qna_prompting.py

import sqlite3
import streamlit as st
from pydantic import BaseModel, Field
from llama_index.core.tools import FunctionTool

import time

db_path = "./database/mock_qna.sqlite"
qna_question_description = """
    Only trigger this when user wants to be tested with a question.
    Use this tool to extract the chapter number from the body of input text, 
    thereafter, chapter number will be used as a filtering criteria for
    extracting the right questions set from database.
    
    Thereafter, the chapter_n argument will be passed to the function for Q&A question retrieval.
    If no chapter number specified or user requested for random question,
    or user has no preference over which chapter of textbook to be tested,
    set function argument `chapter_n` to be `Chapter_0`.
"""
qna_question_data_format = """
    The format of the function argument `chapter_n` looks as follow:
    It should be in the format with `Chapter_` as prefix.
        Example 1: `Chapter_1` for first chapter
        Example 2: For chapter 12 of the textbook, you should return `Chapter_12`
        Example 3: `Chapter_5` for fifth chapter
"""
qna_answer_description = """
    Use this tool to trigger the evaluation of user's provided input with the 
    correct answer of the Q&A question asked. When user provides answer to the
    question asked, they can reply in natural language or giving the alphabet
    letter of which selected choice they think it's the right answer.
    
    If user's answer is not a single alphabet letter, but is contextually 
    closer to a particular answer choice, return the corresponding
    alphabet A, B, C, D or Z for which the answer's meaning is closest to.

    Thereafter, the `user_selected_answer` argument will be passed to the 
    function for Q&A question evaluation.
"""
qna_answer_data_format = """
    The format of the function argument `user_selected_answer` looks as follow:
        It should be in the format of single character such as `A`, `B`, `C`, `D` or `Z`.
        Example 1: User's answer is `a`, it means choice `A`.
        Example 2: User's answer is contextually closer to 3rd answer choice, it means `C`.
        Example 3: User says last is the answer, it means `D`.
        Example 4: If user doesn't know about the answer, it means `Z`.
"""

class Question_Model(BaseModel):
    chapter_n: str = Field(..., 
                           pattern=r'^Chapter_\d*$',
                           description=qna_question_data_format
                    )

class Answer_Model(BaseModel):
    user_selected_answer: str = Field(...,
                                      pattern=r'^[ABCDZ]$',
                                      description=qna_answer_data_format
                            )

def get_qna_question(chapter_n: str) -> str:

    con = sqlite3.connect(db_path)
    cur = con.cursor()

    filter_clause = "WHERE a.id IS NULL" if chapter_n == "Chapter_0" else f"WHERE a.id IS NULL AND chapter='{chapter_n}'"
    sql_string = """SELECT q.id, question, option_1, option_2, option_3, option_4, q.correct_answer, q.reasoning
                    FROM qna_tbl q LEFT JOIN answer_tbl a
                                   ON q.id = a.id
                 """ + filter_clause
    # sql_string = sql_string + " ORDER BY RANDOM() LIMIT 1"
    
    res = cur.execute(sql_string)
    result = res.fetchone()

    id       = result[0]
    question = result[1]
    option_1 = result[2]
    option_2 = result[3]
    option_3 = result[4]
    option_4 = result[5]
    c_answer = result[6]
    reasons  = result[7]

    qna_str  = "As requested, here is the retrieved question: \n" + \
               "============================================= \n" + \
                question.replace("\\n", "\n") + "\n" + \
               "A) " + option_1 + "\n" + \
               "B) " + option_2 + "\n" + \
               "C) " + option_3 + "\n" + \
               "D) " + option_4
    
    st.session_state.question_id = id
    st.session_state.qna_answer = c_answer
    st.session_state.reasons = reasons
    
    con.close()
    
    return qna_str

def evaluate_qna_answer(user_selected_answer: str) -> str:

    try:
        answer_mapping = {
            "A": 1,
            "B": 2,
            "C": 3,
            "D": 4,
            "Z": 0
        }
        num_mapping = dict((v,k) for k,v in answer_mapping.items())
        user_answer_numeric = answer_mapping.get(user_selected_answer, 0)

        question_id = st.session_state.question_id
        qna_answer  = st.session_state.qna_answer
        reasons     = st.session_state.reasons
        
        qna_answer_alphabet = num_mapping.get(int(qna_answer), "ERROR")

        con = sqlite3.connect(db_path)
        cur = con.cursor()
        sql_string = f"""INSERT INTO answer_tbl 
                        VALUES ({question_id}, {qna_answer}, {user_answer_numeric})
        """
        
        res = cur.execute(sql_string)
        con.commit()
        con.close()

        if qna_answer == user_answer_numeric:
            st.toast("🍯 yummy yummy, hooray!", icon="🎉")
            time.sleep(2)
            st.toast("🐻💕🍯 You got it right!", icon="🎊")
            time.sleep(2)
            st.toast("🥇 You are amazing! 💯💯", icon="💪")
            st.balloons()
        else:
            st.toast("🐼 Something doesn't seem right.. 🔥🏠🔥", icon="😂")
            time.sleep(2)
            st.toast("🥶 Are you sure..? 😬😬", icon="😭")
            time.sleep(2)
            st.toast("🤜🤛 Nevertheless, it was a good try!! 🏋️‍♂️🏋️‍♂️", icon="👏")
            st.snow()

        reasoning = "" if "textbook" in reasons else "Rationale is that: " + reasons
        qna_answer_response = (
            f"Your selected answer is `{user_selected_answer}`, "
            f"but the actual answer is `{qna_answer_alphabet}`. " + reasoning
        )
    except Exception as e:
        print(e)

    return qna_answer_response

get_qna_question_tool = FunctionTool.from_defaults(
                            fn=get_qna_question,
                            name="Extract_Question",
                            description=qna_question_description,
                            fn_schema=Question_Model
)

evaluate_qna_answer_tool = FunctionTool.from_defaults(
                            fn=evaluate_qna_answer,
                            name="Evaluate_Answer",
                            description=qna_answer_description,
                            fn_schema=Answer_Model
)