autoqa_chains.py

from langchain_core.pydantic_v1 import BaseModel, Field
from typing import List
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import PromptTemplate


class QA(BaseModel):
    question: str = Field(description="question")
    answer: str = Field(description="answer")


class AutoQA(BaseModel):
    questions: List[QA] = Field(description="list of question and answers")


qa_prompt_template = """
Come up with the 10 questions and answers that could be commonly asked by people about the following research paper.
The question and answers should capture the whole essence of the research paper
The answers should be a bit detailed and strictly based on the research paper.
Your response should be recorded in the following json format: {format_instructions}.

here is the research paper: ####{paper}####
"""

auto_qa_output_parser = JsonOutputParser(pydantic_object=AutoQA)
qa_prompt = PromptTemplate(
    template=qa_prompt_template,
    input_variables=["paper"],
    partial_variables={
        "format_instructions": auto_qa_output_parser.get_format_instructions()
    },
)
auto_qa_chain = lambda model: qa_prompt | model


followup_prompt_template = """
Question: {question}
Answer: {answer}
Based on the above question and answer and the research paper as your context, come up with a followup question and its answer.
The answer should be a bit detailed and strictly based on the research paper.
Your response should be recorded in the following json format: {format_instructions}.

here is the research paper: ####{paper}####
"""

followup_prompt = PromptTemplate(
    template=followup_prompt_template,
    input_variables=["paper"],
    partial_variables={
        "format_instructions": auto_qa_output_parser.get_format_instructions()
    },
)

followup_qa_chain = lambda model: qa_prompt | model