
job-llm
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.
Stars: 70

ResumeFlow is an automated system utilizing Large Language Models (LLMs) to streamline the job application process. It aims to reduce human effort in various steps of job hunting by integrating LLM technology. Users can access ResumeFlow as a web tool, install it as a Python package, or download the source code. The project focuses on leveraging LLMs to automate tasks such as resume generation and refinement, making job applications smoother and more efficient.
README:
For Video Demonstration visit the YouTube link: https://youtu.be/Agl7ugyu1N4
Project can be:
- Access as a Web Tool from https://job-aligned-resume.streamlit.app/
- Install as a Python Package from https://pypi.org/project/zlm/
- download as Source Code from https://github.com/Ztrimus/job-llm.git
All other known bugs, fixes, feedbacks, and feature requests can be reported on the GitHub issues page.
Empower others, just like they helped you! Contribute to this open source project & make a difference. ✨ Create a branch, improve the code, & raise a pull request!
- Saurabh Zinjad | Ztrimus | [email protected]
- Amey Bhilegaonkar | ameygoes | [email protected]
- Amrita Bhattacharjee | Amritabh | [email protected]
In this project, we will investigate how to effectively use Large Language Models (LLMs) to automate various aspects of this pipeline.
Because, Solving a task using machine learning methods requires a series of steps that often require large amounts of human effort or labor. 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.
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.
1.3. Refer to this Paper for more details.
- OS : Linux, Mac
- Python : 3.11.6 and above
- LLM API key: OpenAI OR Gemini Pro
pip install zlm
- Usage
from zlm import AutoApplyModel
job_llm = AutoApplyModel(
api_key="PROVIDE_API_KEY",
provider="ENTER PROVIDER <gemini> or <openai>",
downloads_dir="[optional] ENTER FOLDER PATH WHERE FILE GET DOWNLOADED, By default, 'downloads' folder"
)
job_llm.resume_cv_pipeline(
"ENTER_JOB_URL",
"YOUR_MASTER_RESUME_DATA" # .pdf or .json
) # Return and downloads curated resume and cover letter.
git clone https://github.com/Ztrimus/job-llm.git
cd job-llm
- 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.
- Refer pyproject.toml or poetry.lock for list of packages.
OR
poetry install
- If above command not working, we also provided requirements.txt file. But, we recommend using poetry.
pip install -r resources/requirements.txt
- Refer pyproject.toml or poetry.lock for list of packages.
- We also need to install following packages to conversion of latex to pdf
- For linux
NOTE: try
sudo apt-get install texlive-latex-base texlive-fonts-recommended texlive-fonts-extra
sudo apt-get update
if terminal unable to locate package. - For Mac
brew install basictex sudo tlmgr install enumitem fontawesome
- For linux
- Run following script to get result
>>> python main.py /
--url "JOB_POSTING_URL" /
--master_data="JSON_USER_MASTER_DATA" /
--api_key="YOUR_LLM_PROVIDER_API_KEY" / # put api_key considering provider
--downloads_dir="DOWNLOAD_LOCATION_FOR_RESUME_CV" /
--provider="openai" # openai, gemini
If you find JobLLM useful in your research or applications, please consider giving us a star 🌟 and citing it.
@misc{zinjad2024resumeflow,
title={ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement},
author={Saurabh Bhausaheb Zinjad and Amrita Bhattacharjee and Amey Bhilegaonkar and Huan Liu},
year={2024},
eprint={2402.06221},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
JobLLM is under the MIT License and is supported for commercial usage.
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