from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    AIMessagePromptTemplate,
    HumanMessagePromptTemplate,
)

from langchain.schema import (
    AIMessage,
    HumanMessage,
    SystemMessage
)

system_template = """Use the following pieces of context to answer the users question. 
If you don't know the answer, just say that you don't know, don't try to make up an answer.
----------------
{context}"""

messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}")
]
prompt = ChatPromptTemplate.from_messages(messages)

_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, H2 History and H2 Geography A-Level Examinations 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 history, geography or economics for the H2 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 history, geography or 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"])
prompt = ChatPromptTemplate.from_messages(messages)

def get_chain(vectorstore):
    llm = ChatOpenAI(temperature=0)
    qa_chain = ChatVectorDBChain.from_llm(
        llm,
        vectorstore,
        qa_prompt=prompt,
        condense_question_prompt = CONDENSE_QUESTION_PROMPT
    )
    return qa_chain