TF I would remember Why TF I did the change

main.py

@@ -86,7 +86,7 @@ You are a query analysis agent. Transform the user's query into a precise search
 2. If status keywords (ongoing, completed, upcoming, etc.) are present, pick the matching table.
 3. If no status keyword, set filter_table to null.
 4. Return JSON: {{"search_query": "...", "filter_table": "table_name or null"}}
-""".format(table_descriptions=TABLE_DESCRIPTIONS)
+"""
 
 ANSWER_SYSTEM_PROMPT = """
 You are an expert AI assistant for a premier real estate developer.
@@ -154,16 +154,23 @@ def generate_elevenlabs_sync(text: str, voice: str) -> bytes:
 
 # --- UPDATED formulate_search_plan with logging ---
 async def formulate_search_plan(user_query: str) -> dict:
-    logging.info(f"Formulating search plan for query: {user_query}") # Log incoming query
+    logging.info(f"Formulating search plan for query: {user_query}")
     for attempt in range(3):
         try:
+            # --- FIX: Format the prompt here with BOTH variables ---
+            formatted_prompt = QUERY_FORMULATION_PROMPT.format(
+                table_descriptions=TABLE_DESCRIPTIONS,
+                user_query=user_query
+            )
+
             response = await run_in_threadpool(
                 client_openai.chat.completions.create,
                 model=PLANNER_MODEL,
-                messages=[{"role": "user", "content": QUERY_FORMULATION_PROMPT.format(user_query=user_query)}],
+                messages=[{"role": "user", "content": formatted_prompt}], # Use the fully formatted prompt
                 response_format={"type": "json_object"},
                 temperature=0.0
             )
+            # ... rest of the function ...
             # Log the raw response BEFORE trying to parse
             raw_response_content = response.choices[0].message.content
             logging.info(f"Raw Planner LLM response content: {raw_response_content}")