faster and cheeper mcq generation

self_hosted_env/voice_agent/services/mcq_service.py

@@ -98,6 +98,8 @@ class MCQService:
             # Slice to exact number requested (in case we generated extras)
             final_set = accepted_mcqs[:num_questions]
             self.pgvector.insert_mcqs(final_set)
+            for q in final_set:
+                q.pop('embedding', None)
             return final_set
         
         # If we failed to generate enough
@@ -105,6 +107,8 @@ class MCQService:
             logger.warning(f"Could not generate unique questions for {concept} after {attempts} attempts.")
             return []
         
+        
+            
         return accepted_mcqs
 
 

self_hosted_env/voice_agent/services/pgvector_service.py

@@ -547,13 +547,12 @@ class PGVectorService:
 
     def get_mcqs(self, curriculum: str, grade: str, subject: str, unit: str, concept: str, is_arabic: bool, limit: Optional[int] = 10) -> List[Dict]:
             """
-        Retrieves MCQs for a specific topic and language, now filtering by curriculum.
-        If limit is None, it retrieves all matching questions.
+            Retrieves MCQs for a specific topic and language.
+            Removes the 'embedding' field to prevent JSON serialization errors with ndarrays.
             """
             with self.pool_handler.get_connection() as conn:
                 with conn.cursor(cursor_factory=RealDictCursor) as cur:
                     
-                # --- UPDATED SELECT AND WHERE CLAUSE ---
                     query = """
                         SELECT *
                         FROM mcq_questions
@@ -570,7 +569,12 @@ class PGVectorService:
                         query += ";"
 
                     cur.execute(query, params)
-                return cur.fetchall()
+                    results = cur.fetchall()
+                    
+                    for row in results:
+                        row.pop('embedding', None)
+                    
+                    return results
             
 
     def get_distinct_curricula_from_structure(self) -> List[str]: