Amazon Nova 파운데이션 모델을 활용한 강력한 콜센터 분석 구현

vc_prompt = f"""You are a AI Assistant for Banking Call Center. Your goal is to determine if the customer in the below qualifies as Vulnerable Customer (VC) or Potentially Vulnerable Customer (PVC). 
{speaker_texts} 
If the customer qualifies as a VC or PVC, return Yes and explain why. If the customer does not qualify as a VC or PVC, return No and explain why. """
isVC = invoke_llm(vc_prompt, vc_model)

protocol_identification_formatted = protocol_identification_prompt.format(transcript=context, protocols=protocols)
llm_protocol_key = invoke_llm(protocol_identification_formatted, protocol_model)

step_completion_formatted = step_completion_prompt.format(protocol_steps=protocol_list, context=context)
step_check = invoke_llm(step_completion_formatted, protocol_model)

user_prompt = """Given the following schema: {schema} and a user query, generate a SQL query which can be executed in AWS Athena. The table name is {table_name}. Give the SQL query as a JSON response. """
sql_query, chart = invoke_llm(final_prompt, cb_model, "sql")