# PDF to Markdown & JSON (RAG-Ready) (`basisweb/pdf-to-markdown-rag`) Actor Convert PDFs to clean Markdown and structured JSON (text + tables) for RAG, LLMs, and vector DBs. Batch URLs, pay per page. - **URL**: https://apify.com/basisweb/pdf-to-markdown-rag.md - **Developed by:** [BasisWeb](https://apify.com/basisweb) (community) - **Categories:** AI, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 page processeds 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 & JSON (RAG-Ready) Convert PDFs into clean **Markdown** and structured **JSON** (text + tables) you can drop straight into a RAG pipeline, an LLM prompt, or a vector database. Give it a list of PDF URLs; it returns one record per page. Think of it as the **PDF companion to web crawlers** like [Website Content Crawler](https://apify.com/apify/website-content-crawler) and [RAG Web Browser](https://apify.com/apify/rag-web-browser): point it at the PDFs your crawler discovers and get clean, page-level text + tables back. ### What it does - Downloads each PDF by URL. - Extracts text using the PDF's character layout (natural reading order for standard single-column pages) and detects tables, rendering them as Markdown tables **and** structured rows. - Returns **one dataset item per page**. `url`, `page`, `totalPages`, `tableCount`, and `ok` are always present; `markdown` and/or `text` + `tables` are included depending on `outputFormat` (default `both` returns all of them). ### Use cases - **RAG ingestion**: turn reports, manuals, and whitepapers into clean, page-level Markdown chunks for a vector database. - **LLM document Q&A**: feed structured text and tables to an LLM without copy-paste cleanup. - **Extract tables from PDF**: pull tables out as both Markdown and structured rows. - **Agent pipelines**: chain it after a web crawler so an AI agent can read the PDFs it finds. ### Input | Field | Type | Default | Description | |---|---|---|---| | `pdfUrls` | array of URLs | _(required)_ | Direct links to the PDFs to convert. | | `extractTables` | boolean | `true` | Detect tables and render them as Markdown + structured rows. | | `outputFormat` | `markdown` \| `json` \| `both` | `both` | What each result includes. | #### Example input ```json { "pdfUrls": ["https://example.com/report.pdf"], "extractTables": true, "outputFormat": "both" } ```` #### Example output (one item per page) ```json { "url": "https://example.com/report.pdf", "page": 1, "totalPages": 12, "markdown": "Q3 Report\nRevenue grew 18% YoY...\n\n| Region | Revenue |\n| --- | --- |\n| NA | $4.1M |", "text": "Q3 Report\nRevenue grew 18% YoY...", "tables": [[["Region", "Revenue"], ["NA", "$4.1M"]]], "tableCount": 1, "ok": true } ``` The example above uses the default `outputFormat: "both"`, so it includes every field. Each item also includes `ok` (set to `false` on a failed URL or page, with an `error` field explaining why) and, on pages with no extractable text or tables, a `note` flag. ### Pricing (pay-per-event) - **Run start**: a small flat fee per run (Apify's built-in start event). - **Page processed**: charged per page that returns real content (text and/or tables). **Pages with no extractable text or tables are returned with a `note` and are NOT charged.** Failed URLs and failed pages are reported with an error and are never charged. Your spending limit is always respected: set a max cost per run and the Actor stops once it's reached. ### Use it with AI agents (Apify MCP) This Actor is available as a tool for AI agents through Apify's MCP server (`mcp.apify.com`). An agent can call `basisweb/pdf-to-markdown-rag` to convert a PDF to Markdown mid-task, then chain the page-level output into the next step. The only required input is `pdfUrls`, so an agent can invoke it in one shot, and the output schema tells the agent exactly which fields come back (`markdown`, `text`, `tables`, `tableCount`) before it spends a credit. ### Honest notes - This handles **digital, text-based PDFs**. **Scanned PDFs** (image-only, no text layer) are not OCR'd in this version; those pages come back with a `note` instead of text and are **not charged**. OCR is planned for a future version. - Each PDF must be under **50 MB**. Very large or table-heavy PDFs run best at 2 GB memory or higher (the 50 MB limit caps file size, not parsing memory). - You can also parse PDFs locally for free with open-source libraries. This is the **no-setup, hosted, pay-per-page** version for pipelines that just want it as an API. ### FAQ **Does it work on scanned PDFs?** Not in this version. Image-only pages with no text layer come back with a `note` and are not charged. OCR is planned for a future version. **What does it return per page?** One item per page with `markdown` and/or `text` + `tables` (depending on `outputFormat`), plus `url`, `page`, `totalPages`, and `tableCount`. **How is it priced?** A small per-run start fee plus a per-page fee, charged only for pages with real content. Blank, scanned, and failed pages are never charged. **Can I use it for RAG?** Yes, that is the point. The Markdown is clean and page-scoped, so you can chunk and embed it directly. **How is this different from parsing PDFs locally?** You can parse PDFs locally for free with open-source libraries. This is the no-setup, hosted, pay-per-page version for pipelines that just want it as an API. ### Run locally ```bash apify run ``` ### Deploy ```bash apify login apify push ``` # Actor input Schema ## `pdfUrls` (type: `array`): Direct URLs to the PDF files you want to convert. Each PDF is processed page by page. ## `extractTables` (type: `boolean`): Detect tables on each page and render them as Markdown tables plus structured rows. Turn off for faster text-only extraction. ## `outputFormat` (type: `string`): What to include in each result: clean Markdown, structured JSON (raw text + tables), or both. ## Actor input object example ```json { "pdfUrls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ], "extractTables": true, "outputFormat": "both" } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "pdfUrls": [ "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("basisweb/pdf-to-markdown-rag").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 = { "pdfUrls": ["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("basisweb/pdf-to-markdown-rag").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 '{ "pdfUrls": [ "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" ] }' | apify call basisweb/pdf-to-markdown-rag --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=basisweb/pdf-to-markdown-rag", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF to Markdown & JSON (RAG-Ready)", "description": "Convert PDFs to clean Markdown and structured JSON (text + tables) for RAG, LLMs, and vector DBs. Batch URLs, pay per page.", "version": "0.1", "x-build-id": "O5gfmqJXeThnbFsyb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/basisweb~pdf-to-markdown-rag/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-basisweb-pdf-to-markdown-rag", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/basisweb~pdf-to-markdown-rag/runs": { "post": { "operationId": "runs-sync-basisweb-pdf-to-markdown-rag", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/basisweb~pdf-to-markdown-rag/run-sync": { "post": { "operationId": "run-sync-basisweb-pdf-to-markdown-rag", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "pdfUrls" ], "properties": { "pdfUrls": { "title": "PDF URLs", "minItems": 1, "maxItems": 1000, "type": "array", "description": "Direct URLs to the PDF files you want to convert. Each PDF is processed page by page.", "items": { "type": "string" } }, "extractTables": { "title": "Extract tables", "type": "boolean", "description": "Detect tables on each page and render them as Markdown tables plus structured rows. Turn off for faster text-only extraction.", "default": true }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "json", "both" ], "type": "string", "description": "What to include in each result: clean Markdown, structured JSON (raw text + tables), or both.", "default": "both" } } }, "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 } } } } } } } } } } ```