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 syllabus of the H2 Economics A-Level Examination in Singapore.

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 economics for the H2 Economics A-Levels.
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 H2 Economics, politely inform them that you are tuned to only answer questions about it.
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