How to split pdf text into semantic chunks for ai processing
- Step 1Upload the PDF — Drop the document into the semantic chunker.
- Step 2Select semantic chunking mode — Enable paragraph or section-level splitting rather than fixed character counts.
- Step 3Download the JSON chunks — Save the semantically split text chunks.
- Step 4Embed and index — Pass each chunk to your embedding model and store in a vector database.
Frequently asked questions
How does semantic chunking differ from fixed-size chunking?+
Fixed-size chunking splits at character or token limits regardless of content. Semantic chunking splits at natural language boundaries, keeping related sentences together.
What embedding models work best with semantically chunked PDF text?+
All standard embedding models (OpenAI, Cohere, HuggingFace Sentence Transformers) benefit from semantic chunks — they produce more meaningful embeddings for coherent text units.
Should I include chunk metadata (page number, section title)?+
Yes — include page number and section heading as metadata on each chunk. This allows retrieved chunks to cite their source accurately in LLM responses.
Privacy first
All PDF processing runs locally in your browser using PDF-lib and pdf.js. No file is ever uploaded — only metadata counters are saved for signed-in dashboard stats.