The measurement of similarity between vectors is a fundamental operation in semantic comparison. For example, you need this operation to find the top N number of closest vectors to a query vector, which can be used for a semantic search. Vector search also enables developers to improve the accuracy of their generative AI responses by providing related documents to a large language model.
Snowflake Cortex provides four vector similarity functions:
VECTOR_INNER_PRODUCT
VECTOR_L1_DISTANCE
VECTOR_L2_DISTANCE
VECTOR_COSINE_SIMILARITY
Each function takes two VECTOR arguments of equal element type and dimension and computes the specified metric over them.
Note
Due to computational optimizations in these functions, floating-point errors might be slightly larger than usual (e.g. about 1e-4).
List of functions¶RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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