AI RAG Chunking & Embedding Visualizer

Chunk your documents for RAG architectures, inspect the overlapping context regions live and calculate the embedding cost.

RAG Chunking Parameters and Strategy

Chunk your document and visualize the overlap regions and the embedding cost.

Sample text:

Chunking Strategy

Unit

Chunk Size256 tokens

Chunk Overlap48 tokens

Total Chunks

1chunk

Total Tokens

~242token

Average Size

242token / chunk

Embedding Cost

$0.000005(OpenAI)

Input Document919 characters / ~242 tokens

Vector & Embedding Model:

Visual Chunks (1)(Click a chunk to inspect it)

Chunk #1[0 - 919]

~242 token(919 chars)

# Next.js 15 App Router & Server Components Next.js 15 offers full integration with the stable release of React 19. Thanks to the Server Components architecture, components rendered on the server are sent to the client with zero JavaScript payload. This dramatically improves page load speed (FCP and LCP). ## Data Fetching and Caching Data fetching runs through the standard 'fetch' API. The 'unstable_cache' and React 'cache' functions deduplicate database queries. While unnecessary re-renders are avoided, Stale-While-Revalidate logic keeps fresh content ready in the background. ## RAG and Vector Database Integration In modern AI applications, documents are split into pieces with chunking algorithms. Each piece is sent to an embedding model and turned into a high-dimensional vector. PostgreSQL extensions such as Supabase pgvector or Neon DB run cosine similarity queries in milliseconds using HNSW indexes.

[
  {
    "id": 1,
    "startIndex": 0,
    "endIndex": 919,
    "tokenCount": 242,
    "charCount": 919,
    "text": "# Next.js 15 App Router & Server Components\n\nNext.js 15 offers full integration with the stable release of React 19. Thanks to the Server Components architecture, components rendered on the server are sent to the client with zero JavaScript payload. This dramatically improves page load speed (FCP and LCP).\n\n## Data Fetching and Caching\nData fetching runs through the standard 'fetch' API. The 'unstable_cache' and React 'cache' functions deduplicate database queries.\nWhile unnecessary re-renders are avoided, Stale-While-Revalidate logic keeps fresh content ready in the background.\n\n## RAG and Vector Database Integration\nIn modern AI applications, documents are split into pieces with chunking algorithms. Each piece is sent to an embedding model and turned into a high-dimensional vector. PostgreSQL extensions such as Supabase pgvector or Neon DB run cosine similarity queries in milliseconds using HNSW indexes."
  }
]

Why Is the Right Chunking So Critical in RAG Architectures?

RAG (Retrieval-Augmented Generation) systems succeed or fail on the quality of the text pieces (chunks) stored in the vector database. Large Language Models (LLMs) have limited context windows, and large chunks full of unnecessary noise make the model fall into the "needle in a haystack" (Lost in the Middle) problem.

On the other hand, text chunked too finely destroys the semantic context of a sentence or idea. This visualizer chunks your documents live in your browser with the Recursive Character, Sentence-Aware and Markdown Header strategies, shows the overlap with colored layers and calculates your OpenAI and Cohere embedding costs down to the last cent.

Popular Chunking Strategies and Use Cases

📐 Recursive Character Splitter

The industry standard for LangChain and LlamaIndex. It splits the text on double line breaks (\n\n), single line breaks (\n), periods (. ) and spaces, in that order, preserving natural paragraph and sentence boundaries as much as possible.

📝 Markdown Header Splitter

Ideal for technical documents, API references and Notion docs. It keeps H1 (#), H2 (##) and H3 (###) headings together with the text under them, bringing the loss of hierarchical context down to zero.

🔄 Why Chunk Overlap Matters

Leaving 10% to 20% overlap between chunks ensures that a critical piece of information or formula that falls between two chunks lives on in both vector chunks without losing its context.

Frequently Asked Questions (FAQ)

For general-purpose RAG applications, the most common choice is 256 to 512 tokens. 128-256 tokens is ideal for short Q&A, and 512-1024 tokens for in-depth technical documents.

Leaving 10% to 20% overlap between chunks keeps critical information that falls between two chunks intact without losing its context.

Dimension and cost calculations are available for the OpenAI text-embedding-3-small, text-embedding-3-large and Cohere embed-multilingual models.

For general-purpose RAG applications, the most common choice is 256 to 512 tokens. While 128-256 tokens is enough for Q&A (FAQ) focused searches, 512-1024 tokens is preferred for documents with complex logic, contracts or code blocks.

text-embedding-3-small has 1536 vector dimensions and a very economical cost of $0.02 per million tokens, which is more than enough for most web applications. text-embedding-3-large, with 3072 dimensions, offers higher similarity accuracy (MTEB score) in multilingual legal or academic projects that need deep semantic nuance.

You can store the chunks from the JSON output in a database table with content TEXT and embedding vector(1536) columns. To run similarity searches in milliseconds, defining an HNSW or IVFFlat index is recommended.