Update custom_utils.py

custom_utils.py

@@ -61,8 +61,6 @@ def rag_retrieval_advanced(openai_api_key,
     #        "bedrooms": { "$eq": 1}
     #    }
     #}
-    
-    #additional_stages = [match_stage]
 
     # 2) Average review score and review count boost, sorted in descending order
 
@@ -188,7 +186,7 @@ def vector_search_naive(openai_api_key,
 
     pipeline = [vector_search_stage, get_remove_embedding_stage()]
 
-    return invoke_search(collection, pipeline)
+    return invoke_search(db, collection, pipeline)
 
 def vector_search_advanced(openai_api_key, 
                            user_query, 
@@ -221,28 +219,28 @@ def vector_search_advanced(openai_api_key,
   
     pipeline = [vector_search_stage, get_remove_embedding_stage()] + additional_stages
 
-    return invoke_search(collection, pipeline)
+    return invoke_search(db, collection, pipeline)
 
 def get_remove_embedding_stage():
     return {
         "$unset": "description_embedding"
     }
 
-def invoke_search(collection, pipeline):
+def invoke_search(db, collection, pipeline):
     results = collection.aggregate(pipeline)
     
-    print(f"Vector search millis elapsed: {get_millis_elapsed()}")
+    print(f"Vector search millis elapsed: {get_millis_elapsed(db)}")
     
     return list(results)
 
-def get_millis_elapsed():
+def get_millis_elapsed(db):
     explain_query_execution = db.command(
         "explain", {
             "aggregate": collection.name,
             "pipeline": pipeline,
             "cursor": {}
         }, 
-        verbosity='executionStats')
+        verbosity="executionStats")
 
     explain_vector_search = explain_query_execution["stages"][0]["$vectorSearch"]