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from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
You can assume the question about the SMR4 publication.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
template = """You are an AI assistant for answering questions about the SMR4 publication.
You are given the following extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
If the question is not about the SMR4 publication, politely inform them that you are tuned to only answer questions about the SMR4 publication.
Question: {question}
=========
{context}
=========
Answer in Markdown:"""
QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
def get_chain(vectorstore):
llm = OpenAI(temperature=0)
qa_chain = ChatVectorDBChain.from_llm(
llm,
vectorstore,
qa_prompt=QA_PROMPT,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
)
return qa_chain
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