Dot Product
Vector similarity metric measuring both directional similarity and magnitude of vectors. Used by many LLMs for training and equivalent to cosine similarity for normalized data. Reports both angle and magnitude information.
About this tool
Overview
Dot product (inner product) is a fundamental vector similarity metric that measures both the directional alignment and magnitude of two vectors. It's widely used in machine learning and vector search.
Formula
Dot Product = A · B = Σ(a[i] * b[i])
Characteristics
- Magnitude Aware: Considers both direction and vector length
- Efficient: Computationally cheaper than Euclidean distance
- LLM-Friendly: Many LLMs are trained using dot product
- Normalized Equivalence: If data is normalized, equals cosine similarity
When to Use
- When your embedding model was trained with dot product
- For normalized embeddings (equivalent to cosine)
- When magnitude contains meaningful information
- With LLMs like msmarco-bert-base-dot-v5
Comparison
- Cosine: Reports only angle
- Dot Product: Reports angle and magnitude
- Euclidean: Measures straight-line distance
LLM Training
Many Large Language Models use dot product for training, such as:
- msmarco-bert-base-dot-v5 on Hugging Face
- Various BERT variants
- Custom-trained embedding models
Best Practice
Match your distance metric to your model's training metric. For normalized embeddings, dot product and cosine produce identical rankings.
Vector Database Support
Supported by all major vector databases including Pinecone, Weaviate, Milvus, Qdrant, and Elasticsearch.
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