Getting Started

This guide will help you get started with generatecv to create PDF resumes.

Installation

pip install generatecv

Or using uv:

uv add generatecv

Quick Start with Example Template

The fastest way to get started is using the built-in example system:

1. Get the Example Template

After installing generatecv, use the CLI tool to download a starter template:

# Download example YAML to current directory (creates example.yaml)
generatecv-example

# Or specify a custom output path
generatecv-example --output my_cv_data.yaml

This creates a YAML file with sample CV data that you can customize.

2. Customize Your Data

Edit the generated YAML file with your information:

personal_info:
  name: "Your Name"
  email: "your.email@example.com"
  phone: "+1234567890"
  location: "Your City, Country"
  summary: "Your professional summary"
  title: "Your Job Title"

education:
  - institution: "Your University"
    degree: "Your Degree"
    start_date: "2019"
    end_date: "2023"
    # ... add more fields as needed

experience:
  - company: "Your Company"
    location: "Company Location"
    roles:
      - title: "Your Role"
        start_date: "2023-01"
        end_date: "Present"
        description: "Your role description"
        # ... add achievements, etc.

# Add skills, projects, and other sections as needed

3. Generate Your PDF

from generatecv.pdf_generator import yamltocv, generatepdf

# Load your CV data from YAML
cv_data = yamltocv("example.yaml")  # or your custom filename

# Generate PDF
output_path = generatepdf(
    cv_data=cv_data,
    output_path="my_cv.pdf",
    style="classic",
    page_size="A4"
)

print(f"CV generated successfully: {output_path}")

Alternative: Define Data in Python

If you prefer to define your CV data directly in Python instead of using YAML, here's how:

You'll use models from generatecv.models to structure your information:

from generatecv.pdf_generator import generatepdf
from generatecv.models import (
    CV, PersonalInfo, Education, CompanyExperience, Role, Skill
)

# Construct your CV data
cv_data = CV(
    personal_info=PersonalInfo(
        name="Your Name",
        email="your.email@example.com",
        phone="+1 234 567 8900",
        location="Your City, Country",
        summary="A brief professional summary about yourself.",
        title="Your Professional Title (e.g., Software Engineer)"
    ),
    education=[
        Education(
            institution="University Name",
            degree="Bachelor of Science in Computer Science",
            start_date="2018-09",
            end_date="2022-06",
            location="City, Country",
            gpa="3.8/4.0"
        )
    ],
    experience=[
        CompanyExperience(
            company="Company Name",
            location="Company Location",
            roles=[
                Role(
                    title="Software Engineer",
                    start_date="2022-07",
                    end_date="Present", # or a specific end date like "2023-12"
                    description="Brief description of your role and responsibilities.",
                    achievements=[
                        "Key achievement 1",
                        "Key achievement 2"
                    ]
                )
            ]
        )
    ],
    skills=[
        Skill(category="Programming Languages", name="Python, Java, JavaScript"),
        Skill(category="Tools", name="Git, Docker, Kubernetes")
    ]
    # You can also add projects, certifications, languages, references, etc.
)

# Generate the PDF
try:
    output_file = generatepdf(
        cv_data=cv_data,
        output_path="my_cv.pdf",
        style="classic",  # Currently 'classic' is the primary style
        page_size="A4"    # Options: "A4" or "letter"
    )
    print(f"CV generated successfully: {output_file}")
except ValueError as e:
    print(f"Error generating PDF: {e}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Styles

generatecv uses a style system to define the appearance of the PDF. - classic: This is the default and currently the main implemented style. It provides a traditional CV layout.

You specify the style via the style parameter in the generatepdf function:

generatepdf(cv_data=your_data, output_path="my_cv.pdf", style="classic")

Page Size

You can set the page size for your PDF: - A4 (default) - letter

Specify this using the page_size parameter in generatepdf:

generatepdf(cv_data=your_data, output_path="my_cv.pdf", page_size="letter")

Output Format

The generatepdf function is specifically designed to output PDF files. Other formats like HTML or DOCX are not supported by this function.

Command Line Tool

The generatecv-example command supports these options:

# Download to current directory as example.yaml
generatecv-example

# Download to a specific path
generatecv-example --output my_cv.yaml
generatecv-example -o custom_filename.yaml

# View help
generatecv-example --help

Error Handling

When working with YAML files or generating PDFs, use proper error handling:

from generatecv.pdf_generator import yamltocv, generatepdf
from pydantic import ValidationError
import yaml

try:
    # Load CV data from YAML
    cv_data = yamltocv("example.yaml")

    # Generate PDF
    output_path = generatepdf(
        cv_data=cv_data,
        output_path="my_cv.pdf",
        style="classic",
        page_size="A4"
    )
    print(f"Success: {output_path}")

except FileNotFoundError:
    print("Error: YAML file not found")
except yaml.YAMLError as e:
    print(f"Error: Invalid YAML syntax - {e}")
except ValidationError as e:
    print(f"Error: Invalid CV data - {e}")
except ValueError as e:
    print(f"Error: Invalid parameters - {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Next Steps

• Check out the API Reference for detailed documentation on models and functions.
• See Examples for more comprehensive use cases.
• Learn about Contributing to the project if you're interested in extending its capabilities.