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Test document text chunking strategies, calculate vector embedding density, estimate OpenAI / Cohere indexing costs, and optimize RAG retrieval context windows with 100% browser-local privacy.
Input your API documentation, PDF text, or code. Choose between Recursive Character, Sentence/Paragraph, Semantic Heading, or Fixed Size splitting.
Fine-tune chunk size (100–1500 chars) and overlap parameters (10–30%) while inspecting real-time token gauges and color-coded boundaries.
Evaluate indexing cost estimates across OpenAI and Cohere models, then export ready-to-use LangChain or LlamaIndex Python / TypeScript code.
Zero server uploads. All document text, chunking parsing, token calculations, and embedding density estimates execute locally inside browser memory.
Compare Fixed Character, Sentence & Paragraph, Recursive Character (LangChain-style), and Semantic Heading splitting strategies side-by-side.
Visual color-coded chunk highlights with real-time overlap indicators, token gauges, character counts, and truncation alerts.
Calculates total tokens and estimated vector embedding indexing costs across OpenAI (text-embedding-3-small/large, ada-002), Cohere v3, and Voyage AI.
One-click starter presets for API Documentation, Legal Contracts, Engineering Blog Posts, Python Codebases, and Customer Support KB Articles.
Generates ready-to-use Python and TypeScript code snippets for LangChain (`RecursiveCharacterTextSplitter`) and LlamaIndex (`SentenceSplitter`).
Building enterprise RAG (Retrieval-Augmented Generation) applications requires balancing document chunk granularity against vector database retrieval precision and LLM context window limits. Oversized chunks cause noise and increase token costs during inference, whereas undersized chunks fragment coherent paragraphs, leading to hallucinations.
Choosing the right text splitting strategy (such as LangChain's RecursiveCharacterTextSplitter or LlamaIndex's SentenceSplitter) ensures that headings, code blocks, and lists remain intact inside single embedding vectors.
HiMat's RAG Chunking & Context Window Visualizer executes completely inside your local browser memory. Neither your document text, chunking parameters, vector estimates, nor exported code snippets are transmitted to external servers.
In Retrieval-Augmented Generation (RAG), text documents are split into smaller 'chunks' before vector embedding. If chunks are too small, crucial semantic context is lost; if chunks are too large, vector search precision degrades and LLM context window costs skyrocket.
The tool parses input text using your chosen strategy (Fixed, Sentence, Recursive, or Semantic), computes chunk boundaries, calculates token counts using BPE estimation, highlights overlapping characters, and visualizes exact chunk distribution in real time.
No. The HiMat RAG Chunking Visualizer runs 100% locally inside your web browser. Neither your text content, chunking parameters, nor generated code snippets ever leave your device.
The calculator estimates indexing costs for OpenAI text-embedding-3-small ($0.02 / 1M tokens), text-embedding-3-large ($0.13 / 1M tokens), text-embedding-ada-002 ($0.10 / 1M tokens), Cohere embed-english-v3.0 ($0.10 / 1M tokens), and Voyage AI voyage-3 ($0.06 / 1M tokens).
Yes! The Export Code tab generates copy-paste code snippets for Python (LangChain `RecursiveCharacterTextSplitter`, LlamaIndex `SentenceSplitter`) and TypeScript (`@langchain/textsplitters`).
HiMat Technologies designs citeable, permissioned enterprise RAG pipelines, custom vector database architectures, and autonomous AI agents for high-growth engineering teams.
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