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

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Bookmarked

Total users

Monthly active users

a month 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

$echo '{
<  "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.\\n\\nTo 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.\\n\\nChunking 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."
<}' |
<apify call zenomastro/text-splitter-for-llm --silent --output-dataset

Text Chunker for RAG, Embeddings & LLMs API through CLI

The Apify CLI is the official tool that allows you to use Text Splitter & Chunker for RAG / LLMs locally, providing convenience functions and automatic retries on errors.

Install Apify CLI

Using installation script (macOS/Linux):

$curl -fsSL https://apify.com/install-cli.sh | bash

Using installation script (Windows):

$irm https://apify.com/install-cli.ps1 | iex

Using Homebrew:

$brew install apify-cli

Using npm:

$npm install -g apify-cli

Other API clients include:

Text Splitter & Chunker for RAG / LLMs API in Python

Text Splitter & Chunker for RAG / LLMs API in JavaScript

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

PDF to RAG Markdown Chunks for Embeddings

awesome_highboy/docforge

Convert PDFs into token-bounded Markdown chunks for RAG, embeddings, and vector databases (Pinecone, Chroma, Weaviate, Qdrant). Set maxTokens + overlap; get clean chunks with page number, token count, and SHA-256 content hash for dedup. JSON dataset ready for any LLM pipeline.

Adam

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

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

RAG Post Processor - Text Cleaner & Chunker for LLM Pipelines

jalicia/rag-post-processor

Clean and chunk scraped text for RAG and LLM pipelines. Strips HTML, collapses whitespace, splits into overlapping chunks ready for embedding. Works standalone or chained after any scraper. Per-row billing.

Jordan Wagner

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_

RagChunk: Semantic RAG Splitter & Markdown Cleaner

flaretool/rag-semantic-chunker

Extract, clean, and semantically split documentation URLs into token-safe chunks for Vector DBs. Built-in HTML boilerplate stripping saves up to 40% on LLM token costs.

FlareTool

RAG-Ready Website Crawler — Clean Content for LLMs & Vector DBs

yourwingman/rag-ready-crawler

Crawl websites and output clean, chunked content optimized for RAG pipelines, LLM training data, and vector databases. Built for AI knowledge bases and semantic search.