# PDF to Markdown Converter: Docling Parser for AI & RAG (`raional/pdf-to-markdown-converter`) Actor Convert PDF, DOCX, PPTX, XLSX, HTML and images to clean Markdown and structured JSON using IBM's open-source Docling library. Preserves headings, tables, and page structure. RAG-ready chunked output mode for LLM pipelines. - **URL**: https://apify.com/raional/pdf-to-markdown-converter.md - **Developed by:** [Raion Al](https://apify.com/raional) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 1,000 pages 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 to Markdown Converter — Docling Document Parser for AI & RAG Convert **PDF, DOCX, PPTX, XLSX, HTML, and images to clean Markdown and structured JSON**. Powered by [Docling](https://github.com/docling-project/docling), IBM's open-source document-conversion library (MIT licensed, 51,000+ GitHub stars, 2.4M+ monthly downloads). Headings, tables, and page structure are preserved. Includes a **RAG-ready chunked output mode** for feeding LLM pipelines and vector stores directly. Great for: **pdf to markdown**, **document parser**, **docling**, RAG/LLM ingestion pipelines, AI knowledge bases, research paper processing, and converting legacy DOCX/PPTX archives to Markdown. ### What it does Give it a list of document URLs — it downloads and converts each one, returning clean structured output. No files are stored beyond the run. ### Output formats | `outputFormat` | What you get | |---|---| | `markdown` | Clean Markdown text | | `json` | Full structured document (headings, tables, layout, bounding boxes) | | `both` (default) | Markdown + JSON | | `chunks` | RAG-ready semantic chunks, each with its heading context — ready to embed and index | ### Example input ```json { "documents": [ "https://example.com/report.pdf", "https://example.com/slides.pptx" ], "outputFormat": "both", "ocrEnabled": false } ```` ### Output One row per document: | Field | Description | |---|---| | `documentUrl` | The document converted | | `format` | Detected format (pdf, docx, pptx, xlsx, html, image) | | `pageCount` | Pages in the document | | `markdown` | Markdown output (if requested) | | `json` | Structured JSON output (if requested) | | `chunks` / `chunkCount` | RAG chunks (if `outputFormat: "chunks"`) | | `status` | `ok` or `error` | ### OCR Turn on `ocrEnabled` for **scanned** PDFs and images with no embedded text layer. It's slower and billed separately — leave it off for normal digital PDFs/DOCX/PPTX, which convert faster without it. ### Pricing Billed **per page successfully converted** (fair for a 2-page doc vs. a 200-page one) plus a per-document OCR surcharge when OCR is used. Failed documents are **not charged**. ### FAQ **How do I convert a PDF to Markdown?** Pass the PDF's URL in `documents` and run. The `markdown` field in the output is ready to use as-is. **Does this work for scanned PDFs?** Yes — turn on `ocrEnabled`. It's slower than normal text extraction, so it's billed at a separate rate; leave it off for regular digital PDFs. **What is the "chunks" output for?** It splits the document into semantically coherent pieces (respecting headings and structure) sized for embedding into a vector database — the standard input shape for RAG pipelines. **How is this different from just using Docling myself?** Docling requires installing Python + ML dependencies and managing compute. This runs it as a hosted API — no setup, pay only for what you convert. ### Please note Only convert documents you have the right to process. Documents are processed transiently and not retained beyond the run. Built with [Apify Python SDK](https://docs.apify.com/sdk/python/) + [Docling](https://github.com/docling-project/docling). # Actor input Schema ## `documents` (type: `array`): List of document URLs to convert. Supports PDF, DOCX, PPTX, XLSX, HTML, PNG/JPG/TIFF images. Leave empty if you're only using the file upload field below. ## `uploadedDocument` (type: `string`): Have a file on your computer instead of a URL? Upload it here. Combines with any URLs listed above. ## `outputFormat` (type: `string`): markdown = clean Markdown text. json = full structured document (headings, tables, layout). both = markdown + json. chunks = RAG-ready semantic chunks for LLM/vector-store ingestion. ## `ocrEnabled` (type: `boolean`): Turn on for scanned PDFs/images with no embedded text layer. Slower and priced separately — leave off for normal digital PDFs/DOCX for faster, cheaper conversion. ## `maxDocuments` (type: `integer`): Safety cap on how many documents to process in one run. ## Actor input object example ```json { "documents": [ "https://arxiv.org/pdf/2408.09869" ], "outputFormat": "both", "ocrEnabled": false, "maxDocuments": 50 } ``` # Actor output Schema ## `documents` (type: `string`): One dataset row per document: documentUrl, format, pageCount, markdown, json, chunks, status. ## `summary` (type: `string`): Counts of documents processed and errors. # 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": [ "https://arxiv.org/pdf/2408.09869" ] }; // Run the Actor and wait for it to finish const run = await client.actor("raional/pdf-to-markdown-converter").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": ["https://arxiv.org/pdf/2408.09869"] } # Run the Actor and wait for it to finish run = client.actor("raional/pdf-to-markdown-converter").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": [ "https://arxiv.org/pdf/2408.09869" ] }' | apify call raional/pdf-to-markdown-converter --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=raional/pdf-to-markdown-converter", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF to Markdown Converter: Docling Parser for AI & RAG", "description": "Convert PDF, DOCX, PPTX, XLSX, HTML and images to clean Markdown and structured JSON using IBM's open-source Docling library. Preserves headings, tables, and page structure. RAG-ready chunked output mode for LLM pipelines.", "version": "0.1", "x-build-id": "AhsVcGFwjmyKG8Qdi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/raional~pdf-to-markdown-converter/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-raional-pdf-to-markdown-converter", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/raional~pdf-to-markdown-converter/runs": { "post": { "operationId": "runs-sync-raional-pdf-to-markdown-converter", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/raional~pdf-to-markdown-converter/run-sync": { "post": { "operationId": "run-sync-raional-pdf-to-markdown-converter", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "documents": { "title": "Document URLs", "type": "array", "description": "List of document URLs to convert. Supports PDF, DOCX, PPTX, XLSX, HTML, PNG/JPG/TIFF images. Leave empty if you're only using the file upload field below.", "items": { "type": "string" } }, "uploadedDocument": { "title": "Or upload a file directly", "type": "string", "description": "Have a file on your computer instead of a URL? Upload it here. Combines with any URLs listed above." }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "json", "both", "chunks" ], "type": "string", "description": "markdown = clean Markdown text. json = full structured document (headings, tables, layout). both = markdown + json. chunks = RAG-ready semantic chunks for LLM/vector-store ingestion.", "default": "both" }, "ocrEnabled": { "title": "Enable OCR (scanned documents)", "type": "boolean", "description": "Turn on for scanned PDFs/images with no embedded text layer. Slower and priced separately — leave off for normal digital PDFs/DOCX for faster, cheaper conversion.", "default": false }, "maxDocuments": { "title": "Max documents per run", "minimum": 1, "maximum": 500, "type": "integer", "description": "Safety cap on how many documents to process in one run.", "default": 50 } } }, "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 } } } } } } } } } } ```