Character Text Splitter consultants

Q: What chunk size should I use for Character Text Splitter in n8n?

It depends on your downstream model and use case. For OpenAI embeddings, 500-1000 characters with 50-100 character overlap is a solid starting point. Larger chunks retain more context but reduce retrieval precision in vector search workflows.

Q: How does Character Text Splitter differ from Token Text Splitter?

Character Text Splitter counts raw characters, while Token Text Splitter counts model-specific tokens. Character splitting is simpler and model-agnostic, making it a good default choice. Use token splitting when you need exact control over token counts for a specific model.

Q: Can I use Character Text Splitter with PDFs in n8n?

Yes. You first extract text from the PDF using a node like Extract from File, then pass the extracted text into Character Text Splitter. The splitter works on any plain text input regardless of the original file format.

Q: What is chunk overlap and why does it matter?

Chunk overlap means adjacent chunks share some text at their boundaries. This prevents important information from being cut off mid-sentence or mid-paragraph. A typical overlap of 10-20% of your chunk size ensures continuity without excessive duplication.

Q: Does Character Text Splitter preserve formatting like headings or bullet points?

The node splits based on character boundaries and separator characters, so formatting is only preserved within individual chunks. If you need structure-aware splitting, consider using separators like newline characters to break at paragraph boundaries.

Q: How does Character Text Splitter fit into a RAG pipeline?

It sits between your document ingestion step and your embedding generation step. Documents come in, get split into chunks, each chunk gets embedded into a vector, and those vectors get stored in a database like Pinecone or Qdrant for later retrieval by your AI agent.

We can help you automate your business with Character Text Splitter and hundreds of other systems to improve efficiency and productivity. Get in touch if you’d like to discuss implementing Character Text Splitter.

Get in touch

Book a call

About Character Text Splitter

Character Text Splitter is an n8n node that breaks large text documents into smaller, manageable chunks based on character count. When you feed a massive PDF, webpage, or document into an AI model, it often exceeds token limits or produces poor results because the context window is too large. This node solves that by splitting text at logical breakpoints while respecting your specified chunk size and overlap settings.

For teams building retrieval-augmented generation (RAG) pipelines or document processing workflows, chunking strategy directly affects output quality. Too large and your embeddings lose specificity. Too small and you lose context. Character Text Splitter gives you precise control over chunk size, overlap between chunks, and separator characters — letting you fine-tune how your documents get processed before they hit a vector database or language model.

Osher Digital uses this node extensively in automated data processing pipelines and AI agent builds. In our medical document classification project, getting the chunk size right was critical to accurate categorisation of clinical records. If you are working with large-scale document ingestion and need help tuning your text splitting strategy, our AI consulting team can help you get it right the first time.

Character Text Splitter FAQs

Frequently Asked Questions

Common questions about how Character Text Splitter consultants can help with integration and implementation

What chunk size should I use for Character Text Splitter in n8n?

How does Character Text Splitter differ from Token Text Splitter?

Can I use Character Text Splitter with PDFs in n8n?

What is chunk overlap and why does it matter?

Does Character Text Splitter preserve formatting like headings or bullet points?

How does Character Text Splitter fit into a RAG pipeline?

How it works

We work hand-in-hand with you to implement Character Text Splitter

As Character Text Splitter consultants we work with you hand in hand build more efficient and effective operations. Here’s how we will work with you to automate your business and integrate Character Text Splitter with integrate and automate 800+ tools.

Step 1

Add Character Text Splitter to your workflow

Open your n8n workflow editor and add the Character Text Splitter node from the node panel. Connect it downstream from whichever node provides your source text, whether that is an HTTP Request, Read File, or database query node.

Step 2

Configure your chunk size

Set the chunk size parameter to control how many characters each text segment will contain. Start with 1000 characters for general-purpose use, and adjust based on your embedding model requirements and retrieval accuracy.

Step 3

Set the chunk overlap

Define how many characters should overlap between consecutive chunks. An overlap of 100-200 characters works well for most use cases, ensuring no critical information is lost at chunk boundaries.

Step 4

Choose your separator characters

Specify which characters the splitter should prefer as break points. Newlines and double newlines are common choices that keep paragraphs intact. The node will try to split at these points before falling back to the character limit.

Step 5

Connect to your embedding or processing node

Link the Character Text Splitter output to your next workflow step — typically an embeddings node like OpenAI Embeddings or a vector store insert node. Each chunk will be processed individually through the rest of your pipeline.

Step 6

Test with a sample document and refine

Run the workflow with a representative document to check chunk quality. Review the output to verify chunks are coherent and appropriately sized, then adjust chunk size and overlap until retrieval or processing accuracy meets your needs.

Transform your business with Character Text Splitter

Unlock hidden efficiencies, reduce errors, and position your business for scalable growth. Contact us to arrange a no-obligation Character Text Splitter consultation.

Get in touch

Book a call