AI Resume Screener: An Intelligent Resume Screening System Based on Large Language Models

Core Introduction to AI Resume Screener

AI Resume Screener is an open-source resume screening API project based on FastAPI, Groq LLM, and Redis caching, designed to address the pain points of time-consuming and highly subjective resume screening in the recruitment process. It supports PDF resume parsing, intelligent matching scoring (0-100 points), analysis of strengths/missing skills, and interview suggestion generation, enhancing screening efficiency and objectivity through semantic understanding.

Original author: Yogi1107; Open-source address: GitHub Link; Update date: 2026-06-04.

Project Background and Motivation

In the recruitment process, resume screening is a time-consuming and highly subjective task for HR teams. Traditional keyword matching struggles to capture the true fit of capabilities, and manual review is inefficient. AI Resume Screener leverages the semantic understanding capabilities of LLM to build an end-to-end intelligent screening solution, providing matching scores, strength analysis, and improvement suggestions through structured output to assist recruiters in decision-making.

Technical Architecture and Workflow

Tech Stack:

• FastAPI: High-performance asynchronous web framework
• Groq LLM: Default llama-3.1-8b-instant model
• PyMuPDF: PDF text extraction
• Valkey/Redis: Caching layer (results cached for 24 hours)

Workflow: User uploads PDF resume + job description → Extract text → Generate cache key for duplicate check → New requests call Groq API for analysis → Return structured results and cache.

Core Function Analysis

Intelligent Resume Parsing

Uses PyMuPDF to extract PDF text, standardizes it, and truncates it to within 4000 characters to balance information completeness and token cost.

LLM-Driven Matching Analysis

Uses prompts to make Groq output structured evaluations:

• 0-100 matching score
• Core strengths, missing skills
• Interview/rejection suggestions
• Decision reasons

Intelligent Caching

Based on SHA-256 hashing strategy, identical requests return cached results directly; graceful degradation when caching is unavailable.

API Design and Usage

Main Interfaces:

• POST /screen: Accepts PDF file + job description, returns evaluation report
• GET /health: Checks service status, model, and cache connection

Responses include cache hit status (HIT/MISS) and model information for easy debugging and monitoring.

Application Scenarios and Value

Enterprise Recruitment:

• Batch initial screening of large numbers of resumes
• Improve HR efficiency, focus on interviews
• Unify screening standards, reduce bias
• Caching reduces LLM call costs

Developer Reference: Learn to integrate LLM into business scenarios (complete chain of PDF parsing → caching → structured output).

Limitations and Improvement Directions

Current Limitations: Only supports PDF format resumes.

Improvement Directions:

• Expand to Word and other formats
• Introduce queue management for LLM call rates in high-concurrency scenarios
• Support multi-language resumes

Project Summary

AI Resume Screener is a well-designed open-source project that transforms LLM capabilities into a recruitment assistance tool, with a clear architecture, comprehensive API documentation, and practical functions. It is a project worth attention in the HR Tech field, and an excellent starting point for developers to explore LLM applications in human resources scenarios for learning or secondary development.