srbhr / Resume-Matcher-Docs

Documentation for Resume Matcher

Resume Matcher

AI Based Free & Open Source ATS, Resume Matcher to tailor your resume to a job description. Find the best keywords, and gain deep insights into your resume.

How does it work?

The Resume Matcher takes your resume and job descriptions as input, parses them using Python, and mimics the functionalities of an ATS, providing you with insights and suggestions to make your resume ATS-friendly.

The process is as follows:

1. Parsing: The system uses Python to parse both your resume and the provided job description, just like an ATS would. Parsing is critical as it transforms your documents into a format the system can readily analyze.

2. Keyword Extraction: The tool uses advanced machine learning algorithms to extract the most relevant keywords from the job description. These keywords represent the skills, qualifications, and experiences the employer seeks.

3. Key Terms Extraction: Beyond keyword extraction, the tool uses textacy to identify the main key terms or themes in the job description. This step helps in understanding the broader context of what the resume is about.

4. Vector Similarity Using Qdrant: The tool uses Qdrant, a highly efficient vector similarity search tool, to measure how closely your resume matches the job description. This process is done by representing your resume and job description as vectors in a high-dimensional space and calculating their cosine similarity. The more similar they are, the higher the likelihood that your resume will pass the ATS screening.

On top of that, there are various data visualizations that I've added to help you get started.

Introduction

This guide is intended for anyone who wants to download and install Resume-Matcher Follow these steps to setup the environment and run the application

QuickStart

1. Fork the repository here.

2. Clone the forked repository.

   git clone https://github.com/<YOUR-USERNAME>/Resume-Matcher.git
   cd Resume-Matcher

3. Create a Python Virtual Environment:

   • Using virtualenv:

     Note: Check how to install virtualenv on your system here link.

     virtualenv env

   OR

   • Create a Python Virtual Environment:

     python -m venv env

   OR

   • Create a Python Virtual Environment:

     py -<YOUR_VERSION_OF_PYTHON> -m pip install virtualenv

After you create your Virtual environment you should have a folder in your repo that looks something like myenv

4. Activate the Virtual Environment.

   • On Windows.

     env\Scripts\activate

   • On macOS and Linux.

     source env/bin/activate

5. Install Dependencies:

   pip install -r requirements.txt

6. Prepare Data:

   • Resumes: Place your resumes in PDF format in the Data/Resumes folder. Remove any existing contents in this folder.
   • Job Descriptions: Place your job descriptions in PDF format in the Data/JobDescription folder. Remove any existing contents in this folder.
   • Initially the folders should look like belwo

7. Parse Resumes to JSON:

   python run_first.py

8. Run the Application:

   streamlit run streamlit_app.py

Note: For local versions, you do not need to run "streamlit_second.py" as it is specifically for deploying to Streamlit servers.

Additional Note: The Vector Similarity part is precomputed to optimize performance due to the resource-intensive nature of sentence encoders that require significant GPU and RAM resources. If you are interested in leveraging this feature in a Google Colab environment for free, refer to the upcoming blog (link to be provided) for further guidance.