Turn documentation into chunked JSONL for LLM training

Created by

George Kioko

Actor

AI Training Data Scraper - LLM and RAG-Ready

For ML engineers building fine tuning sets and RAG indexes. The run reads two Python tutorial pages, splits clean text into token counted chunks and returns URL, title, chunk index, token count, word count and quality score as dataset rows in OpenAI JSONL format.

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AI Training Data Scraper - LLM and RAG-Readygeorge.the.developer/ai-training-data-scraper

Source URL

Title

Chunk #

Total Chunks

+4 fields

Input

Start URLs(required)

url:https://docs.python.org/3/tutorial/appetite.html+1

Max Pages:2

Max Crawl Depth:0

Chunk Size (tokens):800

Output Format:jsonl_openai

Output fields

Source URL

Title

Chunk #

Total Chunks

Tokens

Words

Quality

Scraped At

How it works

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The AI Training Data Scraper - LLM and RAG-Ready Actor is ready to run with the inputs already set for "Turn documentation into chunked JSONL for LLM training". Sign up or log in and you'll land in Apify Console with a copy of this exact task.

Start the "Turn documentation into chunked JSONL for LLM training" task

The AI Training Data Scraper - LLM and RAG-Ready Actor will start running based on the input automatically. You can also change all the input fields, like start urls, max pages, and more, to match your specific use case.

Receive the "Turn documentation into chunked JSONL for LLM training" output

Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as source url, title, chunk #, and more.