# 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 Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).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": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shoebill-dev27/rag-text-chunker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "RAG Text Chunker — heading & sentence aware, Japanese ready", "description": "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.", "version": "0.1", "x-build-id": "ySOC8OzD5UWDnsBbw" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shoebill-dev27~rag-text-chunker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shoebill-dev27-rag-text-chunker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shoebill-dev27~rag-text-chunker/runs": { "post": { "operationId": "runs-sync-shoebill-dev27-rag-text-chunker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shoebill-dev27~rag-text-chunker/run-sync": { "post": { "operationId": "run-sync-shoebill-dev27-rag-text-chunker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "documents" ], "properties": { "documents": { "title": "Documents", "type": "array", "description": "Markdown or plain-text documents; one dataset item is produced per chunk", "items": { "type": "string" } }, "max_chars": { "title": "Max characters per chunk", "minimum": 100, "maximum": 20000, "type": "integer", "description": "Upper bound on chunk size in characters", "default": 1500 }, "overlap": { "title": "Overlap characters", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Approximate number of trailing characters repeated at the start of the next chunk (capped at half of max_chars)", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```