resume-yaml-builder

resume-yaml-builder is a Python package that turns structured YAML resume data into a clean, professional PDF resume using a language model. Provide your resume details in YAML format, and the package will validate the structure, extract the key information, and generate a well‑formatted PDF—all without requiring design skills.

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✨ Features

• YAML‑driven: Write your resume once in a simple, human‑readable YAML file.
• LLM powered: Uses a language model (default: ChatLLM7) to verify and format the content.
• Flexible LLM backend: Supply any LangChain‑compatible LLM (OpenAI, Anthropic, Google, etc.).
• Zero‑code PDF output: Get a ready‑to‑use PDF resume.

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📦 Installation

pip install resume_yaml_builder

The package depends on:

• langchain-core
• langchain-llm7 (the default LLM backend)
• llmatch-messages

These will be installed automatically.

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🚀 Quick Start

from resume_yaml_builder import resume_yaml_builder

yaml_input = """
name: Jane Doe
title: Data Scientist
contact:
  email: jane.doe@example.com
  phone: "+1-555-1234"
summary: >
  Passionate data scientist with 5+ years of experience...
experience:
  - company: Acme Corp
    role: Senior Data Scientist
    dates: "2020-2023"
    details: |
      - Built predictive models ...
education:
  - institution: University of Example
    degree: MSc Computer Science
    year: 2019
"""

# Simple call – uses default ChatLLM7 (API key from env var LLM7_API_KEY)
pdf_paths = resume_yaml_builder(user_input=yaml_input)

print(pdf_paths)   # → List of generated PDF file paths

Custom LLM Example (OpenAI)

from langchain_openai import ChatOpenAI
from resume_yaml_builder import resume_yaml_builder

my_llm = ChatOpenAI(model="gpt-4o-mini")
pdf_paths = resume_yaml_builder(user_input=yaml_input, llm=my_llm)

Custom LLM Example (Anthropic)

from langchain_anthropic import ChatAnthropic
from resume_yaml_builder import resume_yaml_builder

anthropic_llm = ChatAnthropic(model="claude-3-haiku-20240307")
pdf_paths = resume_yaml_builder(user_input=yaml_input, llm=anthropic_llm)

Custom LLM Example (Google Gemini)

from langchain_google_genai import ChatGoogleGenerativeAI
from resume_yaml_builder import resume_yaml_builder

gemini_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
pdf_paths = resume_yaml_builder(user_input=yaml_input, llm=gemini_llm)

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📋 Function Signature

def resume_yaml_builder(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]:

Parameter	Type	Description
user_input	str	YAML‑formatted resume data to be processed.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the function creates a ChatLLM7 instance using the provided api_key or the LLM7_API_KEY environment variable.
api_key	Optional[str]	API key for ChatLLM7. Falls back to the LLM7_API_KEY environment variable. Required only when using the default LLM.

The function returns a list of file paths to the generated PDF(s). If the LLM call fails, a RuntimeError is raised with an explanatory message.

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🔑 API Keys & Rate Limits

• ChatLLM7 (default) is available on the free tier; its rate limits are sufficient for most development and small‑scale usage.
• To obtain a free API key, register at https://token.llm7.io/.
• You can pass the key directly:

pdf_paths = resume_yaml_builder(user_input=yaml_input, api_key="your_llm7_api_key")

• Or set the environment variable LLM7_API_KEY before running your script.

If you need higher limits on LLM7, upgrade your account on the provider’s website.

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🐞 Reporting Issues

Found a bug or have a feature request? Please open an issue on GitHub:

https://github.com/chigwell/resume_yaml_builder/issues

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👤 Author

Eugene Evstafev
Email: hi@euegne.plus
GitHub: chigwell

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.