How does the PDF chunker split content?

It extracts words in page order, creates fixed-size word chunks, and carries the selected number of overlapping words into the next chunk. Each chunk records every source page represented in its text.

Which export format should I use for RAG?

JSONL is convenient for line-oriented ingestion, JSON is useful for application code and inspection, and Markdown is easiest for people to review. All formats preserve chunk IDs, page references, text, word counts, and estimated tokens.

Does this tool create embeddings?

No. It prepares deterministic source chunks only. You still choose an embedding model, vector database, metadata schema, retrieval method, and evaluation process in your own RAG pipeline.

What chunk size should I choose?

There is no universal value. Smaller chunks improve retrieval precision while larger chunks retain more context. Start around 300 to 700 words with 10 to 20 percent overlap, then evaluate retrieval quality on real questions.

Free tools

Split a PDF into RAG-Ready Chunks

Choose chunk size and overlap, preserve source-page references, preview the output, and export Markdown, JSON, or JSONL.

Copy-paste outputs

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Tool 01

PDF to RAG Chunks Converter

Create overlapping, page-referenced chunks and export Markdown, JSON, or JSONL.

Prepare deterministic RAG chunks

Split extracted PDF text into overlapping chunks while retaining source-page references.

Drop a PDF here or choose a file

Processed locally in your browser · up to 25 MB · no OCR

How it works

PDF to RAG Chunks Converter: methodology and worked example

How this tool computes its result

After local PDF text extraction, the converter creates an ordered stream of words tagged with their source page number. It slices that stream into a configurable number of words per chunk, advances by chunk size minus overlap, and records all unique PDF pages represented in each chunk. The preview includes chunk ID, pages, words, estimated tokens, and text. JSON and JSONL exports preserve those fields as structured records; Markdown exports create one headed section per chunk with its source pages.

Worked example

For a 9,400-word PDF, selecting 500 words per chunk with 75 words of overlap advances 425 new words at a time. A chunk that begins near the bottom of page 4 and ends on page 5 records pages [4, 5], while the next chunk repeats the final 75 words for continuity. The exported JSONL contains one valid JSON object per line, making it suitable for a later ingestion script without embedding or uploading anything from this page.

When not to use this tool

This is a deterministic baseline chunker, not a semantic segmentation or complete RAG pipeline. It does not detect topic boundaries, generate embeddings, deduplicate headers and footers, choose a vector store, tune retrieval, rerank results, or evaluate answer faithfulness. Complex PDFs should be cleaned before ingestion, especially when extracted reading order is already incorrect.

Common mistakes

- Setting overlap equal to or larger than chunk size. The tool clamps overlap below the effective chunk size.
- Sending chunks directly to production retrieval without checking repeated headers, footers, broken tables, and extraction order.
- Assuming smaller chunks always improve quality. They can lose the context needed to answer multi-sentence questions.

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