![]() ![]() The similarity measure can be based on various metrics, such as cosine similarity, euclidean distance, hamming distance, jaccard index. Then, you need to use a similarity measure that calculates how close or distant two vectors are in the vector space. The query vector can be either derived from the same type of data as the stored vectors (e.g., using an image as a query for an image database), or from different types of data (e.g., using text as a query for an image database). To perform similarity search and retrieval in a vector database, you need to use a query vector that represents your desired information or criteria.
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