# RAG Text Chunker — heading & sentence aware, Japanese ready (`shoebill-dev27/rag-text-chunker`) Actor

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.

- **URL**: https://apify.com/shoebill-dev27/rag-text-chunker.md
- **Developed by:** [Shinobu Otani](https://apify.com/shoebill-dev27) (community)
- **Categories:** Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.20 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## RAG Text Chunker

Split Markdown or plain text into retrieval-ready chunks. Heading-aware,
sentence-aware, Japanese-ready — deterministic, no LLM cost.

- **Cuts at headings first**: chunks never mix sections; fenced code blocks
  are not mistaken for headings
- **Packs whole sentences** up to `max_chars`; oversized sentences are
  hard-split as a last resort
- **Optional overlap** between consecutive chunks for retrieval continuity
- **Japanese-aware boundaries**: 。！？ with closing-quote handling alongside
  Latin `.!?` (decimals like `3.14` stay intact)
- **Heading breadcrumbs**: every chunk carries `heading_path` for citation

### Input

```json
{"documents": ["# 概要\n\n検証は三段階で行う。まず再現する。"], "max_chars": 1500, "overlap": 200}
```

### Output (one dataset item per chunk)

```json
{"id": 0, "document_index": 0, "heading_path": ["概要"], "text": "検証は三段階で行う。 まず再現する。", "char_count": 19}
```

Typical uses: chunking docs/knowledge bases before embedding; Japanese or
mixed-language corpora for vector search; reproducible chunk boundaries.

# Actor input Schema

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

Markdown or plain-text documents; one dataset item is produced per chunk

## `max_chars` (type: `integer`):

Upper bound on chunk size in characters

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

Approximate number of trailing characters repeated at the start of the next chunk (capped at half of max_chars)

## Actor input object example

```json
{
  "documents": [
    "# Retrieval-Augmented Generation\n\nRAG pipelines split long documents into overlapping chunks before embedding. Each chunk should stay under the model's context budget. This sample shows how the chunker segments sentences and packs them into bounded windows. Try lowering max_chars to see multiple chunks per document."
  ],
  "max_chars": 1500,
  "overlap": 0
}
```

# 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 = {
    "documents": [
        "# Retrieval-Augmented Generation\n\nRAG pipelines split long documents into overlapping chunks before embedding. Each chunk should stay under the model's context budget. This sample shows how the chunker segments sentences and packs them into bounded windows. Try lowering max_chars to see multiple chunks per document."
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("shoebill-dev27/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 = { "documents": ["""# Retrieval-Augmented Generation

RAG pipelines split long documents into overlapping chunks before embedding. Each chunk should stay under the model's context budget. This sample shows how the chunker segments sentences and packs them into bounded windows. Try lowering max_chars to see multiple chunks per document."""] }

# Run the Actor and wait for it to finish
run = client.actor("shoebill-dev27/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 '{
  "documents": [
    "# Retrieval-Augmented Generation\\n\\nRAG pipelines split long documents into overlapping chunks before embedding. Each chunk should stay under the model'\''s context budget. This sample shows how the chunker segments sentences and packs them into bounded windows. Try lowering max_chars to see multiple chunks per document."
  ]
}' |
apify call shoebill-dev27/rag-text-chunker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,shoebill-dev27/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/CmlSZI5cDk2RiEnx5/builds/ySOC8OzD5UWDnsBbw/openapi.json