RAG Vector Distance Calculator

Calculate Cosine Similarity and Euclidean Distance between high-dimensional embedding vectors. Built for AI engineers debugging RAG pipelines, semantic search, and vector stores — entirely offline.

Target Vector ASingle Only

Vector BSingle or Batch Mode

Paste embedding vectors to compare

Compare 1-to-1 or use Batch Mode by pasting a 2D array in Vector B to rank multiple vectors.

Vector Calculations FAQ

Should I use Cosine Similarity or Euclidean Distance for RAG?

Cosine Similarity is the industry standard for RAG (Retrieval-Augmented Generation) because it measures the semantic direction of vectors rather than their magnitude. This makes it ideal for text embeddings (like OpenAI or Cohere) where document length can vary. Euclidean Distance is better for numerical data where the absolute scale of features matters.

Can I compare a batch of vectors at once?

Yes. In the 'Vector B' input field, you can paste a 2D Array (a JSON list of multiple vectors). The tool will automatically enter Batch Mode and generate a Top-K ranked table scored by Cosine Similarity, allowing you to simulate a vector database search locally.

What embedding dimensions are supported?

Our calculator supports any dimension count, from small 384D models (like all-MiniLM-L6-v2) to high-density 1536D (OpenAI text-embedding-3-small) or 3072D (text-embedding-3-large) vectors. As long as both vectors share the same length, the math remains accurate.

Engineering Guides

Master This Tool

Deep-dive guides and tutorials for advanced users.

Vector Dimensionality: Why Misaligned Embeddings Break RAG

Discover why projecting 3072-D embeddings into 1536-D indices destroys semantic retrieval. Learn to audit vector math using Cosine Similarity to prevent AI hallucinations.

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Decoding the Wire: A Deep Dive into RSC Flight Data

Master the React Flight format (text/x-component). Learn to decode $L, I, and J prefixes, identify hydration bloat, and prevent secrets leakage in the 2026 stack.

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Securing AI Agents: How to Detect & Prevent Prompt Injection

A Cybersecurity Architect's guide to prompt injection in 2026. Learn about Token to Shell vectors, RAG poisoning, and embedding-based anomaly detection.

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Understanding MCP Transport Layers: stdio vs. HTTP vs. WebSockets

A technical deep dive into Model Context Protocol (MCP) transport mechanisms. Compare stdio, HTTP with SSE, and WebSockets for secure AI agent integration.

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Debugging RAG: Cosine vs Euclidean Distance

A technical guide for AI Architects on measuring embedding proximity. Learn to debug RAG retrieval errors using vector math and Cosine Similarity metrics.

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