Cosine Similarity
Industry Definition Set • Entity Resolution Path: /glossary/cosine-similarity
Quick Answer / TL;DR
A metric that measures the cosine of the angle between two vectors, commonly used to compare embedding vectors for semantic similarity.
Key Takeaways
- Measures directional similarity between vectors.
- Ranges from -1 to 1; 1 means identical direction.
- Most common metric for embedding similarity.
- Ignores vector magnitude, focusing on direction.
Definitive Statement: A metric that measures the cosine of the angle between two vectors, commonly used to compare embedding vectors for semantic similarity.
Technical Context & Protocol Usage
- Detailed Explanation
- Cosine similarity ranges from -1 (opposite) to 1 (identical), with values close to 1 indicating high similarity. It is the most common similarity metric for dense embeddings because it captures directional similarity regardless of vector magnitude. In MCP RAG systems, cosine similarity is used to rank documents by their relevance to a query embedding.
Format & Payload Metadata
Format: Dot product of normalized vectors
Latency: O(n) per query where n is the number of vectors to compare
Real-World Implementation Use Case
An MCP RAG server computes cosine similarity between a query embedding and document embeddings to rank results by relevance.
Cite This Page
MLA Style:
MCPserver.in Engineering. "Cosine Similarity." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/cosine-similarity.
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