Extract text from a public PDF for RAG
Textokit
Extract the embedded text of a public, text-based PDF page by page, ready for retrieval-augmented generation (RAG), summarization or indexing. Each page keeps its page number and character counts, with truncation warnings when a limit applies. Replace the example URL with your own PDF. Scanned PDFs need OCR and are not supported. You pay only for documents and pages that were extracted.
Input
Output fields
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The PDF to Text & HTML to Markdown for LLMs and RAG Actor is ready to run with the inputs already set for "Extract text from a public PDF for RAG". Sign up or log in and you'll land in Apify Console with a copy of this exact task.
Start the "Extract text from a public PDF for RAG" task
The PDF to Text & HTML to Markdown for LLMs and RAG Actor will start running based on the input automatically. You can also change all the input fields, like single document url, output format, and more, to match your specific use case.
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Monitor the progress in real time. You will be notified as soon as your dataset is complete and ready for review. Each result includes data such as url, status, document type, and more.
Integrate into your workflow
The final output is delivered in JSON, CSV, or Excel format, ready to be plugged into your workflow. You can also run the "Extract text from a public PDF for RAG" task via API.
No. It reads the embedded text layer only and has no OCR. Pages without embedded text come back empty with a warning, and the document is still charged as an extracted document.
USD 0.005 per successfully extracted document plus USD 0.001 per processed PDF page. A three-page PDF costs USD 0.008 and a ten-page PDF USD 0.015. Failed downloads and failed extractions are not charged.
Yes. Put up to 50 public HTTPS URLs in the Document URLs field instead of the single URL. Each document gets its own character budget, and PDFs can have up to 200 pages.