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 = """ Not to trigger this when questions being asked, come directly from user. Only use this tool to trigger the evaluation of user's provided input with the correct answer of the Q&A question asked by Assistant. 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.question_id IS NULL" \ if chapter_n == "Chapter_0" \ else f"WHERE a.question_id IS NULL AND chapter='{chapter_n}'" sql_string = f"""SELECT q.id, question, option_1, option_2, option_3, option_4, q.correct_answer, q.reasoning FROM qna_tbl q LEFT JOIN (SELECT * FROM answer_tbl WHERE user_id = '{st.session_state.user_id}') a ON q.id = a.question_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] c_answer = int(c_answer) option_dict = { 1: option_1, 2: option_2, 3: option_3, 4: option_4 } qna_answer_str = option_dict.get(c_answer, "NA") 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 + "\n" system_prompt = ( "#### System prompt to assistant #### \n" "Be reminded to ask user the question \n" "#################################### \n" ) st.session_state.question_id = id st.session_state.qna_answer_int = c_answer st.session_state.reasons = reasons st.session_state.qna_answer_str = qna_answer_str con.close() return qna_str + system_prompt 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_int = st.session_state.qna_answer_int reasons = st.session_state.reasons qna_answer_str = st.session_state.qna_answer_str ### convert to numeric type qna_answer_int = int(qna_answer_int) qna_answer_alphabet = num_mapping.get(qna_answer_int, "ERROR") con = sqlite3.connect(db_path) cur = con.cursor() sql_string = f"""INSERT INTO answer_tbl VALUES ('{st.session_state.user_id}', {question_id}, {qna_answer_int}, {user_answer_numeric}) """ res = cur.execute(sql_string) con.commit() con.close() reasoning = "" if "textbook" in reasons else f"Rationale is that: {reasons}. " qna_answer_response = ( f"Your selected answer is `{user_selected_answer}`, " f"but the actual answer is `{qna_answer_alphabet}`) {qna_answer_str}. " ) qna_not_knowing_response = ( f"No problem! The answer is `{qna_answer_alphabet}`. " f"Let me explain to you why the correct answer is '{qna_answer_str}'. " ) to_know_more = ( "######## System prompt to assistant ######### \n" "Be reminded to provide explanation to user \n" "############################################# \n" ) if user_answer_numeric == 0: st.toast("🍯❓ couldn't find the honey? 👌 no worries!", icon="🫠") time.sleep(2) st.toast("🐻 Let me bring it to you! 🍯💕", icon="💌") time.sleep(2) st.toast("✨ You will do great next time! 💆", icon="🎁") final_response = qna_not_knowing_response + reasoning + to_know_more elif qna_answer_int == 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() final_response = qna_answer_response + reasoning + to_know_more else: st.toast("🐼 Something doesn't feel 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() final_response = qna_answer_response + reasoning + to_know_more st.session_state.question_id = None st.session_state.qna_answer_int = None st.session_state.reasons = None st.session_state.qna_answer_str = None except Exception as e: print(e) return final_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 )