Build a Resume Parser & Job Matcher API with FastAPI and NLP
This article guides readers through building a Resume Parser & Job Matcher API using FastAPI, spaCy, scikit-learn, and other Python libraries. The API extracts text from resumes, compares them against job descriptions, and returns a match score.
Why it matters
This project demonstrates how to build a practical AI-powered tool that can automate the tedious task of resume screening, helping recruiters and hiring managers work more efficiently.
Key Points
- 1Extracts text from PDF, DOCX, or TXT resumes
- 2Compares resumes against job descriptions using cosine similarity and skill overlap
- 3Exposes the functionality as a modern, self-documenting API
- 4Uses spaCy for text preprocessing, scikit-learn for TF-IDF vectorization
- 5Supports multiple file formats and combines multiple matching heuristics
Details
The article covers building a Resume Parser & Job Matcher API using the FastAPI web framework, spaCy for text preprocessing, scikit-learn for TF-IDF vectorization, and various libraries for file parsing (pdfplumber, python-docx). The API extracts plain text from uploaded resumes, cleans and tokenizes the text using spaCy, and compares the resume against one or more job descriptions. It calculates a match score based on cosine similarity and skill overlap, and returns the results through a modern, self-documenting API. The project setup, file structure, and key implementation details are provided, covering topics like parsing different resume formats, computing document similarity, and combining multiple matching heuristics into a final score.
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