# PDF to Markdown & JSON Extractor — LLM-Ready (`f0rty7even/pdf-extractor`) Actor Turn any PDF URL into clean, LLM-ready Markdown and structured JSON. Extracts text + tables + document metadata for RAG, agents, and fine-tuning. No AGPL components. - **URL**: https://apify.com/f0rty7even/pdf-extractor.md - **Developed by:** [F0rty7even](https://apify.com/f0rty7even) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 page-parseds 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 Extractor — LLM-Ready **Turn any PDF into clean, LLM-ready Markdown and structured JSON.** Point this **PDF to JSON** / **PDF to Markdown** converter at one or many PDF URLs and get back tidy Markdown (text **plus tables**), plain text, and document metadata — one structured record per PDF. Built for feeding **LLMs, RAG pipelines, AI agents, and fine-tuning datasets** clean document content instead of raw, messy PDF bytes. No headless browser, no external services — just fast, reliable extraction. ### What it does - **PDF to Markdown** — converts a PDF's text into clean Markdown ready to drop into an LLM prompt or vector database. - **Table extraction** — detects tables and renders them as Markdown **pipe tables**, so structure survives the conversion. - **Metadata** — title, author, subject, keywords, producer, and creation/modification dates from the PDF's own info dictionary. - **Per-document or per-page output** — one record per PDF by default, or one record per page (`splitPages`) for easy RAG chunking. - **Page ranges & caps** — extract only the pages you need (`pageRange`) and bound cost with `maxPagesPerPdf`. - **Structured output** — exportable to JSON, JSONL, CSV, or Excel, or via the Apify API. ### Use cases - Build a **RAG knowledge base** from reports, papers, manuals, or contracts. - Feed **LLM agents** clean document text instead of raw PDF. - Assemble **fine-tuning / training datasets** from public PDFs. - Convert **research papers, invoices, or datasheets** into structured, queryable data. ### Input | Field | Description | |---|---| | `startUrls` | Direct links to the PDF files to extract. | | `outputFormat` | `markdown` (LLM-ready) or `text`. | | `extractTables` | Detect tables and render them as Markdown pipe tables. | | `splitPages` | Output one item per page instead of one per document. | | `pageRange` | Pages to extract, e.g. `1-10` or `3` (empty = all). | | `maxPagesPerPdf` | Hard cap on pages parsed per document (main cost lever). | | `maxFileSizeMb` | Skip PDFs larger than this, without charging. | ### Output Each PDF becomes one dataset item (or one per page with `splitPages`): ```json { "url": "https://arxiv.org/pdf/1706.03762", "title": "Attention Is All You Need", "content": "## Page 1\n\nClean markdown of the page text...\n\n| Layer | Complexity |\n| --- | --- |\n| Self-Attention | O(n²·d) |", "format": "markdown", "wordCount": 8123, "pageCount": 15, "totalPages": 15, "metadata": { "author": "Vaswani et al.", "creationDate": "D:20170606", "producer": "pdfTeX", "subject": null, "keywords": null } } ```` ### Pricing Pay-per-result: you're charged **per page successfully parsed** — no monthly fee, and no charge for PDFs that fail, are password-protected, are too large, or have no extractable text. ### Notes - Works on **text-based PDFs** (papers, reports, docs, invoices, datasheets). **Scanned / image-only PDFs** have no text layer; they're detected and skipped free. **OCR is on the roadmap** (future toggle). - Password-protected and corrupt PDFs are reported as error records (not charged). - Processes **public PDF URLs**; it does not log in or bypass access controls. ### Licensing Built entirely on permissively licensed libraries — **pdfplumber (MIT)**, **pdfminer.six (MIT)**, **pypdfium2 (BSD/Apache)**, and **Pillow**. **No AGPL components.** ### FAQ **Does it keep tables?** Yes — with `extractTables` on and Markdown output, detected tables are rendered as Markdown pipe tables beneath the page text. **Can I extract just some pages?** Yes — set `pageRange` (e.g. `1-5`) and/or `maxPagesPerPdf`. **What about scanned PDFs?** They have no text layer, so v1 skips them for free (no charge). OCR support is planned. **What formats can I export?** JSON, JSONL, CSV, or Excel, or via the Apify API. # Actor input Schema ## `startUrls` (type: `array`): Direct links to PDF files to extract. Each becomes one dataset item (or one item per page if 'One item per page' is on). ## `outputFormat` (type: `string`): Content format returned per PDF. ## `extractTables` (type: `boolean`): Detect tables and render them as Markdown pipe tables (Markdown format only). ## `splitPages` (type: `boolean`): Output one dataset item per page instead of one per document. Useful for chunking into a vector DB. ## `pageRange` (type: `string`): Pages to extract, e.g. "1-10" or "3". Leave empty for all pages. ## `maxPagesPerPdf` (type: `integer`): Hard cap on pages parsed per document (main cost lever). ## `maxFileSizeMb` (type: `integer`): Skip PDFs larger than this, without charging. ## Actor input object example ```json { "startUrls": [ { "url": "https://arxiv.org/pdf/1706.03762" } ], "outputFormat": "markdown", "extractTables": true, "splitPages": false, "maxPagesPerPdf": 100, "maxFileSizeMb": 50 } ``` # Actor output Schema ## `extractedPdfs` (type: `string`): Clean content and metadata for every successfully parsed PDF. Export as JSON, JSONL, CSV, or Excel. # 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 = { "startUrls": [ { "url": "https://arxiv.org/pdf/1706.03762" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("f0rty7even/pdf-extractor").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 = { "startUrls": [{ "url": "https://arxiv.org/pdf/1706.03762" }] } # Run the Actor and wait for it to finish run = client.actor("f0rty7even/pdf-extractor").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 '{ "startUrls": [ { "url": "https://arxiv.org/pdf/1706.03762" } ] }' | apify call f0rty7even/pdf-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=f0rty7even/pdf-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PDF to Markdown & JSON Extractor — LLM-Ready", "description": "Turn any PDF URL into clean, LLM-ready Markdown and structured JSON. Extracts text + tables + document metadata for RAG, agents, and fine-tuning. No AGPL components.", "version": "0.1", "x-build-id": "QWtDhFxssTurxNA62" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/f0rty7even~pdf-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-f0rty7even-pdf-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/f0rty7even~pdf-extractor/runs": { "post": { "operationId": "runs-sync-f0rty7even-pdf-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/f0rty7even~pdf-extractor/run-sync": { "post": { "operationId": "run-sync-f0rty7even-pdf-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "PDF URLs", "type": "array", "description": "Direct links to PDF files to extract. Each becomes one dataset item (or one item per page if 'One item per page' is on).", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "outputFormat": { "title": "Output format", "enum": [ "markdown", "text" ], "type": "string", "description": "Content format returned per PDF.", "default": "markdown" }, "extractTables": { "title": "Extract tables", "type": "boolean", "description": "Detect tables and render them as Markdown pipe tables (Markdown format only).", "default": true }, "splitPages": { "title": "One item per page", "type": "boolean", "description": "Output one dataset item per page instead of one per document. Useful for chunking into a vector DB.", "default": false }, "pageRange": { "title": "Page range", "type": "string", "description": "Pages to extract, e.g. \"1-10\" or \"3\". Leave empty for all pages." }, "maxPagesPerPdf": { "title": "Max pages per PDF", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Hard cap on pages parsed per document (main cost lever).", "default": 100 }, "maxFileSizeMb": { "title": "Max file size (MB)", "minimum": 1, "maximum": 500, "type": "integer", "description": "Skip PDFs larger than this, without charging.", "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 } } } } } } } } } } ```