Simplify and improve the job hunting experience by integrating LLMs to automate tasks such as resume and cover letter generation, as well as application submission, saving users time and effort.
Project description
Streamlining Job Applications with LLM Automation Pipeline
Project source can be downloaded from https://github.com/Ztrimus/job-llm.git
All other known bugs and fixes can be sent to following emails with the subject "[BUG] JOB LLM". Reported bugs/fixes will be submitted to correction.
Author & Contributor List
Introduction:
1. Motivation: LLMs as Components in an ML Pipeline
Solving a task using machine learning methods requires a series of steps that often require large amounts of human effort or labor. Some examples of this are:
- data collection
- data curation or something similar
- data augmentation, etc.
Furthermore there might be more steps after the training the ML model, such as evaluation, explaining the behavior of the model, interpreting model outputs, etc. Many of these steps are also often human labor intensive. In this project, we will investigate how to effectively use Large Language Models (LLMs) to automate various aspects of this pipeline.
2. Our Proposal
We're aiming to create a automated system that makes applying for jobs a breeze. Job hunting has many stages, and we see a chance to automate things and use LLM (Language Model) to make it even smoother. We're looking at different ways, both the usual and some new ideas, to integrate LLM into the job application process. The goal is to reduce how much you have to do and let LLM do its thing, making the whole process easier for you.
3. Refer Project Report for more details.
Get Started and Setup
- Prerequisites
- OS - Linux (Ubuntu 22.04)
- Python - 3.10.12
- OpenAI API key - Store it in your environment variable called
OPENAI_API_KEY
. you can access it thorugh config.py.
- Create and activate python environment (use
python -m venv .env
or conda or etc.) to avoid any package dependency conflict. - Install Poetry package (dependency management and packaging tool)
pip install poetry
- Install all required packages.
4.1. Refer pyproject.toml or poetry.lock for list of packages.
bash poetry install
OR 4.2. We recommend using poetry, if above command not working, we also provided requirements.txt file.pip install -r resources/requirements.txt
- on linux you also need to install following pakages to convert latex to pdf.
sudo apt-get install texlive-latex-base texlive-fonts-recommended texlive-fonts-extra
NOTE: trysudo apt-get update
if terminal unable to locate package.
Run Code
python main.py --url "JOB_POSTING_URL" --master_data="JSON_USER_MASTER_DATA"
- Refer following example
python main.py --url "https://www.squarespace.com/careers/jobs/5369485?ref=Simplify" --master_data="master_data/user_profile.json"
References
Limitation and Further growth :
TODO:
- Evaluation of generated resumes: metrics can be
- Content Preservation: overlap b/w keywords from resume and master data.
- Goodness of resume for certain job: Overlap b/w generated resume and job description.
- Streamlit app development
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file zlm-0.1.1.tar.gz
.
File metadata
- Download URL: zlm-0.1.1.tar.gz
- Upload date:
- Size: 15.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.6.1 CPython/3.10.12 Linux/6.2.0-37-generic
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | f9f7f09fad6ab4bc300eb4936cabdd19c10c9c2cc832a41359e7d2659f7670b1 |
|
MD5 | a354debacf4588558978bacc5488be64 |
|
BLAKE2b-256 | 0fca9c9ab327467230778c58d5b978beb420385e7301f663fff9aeeaf70c92d3 |
File details
Details for the file zlm-0.1.1-py3-none-any.whl
.
File metadata
- Download URL: zlm-0.1.1-py3-none-any.whl
- Upload date:
- Size: 19.1 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.6.1 CPython/3.10.12 Linux/6.2.0-37-generic
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 60d23221a21fa0dddb4679930975d216d3cd0e1e02aa31b3c2f62fa68917b688 |
|
MD5 | e91dfc8024372f7cdddcefc73b90502f |
|
BLAKE2b-256 | 72b4186137e5c5c000bace8e9e990ca650dd9692a98036bef19bc26185f8ca5c |