/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.

Primary LanguagePythonMIT LicenseMIT

ResumeFlow: An LLM-facilitated Pipeline for Personalized Resume Generation and Refinement

Demo Page arxiv paper PyPI Latest Release PyPI Downloads GitHub issues open License: MIT

Click here to see image of "Auto Job Aligned Personalized Resume Generation Pipeline"
For Video Demonstration visit the YouTube link: https://youtu.be/Agl7ugyu1N4

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Author & Contributor List

1. Introduction:

1.1. Motivation: LLMs as Components in an ML Pipeline

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.

1.2. 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.

1.3. Refer to this Paper for more details.

2. Setup, Installation and Usage

2.1. Prerequisites

2.2. Package Installation - Use as Library

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.

2.4. Setup & Run Code - Use as Project

git clone https://github.com/Ztrimus/job-llm.git
cd job-llm
  1. Create and activate python environment (use python -m venv .env or conda or etc.) to avoid any package dependency conflict.
  2. Install Poetry package (dependency management and packaging tool)
    pip install poetry
  3. Install all required packages.
    • Refer pyproject.toml or poetry.lock for list of packages.
      poetry install
      OR
    • If above command not working, we also provided requirements.txt file. But, we recommend using poetry.
      pip install -r resources/requirements.txt
  4. We also need to install following packages to conversion of latex to pdf
    • For linux
      sudo apt-get install texlive-latex-base texlive-fonts-recommended texlive-fonts-extra
      NOTE: try sudo apt-get update if terminal unable to locate package.
    • For Mac
      brew install basictex
      sudo tlmgr install enumitem fontawesome
  5. 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, together, g4f

3. Citations

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}
}

4. License

JobLLM is under the MIT License and is supported for commercial usage.

4. References