$megatools --rag-chunker --recursive-split▊
RAG Document Chunker & Overlap Visualizer
Partition documents into embedding-ready chunks with configurable token windows, overlap buffers, and Vector DB JSON exports.
PRESETS:|
> CHUNKING_HYPERPARAMETERSAlgorithm: Recursive Character Split
450 chars
≈ 113 tokens per vector record60 chars
Carries last 60 chars across adjacent chunk boundaries> SOURCE_DOCUMENT
1389 chars · ~347 tokens
#1289 chars · ~72 tokens
# Retrieval-Augmented Generation (RAG) Architecture
Retrieval-Augmented Generation (RAG) is an architectural pattern that optimizes the output of large language models (LLMs) by referencing an authoritative knowledge base outside of its training data sources before generating a response.
#2333 chars · ~83 tokens
[overlap: 60c] e of its training data sources before generating a response.
## Why Chunking Matters in RAG
Large Language Models have fixed context windows and attention limitations. When indexing extensive knowledge documents—such as company handbooks, medical journals, or technical source code—we cannot inject millions of tokens in one prompt.
#3259 chars · ~65 tokens
[overlap: 60c] urce code—we cannot inject millions of tokens in one prompt.
Instead, documents are broken down into discrete segments called "chunks". Each chunk is converted into a dense vector embedding and indexed into a vector database like Pinecone, Milvus, or Qdrant.
#4408 chars · ~102 tokens
[overlap: 60c] xed into a vector database like Pinecone, Milvus, or Qdrant.
## The Overlap Strategy
When a text boundary is cut abruptly mid-sentence, essential relational context can be lost. Introducing an overlap buffer ensures that consecutive chunks share boundary context. For example, with a chunk size of 500 characters and an overlap of 100 characters, the last 100 characters of Chunk 1 are prepended to Chunk 2.
#5340 chars · ~85 tokens
[overlap: 60c] the last 100 characters of Chunk 1 are prepended to Chunk 2.
## Embedding Search and Retrieval
At query time, the user prompt is embedded into the same vector space. A Cosine Similarity or Approximate Nearest Neighbor (ANN) search retrieves the top-K relevant chunks, which are then injected into the LLM system prompt as verified context.