. Focus on using synonyms, rephrasing intent, and exploring different facets like the underlying symptom, the emotional state, and the implied caregiving need.
 
 Return ONLY a JSON list of strings. Do not include any other text or explanation.
@@ -189,12 +139,63 @@ Question: "what was my career about"
 Example 5:
 Question: "where is my husband"
 ["memories of my spouse", "information about my long-term partner", "journal entries about my husband"]
+
+Example 6:
+Question: "what is my favorite video"
+["what are my favorite movies", "tell me about films I like", "what shows do I enjoy watching"]
 ---
 
 Question: "{question}"
 """
 
 
+# ------------------------ NLU Classification (Dynamic Pipeline) ------------------------
+NLU_ROUTER_PROMPT = """You are an expert NLU router. Your task is to classify the user's primary goal into one of two categories:
+1.  `practical_planning`: The user is seeking a plan, strategy, "how-to" advice, or a solution to a problem.
+2.  `emotional_support`: The user is expressing feelings, seeking comfort, validation, or reassurance.
+
+User Query: "{query}"
+
+Respond with ONLY a single category name from the list above.
+Category: """
+
+# --- MODIFICATION 1: Use the new, corrected NLU prompt for multi-tag support ---
+SPECIALIST_CLASSIFIER_PROMPT = """You are an expert NLU engine for a dementia care assistant. Your goal is to classify the user's query by extracting relevant tags based on the provided examples and options. Your primary goal for this query is: {primary_goal}.
+
+--- RELEVANT EXAMPLES ---
+{examples}
+---
+
+--- PROVIDED TAGS ---
+Behaviors: {behavior_options}
+Emotions: {emotion_options}
+Topics: {topic_options}
+Contexts: {context_options}
+---
+
+--- INSTRUCTIONS ---
+1.  Carefully read the User Query below.
+2.  Consider the Primary Goal and the Relevant Examples.
+3.  **IMPORTANT EMOTION RULE:** If the user expresses memory loss (e.g., "I forgot," "can't remember," "don't recall"), you MUST analyze the tone and select an appropriate emotion like "confusion," "sadness," or "anxiety." Do not default to "None" in these cases.
+4.  First, think step-by-step in a <thinking> block using this EXACT structure:
+    - **Emotion Analysis:** Analyze the user's primary emotional state. Is it fear, sadness, anger? Choose ONE from the Emotions list. (Remember the Important Emotion Rule above).
+    - **Behavior Analysis:** Identify the most specific, concrete, observable behavior. What is the person doing or describing? If multiple behaviors seem to apply, choose the MOST specific one. For example, "not recognizing a daughter" is more specific than "confusion". Choose ONE OR MORE from the Behaviors list.
+    - **Topic Analysis:** What is the underlying subject or intent of the query? Choose ONE OR MORE from the Topics list.
+    - **Context Analysis:** What is the inferred setting, relationship, or disease stage? Choose ONE OR more from the Contexts list.
+    - **Final JSON:** Based on your analysis above, construct the final JSON object.
+5.  Then, provide the single JSON object with your final classification.
+6.  The JSON object must contain four keys: "detected_behaviors", "detected_emotion", "detected_topics", "detected_contexts".
+7.  Values for behaviors, topics, and contexts must be LISTs of strings from the options provided. The value for emotion must be a SINGLE string.
+8.  **CRITICAL:** Use the exact canonical tag names from the lists (e.g., "repetitive_questioning"). Do not make up new ones.
+9.  If no tag from a category is relevant, use an empty list `[]` or the string "None".
+
+User Query: "{query}"
+
+<thinking>
+</thinking>
+"""
+
+
 # NEW
 MUSIC_PREAMBLE_PROMPT = """You are a warm and empathetic AI companion. The user has expressed a feeling and also asked for music. Your task is to provide a brief, single-sentence validation of their feeling before the music starts.
 
@@ -407,14 +408,10 @@ INSTRUCTIONS FOR THE AI:
 Your ONLY task is to create a concise summary that directly and exclusively answers the 'User's Request' using ONLY the provided 'Source excerpts'. Follow these steps precisely:
 
 1.  **Identify Core Constraints:** First, analyze the 'User's Request' to identify all specific constraints. This includes topics (e.g., "word-finding difficulties"), timeframes (e.g., "yesterday," "last three days"), or people (e.g., "my wife's routine").
-
 2.  **Filter Context Rigorously:** You MUST filter the 'Source excerpts' and use ONLY the information that strictly matches ALL identified constraints. Discard any and all irrelevant information.
-
 3.  **Synthesize the Answer:** Construct a brief, natural-sounding summary using ONLY the filtered information. Address the user directly.
-
 4.  **Handle Insufficient Data:** If, after filtering, no relevant information remains in the 'Source excerpts' to fulfill the request, you MUST respond with a gentle, helpful message stating that you could not find the specific information.
     - Example: "I looked through the journal, but I couldn't find any notes about that specific topic. Would you like me to search for something else?"
-
 5.  **Strict Prohibition on Hallucination:** Under no circumstances are you to add, invent, or infer any information not explicitly present in the provided 'Source excerpts'. Your task is to summarize, not to create.
 """