# Doc To Markdown (`abotapi/doc-to-markdown`) Actor Convert documents (PDF, Word, PowerPoint, Excel, HTML, images) to clean Markdown. Supports batch processing, metadata extraction, and customizable output formatting. - **URL**: https://apify.com/abotapi/doc-to-markdown.md - **Developed by:** [AbotAPI](https://apify.com/abotapi) (community) - **Categories:** Developer tools, AI, SEO tools - **Stats:** 15 total users, 4 monthly users, 97.2% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Document to Markdown Converter An Apify Actor that converts documents to clean Markdown. ### Features - **File Upload**: Upload documents directly via Apify Console - **URL Support**: Convert documents from any URL - **Batch Processing**: Process multiple files at once - **15+ Formats**: PDF, Word, PowerPoint, Excel, HTML, images, and more - **Metadata Extraction**: Preserve file information - **Customizable Output**: Table of contents, heading styles - **Flexible Output**: Save to Dataset, Key-Value Store, or both ### Supported Formats | Category | Formats | |----------|---------| | Documents | PDF, Word (.docx), PowerPoint (.pptx), Excel (.xlsx, .xls) | | Web | HTML, XML | | Images | JPEG, PNG, GIF, BMP | | Audio | WAV, MP3 (with transcription) | | Data | CSV, JSON, YAML | | Archives | ZIP | ### Input | Field | Type | Description | |-------|------|-------------| | `files` | array | Upload files directly (uses fileupload widget) | | `urls` | array | List of document URLs to convert | | `includeMetadata` | boolean | Include file metadata in output (default: true) | | `includeToc` | boolean | Generate table of contents (default: false) | | `headingStyle` | string | "atx" (# Heading) or "setext" (underlined) | | `outputFormat` | string | "dataset", "keyValueStore", or "both" | # Actor input Schema ## `files` (type: `array`): Upload files to convert (PDF, Word, PowerPoint, Excel, HTML, images, etc.) ## `urls` (type: `array`): URLs of documents to convert ## `includeMetadata` (type: `boolean`): Include file metadata (filename, format, size, conversion time) in output ## `includeToc` (type: `boolean`): Auto-generate table of contents from headings ## `headingStyle` (type: `string`): Markdown heading style ## `outputFormat` (type: `string`): How to output the converted markdown ## `memoryMbytes` (type: `integer`): Override automatic memory allocation. Leave empty to auto-scale based on number of files/URLs. Use higher values for large PDFs or image-heavy documents with OCR. ## Actor input object example ```json { "includeMetadata": true, "includeToc": false, "headingStyle": "atx", "outputFormat": "both" } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("abotapi/doc-to-markdown").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 = {} # Run the Actor and wait for it to finish run = client.actor("abotapi/doc-to-markdown").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 '{}' | apify call abotapi/doc-to-markdown --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=abotapi/doc-to-markdown", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Doc To Markdown", "description": "Convert documents (PDF, Word, PowerPoint, Excel, HTML, images) to clean Markdown. Supports batch processing, metadata extraction, and customizable output formatting.", "version": "1.0", "x-build-id": "B6P0rVlpaL878KrKs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/abotapi~doc-to-markdown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-abotapi-doc-to-markdown", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/abotapi~doc-to-markdown/runs": { "post": { "operationId": "runs-sync-abotapi-doc-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/abotapi~doc-to-markdown/run-sync": { "post": { "operationId": "run-sync-abotapi-doc-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "files": { "title": "Upload Files", "type": "array", "description": "Upload files to convert (PDF, Word, PowerPoint, Excel, HTML, images, etc.)" }, "urls": { "title": "URLs", "type": "array", "description": "URLs of documents to convert", "items": { "type": "string" } }, "includeMetadata": { "title": "Include Metadata", "type": "boolean", "description": "Include file metadata (filename, format, size, conversion time) in output", "default": true }, "includeToc": { "title": "Generate Table of Contents", "type": "boolean", "description": "Auto-generate table of contents from headings", "default": false }, "headingStyle": { "title": "Heading Style", "enum": [ "atx", "setext" ], "type": "string", "description": "Markdown heading style", "default": "atx" }, "outputFormat": { "title": "Output Format", "enum": [ "dataset", "keyValueStore", "both" ], "type": "string", "description": "How to output the converted markdown", "default": "both" }, "memoryMbytes": { "title": "Memory (MB)", "minimum": 256, "maximum": 8192, "type": "integer", "description": "Override automatic memory allocation. Leave empty to auto-scale based on number of files/URLs. Use higher values for large PDFs or image-heavy documents with OCR." } } }, "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 } } } } } } } } } } ```