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PDF to RAG-Ready Dataset

Try for free

Convert public text-based PDFs into clean, token-aware RAG chunks with Markdown, page references, metadata, stable document IDs, and ready-to-use Apify Dataset output.

Pricing

from $30.00 / 1,000 document processeds

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0.0

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Developer

UtilityForgeLab

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20 days ago

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PDF to RAG-Ready Dataset API

Below, you can find a list of relevant HTTP API endpoints for calling the PDF to RAG-Ready Dataset Actor. For this, you’ll need an Apify account. Replace <YOUR_API_TOKEN> in the URLs with your Apify API token, which you can find under API & Integrations in Apify Console. For details, see the API reference.

Run Actor

POST
https://api.apify.com/v2/actors/utilityforgelab~pdf-to-rag-ready-dataset/runs?token=<YOUR_API_TOKEN>

Note: By adding the method=POST query parameter, this API endpoint can be called using a GET request and thus used in third-party webhooks. Please refer to our Run Actor API documentation.

Run Actor synchronously and get dataset items

POST
https://api.apify.com/v2/actors/utilityforgelab~pdf-to-rag-ready-dataset/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>

Note: This endpoint supports both POST and GET request methods. However, only the POST method allows you to pass input data. For more information, please refer to our Run Actor synchronously and get dataset items API documentation.

Get Actor

GET
https://api.apify.com/v2/actors/utilityforgelab~pdf-to-rag-ready-dataset?token=<YOUR_API_TOKEN>

For more information, please refer to our Get Actor API documentation.

Actors can be used to scrape web pages, extract data, or automate browser tasks. Use the PDF to RAG-Ready Dataset API programmatically via the Apify API.

You can choose from:

PDF to RAG-Ready Dataset API in Python

PDF to RAG-Ready Dataset API in JavaScript

PDF to RAG-Ready Dataset API through CLI

PDF to RAG-Ready Dataset OpenAPI definition

You can start PDF to RAG-Ready Dataset with the Apify API by sending an HTTP POST request to the Run Actorendpoint. An Actor’s input and its content type can be passed as a payload of the POST request, and additional options can be specified using URL query parameters. The PDF to RAG-Ready Dataset is identified within the API by its ID, which is the creator’s username and the name of the Actor.

When the PDF to RAG-Ready Dataset run finishes you can list the data from its default dataset(storage) via the API or you can preview the data directly on Apify Console.

PDF URL to Markdown, Tables & RAG Extractor

thescrapelab/Apify-PDF-url-scraper

Extract clean Markdown, page text, tables, metadata, summaries, and AI-ready RAG chunks from PDF URLs.

Inus Grobler

Website to RAG Dataset

sebastian-actors/website-to-rag-dataset

Convert public websites, docs, blogs, and XML sitemaps into clean Markdown, structured metadata, and stable chunks for RAG pipelines and vector databases.

Sebastián S

PDF to Markdown & JSON (RAG-Ready)

basisweb/pdf-to-markdown-rag

Convert PDFs to clean Markdown and structured JSON (text + tables) for RAG, LLMs, and vector DBs. Batch URLs, pay per page.

BasisWeb

PDF Citation Chunker

skilled_glee/pdf-citation-chunker

Deterministically convert public embedded-text PDFs into page-aware, citation-ready JSON chunks with provenance, SHA-256 stable IDs, and RAG-ready output for API, MCP, and AI-agent workflows.

Dakota Myers

YouTube Transcript to RAG Dataset

ghostgrid/youtube-transcript-to-rag

Extract YouTube transcripts and convert them into RAG-ready chunks with timestamps, metadata, and content hashes.

GhostGrid

News to Markdown — RAG-Ready News Chunks API

nexgendata/news-announcements-rag-markdown

Convert news and announcements into RAG-ready Markdown chunks. Clean JSON for PR, media-monitoring teams and AI agents.

NexGenData

Sitemap to RAG Dataset

ghostgrid/sitemap-to-rag-dataset

Convert any website sitemap into an LLM-ready dataset for RAG pipelines.

GhostGrid

Doc-to-Markdown/JSON RAG Prep - Convert PDF & DOCX for RAG

bigjoecoding/doc-to-markdown-json-rag-prep

Convert PDF, DOCX, PPTX and webpages to clean Markdown and RAG-ready JSON chunks for your embedding pipeline. No LLM cost. $0.03 per document.

Joseph Curry

PDF to RAG Markdown Chunks for Embeddings

awesome_highboy/docforge

Convert PDFs into token-bounded Markdown chunks for RAG, embeddings, and vector databases (Pinecone, Chroma, Weaviate, Qdrant). Set maxTokens + overlap; get clean chunks with page number, token count, and SHA-256 content hash for dedup. JSON dataset ready for any LLM pipeline.