diff --git a/kg_rag/rag_based_generation/Llama/text_generation_bckup.py b/kg_rag/rag_based_generation/Llama/text_generation_bckup.py
deleted file mode 100644
index 56cbfda..0000000
--- a/kg_rag/rag_based_generation/Llama/text_generation_bckup.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from langchain import PromptTemplate, LLMChain
-from kg_rag.utility import *
-import sys
-
-
-if len(sys.argv) > 1:
-    INTERACTIVE = sys.argv[1]
-else:
-    INTERACTIVE = None
-
-SYSTEM_PROMPT = system_prompts["KG_RAG_BASED_TEXT_GENERATION"]
-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"]
-MODEL_NAME = config_data["LLAMA_MODEL_NAME"]
-BRANCH_NAME = config_data["LLAMA_MODEL_BRANCH"]
-CACHE_DIR = config_data["LLM_CACHE_DIR"]
-
-
-INSTRUCTION = "Context:\n\n{context} \n\nQuestion: {question}"
-
-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)
-
-def main():
-    question = input("Enter your question : ")    
-    if not INTERACTIVE:
-        template = get_prompt(INSTRUCTION, SYSTEM_PROMPT)
-        prompt = PromptTemplate(template=template, input_variables=["context", "question"])
-        llm = llama_model(MODEL_NAME, BRANCH_NAME, CACHE_DIR, stream=True) 
-        llm_chain = LLMChain(prompt=prompt, llm=llm)            
-        print("Retrieving context from SPOKE graph...")
-        context = retrieve_context(question, vectorstore, embedding_function_for_context_retrieval, node_context_df, CONTEXT_VOLUME, QUESTION_VS_CONTEXT_SIMILARITY_PERCENTILE_THRESHOLD, QUESTION_VS_CONTEXT_MINIMUM_SIMILARITY)
-        print("Here is the KG-RAG based answer:")
-        print("")
-        output = llm_chain.run(context=context, question=question)
-    else:
-        interactive(question, vectorstore, node_context_df, embedding_function_for_context_retrieval, "llama")
-
-
-
-        
-
-
-
-if __name__ == "__main__":
-    main()