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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
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