# RAG Text Chunker (Markdown to Chunks, Token Counts) (`webdatatools/rag-text-chunker`) Actor

Split Markdown or text into RAG-ready chunks with exact OpenAI token counts (o200k/cl100k): heading-aware, code blocks kept whole, sentence-boundary overlap, heading path on every chunk. Chunks any dataset from Website to Markdown, PDF to Markdown or your own texts.

- **URL**: https://apify.com/webdatatools/rag-text-chunker.md
- **Developed by:** [Murat Uzun](https://apify.com/webdatatools) (community)
- **Categories:** AI, Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.12 / 1,000 chunks

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### What is RAG Text Chunker?

**RAG Text Chunker** splits Markdown or plain text into **retrieval-ready chunks** for vector databases (Pinecone, Qdrant, Weaviate, pgvector, Chroma) and RAG apps, with **exact OpenAI token counts** (`o200k_base` for GPT-4o/GPT-5, or `cl100k_base` for GPT-4 and `text-embedding-3`). It is **heading-aware**: every chunk stays inside one section and carries its heading path (`Guide > Install > Linux`), code blocks are never cut mid-line, oversized paragraphs split at sentence boundaries, tiny fragments are folded into their neighbours, and inline base64 images are dropped. **$0.20 per 1,000 chunks.**

Feed it the dataset of a scraping run (Website to Markdown, llms.txt Generator, PDF to Markdown, YouTube transcripts, articles) or paste your own texts, and get one row per chunk ready to embed.

### What does RAG Text Chunker output?

| Field | Description |
|---|---|
| `documentId`, `url`, `title` | The source document (`documentId` is its URL, or `document-N`) |
| `chunkIndex`, `chunkCount`, `chunkId` | Position of the chunk in the document; `chunkId` = `documentId#index`, a stable key for upserts |
| `headingPath`, `headings` | Headings the chunk sits under, as an array and as `A > B > C` |
| `text` | The chunk text, with the heading path on top and the overlap from the previous chunk |
| `tokens`, `overlapTokens`, `characters` | Exact token count with the chosen tokenizer, tokens of overlap, length |
| `encoding` | `o200k_base` or `cl100k_base` |
| any **Columns to keep** | Fields copied from the source rows, e.g. `lang`, `crawledAt`, `site` |

### How to use RAG Text Chunker

1. Give it text: an **Input dataset ID** from another run (the text column is found automatically), **Documents** as JSON, or **Texts**.
2. Set **Chunk size** (default 500 tokens) and **Overlap** (default 50), and pick the **Tokenizer** your embedding model uses.
3. Run, then embed the `text` column of the dataset and store it with `chunkId`, `url` and `headings` as metadata.

#### Chaining with a crawler

Run [Website to Markdown](https://apify.com/webdatatools/website-to-markdown) or [PDF to Markdown Converter](https://apify.com/webdatatools/pdf-to-markdown), copy the run's dataset ID, and pass it as **Input dataset ID**. With Apify integrations or the API you can start the chunker automatically when the crawl finishes.

### Example input

```json
{
    "inputDatasetId": "NJMrOuJvOh0X9IP4r",
    "chunkSize": 400,
    "overlap": 40,
    "encoding": "o200k_base",
    "keepFields": ["lang"]
}
```

### Example output

````json
{
    "documentId": "https://crawlee.dev/js/docs/3.10/quick-start",
    "url": "https://crawlee.dev/js/docs/3.10/quick-start",
    "title": "Quick Start",
    "chunkIndex": 4,
    "chunkCount": 26,
    "chunkId": "https://crawlee.dev/js/docs/3.10/quick-start#4",
    "headingPath": [
        "Quick Start",
        "Crawling"
    ],
    "headings": "Quick Start > Crawling",
    "text": "Quick Start > Crawling\n\nYou need to explicitly install it with NPM. 👇\n\n```\nnpm install crawlee puppeteer\n```\n\nRun the following example to perform a recursive crawl of the Crawlee website using the selected crawler.\n\nDon't forget about module imports\n\nTo run  ...",
    "tokens": 145,
    "overlapTokens": 23,
    "characters": 589,
    "encoding": "o200k_base"
}
````

In a test on 120 pages of the Crawlee and Apify docs with 400-token chunks and 40-token overlap, the 898 chunks ranged from 23 to 463 tokens (content up to 400, plus heading path and overlap).

### How much does it cost?

Pay per result: **$0.0002 per chunk ($0.20 per 1,000)**, with volume discounts on paid Apify plans. A typical documentation page gives 5 to 10 chunks at 400 tokens. A dataset that cannot be read is listed in the `ERRORS` record and costs nothing. Set **Maximum cost per run** to cap spend.

### Limits

- **Chunk size** limits the content; the heading path and the overlap are added on top, so a chunk can be up to about chunk size + overlap + the heading path.
- Chunking follows Markdown headings (`#` … `######`). Plain text without headings is split by paragraphs and sentences.
- Tokens are counted with OpenAI's tokenizers; other models (Claude, Gemini, open-source embeddings) count somewhat differently.
- Datasets are read in batches of 1,000 rows; very large datasets take longer.

### Using RAG Text Chunker with AI agents

The Actor is pay-per-event with limited permissions, so AI agents can call it through Apify's MCP server (`mcp.apify.com`), for example with `{"texts": ["# Doc\n\nLong text..."], "chunkSize": 300}`, or with `inputDatasetId` right after a crawl.

### FAQ

**Why heading-aware chunks?** A chunk that mixes two sections embeds to a blurry vector and is hard to cite. Keeping one section per chunk and putting its heading path in the text makes retrieval and citations more precise.

**Which chunk size should I use?** 300 to 500 tokens with 10 % overlap is a common starting point for question answering; use larger chunks for summarisation.

### Related Actors

Part of the **webdatatools** web-intelligence suite — every Actor is pay-per-event, reads public data
without a login, and returns one clean row per entity:

Browse the whole suite at [webdatatools](https://paulet4a-commits.github.io/webdatatools/), or call ten of
these Actors straight from Claude, Cursor or Cline with the
[webdatatools MCP server](https://github.com/paulet4a-commits/webdatatools-mcp-server).

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- [App Store (iOS) App Metadata, Ratings & Top Charts Lookup](https://apify.com/webdatatools/app-store-lookup) — ratings, price, version and charts per app
- [CrossRef DOI & Citation Metadata Lookup](https://apify.com/webdatatools/crossref-doi-lookup) — papers, authors, journals and citation counts
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# Actor input Schema

## `inputDatasetId` (type: `string`):

Dataset of another run to chunk, e.g. from Website to Markdown, llms.txt Generator or PDF to Markdown. The text column is found automatically (`markdown`, `text`, `content`, `body`, `transcript`).

## `textField` (type: `string`):

Optional: the dataset column holding the text, if it is not one of the usual names.

## `documents` (type: `array`):

Or paste documents as JSON: `[{"url": "...", "title": "...", "text": "..."}]`.

## `texts` (type: `array`):

Or paste plain texts or Markdown, one document per item.

## `chunkSize` (type: `integer`):

Maximum tokens of content per chunk (heading path and overlap come on top).

## `overlap` (type: `integer`):

Tokens from the end of the previous chunk repeated at the start of the next, cut at a sentence boundary. 0 for none.

## `encoding` (type: `string`):

o200k\_base: GPT-4o, GPT-4.1, GPT-5, o-series. cl100k\_base: GPT-4, GPT-3.5, text-embedding-3.

## `respectHeadings` (type: `boolean`):

Never mix two sections (headings up to the level below) in one chunk.

## `headingLevel` (type: `integer`):

2 = split at # and ## headings; 3 also at ###.

## `includeHeadingInText` (type: `boolean`):

Prefix each chunk with "Title > Section" so the embedding knows the context.

## `keepFields` (type: `array`):

Optional: dataset or document fields copied onto every chunk, e.g. `lang`, `crawledAt`.

## Actor input object example

```json
{
  "texts": [
    "# Guide\n\nFirst section text.\n\n## Details\n\nMore text here."
  ],
  "chunkSize": 500,
  "overlap": 50,
  "encoding": "o200k_base",
  "respectHeadings": true,
  "headingLevel": 2,
  "includeHeadingInText": true
}
```

# Actor output Schema

## `results` (type: `string`):

All chunks from this run.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "texts": [
        "# Guide\n\nFirst section text.\n\n## Details\n\nMore text here."
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("webdatatools/rag-text-chunker").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "texts": ["""# Guide

First section text.

## Details

More text here."""] }

# Run the Actor and wait for it to finish
run = client.actor("webdatatools/rag-text-chunker").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "texts": [
    "# Guide\\n\\nFirst section text.\\n\\n## Details\\n\\nMore text here."
  ]
}' |
apify call webdatatools/rag-text-chunker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,webdatatools/rag-text-chunker"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/QIZmifxee1cNc3cjl/builds/PMLYmimzyLcPFiWwP/openapi.json