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from $3.00 / 1,000 pdf processeds

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PDF to Text & Markdown Extractor for RAG

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Convert PDFs from URLs or a dataset to clean text or Markdown with tables, metadata and RAG-ready chunks (page ranges + token estimates). Optional OCR for scanned pages. $0.003 per PDF; failed or password-protected PDFs are free.

Pricing

from $3.00 / 1,000 pdf processeds

Rating

0.0

(0)

Developer

Majd Hijazin

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

Categories

Developer tools

You can access the PDF to Text & Markdown Extractor for RAG 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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  "components": {
    "schemas": {
      "inputSchema": {
        "type": "object",
        "properties": {
          "pdfUrls": {
            "title": "PDF URLs",
            "type": "array",
            "description": "Direct links to PDF files (http/https). arXiv /abs/ links, Google Drive share links, Dropbox ?dl=0 links and GitHub blob links are converted to direct downloads automatically. Duplicates are removed. Up to 10,000 per run.",
            "items": {
              "type": "string"
            }
          },
          "datasetId": {
            "title": "Read URLs from dataset (optional)",
            "type": "string",
            "description": "ID or name of an Apify dataset whose items contain PDF URLs, e.g. the output of a crawler or another Actor. Combined with the list above."
          },
          "datasetUrlField": {
            "title": "URL field in the dataset",
            "type": "string",
            "description": "Field that holds the PDF URL. Dot paths (e.g. \"document.pdfUrl\") and arrays of URLs are supported.",
            "default": "url"
          },
          "outputFormat": {
            "title": "Output format",
            "enum": [
              "markdown",
              "text"
            ],
            "type": "string",
            "description": "Markdown keeps headings, lists and tables (best for LLMs and RAG). Text is plain reflowed text (tables are tab-separated).",
            "default": "markdown"
          },
          "extractTables": {
            "title": "Extract tables",
            "type": "boolean",
            "description": "Detect tables from column alignment and return them as rows (and Markdown tables). Best effort: works on most digitally-created PDFs; merged cells and borderless complex layouts may split.",
            "default": true
          },
          "chunkSize": {
            "title": "RAG chunk size (tokens)",
            "minimum": 0,
            "maximum": 8000,
            "type": "integer",
            "description": "Target maximum tokens per chunk (estimate: ~4 characters per token). Chunks follow paragraph, heading and sentence boundaries. Set 0 to disable chunks.",
            "default": 800
          },
          "chunkOverlap": {
            "title": "Chunk overlap (tokens)",
            "minimum": 0,
            "maximum": 2000,
            "type": "integer",
            "description": "Tokens repeated from the end of the previous chunk at the start of the next one. Capped at half the chunk size.",
            "default": 100
          },
          "removeHeadersFooters": {
            "title": "Remove running headers, footers and page numbers",
            "type": "boolean",
            "description": "Drops lines that repeat in the top/bottom margin of most pages, plus bare page numbers. Recommended for RAG.",
            "default": true
          },
          "includePages": {
            "title": "Include per-page text",
            "type": "boolean",
            "description": "Adds a pages[] array with the text of each page and its kind (text, ocr, needs_ocr, blank). Increases item size.",
            "default": false
          },
          "ocr": {
            "title": "OCR scanned pages",
            "type": "boolean",
            "description": "Run OCR (Tesseract, English) on pages that have no text layer. Adds $0.002 per OCR page read. Off: scanned pages are listed in pagesNeedingOcr and a fully scanned PDF gets status needs_ocr (free).",
            "default": false
          },
          "maxOcrPagesPerPdf": {
            "title": "Max OCR pages per PDF",
            "minimum": 1,
            "maximum": 500,
            "type": "integer",
            "description": "Upper bound on OCR pages per PDF to keep cost and time predictable.",
            "default": 20
          },
          "maxPages": {
            "title": "Max pages per PDF",
            "minimum": 0,
            "maximum": 2000,
            "type": "integer",
            "description": "Only the first N pages are processed (item.truncated = true when the PDF is longer). 0 = all pages, up to the hard cap of 2,000.",
            "default": 200
          },
          "maxFileSizeMb": {
            "title": "Max file size (MB)",
            "minimum": 1,
            "maximum": 200,
            "type": "integer",
            "description": "Larger files are skipped with status too_large (not charged).",
            "default": 50
          },
          "timeoutSecs": {
            "title": "Time limit per PDF (seconds)",
            "minimum": 10,
            "maximum": 900,
            "type": "integer",
            "description": "Download + extraction budget per PDF. If reached after some pages, the item is returned with status partial; with no text, status timeout (not charged).",
            "default": 180
          },
          "maxConcurrency": {
            "title": "Parallel PDFs",
            "minimum": 1,
            "maximum": 10,
            "type": "integer",
            "description": "PDFs processed at the same time. 3 fits comfortably in 1 GB; raise memory before raising this.",
            "default": 3
          }
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              "userId": {
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              "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"
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              "status": {
                "type": "string",
                "example": "READY"
              },
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                "type": "object",
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                  "origin": {
                    "type": "string",
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                    "example": 0
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                }
              },
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                "example": 0.00005
              },
              "usageUsd": {
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                "properties": {
                  "ACTOR_COMPUTE_UNITS": {
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                    "example": 0
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                    "example": 0
                  },
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                    "type": "integer",
                    "example": 0
                  },
                  "KEY_VALUE_STORE_READS": {
                    "type": "integer",
                    "example": 0
                  },
                  "KEY_VALUE_STORE_WRITES": {
                    "type": "number",
                    "example": 0.00005
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                  "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
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                  "PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
                    "type": "integer",
                    "example": 0
                  },
                  "PROXY_SERPS": {
                    "type": "integer",
                    "example": 0
                  },
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                    "type": "integer",
                    "example": 0
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}

PDF to Text & Markdown API — Tables, RAG Chunks 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 Text & Markdown Extractor for RAG 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 Text & Markdown Extractor for RAG API in Python

PDF to Text & Markdown Extractor for RAG API in JavaScript

PDF to Text & Markdown Extractor for RAG API through CLI

PDF to Text & Markdown Extractor for RAG API

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PDF Text Extractor — Text, Markdown, Tables and OCR

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[$0.003/PDF] Extract text from PDF files by URL, upload or base64: full text, per-page text, Markdown, tables and metadata. Scanned pages are read with OCR in 19 languages. Password PDFs, page ranges, RAG chunks. No fee per run.

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PDF to Markdown & JSON Extractor for LLMs

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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.

Michael Yousrie

PDF Text Extractor & Change Monitor

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Extract text, page content, metadata, and RAG-ready chunks from PDFs. Monitor content, page, and metadata changes across runs.

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hedelka/pdf-to-markdown-rag

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Paste PDF links, get clean text, per-page text, RAG-ready chunks and metadata (title, author, dates, page count). Scanned pages are read with Tesseract OCR in 7 languages. Failed, encrypted and empty files are free. Free while in launch; planned price $2 per 1,000 PDFs.

Adam Hale

PDF Text Extractor + OCR for Scanned PDFs

sumitr_mardy/pdf-text-extractor

Extract text from PDFs by URL, with automatic OCR for scanned pages. One flat row per URL, never null, only successful PDFs billed. Fast, low-memory engine; Google Drive, Dropbox and arXiv links accepted. Built for pipelines and AI agents.

Sumitr Mardy

PDF Text Extractor: Text and Pages from PDF Links | $1/1K

one_dollar_store/pdf-text-extractor-scraper

$1 per 1,000 results, platform usage included. Extracts text, page count and document details (title, author, dates) from PDF files at web links, one row per PDF or per page. Text layer only, no OCR. Up to 25 MB per file. No login or API key.

One Dollar Store

PDF to Text & Markdown Converter (Tables + OCR Fallback)

mrlarryjohnson/pdf-to-text-markdown

Convert PDFs to clean text and Markdown in bulk: headings, bullet lists and tables as Markdown pipe tables, with automatic OCR for scanned pages (7 languages). Ideal for RAG/LLM pipelines, search indexing and data entry. Pay per page; failed files are free.