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from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAIChat
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 is about subjects covered in The Bot Forge website about conversational AI
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 conversational AI
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 conversational AI, politely inform them that you are tuned to only answer questions about conversational AI.
Question: {question}
=========
{context}
=========
Answer in Markdown:"""
QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
prefix_messages = [{"role": "system", "content": "You are a helpful assistant that is very good at answering questions about conversational AI and the bot forge."}]
def get_chain(vectorstore):
llm = OpenAIChat(temperature=0,prefix_messages=prefix_messages)
qa_chain = ChatVectorDBChain.from_llm(
llm,
vectorstore,
qa_prompt=QA_PROMPT,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
)
return qa_chain
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