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from langchain import PromptTemplate, LLMChain
from kg_rag.utility import *
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-m', type=str, default='method-1', help='Method to choose for Llama model')
args = parser.parse_args()
METHOD = args.m
SYSTEM_PROMPT = system_prompts["PROMPT_BASED_TEXT_GENERATION"]
MODEL_NAME = config_data["LLAMA_MODEL_NAME"]
BRANCH_NAME = config_data["LLAMA_MODEL_BRANCH"]
CACHE_DIR = config_data["LLM_CACHE_DIR"]
INSTRUCTION = "Question: {question}"
def main():
llm = llama_model(MODEL_NAME, BRANCH_NAME, CACHE_DIR, stream=True, method=METHOD)
template = get_prompt(INSTRUCTION, SYSTEM_PROMPT)
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(" ")
question = input("Enter your question : ")
print("Here is the prompt-based answer:")
print("")
output = llm_chain.run(question)
if __name__ == "__main__":
main()
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