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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 | |
| ) |