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diff --git a/kg_rag/rag_based_generation/GPT/run_mcq_qa.py b/kg_rag/rag_based_generation/GPT/run_mcq_qa.py
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+++ b/kg_rag/rag_based_generation/GPT/run_mcq_qa.py
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+'''
+This script takes the MCQ style questions from the csv file and save the result as another csv file. 
+Before running this script, make sure to configure the filepaths in config.yaml file.
+Command line argument should be either 'gpt-4' or 'gpt-35-turbo'
+'''
+
+from kg_rag.utility import *
+import sys
+
+
+from tqdm import tqdm
+CHAT_MODEL_ID = sys.argv[1]
+
+QUESTION_PATH = config_data["MCQ_PATH"]
+SYSTEM_PROMPT = system_prompts["MCQ_QUESTION"]
+CONTEXT_VOLUME = int(config_data["CONTEXT_VOLUME"])
+QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD = float(config_data["QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD"])
+QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY = float(config_data["QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY"])
+VECTOR_DB_PATH = config_data["VECTOR_DB_PATH"]
+NODE_CONTEXT_PATH = config_data["NODE_CONTEXT_PATH"]
+SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL"]
+SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL = config_data["SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL"]
+TEMPERATURE = config_data["LLM_TEMPERATURE"]
+SAVE_PATH = config_data["SAVE_RESULTS_PATH"]
+
+
+CHAT_DEPLOYMENT_ID = CHAT_MODEL_ID
+
+save_name = "_".join(CHAT_MODEL_ID.split("-"))+"_kg_rag_based_mcq_{mode}.csv"
+
+
+vectorstore = load_chroma(VECTOR_DB_PATH, SENTENCE_EMBEDDING_MODEL_FOR_NODE_RETRIEVAL)
+embedding_function_for_context_retrieval = load_sentence_transformer(SENTENCE_EMBEDDING_MODEL_FOR_CONTEXT_RETRIEVAL)
+node_context_df = pd.read_csv(NODE_CONTEXT_PATH)
+edge_evidence = False
+
+
+MODE = "0"
+### MODE 0: Original KG_RAG                     ### 
+### MODE 1: jsonlize the context from KG search ### 
+### MODE 2: Add the prior domain knowledge      ### 
+### MODE 3: Combine MODE 1 & 2                  ### 
+
+def main():
+    start_time = time.time()
+    question_df = pd.read_csv(QUESTION_PATH)
+    answer_list = []
+    
+    for index, row in tqdm(question_df.iterrows(), total=306):
+        try: 
+            question = row["text"]
+            if MODE == "0":
+                ### MODE 0: Original KG_RAG                     ### 
+                context = retrieve_context(row["text"], vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY, edge_evidence, model_id=CHAT_MODEL_ID)
+                enriched_prompt = "Context: "+ context + "\n" + "Question: "+ question
+                output = get_Gemini_response(enriched_prompt, SYSTEM_PROMPT, temperature=TEMPERATURE)
+
+            if MODE == "1":
+                ### MODE 1: jsonlize the context from KG search ### 
+                ### Please implement the first strategy here    ###
+                output = '...'
+
+            if MODE == "2":
+                ### MODE 2: Add the prior domain knowledge      ### 
+                ### Please implement the second strategy here   ###
+                output = '...'
+            
+            if MODE == "3":
+                ### MODE 3: Combine MODE 1 & 2                  ### 
+                ### Please implement the third strategy here    ###
+                output = '...'
+
+            answer_list.append((row["text"], row["correct_node"], output))
+        except Exception as e:
+            print("Error in processing question: ", row["text"])
+            print("Error: ", e)
+            answer_list.append((row["text"], row["correct_node"], "Error"))
+
+
+    answer_df = pd.DataFrame(answer_list, columns=["question", "correct_answer", "llm_answer"])
+    output_file = os.path.join(SAVE_PATH, f"{save_name}".format(mode=MODE),)
+    answer_df.to_csv(output_file, index=False, header=True) 
+    print("Save the model outputs in ", output_file)
+    print("Completed in {} min".format((time.time()-start_time)/60))
+
+        
+        
+if __name__ == "__main__":
+    main()
+
+