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Cosine similarity langchain Examples using cosine_similarity. Raises: ValueError – If the number of columns in X and Y are not the same. Dec 9, 2024 · langchain_community. Parameters: X (List[List[float]] | List[ndarray] | ndarray) – Y (List[List[float]] | List[ndarray] | ndarray) – Return type: ndarray. utils. Cosine Distance: Measures the dissimilarity between vectors as the complement of the cosine similarity. Measures the cosine of the angle between two vectors in a vector space. Higher values mean greater similarity. How to route between sub-chains Sep 6, 2024 · Cosine similarity is a metric used to measure how similar two vectors are. Oct 30, 2023 · The cosine_similarity function in the LangChain codebase calculates the cosine similarity between two matrices X and Y. It first checks if the matrices are empty, and if so, returns an empty numpy array. . It ranges from -1 to 1, where 1 represents identical vectors, 0 represents orthogonal vectors, and -1 represents vectors that are diametrically opposed. cosine_similarity (X: Union [List [List [float]], List [ndarray], ndarray], Y: Union [List [List [float]], List [ndarray], ndarray]) → ndarray [source] ¶ Row-wise cosine similarity between two equal-width matrices. math. Jul 13, 2023 · Cosine Similarity: Measures the cosine of the angle between vectors, indicating their similarity. To be more precise, it determines the cosine of the angle between two non-zero vectors in a multi-dimensional space. Nov 9, 2024 · Learn how to calculate cosine similarity between vectors in LangChain using the cosine_similarity utility function, with practical examples for text embeddings and semantic routing. Row-wise cosine similarity between two equal-width matrices. eqsum ahbkg kidsqg lcd mnedom dbwhp ivxi rpoka hjbwvmj myen |
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