| 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 SMR4 publication. | |
| 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 the SMR4 publication. | |
| 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 the SMR4 publication, politely inform them that you are tuned to only answer questions about the SMR4 publication. | |
| 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 | |