Java: Integrate EmbeddingVector into Embedding #2328
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johnoliver
previously approved these changes
Aug 8, 2023
…el into dsgrieve/move-embeddingvector
johnoliver
approved these changes
Aug 9, 2023
johnoliver
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Jun 5, 2024
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> EmbeddingVector and the vectoroperations package are down in semantickernel-core, which makes it impossible to use the cosine similarity function needed to calculate the similarity between embeddings. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> EmbeddingVector was integrated into Embedding and the vectoroperations package was refactored into a VectorOperations class which is in the same package as Embedding. Now, one can do: `embedding.cosineSimilarity(otherEmbedding)` instead of having to new up an EmbeddingVector from embedding.getVector(). ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄
johnoliver
pushed a commit
to johnoliver/semantic-kernel
that referenced
this pull request
Jun 5, 2024
### Motivation and Context <!-- Thank you for your contribution to the semantic-kernel repo! Please help reviewers and future users, providing the following information: 1. Why is this change required? 2. What problem does it solve? 3. What scenario does it contribute to? 4. If it fixes an open issue, please link to the issue here. --> EmbeddingVector and the vectoroperations package are down in semantickernel-core, which makes it impossible to use the cosine similarity function needed to calculate the similarity between embeddings. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> EmbeddingVector was integrated into Embedding and the vectoroperations package was refactored into a VectorOperations class which is in the same package as Embedding. Now, one can do: `embedding.cosineSimilarity(otherEmbedding)` instead of having to new up an EmbeddingVector from embedding.getVector(). ### Contribution Checklist <!-- Before submitting this PR, please make sure: --> - [X] The code builds clean without any errors or warnings - [X] The PR follows the [SK Contribution Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md) and the [pre-submission formatting script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts) raises no violations - [X] All unit tests pass, and I have added new tests where possible - [X] I didn't break anyone 😄
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Motivation and Context
EmbeddingVector and the vectoroperations package are down in semantickernel-core, which makes it impossible to use the cosine similarity function needed to calculate the similarity between embeddings.
Description
EmbeddingVector was integrated into Embedding and the vectoroperations package was refactored into a VectorOperations class which is in the same package as Embedding. Now, one can do:
embedding.cosineSimilarity(otherEmbedding)instead of having to new up an EmbeddingVector from embedding.getVector().Contribution Checklist