Pricing

from $0.50 / 1,000 results

Rag Content Chunker

Turn raw text, Markdown, or Apify datasets into token-perfect RAG chunks with deterministic IDs, source metadata, and a billing-ready summary—ready for embeddings or vector DBs without extra glue code.

Pricing

from $0.50 / 1,000 results

Rating

0.0

(0)

Developer

Mick

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

Categories

Agents

Integrations

You can access the Rag Content Chunker 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 Integrations settings in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

1import { ApifyClient } from 'apify-client';
2
3// Initialize the ApifyClient with your Apify API token
4// Replace the '<YOUR_API_TOKEN>' with your token
5const client = new ApifyClient({
6    token: '<YOUR_API_TOKEN>',
7});
8
9// Prepare Actor input
10const input = {
11    "text": `# Introduction
12        
13        This is a sample document with multiple sections.
14        
15        ## Section One
16        
17        Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
18        
19        ## Section Two
20        
21        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.`
22};
23
24// Run the Actor and wait for it to finish
25const run = await client.actor("labrat011/rag-content-chunker").call(input);
26
27// Fetch and print Actor results from the run's dataset (if any)
28console.log('Results from dataset');
29console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
30const { items } = await client.dataset(run.defaultDatasetId).listItems();
31items.forEach((item) => {
32    console.dir(item);
33});
34
35// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

Rag Content Chunker API in JavaScript

The Apify API client for JavaScript is the official library that allows you to use Rag Content Chunker API in JavaScript or TypeScript, providing convenience functions and automatic retries on errors.

Install the apify-client

$npm install apify-client

Other API clients include:

Rag Content Chunker API in Python

Rag Content Chunker API through CLI

Rag Content Chunker OpenAPI definition

Rag Content Chunker API

Rag Embedding Generator

labrat011/rag-embedding-generator

Generate vector embeddings from text or chunked datasets using OpenAI or Cohere. Chains with RAG Content Chunker for end-to-end RAG pipelines. Outputs raw vectors ready for any vector database.

Mick

Docs Markdown Rag Ready Crawler

devwithbobby/docs-markdown-rag-ready-crawler

Turn any documentation site or website into clean, structured markdown—ready for RAG, embeddings, and AI agents.

Dev with Bobby

Web Scraper RAG Ready

traorealexy/Web-Sraper-RAG-Ready

Turn any website into clean, token-efficient Markdown ready for RAG and LLM pipelines. Removes boilerplate, handles JavaScript rendering, and outputs structured JSON for LangChain, LlamaIndex, and vector databases.

Alexy Traore

Docs To Rag

gabrielaxy/docs-to-rag

Transform documentation websites into RAG-ready chunks with semantic understanding, quality scoring, and direct vector database integration.

Gabriel Antony Xaviour

RAG Markdown Cleaner

cooldev/rag-markdown-cleaner

Transform web pages into RAG-ready Markdown with smart chunking, metadata, code detection & quality scoring. Production-tested deduplication. Fully open-source (Apache 2.0)—review code, contribute, or self-host. Turn messy HTML into embedding-ready knowledge instantly.

Mohamed khalil Zouitni

Universal RAG Web Scraper

express_kingfisher/rag-web-scraper

Turn any website into clean, LLM-ready Markdown. Automatically strips ads, navigation, and noise using Mozilla Readability. Perfect for feeding data to ChatGPT, Claude, or Vector Databases (RAG).

Prince Raj

Docs to Markdown + AI Embeddings → Vector DB Crawler

badruddeen/docs-to-markdown-ai-embeddings---vector-db-crawler

Turn any documentation site into clean Markdown, intelligently chunked content with embeddings (Azure/OpenAI), and directly upsert into MongoDB Atlas, Pinecone, Weaviate, Qdrant, or Milvus — ready for RAG, AI assistants, and semantic search in minutes.

Badruddeen Naseem

5.0

Website Content Crawler Pro

datascoutapi/website-content-crawler-pro

Crawl websites and extract clean, structured content in Markdown, JSON, or plain text for AI models, LLMs, vector DBs, or RAG pipelines. Fast, reliable, and stealthy, with bulk processing, advanced metadata extraction, and seamless integration with LangChain, LlamaIndex, and AI workflows.