Note

This project is no longer maintained, the HDBscan library keeps bumping versions and makes it difficult for me to always keep a look at it. If you desperatly want to use this, check out this , its source code is earlier version of the lib before the branch contradictions had occured and had to delete the history. Its more stabel cause its the backend of our website . You would need to clone the repo and use it as a local module, if you want to install it take the setup.py and pypriject.toml form this repo put it in the root foler of that repo and do pip install .

JEE Mains PYQS Database

This project provides a structured database of more than 14,000 previous year questions (PYQS) from JEE Mains. The questions are reverse engineered from API endpoints of a subscription site and cached for efficient use. It supports clustering, filtering, and rendering of questions into HTML for easy study.

Features

• Access to 14k+ JEE Mains PYQS
• Precomputed embeddings using the intfloat/e5-large-v2 model for efficient clustering
• Cluster similar questions together based on semantic embeddings
• Apply chainable filters (by chapter, topic, year, etc.)
• Render filtered or clustered questions into HTML using themed styles

Project Structure

The core folder contains the following modules:

• cache.py – Defines the Cache class for creating and loading internal caches. Not intended for direct user interaction.
• chapter.py – Defines the Chapter class, which is stored in the DataBaseChapters cache file. Internal use only.
• data_base.py – Defines the DataBase class. This must be initialized before any operations.
• filter.py – Defines the Filter class. Provides chainable methods to filter questions and update the current set.
• question.py – Defines the Question object.
• styles.py – Contains themed HTML styles for rendering.
• pdfy.py – Provides functions to convert clusters or sets of questions into HTML.

Installation

• Install using pip:
• Install the package

pip install jee_data_base

• Install chromium plawright install chromium

• Clone the repository:

git clone https://github.com/HostServer001/jee_mains_pyqs_data_base

Navigate into the project directory and ensure dependencies are installed.

Usage

Basic Initialization

import os
from jee_data_base import DataBase, Filter, pdfy

# Initialize database
db = DataBase()

# Initialize filter
filter = Filter(db.chapters_dict)

# Inspect available chapters
print(filter.get_possible_filter_values()["chapter"])

Its highly recommended to filter as much as possible so that your html files open smoothly in browser

Its always good to use the cluster method and render_cluster_to_html method to get your output, it provides the most efficeint way of practice

The render_cluster_to_html_skim is great if you have prepared chapter loosely and want to skin thorugh and get most out of it (use it after cluster)

Most useful feature

from jee_data_base import DataBase,Filter
import asyncio

path = "<path where chpater folder will be created>"
chpater = "<your example chpater>"

#Load the data base
db = DataBase()

#Initialize filter
filter = Filter(db.chapter_dict)

#Create html file
asyncio.run(filter.render_chap_last5yrs(path,chpater,skim=False,output_file_format="pdf"))

Filtering by Chapter and Year

# Get all questions from a specific chapter in the last 3 years
questions = filter.by_chapter("thermodynamics").by_n_last_yrs(3).get()

for q in questions:
    print(q.question)

Clustering and Rendering

# Cluster questions by topic and render to HTML
filter.current_set = filter.by_chapter("organic-compounds").by_n_last_yrs(5).get()
cluster = filter.cluster()

pdfy.render_cluster_to_html(
    cluster,
    "organic_compounds.html",
    "Organic Compounds - Last 5 Years"
)# can use render_cluster_to_html_skim() function to make a file which 
#makes a html file perfected for skiming through a chapter

Example: Render Chapter Questions by Topic

def render_chapter(chapter_name: str):
    all_q = filter.by_chapter(chapter_name).by_n_last_yrs(5).get()
    os.makedirs(chapter_name, exist_ok=True)

    for topic in filter.get_possible_filter_values()["topic"]:
        filter.current_set = all_q
        filter.by_topic(topic)
        cluster = filter.cluster()
        pdfy.render_cluster_to_html_skim(
            cluster,
            f"{chapter_name}/{topic}.html",
            topic
        )

render_chapter("alcohols-phenols-and-ethers")

Ouput

• The output will look somthing like this PDF 📄

Data Caches

• DataBaseChapters – Contains a dictionary with chapter names as keys and Chapter objects as values.
• EmbeddingsChapters – Contains precomputed embeddings of all questions to save computation time.

Contributing

Contributions are welcome. You can help by:

• Improving documentation
• Adding new filters or clustering strategies
• Enhancing rendering styles
• Reporting issues and suggesting features

Fork the repository, create a new branch for your changes, and submit a pull request.

License

This project is provided for educational purposes. Please review the repository for licensing details.