Pricing

from $5.00 / 1,000 text chunkeds

Text Splitter & Chunker for RAG / LLMs

Split text into clean, overlapping chunks ready for embeddings, vector databases, RAG and LLM context. Configurable size, overlap, and split strategy.

Pricing

from $5.00 / 1,000 text chunkeds

Rating

0.0

(0)

Developer

Rosario Vitale

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

Categories

Developer tools

Automation

You can access the Text Splitter & Chunker for RAG / LLMs 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

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 = { "text": """Retrieval-Augmented Generation (RAG) combines a language model with an external knowledge base. Instead of relying only on what the model memorized during training, RAG retrieves relevant chunks of text and feeds them to the model as context.
9
10To build a RAG system you first split your documents into chunks, create embeddings for each chunk, and store them in a vector database. At query time you embed the user's question, find the most similar chunks, and pass them to the model alongside the prompt.
11
12Chunking matters a lot. Chunks that are too large dilute relevance and waste tokens, while chunks that are too small lose context. A common starting point is around 1000 characters per chunk with a small overlap, so that ideas spanning a boundary are not lost between neighbouring chunks.""" }
13
14# Run the Actor and wait for it to finish
15run = client.actor("zenomastro/text-splitter-for-llm").call(run_input=run_input)
16
17# Fetch and print Actor results from the run's dataset (if there are any)
18print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
19for item in client.dataset(run["defaultDatasetId"]).iterate_items():
20    print(item)
21
22# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

Text Chunker for RAG, Embeddings & LLMs API in Python

The Apify API client for Python is the official library that allows you to use Text Splitter & Chunker for RAG / LLMs API in Python, providing convenience functions and automatic retries on errors.

Install the apify-client

$pip install apify-client

Other API clients include:

Text Splitter & Chunker for RAG / LLMs API in JavaScript

Text Splitter & Chunker for RAG / LLMs API through CLI

Text Splitter & Chunker for RAG / LLMs OpenAPI definition

Text Splitter & Chunker for RAG / LLMs API

RAG-Ready Markdown Converter & Chunker

foxpink/apify-rag-markdown-chunker

Convert raw HTML/text into clean Markdown and split into ready-to-ingest chunks for RAG pipelines, Vector DBs, and LLM fine-tuning workflows.

Nguyễn Anh Duy

4.7

RAG-Ready Web Scraper & Smart Chunker for AI Knowledge Bases

adinfosys-labs/rag-ready-web-scraper-smart-chunker-for-ai-knowledge-bases

RAG-ready web scraper that collects, cleans, deduplicates, filters, and chunks web content into structured datasets for AI pipelines. Generates high-quality knowledge-base data optimized for LLMs, embeddings, and vector databases

Artashes Arakelyan

Rag Content Chunker

labrat011/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.

mick_

RAG Text Chunker — heading & sentence aware, Japanese ready

shoebill-dev27/rag-text-chunker

Split Markdown or plain text into retrieval-ready chunks for RAG pipelines: cuts at headings, packs whole sentences up to a size limit with optional overlap, and tags every chunk with its heading breadcrumb. Handles Japanese sentence boundaries. No LLM cost.

Shinobu Otani

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_

PDF to Text API | Document Extraction for LLMs & RAG

andok/pdf-text-converter

Convert bulk PDF documents via URL into clean, raw text. The perfect document scraper for LLMs, vector databases, and RAG pipelines.

Andok

Website to Text & Markdown — AI / RAG Content Crawler

inexhaustible_glass/rag-website-crawler

Scrape any website into clean text & Markdown with RAG-ready chunks and token counts for LLMs, vector databases (Pinecone, Qdrant) and AI chatbots. Also extracts linked PDF/Word/Excel. Anti-block, robots.txt-aware. Website-to-text for beginners, full RAG pipeline for pros. CPU only, no API key.

Hitman studio

AI Context Fetcher: Clean Text for RAG

sarvesh_bijawe/ai-context-fetcher-clean-text-for-rag

Instantly extracts clean, ad-free text from any URL. Designed for AI Agents, RAG pipelines, and LLM context windows.

Sarvesh Bijawe

Website to Markdown for LLM and RAG

jeweled_jockstrap/my-actor-3

Convert any URL to clean Markdown text for AI applications. Strips HTML extracts content. For LLM training RAG pipelines and vector databases. Free Firecrawl alternative.