d

ai_chat.py

@@ -218,8 +218,8 @@ async def process_message(user_message: str, user_info: dict = None, session_id:
     
     # Proactive persona extraction from any message
     extracted_persona = {}
-    if not is_persona_question and wa_id:
-        # Try to extract persona fields from the current message
+    if wa_id:
+        # Try to extract persona fields from the current message (ALWAYS)
         extracted_persona = await extract_persona_from_message(user_message, persona)
         print(f"Extracted persona from message: {extracted_persona}")
     

persona_manager.py

@@ -75,9 +75,19 @@ async def update_persona_field(wa_id: str, field: str, value) -> bool:
         }
         print(f"Updating persona field {field} with value {value} for user {wa_id}")
         print(f"Update data: {update_data}")
+        
+        # First check if the persona exists
+        check_response = supabase.table("user_personas").select("*").eq("wa_id", wa_id).execute()
+        print(f"Current persona data: {check_response.data}")
+        
         response = supabase.table("user_personas").update(update_data).eq("wa_id", wa_id).execute()
         print(f"Persona update response: {response.data if response.data else 'No data returned'}")
         print(f"Response status: {response.status_code if hasattr(response, 'status_code') else 'No status'}")
+        
+        # Verify the update
+        verify_response = supabase.table("user_personas").select("*").eq("wa_id", wa_id).execute()
+        print(f"Updated persona data: {verify_response.data}")
+        
         return True
     except Exception as e:
         print(f"Error updating persona field {field}: {e}")
@@ -414,18 +424,21 @@ async def extract_persona_from_message(user_message: str, current_persona: Dict)
         return {}
     
     try:
+        print(f"Starting AI extraction for message: '{user_message}'")
         # Create a comprehensive prompt for AI extraction
         system_prompt = """You are a property agent assistant. Extract any persona information from the user's message.
 
 Available persona fields:
 - intent: "buy" or "lease" (extract from words like buy, purchase, own, lease, rent)
-- location_preference: Extract location names, including abbreviations (e.g., "cpt" = "cape town", "jhb" = "johannesburg", "pta" = "pretoria")
+- location_preference: Extract location names, ALWAYS use full names (e.g., "cpt" = "cape town", "jhb" = "johannesburg", "pta" = "pretoria")
 - budget: Extract numeric values with currency/budget indicators (e.g., "500k" = 500000, "$1m" = 1000000)
 - size_preference_sqm: Extract size in square meters (convert from sq ft if needed)
 - must_have: Extract features as array (e.g., ["truck access", "office space"])
+- language: Extract language preference (e.g., "afrikaans", "english", "afrikaans praat", "speak afrikaans")
 
 IMPORTANT: You MUST return a valid JSON object. If no information is found, return {} (empty object).
-For location_preference, be very generous in extraction. If the user mentions ANY location (including "pretoria", "pta", "johannesburg", "jhb", "cape town", "cpt", etc.), extract it.
+For location_preference, be very generous in extraction and ALWAYS use full city names, not abbreviations.
+For language, extract if user mentions language preference (e.g., "praat afrikaans", "speak english", etc.)
 
 Examples:
 - "I want a property in cpt" → {"location_preference": "cape town"}
@@ -433,6 +446,8 @@ Examples:
 - "Looking for a property in pretoria for my clothing business" → {"location_preference": "pretoria"}
 - "Need space for clothing business with office" → {"must_have": ["office"]}
 - "I want one big enough for a phara manufacturing plant" → {"size_preference_sqm": 5000, "must_have": ["manufacturing"]}
+- "Praat van nou af net afrikaans met my" → {"language": "afrikaans"}
+- "Speak English from now on" → {"language": "english"}
 
 Return ONLY the JSON object, no other text."""
 
@@ -441,9 +456,12 @@ Return ONLY the JSON object, no other text."""
             {"role": "user", "content": user_message}
         ]
         
+        print(f"Calling LLM with messages: {messages}")
         response = llm.invoke(messages)
+        print(f"LLM response received: {type(response)}")
         
         print(f"AI extraction response: {response.content}")  # Debug
+        print(f"User message being extracted: {user_message}")  # Debug
         
         # Parse the JSON response
         import json
@@ -454,9 +472,9 @@ Return ONLY the JSON object, no other text."""
             # Validate and clean the extracted data
             cleaned_extracted = {}
             
-            # Only include fields that aren't already set in current_persona
+            # Include fields that can be extracted, even if they're already set (allow updates)
             for field, value in extracted.items():
-                if field in PERSONA_FIELDS and not current_persona.get(field) and value is not None:
+                if field in PERSONA_FIELDS and value is not None:
                     cleaned_extracted[field] = value
             
             print(f"Cleaned extraction: {cleaned_extracted}")  # Debug