Pricing

from $0.50 / 1,000 page processed (text layer)s

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PDF to Markdown, Tables & RAG Chunks (with OCR)

Try for free

Convert PDF, DOCX and image files to clean Markdown, tables as CSV and RAG-ready chunks with page numbers. Scanned pages are read with OCR. No API keys, pay per page, built for AI agents and LLM pipelines.

Pricing

from $0.50 / 1,000 page processed (text layer)s

Rating

0.0

(0)

Developer

Killis Haraway

Actor stats

Bookmarked

Total users

Monthly active users

6 hours ago

Last modified

Categories

Developer tools

You can access the PDF to Markdown, Tables & RAG Chunks (with OCR) programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in API & Integrations in Apify Console.

Python

JavaScript

CLI

OpenAPI

HTTP

MCP

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          "documentUrls": {
            "title": "Document URLs",
            "type": "array",
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            "items": {
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            "type": "string",
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          "documents": {
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            "type": "array",
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          "outputMode": {
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            "enum": [
              "documents",
              "chunks"
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            "type": "string",
            "description": "documents: one dataset item per document (Markdown, tables, chunks, metadata). chunks: one item per chunk, ready for a vector database.",
            "default": "documents"
          },
          "pages": {
            "title": "Pages (PDF)",
            "type": "string",
            "description": "Page selection, 1-based, e.g. 1-3,7. Leave empty for all pages."
          },
          "ocr": {
            "title": "OCR",
            "enum": [
              "auto",
              "force",
              "off"
            ],
            "type": "string",
            "description": "auto: OCR only pages without a text layer (scans). force: OCR every page. off: never (scanned pages come back empty).",
            "default": "auto"
          },
          "ocrLanguage": {
            "title": "OCR language",
            "enum": [
              "eng",
              "spa",
              "fra",
              "deu",
              "por",
              "ita",
              "eng+spa",
              "eng+fra"
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            "type": "string",
            "description": "Tesseract language code. Combine with + (e.g. eng+spa).",
            "default": "eng"
          },
          "tables": {
            "title": "Extract tables",
            "type": "boolean",
            "description": "Detect tables with drawn cell borders in PDFs (and all DOCX tables) and return them as rows and CSV.",
            "default": true
          },
          "chunking": {
            "title": "Make RAG chunks",
            "type": "boolean",
            "description": "Split each document into chunks that keep the nearest heading and the page range.",
            "default": true
          },
          "chunkMaxChars": {
            "title": "Chunk size (characters)",
            "minimum": 200,
            "maximum": 20000,
            "type": "integer",
            "description": "Target maximum characters per chunk.",
            "default": 1500
          },
          "chunkOverlapChars": {
            "title": "Chunk overlap (characters)",
            "minimum": 0,
            "maximum": 2000,
            "type": "integer",
            "description": "Characters repeated from the end of the previous chunk.",
            "default": 150
          },
          "includeMarkdown": {
            "title": "Include full Markdown in results",
            "type": "boolean",
            "description": "Turn off to keep dataset items small when you only need chunks or tables.",
            "default": true
          },
          "saveMarkdownFiles": {
            "title": "Save .md files",
            "type": "boolean",
            "description": "Also save each document's Markdown as a file in the key-value store.",
            "default": true
          },
          "maxPagesPerDocument": {
            "title": "Max pages per document",
            "minimum": 1,
            "maximum": 5000,
            "type": "integer",
            "description": "Pages beyond this are skipped (and not charged).",
            "default": 500
          },
          "failOnEmpty": {
            "title": "Fail the run if no text is found",
            "type": "boolean",
            "description": "Marks the run failed (not succeeded-but-empty) when no document yields any text.",
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          }
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PDF to Markdown, Tables & RAG Chunks (with OCR) OpenAPI definition

OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.

OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.

By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.

You can download the OpenAPI definitions for PDF to Markdown, Tables & RAG Chunks (with OCR) from the options below:

OpenAPI.json

If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.

You can also check out our other API clients:

PDF to Markdown, Tables & RAG Chunks (with OCR) API in Python

PDF to Markdown, Tables & RAG Chunks (with OCR) API in JavaScript

PDF to Markdown, Tables & RAG Chunks (with OCR) API through CLI

PDF to Markdown, Tables & RAG Chunks (with OCR) API

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