# Character splitter Node

### Overview

The Character Splitter Node splits text or documents into smaller chunks using a recursive character-based approach. It's particularly useful for preparing text for LLMs that have token limits or when you need to process long documents in smaller segments.

Usage cost: 1 credit

### Configuration

#### Settings

1. **Chunk Configuration**
   * Chunk Size\*: Number of characters per chunk
   * Chunk Overlap\*: Number of overlapping characters between chunks
   * Separators: List of strings that define where to split the text (comma-separated)
2. **Input Selection**
   * Documents/Text to Split\*: Select one or more inputs to process
   * Supports:
     * Single text strings
     * Document objects
     * Arrays of text strings
     * Arrays of documents

#### Output Ports

* `split_documents` (Document\[]): Array of split documents
  * Each document maintains original metadata
  * Chunks respect natural text boundaries based on separators

### Best Practices

1. **Chunk Size Selection**
   * Consider model token limits
   * Balance information density
   * Account for desired context window
   * Test with representative content
2. **Overlap Configuration**
   * Use overlap to maintain context
   * Consider semantic boundaries
   * Avoid too large overlaps (waste)
3. **Separator Usage**
   * Use natural text boundaries
   * Consider document structure
   * Common separators: "\n\n", "\n", ".", "!", "?"
   * Order separators from specific to general

### Common Issues

* Memory issues with large documents
* Inconsistent chunk sizes
* Improper handling of special characters


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