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.
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.
Master This Tool
Deep-dive guides and tutorials for advanced users.
The 2026 Developer Manifesto: Mastering the AI-Native and RSC Stack
A technical guide to navigating the shift from legacy web patterns to the era of React Server Components (RSC) and LLM-driven application logic.
Debugging RAG: When to Use Cosine Similarity vs. Euclidean Distance
A technical guide for AI Architects on measuring embedding proximity. Learn to debug RAG retrieval errors using vector math and similarity metrics.
Token Counting for GPT-5.4, Claude 4, and Gemini 3.1: The 2026 Developer Guide
Master token counting for 2026 frontier models. Learn how to calculate tokens for GPT-5.4, Claude 4, and Gemini 3.1, including agentic reasoning and tool use tokens.
Token Counting for GPT-5.4, Claude 4, and Gemini 3.1: The 2026 Developer Guide
Count tokens for GPT-5.4, Claude 4, and Gemini 3.1 APIs. Free tool, Python/JS code, 2026 pricing, and strategies for agentic token management.
GPT-5.4 vs Claude 4.6: Calculating the Real Cost of 1M Token Context Windows
Complete technical breakdown of March 2026 LLM context limits. Learn how reasoning tokens affect GPT-5.4 and Claude 4.6 pricing.
The Power of JSON Prompting: Why Structured Outputs are the Future of AI Agents
Stop relying on unpredictable text parsing. Learn why framing your LLM prompts as JSON payloads is the only way to build deterministic, reliable AI agents in 2026.