Unlocking Semantic Understanding: Cosine Similarity in AI
2025-03-10
This article provides a clear explanation of cosine similarity and its applications in AI, particularly in understanding semantic relationships between words. It starts by explaining vectors, then details the cosine similarity calculation with a step-by-step example. A TypeScript implementation of the cosine similarity function is provided, along with an optimized version. The article then explores real-world web application use cases, such as product recommendations and semantic search, and shows how to leverage OpenAI's embedding models for improved accuracy. The article also emphasizes efficient implementation using Math.hypot() and the importance of pre-computing embeddings in production environments.
AI
vectors