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import sqlite3
from pydantic import BaseModel, Field
from llama_index.core.tools import FunctionTool
db_path = "./database/mock_qna.db"
description = """
Use this tool to extract the chapter information from the body of the input text,
when user wants to learn more about a particular chapter and requested to be asked
with a question to test his/her understanding.
The format of the function argument 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
Thereafter, the chapter_n argument will be passed to the function for Q&A question retrieval.
"""
class QnA_Model(BaseModel):
chapter_n: str = Field(...,
pattern=r'^Chapter_\d*$',
description=(
"which chapter to extract, the format of this function argumet"
"is with `Chapter_` as prefix concatenated with chapter number"
"in integer. For example, `Chapter_2`, `Chapter_10`."
"if no chapter number specified or user requested for random question"
"or user has no preference over which chapter of textbook to be tested"
"return `Chapter_0`"
)
)
def get_qna_question(chapter_n: str) -> str:
"""
Use this tool to extract the chapter information from the body of the input text,
the format looks as follow:
The output 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
Thereafter, the chapter_n argument will be passed to the function for Q&A question retrieval.
"""
con = sqlite3.connect(db_path)
cur = con.cursor()
filter_clause = "" if chapter_n == "Chapter_0" else f"WHERE chapter='{chapter_n}'"
sql_string = """SELECT id, question, option_1, option_2, option_3, option_4, correct_answer
FROM qna_tbl
""" + filter_clause
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]
qna_str = "Question: \n" + \
"========= \n" + \
question.replace("\\n", "\n") + "\n" + \
"A) " + option_1 + "\n" + \
"B) " + option_2 + "\n" + \
"C) " + option_3 + "\n" + \
"D) " + option_4
con.close()
return qna_str
get_qna_question_tool = FunctionTool.from_defaults(
fn=get_qna_question,
name="Extract_Question",
description=description,
fn_schema=QnA_Model
) |