zlm 0.1.26

Simplify and improve your 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.

Streamlining Job Applications with LLM Automation Pipeline

Project source can be:

• downloaded from https://github.com/Ztrimus/job-llm.git
• Installed from https://pypi.org/project/zlm/

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

• Saurabh Zinjad - Ztrimus | [e-mail]
• Amey Bhilegaonkar - ameygoes | [e-mail]

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 Project Report for more details.

2. Setup, Installation and Usage

2.1. Prerequisites

• OS : Linux, Mac
• Python : 3.10.12 and above
• OpenAI API key:
  • Store it in your environment variable called OPENAI_API_KEY.
  • Or you can pass it as class Arugment. like

    from zlm import AutoApplyModel
    job_llm = AutoApplyModel(YOUR_OPENAI_API_KEY_HERE)
    

2.2. Package Installation

pip install zlm

• Usage

from zlm import AutoApplyModel

job_llm = AutoApplyModel("OPENAI_API_KEY")
job_llm.resume_cv_pipeline("JOB_LINK_YOU_WANT_APPLY_FOR") # Return and downloads curated resume and cover letter.

2.3. Setup as projects

• 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.

    poetry install
    

    OR
  • We recommend using poetry, if above command not working, we also provided requirements.txt file.

    pip install -r resources/requirements.txt
    

• 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
    

2.4. Run Code

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. References

• Overleaf LaTex Resume Template
• Combining LaTeX with Python
• OpenAI Documentation

4. Limitation and Further growth :

• 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
• When ship as package give option for
  • Passing OPENAI_API_KEY
  • Where to Download folder or path