What is the ideal chunk size and overlap for a RAG pipeline?

For general Q&A and technical documentation, a chunk size of 250–512 tokens (1,000–2,000 characters) with a 10%–20% sliding window overlap works best. This preserves enough surrounding context for the LLM while keeping vector embeddings focused on a single topic.

How do you calculate RAM required for a Vector Database (HNSW)?

Raw vector memory equals Vectors × Dimensions × BytesPerDimension (4 bytes for Float32). For example, 1 million 1536-dimension FP32 vectors require 6.14 GB of raw memory. Adding an HNSW graph index (M=16) and a 25% operating system safety buffer brings recommended server RAM to 9.0 GB.

How much RAM does INT8 Scalar or Binary Quantization save?

INT8 scalar quantization compresses 32-bit floats into 8-bit integers, reducing vector RAM usage by 75% (4x compression) with less than 1% loss in retrieval recall. Binary quantization compresses each dimension to a single bit, achieving a 32x memory reduction on high-dimensional models like OpenAI 1536d and 3072d.

RAG Text Chunking Visualizer & Vector Database RAM Calculator

Visualize RAG text splitting & sliding window overlap in real time, and calculate HNSW vector database RAM.

Security Guarantee: 100% Client-Side In-Browser Execution. Computations run locally within browser RAM. Zero remote telemetry.

Technical Overview & Architecture

A dual-engine AI engineering utility for Retrieval-Augmented Generation (RAG). Visually inspect how character, sentence, and Markdown splitting strategies segment your documents with highlighted sliding window overlaps, and calculate exact production RAM and HNSW index overhead for Qdrant, Pinecone, Milvus, and pgvector.

The chunking engine segments input text according to your selected boundary strategy and slices trailing characters from chunk N-1 into chunk N to visualize context continuity. The Vector RAM engine multiplies vector count by dimension byte width (4B for FP32, 2B for FP16, 1B for INT8, 0.125B for Binary) and adds HNSW graph pointer overhead.

Key Capabilities & Features

Live Sliding-Window Overlap Highlighting: Visually highlights exact overlapping prefix characters in amber across adjacent chunks so you never lose boundary context.
Multi-Strategy Text Segmentation: Compare Fixed Character Window, Recursive Sentence/Paragraph boundaries, and Markdown Header (# / ##) splitting.
HNSW Vector Index RAM Sizer: Calculates raw vector memory plus HNSW bi-directional graph edge overhead (M=8 to M=64) from 10K to 20M vectors.
FP16, INT8 Scalar & Binary Quantization: Compare memory savings across 384d, 768d, 1024d, 1536d, and 3072d embedding models with 1-click JSON chunk export.

Step-by-Step Usage Guide

Paste Sample Document & Choose Splitter: Select Character, Recursive Sentence, or Markdown Header splitting and adjust chunk size and overlap sliders.
Inspect Highlighted Overlap Boundaries: Verify that sentences and headings are cleanly segmented without cutting critical semantic context.
Size Vector DB Server RAM: Select your total vector count, embedding dimension (e.g. 1536d), and quantization mode to size your production cluster.

Frequently Asked Questions (FAQ)

What is the ideal chunk size and overlap for a RAG pipeline?: For general Q&A and technical documentation, a chunk size of 250–512 tokens (~1,000–2,000 characters) with a 10%–20% sliding window overlap works best. This preserves enough surrounding context for the LLM while keeping vector embeddings focused on a single topic.
How do you calculate RAM required for a Vector Database (HNSW)?: Raw vector memory equals Vectors × Dimensions × BytesPerDimension (4 bytes for Float32). For example, 1 million 1536-dimension FP32 vectors require ~6.14 GB of raw memory. Adding an HNSW graph index (M=16) and a 25% operating system safety buffer brings recommended server RAM to ~9.0 GB.
How much RAM does INT8 Scalar or Binary Quantization save?: INT8 scalar quantization compresses 32-bit floats into 8-bit integers, reducing vector RAM usage by 75% (4x compression) with less than 1% loss in retrieval recall. Binary quantization compresses each dimension to a single bit, achieving a 32x memory reduction on high-dimensional models like OpenAI 1536d and 3072d.