Tencent Cloud Vector Database supports several similarity calculation methods, which are essential for comparing and retrieving high-dimensional vector data. The main methods include:
Inner Product (IP) – Measures the dot product of two vectors. It is commonly used when vectors are normalized, as it correlates with cosine similarity.
Example: In a recommendation system, inner product can quickly find similar user preferences by comparing normalized embedding vectors.
Euclidean Distance (L2) – Computes the straight-line distance between two points in space. It is useful for applications where absolute differences matter.
Example: In image search, Euclidean distance can help find visually similar images by measuring pixel-level differences.
Cosine Similarity – Measures the angle between two vectors, focusing on their orientation rather than magnitude. It is widely used in text and semantic search.
Example: In a document retrieval system, cosine similarity helps find articles with similar topics by comparing their embedding vectors.
IP (Inner Product) with Normalization – Equivalent to cosine similarity when vectors are normalized, but computationally more efficient.
Example: In a chatbot application, normalized inner product can efficiently retrieve semantically similar responses from a large vector database.
Tencent Cloud Vector Database is optimized for these methods, enabling fast and scalable similarity searches for AI and machine learning applications. For more details, you can explore Tencent Cloud’s vector database services.