# Convert To Markdown (`datavault/convert-to-markdown`) Actor Convert to Markdown, converts documents, spreadsheets, images (OCR), audio (transcription), and web/data files into clean Markdown. It runs fully locally, requires no API keys, and is ideal for LLMs, docs, and archiving. - **URL**: https://apify.com/datavault/convert-to-markdown.md - **Developed by:** [Datavault](https://apify.com/datavault) (community) - **Categories:** Automation, Other - **Stats:** 9 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $15.00 / 1,000 file conversions 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 ## Convert to Markdown - Versatile File Converter The **Convert to Markdown** Actor is a high-performance, all-in-one utility designed to transform a wide variety of file formats into clean, structured Markdown. It is ideal for preparing data for LLMs (Large Language Models), documentation workflows, or archiving. ### Features - **Documents**: Converts **PDF** (preserving layout and structure), **Word (.docx)**, and **PowerPoint (.pptx)** into clean Markdown. - **Spreadsheets**: Transforms **Excel (.xlsx)** and **CSV** files into readable Markdown tables. - **Images (OCR)**: Extracts text from images (JPG, PNG, WebP, etc.) using automated OCR. - **Audio (Transcription)**: Transcribes speech from audio files (MP3, WAV, etc.) into text using local AI models. - **Web & Data**: Converts **HTML**, **JSON**, and **XML** into formatted Markdown blocks or tables. - **Metadata Extraction**: Automatically extracts technical metadata for images and audio files. - **No External API Keys**: Everything runs locally inside the container (including OCR and Transcription). ### Supported Formats | Category | Formats | | --- | --- | | **Documents** | PDF, DOCX, PPTX, TXT | | **Data** | JSON, XML, CSV, HTML | | **Spreadsheets** | XLSX | | **Images** | PNG, JPG, JPEG, WEBP, BMP, TIFF | | **Audio** | MP3, WAV, OGG, M4A, FLAC | ### Input Parameters - `urls`: A list of URLs pointing to the files you want to convert. - `performOcr`: (Default: `true`) Enable/disable OCR for images and scanned PDFs. - `extractMetadata`: (Default: `true`) Enable/disable technical metadata extraction. - `proxyConfiguration`: Use Apify Proxy if your target files are protected or geo-blocked. ### Output The Actor outputs a dataset where each item represents a converted file: - `url`: The original source URL. - `title`: The filename or detected title. - `markdown`: The full converted content in Markdown format. - `mimeType`: The detected MIME type of the file. - `metadata`: A JSON object containing technical metadata (e.g., Image dimensions, Audio duration, GPS data). #### Sample Input ```json { "urls": [ "https://example.com/document.pdf", "https://example.com/photo.jpg" ], "performOcr": true, "extractMetadata": true } ```` ### How it works 1. **Download**: The Actor downloads the file from the provided URL. 2. **Identification**: It detects the file type based on headers and extensions. 3. **Conversion**: - **PDFs** use specialized tools to preserve layout and then convert to Markdown. - **Word/PowerPoint** are transformed using robust document processors. - **Images** use advanced OCR for text and technical metadata extraction. - **Audio** uses local AI models for speech-to-text transcription. - **Web/Data** use specialized HTML and data parsers to build tables and lists. 4. **Formatting**: All outputs are normalized into valid Markdown. 5. **Storage**: Results are saved to the Apify Dataset and a `conversion` event is billed. ### Performance Note - **Transcription/OCR**: Processing large audio files or complex images can be CPU-intensive. The Actor uses optimized models for a balance between speed and accuracy. - **Memory**: For very large Excel files or PDFs, ensure the Actor has at least 2GB of memory allocated. *** **Feedback & Improvements** If you encounter a file format that isn't supported or have ideas for improvements, please leave us a message in the Issues tab! # Actor input Schema ## `urls` (type: `array`): List of URLs of files to convert to Markdown. ## `performOcr` (type: `boolean`): If enabled, images and scanned PDFs will be processed with automated OCR. ## `extractMetadata` (type: `boolean`): If enabled, technical metadata will be extracted for images and audio files. ## `proxyConfiguration` (type: `object`): Use Apify Proxy if files are behind blocks. ## `concurrency` (type: `integer`): Number of URLs to process in parallel. (Default: 2) ## Actor input object example ```json { "urls": [ { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" } ], "performOcr": true, "extractMetadata": true, "concurrency": 2 } ``` # Actor output Schema ## `results` (type: `string`): The converted Markdown results. # 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 = { "urls": [ { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("datavault/convert-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 = { "urls": [{ "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" }] } # Run the Actor and wait for it to finish run = client.actor("datavault/convert-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 '{ "urls": [ { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" } ] }' | apify call datavault/convert-to-markdown --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datavault/convert-to-markdown", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Convert To Markdown", "description": "Convert to Markdown, converts documents, spreadsheets, images (OCR), audio (transcription), and web/data files into clean Markdown. It runs fully locally, requires no API keys, and is ideal for LLMs, docs, and archiving.", "version": "1.0", "x-build-id": "mUiYmCf97Z3g8rAcH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datavault~convert-to-markdown/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datavault-convert-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datavault~convert-to-markdown/runs": { "post": { "operationId": "runs-sync-datavault-convert-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datavault~convert-to-markdown/run-sync": { "post": { "operationId": "run-sync-datavault-convert-to-markdown", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "urls" ], "properties": { "urls": { "title": "URLs to Convert", "type": "array", "description": "List of URLs of files to convert to Markdown.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "performOcr": { "title": "Perform OCR", "type": "boolean", "description": "If enabled, images and scanned PDFs will be processed with automated OCR.", "default": true }, "extractMetadata": { "title": "Extract Metadata", "type": "boolean", "description": "If enabled, technical metadata will be extracted for images and audio files.", "default": true }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Use Apify Proxy if files are behind blocks." }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Number of URLs to process in parallel. (Default: 2)", "default": 2 } } }, "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 } } } } } } } } } } ```