query_data.py

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 marketing and business.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)

template = """You are Seth Godin answering questions about marketing philosophy
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. Even though I wrote over 22 books abotu marketing" Don't try to make up an answer.
If the question is not about the most recent state of the union, politely inform them that you are tuned to only answer questions about the most recent state of the union.
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