You can access the RAG Docs Extractor - Documentation to Chunks programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1from apify_client import ApifyClient
2
3# Initialize the ApifyClient with your Apify API token
4# Replace '<YOUR_API_TOKEN>' with your token.
5client = ApifyClient("<YOUR_API_TOKEN>")
6
7# Prepare the Actor input
8run_input = { "startUrl": "https://docs.python.org/3/library/functions.html" }
9
10# Run the Actor and wait for it to finish
11run = client.actor("ambitious_door/ragdocs-extractor").call(run_input=run_input)
12
13# Fetch and print Actor results from the run's dataset (if there are any)
14print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
15for item in client.dataset(run.default_dataset_id).iterate_items():
16    print(item)
17
18# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

RAG Docs Extractor - Docs to LLM Chunks API in Python

The Apify API client for Python is the official library that allows you to use RAG Docs Extractor - Documentation to Chunks API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

RAG Docs Extractor - Documentation to Chunks API in JavaScript

RAG Docs Extractor - Documentation to Chunks API through CLI

RAG Docs Extractor - Documentation to Chunks OpenAPI definition

RAG Docs Extractor - Documentation to Chunks API

Rag Knowledge Graph Builder

cspnair/rag-knowledge-graph-builder

Transform websites into RAG-ready datasets. Crawls pages, chunks content into semantic segments (500-1000 tokens), and generates hypothetical questions for each chunk. No API key needed with native mode. Output: pre-indexed JSON optimized for AI retrieval with 3x better accuracy than raw text.

csp

132

5.0

(7)

GitHub Scraper - Repos, Profiles & Contributors

ambitious_door/github-scraper

Extract repositories, user profiles, contributors, and stargazers from GitHub. Perfect for developer lead generation, open source intelligence, and tech stack research.

C. K.

AI RAG Feeder V2

mickeywmoore/ai-rag-feeder-v2

Turn any website into AI-ready Markdown. Scrapes entire domains, removes ads/clutter, and formats text specifically for RAG pipelines and LLM training data.

Mickey Moore

Website Content Extractor for RAG: Markdown, HTML, Text

nezha/website-content-crawler

Turn docs sites, help centers, blogs, and websites into clean markdown, text, or HTML for RAG, AI knowledge bases, and internal search. Crawl from start URLs or sitemaps and keep the crawl in scope.

nezha

Website Content Crawler - Clean Text, Markdown & RAG Chunks

topapi/website-content-crawler

Crawl any website or URL list for $1.20 per 1,000 pages: one row per page with clean text, Markdown, HTML, metadata, links, headings, tables and RAG chunks. Plain HTTP first, headless Chrome only for JavaScript pages. Sitemaps, llms.txt, PDF/DOCX text, Bing/DuckDuckGo search, change monitor.

Top Api

PDF to Markdown RAG-Ready

hedelka/pdf-to-markdown-rag

Premium PDF scraper that preserves tables and structure. Optimized for RAG.

Dmitry Goncharov

Webpage Text Extractor

maximedupre/webpage-text-extractor

Extract clean text, article text, and Markdown from public web pages. Get titles, metadata, headings, links, word counts, final URLs, and timestamps for LLM prompts, RAG inputs, reviews, and exports.