DocSet Gen

Transform documentation into LLM training datasets

DocSet Gen scrapes documentation websites and generates high-quality Q&A training datasets for fine-tuning LLMs.

Features

• Smart Scraping - Uses Firecrawl to handle JS-rendered sites, anti-bot measures, and content cleaning
• AI-Powered Generation - Generates Q&A pairs using GPT-4o/GPT-4o-mini
• llms.txt Generation - Generate llms.txt files to help LLMs understand your documentation
• Interactive CLI - Guided prompts walk you through the process
• Quality Controls - Automatic deduplication, validation, and filtering
• Ready-to-Use Output - JSONL format with automatic train/val/test splits

Installation

# Clone the repository
git clone https://github.com/t21dev/docset-gen.git
cd docset-gen

# Create virtual environment
# Windows (the py launcher avoids picking up an unrelated venv on PATH):
py -m venv venv
# macOS/Linux:
python3 -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Activate before installing. Every command below assumes the venv is active. If you skip activation, pip and python may resolve to two different interpreters and nothing will work — see Troubleshooting.

Configuration

1. Copy the example environment file:

cp .env.example .env

2. Edit .env with your API keys:

FIRECRAWL_API_KEY=fc-your-key-here
OPENAI_API_KEY=sk-your-key-here

Note: The default model is gpt-5.1. Make sure you have access to this model enabled in your OpenAI developer account. You can change the model in .env by setting OPENAI_MODEL=gpt-4o or another supported model.

Usage

Just run:

python app.py

The interactive CLI will guide you:

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                        by t21.dev
Transform documentation into LLM training datasets

Enter documentation URL: docs.example.com
Crawl depth [3]: 3

──────── Step 1/3: Scraping Documentation ────────

Found 45 pages (23,450 words total)

Output format? [dataset/llms.txt/both]: dataset

How many Q&A pairs to generate? [225]: 200
Output file [dataset.jsonl]:

──────── Step 2/3: Generating Q&A Pairs ──────────

Generated 200 Q&A pairs

──────── Step 3/3: Cleaning and Saving ───────────

┌────────────── Dataset Complete ──────────────┐
│ Pages Scraped       │ 45                     │
│ Q&A Pairs Generated │ 200                    │
│ Train / Val / Test  │ 160 / 20 / 20          │
│ Output              │ dataset.jsonl          │
└──────────────────────────────────────────────┘

Manual Commands

You can also run individual steps:

# Just scrape (save for later)
python app.py scrape https://docs.example.com --output ./scraped

# Generate from previously scraped content
python app.py generate ./scraped --pairs 500 --output dataset.jsonl

# Generate llms.txt directly
python app.py llms-txt https://docs.example.com --output llms.txt

# Create config file
python app.py init

llms.txt Generation

Generate llms.txt files - a proposed standard to help LLMs understand your documentation structure.

Two modes available:

• minimal (default) - Links with brief descriptions
• full - Complete page content included (llms-full.txt)

# Generate minimal llms.txt (links only)
python app.py llms-txt https://docs.example.com

# Generate full llms.txt with complete content
python app.py llms-txt https://docs.example.com --mode full

# Specify project name and output file
python app.py llms-txt https://docs.example.com --name "My Project" --output my-llms.txt

Or choose llms.txt or both when prompted for output format in interactive mode.

Minimal mode output:

# Example Project

> A comprehensive toolkit for building modern web applications.

## Docs
- [Getting Started](https://example.com/docs/getting-started): Quick start guide
- [Configuration](https://example.com/docs/config): Configuration options

## API Reference
- [Authentication](https://example.com/api/auth): Auth endpoints
- [Users](https://example.com/api/users): User management

Full mode output:

# Example Project

> A comprehensive toolkit for building modern web applications.

## Docs

### [Getting Started](https://example.com/docs/getting-started)

> Quick start guide

# Getting Started

Welcome to the project! This guide will help you get up and running...

### [Configuration](https://example.com/docs/config)

> Configuration options

# Configuration

The following configuration options are available...

Output Format

{"instruction": "What is dependency injection?", "input": "", "output": "Dependency injection is..."}
{"instruction": "How do I configure logging?", "input": "", "output": "To configure logging..."}

Configuration File (Optional)

Create docset-gen.yaml for advanced settings:

firecrawl:
  max_depth: 3
  exclude_patterns:
    - "/changelog/*"
    - "/blog/*"

openai:
  model: gpt-4o-mini
  temperature: 0.7

generation:
  mode: qa
  pairs_per_page: 5

output:
  split_ratio: [0.8, 0.1, 0.1]

llms_txt:
  include_optional_section: true
  max_links_per_section: 20

Troubleshooting

ModuleNotFoundError: No module named 'typer' (or any other dependency)

Your shell is running a different Python than the one pip installed into. This is common on Windows when another tool has put its own venv on your PATH. Check both:

# Windows
where python
where pip

# macOS/Linux
which python
which pip

If the two paths don't point into the same directory, that's the problem — installing with one never affects the other. Activate the project venv and confirm the paths now agree:

# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

To bypass PATH entirely, call the venv's interpreter directly:

# Windows
venv\Scripts\python.exe app.py
# macOS/Linux
venv/bin/python app.py

ImportError: cannot import name 'Firecrawl' from 'firecrawl'

You have firecrawl-py 1.x installed, which exposes FirecrawlApp instead. This project uses the v2 API. Reinstall with pip install -r requirements.txt in an active venv.

TypeError: Parameter.make_metavar() missing 1 required positional argument: 'ctx'

typer 0.15.x is incompatible with click 8.2+. requirements.txt pins typer>=0.16; make sure your install picked it up.

Requirements

• Python 3.10+
• Firecrawl API key
• OpenAI API key

Fine-tune with LoCLI

This tool pairs perfectly with LoCLI - a CLI tool that makes fine-tuning LLMs accessible to developers. Just point it at your dataset and go.

# Generate dataset with DocSet Gen
python app.py

# Fine-tune with LoCLI
lo-cli train --dataset dataset.jsonl

License

MIT License - see LICENSE

Author

Created by @TriptoAfsin | t21dev