# Cosine Similarity

> Cosine similarity measures how alike two embeddings are by the angle between them — the standard relevance score behind semantic search and RAG retrieval.

**Cosine similarity scores how similar two [embeddings](/glossary/embedding) are by measuring the angle between them: 1.0 means pointing the same way (very similar), 0 means unrelated — the default relevance metric of [semantic search](/glossary/semantic-search).**

It won the default slot because embedding spaces encode meaning *directionally*: two texts about the same thing point the same way regardless of length or emphasis, so comparing angles (and ignoring magnitude) matches semantic intuition. A practical simplification follows: most modern embedding models output **normalized** vectors, where cosine similarity equals the dot product and ranks identically to Euclidean distance — the metric choice in your [vector database](/glossary/vector-database) matters less than tutorials imply, as long as it matches what the embedding model was trained for (check the model card).

Two field notes save real debugging time. **Scores aren't portable**: each model has its own score distribution, so thresholds like "similarity > 0.8" must be calibrated per model, never copied. And **similarity isn't relevance**: cosine retrieves what's *alike*, which is why pipelines add [reranking](/glossary/reranking) to sort the genuinely-relevant out of the merely-similar.

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_Source: https://agentscamp.com/glossary/cosine-similarity — Term on AgentsCamp._