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.

• 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

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

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.

• Prompt engineering Guidelines
• Overleaf LaTex Resume Template
• Combining LaTeX with Python
• OpenAI Documentation