📝 Input Text
What Can the Text Chunk Splitter Do?
The Text Chunk Splitter is a free online tool designed for AI/LLM/RAG scenarios, intelligently splitting long text into shorter segments. It supports four chunking strategies — by character count, token count, paragraph, and sentence — each with configurable chunk size and overlap length to ensure semantic continuity. All processing is done locally in your browser with no data uploaded to any server.
Key Features
- Four Chunking Strategies: By character count, by token count (approximate GPT Tokenizer), by paragraph, by sentence — flexible for different use cases
- Configurable Overlap: Set overlap between chunks to prevent key information from being cut at boundaries
- Token Estimation: Approximate GPT Tokenizer token counting to match model context window limits
- Real-time Statistics: Display chunk count, total characters, total tokens, average chunk length
- Multiple Export Formats: Copy as JSON, download as JSON or TXT files
- Per-chunk View: Each chunk displayed independently with index, character count, and token count
Tutorial
1. Paste your long text in the input area.
2. Select a chunking strategy: by character for simple splitting, by token to match LLM context limits, by paragraph/sentence to preserve semantic completeness.
3. Set chunk size (maximum length per chunk) and overlap length (shared content between adjacent chunks).
4. Click "Split Text" to see results, each chunk showing its index, character count, and token count.
5. Copy or download chunked results for your RAG pipeline or LLM processing.
Use Cases
Case 1: RAG (Retrieval-Augmented Generation)
Split documents from your knowledge base into chunks, store them in a vector database. When users query, retrieve the most relevant chunks and feed them to the LLM for answer generation.
Case 2: LLM Long Text Processing
When text exceeds the model's context window limit, split it into chunks and process each chunk sequentially, then merge the results.
Case 3: Data Preprocessing
In ML data preprocessing, split long documents into training samples. Paragraph or sentence chunking preserves semantic completeness.
Case 4: Content Summarization
Summarize each chunk of a long article individually, then combine the summaries for a complete overview, avoiding LLM missing key information.
Extended Knowledge
Text chunking is a core preprocessing step in RAG systems. Common strategies include: fixed-size chunking (simple but may break semantics), sentence-based chunking (preserves sentence integrity but uneven sizes), paragraph-based chunking (preserves paragraph integrity but may be too long), and recursive chunking (LangChain's RecursiveCharacterTextSplitter, recursively splitting by paragraph → sentence → character). Overlap is key for maintaining semantic continuity between chunks, typically set to 10-20% of chunk size. For token counting, GPT models use BPE Tokenizer where 1 Chinese character ≈ 1-2 tokens and 1 English word ≈ 1 token; this tool uses an approximate estimation algorithm.
FAQ
What is text chunking?
Text chunking splits long text into shorter segments, commonly used in RAG (Retrieval-Augmented Generation), LLM context window management, and vector database indexing.
What is the difference between token and character chunking?
Token chunking splits by AI model token count (1 Chinese char ≈ 1-2 tokens, 1 English word ≈ 1 token), more accurately matching model context limits. Character chunking splits by character count, simpler but may not match actual model consumption.
Why is overlap needed?
Overlap ensures adjacent chunks share some content, preventing key information from being cut at chunk boundaries. Typically 10-20% overlap rate maintains semantic continuity.
Is my data uploaded to a server?
No. This tool uses pure frontend technology. All chunking calculations are done locally in your browser. Your data never leaves your device, even offline.
What chunking strategies are supported?
Four strategies: by character count, by token count (approximate GPT Tokenizer), by paragraph, and by sentence. Each strategy supports configurable chunk size and overlap length.
Can I export the chunked results?
Yes. Copy individual chunks, copy all as JSON, download as JSON or TXT files for direct use in RAG pipelines.