RAG Chunking & Overlap Visualizer Studio

Simulate document text chunking strategies, visualize token overlap, and generate LangChain and LlamaIndex splitters

Chunking Presets

Load typical RAG segmentation profiles for documentation, code, and Q&A

Splitter Configuration

Tune chunk capacity, overlap margins, and split units

Segmentation Unit:

Chunk Size:350 characters

Chunk Overlap:70 characters (20%)

Source Document:1068 chars

# Next.js Architecture and Server Components

Next.js is a flexible React framework that gives you building blocks to create fast, full-stack web applications. By framework, we mean Next.js handles the tooling and configuration needed for React, and provides additional structure, features, and optimizations for your application.

## React Server Components (RSC)
React Server Components allow developers to build applications that span the server and client, combining the rich interactivity of client-side apps with the improved performance of traditional server rendering.

### Benefits of Server Rendering
- **Data Fetching**: Server Components allow you to move data fetching to the server, closer to your data source.
- **Security**: Server Components keep sensitive data and logic on the server, such as tokens and API keys.
- **Caching**: By rendering on the server, the result can be cached and reused across subsequent requests.
- **Bundle Sizes**: Server Components allow you to keep large dependencies on the server, reducing the client JavaScript bundle.

Total Chunks

Avg Chunk Length

320 characters

Overlap Ratio

20%

Interactive Chunk Segmentation Stream

4 chunks generated

Chunk #1350 characters

# Next.js Architecture and Server Components Next.js is a flexible React framework that gives you building blocks to create fast, full-stack web applications. By framework, we mean Next.js handles the tooling and configuration needed for React, and provides additional structure, features, and optimizations for your application. ## React Server Co

Chunk #2350 characters

↳ Overlap Span from Previous Chunk: features, and optimizations for your application. ## React Server Co

features, and optimizations for your application. ## React Server Components (RSC) React Server Components allow developers to build applications that span the server and client, combining the rich interactivity of client-side apps with the improved performance of traditional server rendering. ### Benefits of Server Rendering - **Data Fetching**

Chunk #3350 characters

↳ Overlap Span from Previous Chunk:erver rendering. ### Benefits of Server Rendering - **Data Fetching**

erver rendering. ### Benefits of Server Rendering - **Data Fetching**: Server Components allow you to move data fetching to the server, closer to your data source. - **Security**: Server Components keep sensitive data and logic on the server, such as tokens and API keys. - **Caching**: By rendering on the server, the result can be cached and reuse

Chunk #4228 characters

↳ Overlap Span from Previous Chunk:hing**: By rendering on the server, the result can be cached and reuse

hing**: By rendering on the server, the result can be cached and reused across subsequent requests. - **Bundle Sizes**: Server Components allow you to keep large dependencies on the server, reducing the client JavaScript bundle.

# LangChain Python Document Splitter
from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=350,
    chunk_overlap=70,
    length_function=len,
    separators=["\n\n", "\n", ". ", " ", ""]
)

docs = text_splitter.create_documents([raw_text])
print(f"Created {len(docs)} chunks for RAG embedding.")

# LlamaIndex Python Sentence / Token Splitter
from llama_index.core.node_parser import SentenceSplitter

splitter = SentenceSplitter(
    chunk_size=350,
    chunk_overlap=70
)

nodes = splitter.get_nodes_from_documents([document])
print(f"Extracted {len(nodes)} semantic nodes.")

// LangChain TypeScript Text Splitter
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 350,
  chunkOverlap: 70,
  separators: ["\n\n", "\n", ". ", " ", ""],
});

const output = await splitter.createDocuments([rawText]);
console.log(`Created ${output.length} document chunks.`);

# LangChain Python Document Splitter from langchain_text_splitters import RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter( chunk_size=350, chunk_overlap=70, length_function=len, separators=["\n\n", "\n", ". ", " ", ""] ) docs = text_splitter.create_documents([raw_text]) print(f"Created {len(docs)} chunks for RAG embedding.")

# LlamaIndex Python Sentence / Token Splitter from llama_index.core.node_parser import SentenceSplitter splitter = SentenceSplitter( chunk_size=350, chunk_overlap=70 ) nodes = splitter.get_nodes_from_documents([document]) print(f"Extracted {len(nodes)} semantic nodes.")

// LangChain TypeScript Text Splitter import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 350, chunkOverlap: 70, separators: ["\n\n", "\n", ". ", " ", ""], }); const output = await splitter.createDocuments([rawText]); console.log(`Created ${output.length} document chunks.`);