Refactor property selection logic and enhance conversation context handling

- Removed the previous check for existing properties in the `extract_and_search_properties` function to streamline property request handling.
- Introduced a new mechanism in the `handle_image_request` function to analyze recent user messages for specific property selections, improving the AI's ability to respond to user preferences.
- Added debug statements to track user-selected options and property mentions, enhancing traceability during property inquiries.
- These changes aim to improve the responsiveness and accuracy of property selections based on user interactions, ultimately enhancing the overall user experience.

ai_chat.py

@@ -371,17 +371,6 @@ async def extract_and_search_properties(state):
     classification = state.get("classification")
     print(f"DEBUG - Property search classification check: '{classification}'")
     
-    # Check if this is a detail request for existing properties in state
-    existing_properties = state.get("properties", [])
-    if (classification.startswith("request_images") or 
-        classification == "request_address" or 
-        classification == "request_details") and existing_properties:
-        print(f"DEBUG - Using existing properties from state, count: {len(existing_properties)}")
-        return {
-            "properties": existing_properties,
-            "classification": classification
-        }
-    
     # Check if classification matches our search categories
     is_search_request = (
         classification == "search_listings" or
@@ -586,6 +575,40 @@ async def handle_image_request(state):
                     best_match_score = score
                     selected_property = prop
     
+    # Look for specific property selections in conversation
+    session_messages = state.get("session_messages", [])
+    recent_messages = session_messages[-20:]  # Look at more messages
+
+    # Look for patterns like "option 3", "the warehouse", "this property"
+    selected_property_context = None
+
+    for msg in recent_messages:
+        if msg.get("role") == "user":
+            content = msg.get("content", "").lower()
+            # Look for option selections
+            if "option" in content:
+                import re
+                option_match = re.search(r'option\s+(\d+)', content)
+                if option_match:
+                    option_num = int(option_match.group(1))
+                    if 1 <= option_num <= len(props):
+                        selected_property_context = props[option_num - 1]
+                        print(f"DEBUG - Found user selected option {option_num}: {selected_property_context.get('title')}")
+                        break
+            
+            # Look for property type mentions that user specifically asked about
+            for i, prop in enumerate(props):
+                title_words = prop.get("title", "").lower().split()
+                for word in ["warehouse", "office", "space", "unit"]:
+                    if word in content and word in title_words:
+                        selected_property_context = prop
+                        print(f"DEBUG - Found user interest in {word}: {prop.get('title')}")
+                        break
+
+    # Use the property the user specifically selected/discussed
+    if selected_property_context:
+        selected_property = selected_property_context
+    
     # Fallback: Use conversation context to find which property user was discussing
     if not selected_property and len(props) > 1:
         # Check conversation history for property context

database.py

@@ -329,8 +329,8 @@ async def search_properties(filters: dict) -> list:
         # Note: Must-have features are removed from database search
         # They will be handled by the LLM in the chat response
         
-        # e. Order & limit - prioritize recent activity, then featured, then price
-        query = query.order("updated_at", desc=True).order("is_featured", desc=True).order("price").limit(5)
+        # e. Order & limit
+        query = query.order("is_featured", desc=True).order("price").limit(5)
         
         # f. Execute and return
         resp = query.execute()