# Pdf Page Splitter (`vivianferreira/pdf-page-splitter`) Actor Split any PDF into individual pages instantly. Extract all pages, specific pages (1,3,5), or ranges (1-5). Handles up to 50,000 pages. Flat $0.005 per run. Perfect first step for document processing pipelines — chain with OCR, table extraction, and text analysis actors. - **URL**: https://apify.com/vivianferreira/pdf-page-splitter.md - **Developed by:** [Vivian Ferreira](https://apify.com/vivianferreira) (community) - **Categories:** Integrations, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $0.005 / actor start 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/platform/actors/running/actors-in-store#pay-per-event ## 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 ## ✂️ PDF Page Splitter > Split any PDF into individual pages — then chain into 100+ Apify actors for extraction, OCR, or analysis. ### What does PDF Page Splitter do? PDF Page Splitter takes a multi-page PDF and splits it into individual single-page PDFs. You can extract **all pages**, pick **specific pages** (e.g., `1,3,5`), or select a **range** (e.g., `1-5`). This is the essential first step in any document processing pipeline. #### Key features - ✅ **Flexible page selection** — `all`, `1`, `1,3,5`, `1-5`, `1,3-5,7` - ✅ **Two output modes** — Dataset (base64 JSON for chaining) or Key-Value Store (binary files for download) - ✅ **Upload or URL** — Upload a PDF directly or provide a URL - ✅ **Memory efficient** — Processes pages one-by-one with periodic cleanup - ✅ **Lightweight** — Runs on just 256 MB of memory - ✅ **Handles massive PDFs** — Up to 50,000 pages / 500 MB - ✅ **Flat pricing** — One flat fee per run, no matter how many pages --- ### Use cases | Use Case | Pages Setting | Output Format | |----------|---------------|---------------| | Split batched invoices for processing | `all` | Dataset (base64) | | Extract a specific contract page for review | `3` | Key-Value Store | | Pull first 5 pages from a report | `1-5` | Dataset (base64) | | Cherry-pick pages from a large document | `1,5,10-15,20` | Key-Value Store | #### Who is this for? - **n8n / Make.com automation builders** who process documents in workflows - **Finance teams** splitting batched invoices and payment advices - **Legal teams** extracting specific pages from large contracts - **AI/RAG pipeline developers** who need individual pages for processing --- ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `pdfFile` | File Upload | — | Upload a PDF file directly | | `pdfUrl` | String | — | URL to a PDF file (for chaining from other actors) | | `pages` | String | `all` | Page selection: `all`, `1`, `1,3,5`, `1-5`, `1,3-5,7` | | `outputFormat` | Enum | `dataset_base64` | `dataset_base64` (JSON with base64) or `key_value_store` (binary files) | > **Note:** Provide either `pdfFile` or `pdfUrl`. If both are provided, `pdfFile` takes priority. #### Page selection examples ```` all → Extract every page 1 → Just the first page 1,3,5 → Pages 1, 3, and 5 1-5 → Pages 1 through 5 1,3-5,7 → Pages 1, 3, 4, 5, and 7 ```` --- ### Output #### Dataset output (`dataset_base64`) Each page becomes one row in the dataset: ```json { "page_number": 1, "filename": "page_1_invoice.pdf", "size_bytes": 12345, "content_base64": "JVBERi0xLjQK...", "original_filename": "invoice.pdf", "total_pages": 10 } ```` #### Key-Value Store output (`key_value_store`) Each page is saved as a binary PDF file in the default Key-Value Store: - `page_1_invoice.pdf` - `page_2_invoice.pdf` - `page_3_invoice.pdf` - etc. *** ### Pricing This Actor uses **flat per-run** pricing: | Event | Price | |-------|-------| | `run-completed` (per run, any number of pages) | **$0.005** | **Example costs:** - Split a 10-page PDF → **$0.005** - Extract 3 specific pages → **$0.005** - Split a 10,000-page document → **$0.005** Same price whether you split 1 page or 10,000. Platform compute costs vary by memory and run time. *** ### Chaining with other actors PDF Page Splitter is designed as the **gateway** to document processing pipelines: ``` ┌─→ Resume Text Extractor │ PDF Page Splitter ──┼─→ Indian Payment Advice Parser │ ├─→ Document Table Extractor │ └─→ PDF to PNG Converter ``` #### Input sources - Any file download actor - Gmail attachment scraper - Website crawler (PDF links) - Manual upload #### Output destinations - **Resume Text Extractor** — Extract structured text from resume PDFs - **Indian Payment Advice Parser** — Parse payment details from bank advices - **Document Table Extractor** — Extract tables from document pages - **PDF to PNG Converter** — Convert pages to images for OCR or vision AI *** ### Integration with n8n / Make.com #### n8n HTTP Request Node 1. Set **Method** to `POST` 2. Use the Apify API endpoint to run this actor 3. Pass your PDF as binary data or provide a URL 4. Use `dataset_base64` output for easy downstream processing #### Make.com 1. Use the **Apify** module to run this actor 2. Pass your PDF URL in the `pdfUrl` input field 3. Iterate over dataset results for downstream processing *** ### Limits | Resource | Limit | |----------|-------| | File size | 500 MB | | Page count | 50,000 pages | | Default memory | 256 MB | | Max memory | 4096 MB | #### Memory recommendations | PDF Size | Pages | Recommended Memory | |----------|-------|--------------------| | ≤ 10 MB | ≤ 100 | 256 MB | | ≤ 50 MB | ≤ 500 | 512 MB | | ≤ 200 MB | ≤ 2,000 | 1024 MB | | ≤ 500 MB | ≤ 5,000 | 2048 MB | | > 500 MB | > 5,000 | 4096 MB | > **Tip:** For PDFs with 1,000+ pages, use `key_value_store` output format instead of `dataset_base64` to avoid base64 encoding overhead. *** ### Changelog #### v0.2 (Large PDF Support) - Flat per-run pricing ($0.005 per run, unlimited pages) - Raised limits: 50,000 pages, 500 MB file size, 4 GB memory - Streaming download for large files - Batch dataset pushes (50 rows per API call) - Aggressive memory cleanup with gc for 10K+ page PDFs - Progress logging every 100 pages - Memory recommendations in logs #### v0.1 (Initial Release) - PDF splitting with page selection (`all`, ranges, specific pages) - Two output modes: Dataset (base64) and Key-Value Store (binary) - File upload and URL input support - Memory-efficient processing with periodic cleanup # Actor input Schema ## `pdfFile` (type: `string`): Upload a PDF file to split. The file will be stored in a key-value store and its URL passed here. ## `pdfUrl` (type: `string`): Direct URL to a PDF file. Use this when chaining from another actor or when the PDF is hosted online. If both pdfFile and pdfUrl are provided, pdfFile takes priority. ## `pages` (type: `string`): Which pages to extract. Examples: 'all' (every page), '1' (first page only), '1,3,5' (specific pages), '1-5' (range), '1,3-5,7' (mixed). Default: all pages. ## `outputFormat` (type: `string`): How to output the split pages. 'Dataset (base64)' stores each page as a JSON row with base64-encoded PDF — best for chaining to other actors. 'Key-Value Store' saves each page as a binary PDF file — best for direct download. ## Actor input object example ```json { "pdfUrl": "https://www.w3.org/WAI/WCAG21/Techniques/pdf/img/table-word.pdf", "pages": "all", "outputFormat": "dataset_base64" } ``` # Actor output Schema ## `splitPages` (type: `string`): Each row contains a single split page with page\_number, filename, size\_bytes, content\_base64, original\_filename, and total\_pages fields. ## `splitFiles` (type: `string`): Binary PDF files saved as page\_N\_filename.pdf in the default key-value store. Only populated when outputFormat is set to key\_value\_store. # 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 = { "pdfUrl": "https://www.w3.org/WAI/WCAG21/Techniques/pdf/img/table-word.pdf" }; // Run the Actor and wait for it to finish const run = await client.actor("vivianferreira/pdf-page-splitter").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 = { "pdfUrl": "https://www.w3.org/WAI/WCAG21/Techniques/pdf/img/table-word.pdf" } # Run the Actor and wait for it to finish run = client.actor("vivianferreira/pdf-page-splitter").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 '{ "pdfUrl": "https://www.w3.org/WAI/WCAG21/Techniques/pdf/img/table-word.pdf" }' | apify call vivianferreira/pdf-page-splitter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vivianferreira/pdf-page-splitter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Pdf Page Splitter", "description": "Split any PDF into individual pages instantly. Extract all pages, specific pages (1,3,5), or ranges (1-5). Handles up to 50,000 pages. Flat $0.005 per run. Perfect first step for document processing pipelines — chain with OCR, table extraction, and text analysis actors.", "version": "0.2", "x-build-id": "bGC3ZDm5qpheJQS3Q" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vivianferreira~pdf-page-splitter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vivianferreira-pdf-page-splitter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/vivianferreira~pdf-page-splitter/runs": { "post": { "operationId": "runs-sync-vivianferreira-pdf-page-splitter", "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/vivianferreira~pdf-page-splitter/run-sync": { "post": { "operationId": "run-sync-vivianferreira-pdf-page-splitter", "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", "properties": { "pdfFile": { "title": "Upload PDF", "type": "string", "description": "Upload a PDF file to split. The file will be stored in a key-value store and its URL passed here." }, "pdfUrl": { "title": "OR: PDF URL", "type": "string", "description": "Direct URL to a PDF file. Use this when chaining from another actor or when the PDF is hosted online. If both pdfFile and pdfUrl are provided, pdfFile takes priority." }, "pages": { "title": "Pages to extract", "type": "string", "description": "Which pages to extract. Examples: 'all' (every page), '1' (first page only), '1,3,5' (specific pages), '1-5' (range), '1,3-5,7' (mixed). Default: all pages.", "default": "all" }, "outputFormat": { "title": "Output format", "enum": [ "dataset_base64", "key_value_store" ], "type": "string", "description": "How to output the split pages. 'Dataset (base64)' stores each page as a JSON row with base64-encoded PDF — best for chaining to other actors. 'Key-Value Store' saves each page as a binary PDF file — best for direct download.", "default": "dataset_base64" } } }, "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 } } } } } } } } } } ```