Simple Resume Matcher

A Python script that matches resumes against job descriptions and provides improvement suggestions using AI with support for multiple LLM providers.

Note: This is a simplified, standalone version inspired by the Resume-Matcher project. The main Resume-Matcher is a full-stack web application with advanced features, while this version provides the core functionality in a single Python script for easier setup and use.

Why This Project Exists

I built this application because I wanted a simple way to take my resume, compare it to a job description, and interact with both in a conversational way. Having an LLM look at both documents and ask targeted questions to help improve the quality of my resume is incredibly powerful.

Resume matching and optimization isn't something we do every day, so having an AI-powered tool that can:

Analyze gaps between your resume and job requirements
Ask intelligent questions to uncover missing experiences and achievements
Provide specific improvement suggestions based on the job description
Guide you through an interactive improvement process

...is incredibly valuable when you need to tailor your resume for a specific role.

This tool bridges the gap between generic resume advice and the specific requirements of each job, making the resume optimization process more targeted and effective.

Features

Extract text from PDF and DOCX resume files
Extract job descriptions from text, URLs, or PDF files
Analyze keyword matching between resume and job description
Provide AI-powered resume improvement suggestions
Interactive improvement sessions with targeted questions
Generate formatted resume templates with 5 different styles
Multi-provider LLM support: Ollama, OpenAI, Gemini, and LM Studio
Advanced logging system with multiple verbosity levels (quiet, normal, verbose, debug)
Professional progress tracking with spinners and detailed status updates
Flexible input/output with JSON save/load capabilities
Template generation from previous analysis results
Comprehensive error handling with troubleshooting guidance

Installation

Install Python dependencies:

pip install -r requirements.txt

Set up your preferred LLM provider (see Provider Setup below)

Provider Setup

Ollama (Local - Recommended) 🏠

Free, private, runs locally on your machine

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start server
ollama serve

# Pull a model
ollama pull llama3.1

LM Studio (Local) 🏠

Free, private, runs locally on your machine

Download from https://lmstudio.ai/
Start local server in LM Studio app
Download a model (e.g., google/gemma-3-27b)

OpenAI (Cloud) ☁️

Paid service, requires API key

Get API key from https://platform.openai.com/
Set environment variable: export OPENAI_API_KEY="your-key"

Gemini (Cloud) ☁️

Free tier available, requires API key

Get API key from https://makersuite.google.com/app/apikey
Set environment variable: export GEMINI_API_KEY="your-key"

Usage

Basic Usage

# Using Ollama (default, recommended)
python simple_resume_matcher.py resume.pdf "job description text"
python simple_resume_matcher.py resume.pdf https://example.com/job-posting
python simple_resume_matcher.py resume.pdf job_description.pdf

# Using OpenAI
python simple_resume_matcher.py resume.pdf job_description.pdf --provider openai

# Using Gemini
python simple_resume_matcher.py resume.pdf job_description.pdf --provider gemini

# Using LM Studio
python simple_resume_matcher.py resume.pdf job_description.pdf --provider lmstudio

Interactive Mode

python simple_resume_matcher.py resume.pdf job_description.pdf --interactive

Generate Formatted Resume

python simple_resume_matcher.py resume.pdf job_description.pdf --template-output improved_resume.txt

Advanced Usage with Logging

# Verbose output for detailed progress
python simple_resume_matcher.py resume.pdf job_description.pdf --verbose

# Quiet mode (errors only)
python simple_resume_matcher.py resume.pdf job_description.pdf --quiet

# Debug mode (maximum verbosity)
python simple_resume_matcher.py resume.pdf job_description.pdf --debug

Save and Load Analysis Results

# Save analysis results to JSON
python simple_resume_matcher.py resume.pdf job_description.pdf --output analysis.json

# Load previous results and generate template
python simple_resume_matcher.py --input analysis.json --generate-template accomplishments --template-output final_resume.txt

List Available Options

# List available resume templates
python simple_resume_matcher.py --list-templates

# List available LLM providers with setup instructions
python simple_resume_matcher.py --list-providers

Command Line Options

Provider Selection

--provider {ollama,openai,gemini,lmstudio}: Choose LLM provider (default: ollama)
--model: Specify model name (auto-detected if not specified)
--api-key: API key for cloud providers (OpenAI, Gemini)
--provider-url: Custom URL for provider server

Logging and Verbosity

--verbose, -v: Show detailed progress information
--quiet, -q: Show only errors and critical messages
--debug: Show debug information (very verbose)

Input/Output Options

--output: Save analysis results to JSON file
--input: Load previous analysis results from JSON file
--preview: Show formatted resume preview
--interactive: Run interactive improvement session

Template Options

--template: Choose resume template (classic_ats, skills_forward, accomplishments, dual_column, minimalist)
--template-output: Save formatted resume to file
--generate-template: Generate specific template from input file (requires --input)
--list-templates: Show available resume templates
--list-providers: Show available LLM providers and setup instructions

Resume Templates

The tool includes 5 professionally designed resume templates, each optimized for different career stages and industries:

1. Classic ATS-Optimized (Default)

Best For: All technology professionals, high-volume applications
Features: Single-column design, standard fonts, clear headings, no graphics
Use Case: Universal template that works with most Applicant Tracking Systems

2. Skills-Forward Hybrid

Best For: Recent graduates, career changers, diverse backgrounds
Features: Skills section at top, reverse-chronological history, transferable skills focus
Use Case: When your skills are more relevant than your work history

3. Accomplishments-Centric

Best For: Mid-to-senior-level professionals with clear progression
Features: Quantified achievements, strong action verbs, career story focus
Use Case: When you have impressive metrics and career growth to highlight

4. Dual-Column Modern

Best For: Candidates with many projects, skills, and certifications
Features: Two-column format, visual balance, high information density
Use Case: When you need to fit a lot of information in a compact format

5. Minimalist LaTeX/Plain-Text

Best For: Software engineers, developers, data scientists
Features: LaTeX/plain-text, technical elegance, function over form
Use Case: Technical roles where clean, simple formatting is preferred

Examples

Basic Analysis

# Simple analysis with Ollama (default)
python simple_resume_matcher.py resume.pdf job.pdf

# Interactive improvement session
python simple_resume_matcher.py resume.pdf job.pdf --interactive

Provider-Specific Examples

# Using OpenAI with API key
python simple_resume_matcher.py resume.pdf job.pdf --provider openai --api-key sk-...

# Using Gemini with environment variable
export GEMINI_API_KEY="your-key"
python simple_resume_matcher.py resume.pdf job.pdf --provider gemini

# Using LM Studio with custom URL
python simple_resume_matcher.py resume.pdf job.pdf --provider lmstudio --provider-url http://localhost:1234

# Using Ollama with custom URL
python simple_resume_matcher.py resume.pdf job.pdf --provider ollama --provider-url http://192.168.1.100:11434

Advanced Workflow Examples

# Save analysis results for later use
python simple_resume_matcher.py resume.pdf job.pdf --output analysis.json --verbose

# Generate different templates from saved analysis
python simple_resume_matcher.py --input analysis.json --generate-template accomplishments --template-output accomplishments_resume.txt
python simple_resume_matcher.py --input analysis.json --generate-template minimalist --template-output minimalist_resume.txt

# Complete workflow: analyze, save, and generate multiple templates
python simple_resume_matcher.py resume.pdf job.pdf --interactive --output analysis.json --template-output improved_resume.txt

Debugging and Troubleshooting

# Debug mode for troubleshooting issues
python simple_resume_matcher.py resume.pdf job.pdf --debug

# Quiet mode for automated scripts
python simple_resume_matcher.py resume.pdf job.pdf --quiet --output results.json

# Verbose mode to see detailed progress
python simple_resume_matcher.py resume.pdf job.pdf --verbose --interactive

Template-Specific Examples

# Generate skills-forward template for career changers
python simple_resume_matcher.py resume.pdf job.pdf --template skills_forward --template-output skills_resume.txt

# Generate accomplishments template for senior professionals
python simple_resume_matcher.py resume.pdf job.pdf --template accomplishments --template-output accomplishments_resume.txt

# Generate minimalist template for developers
python simple_resume_matcher.py resume.pdf job.pdf --template minimalist --template-output dev_resume.txt

Provider Comparison

Provider	Privacy	Cost	Setup Difficulty	Performance	Recommended For
Ollama	✅ Local	✅ Free	🟢 Easy	🟡 Good	Privacy-conscious users
LM Studio	✅ Local	✅ Free	🟡 Medium	🟢 Excellent	Power users
OpenAI	❌ Cloud	💰 Paid	🟢 Easy	🟢 Excellent	Professional use
Gemini	❌ Cloud	🟡 Free tier	🟢 Easy	🟡 Good	Budget-conscious users

Requirements

Python 3.7+
One of the supported LLM providers (see Provider Setup)
Internet connection (for URL job descriptions and cloud providers)

Dependencies

markitdown[all]: PDF/DOCX text extraction
requests: HTTP requests
beautifulsoup4: Web scraping

Troubleshooting

Ollama Issues

"Connection refused": Make sure ollama serve is running
"Model not found": Run ollama pull llama3.1 to download a model
"Permission denied": Run sudo ollama serve on Linux

OpenAI Issues

"API key not provided": Set export OPENAI_API_KEY="your-key"
"Rate limit exceeded": Wait a moment and try again
"Model not found": Check if the model name is correct

Gemini Issues

"API key not provided": Set export GEMINI_API_KEY="your-key"
"Quota exceeded": Check your Google Cloud quota

LM Studio Issues

"Connection refused": Make sure LM Studio local server is running
"Model not loaded": Load a model in LM Studio app

Benefits of Multi-Provider Support

Flexibility: Choose the provider that best fits your needs
Privacy: Local providers keep your data on your machine
Cost Control: Free local options vs paid cloud services
Reliability: Fallback options if one provider is unavailable
Performance: Different providers have different strengths
Accessibility: More users can use the tool regardless of their setup

Comparison with Main Resume-Matcher Project

This Simple Resume Matcher is inspired by the main Resume-Matcher project but designed for different use cases:

Feature	Simple Resume Matcher	Main Resume-Matcher
Architecture	Single Python script	Full-stack web app (FastAPI + Next.js)
Setup	Simple pip install	Docker/containerized setup
UI	Command-line interface	Modern web interface
Database	None (in-memory processing)	SQLite with structured models
Deployment	Run locally	Web application
Features	Core matching & improvement	Advanced ATS analysis, templates, collaboration
Use Case	Quick analysis, learning, CLI users	Production use, teams, web interface

Choose Simple Resume Matcher if you want:

Quick setup and immediate use
Command-line workflow
Local processing only
Learning the core algorithms
Simple deployment

Choose Main Resume-Matcher if you want:

Full web application experience
Advanced ATS compatibility analysis
Team collaboration features
Production-ready deployment
Comprehensive resume templates

Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt
simple_resume_matcher.py		simple_resume_matcher.py

License

beettlle/Resumator

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