Embeddings can be thought of as entities that provide a rich representation of the data we encounter in our everyday lives. They transform complex, high-dimensional data into a simpler, more meaningful form, allowing us to understand and manipulate it more effectively.
Semantic search is a powerful technique that allows us to retrieve data based on the meaning and context of the search query, rather than relying solely on keyword matching. This is particularly useful in scenarios where we want to find images, documents, or other data types that are semantically similar to a given query.
Manually implementing semantic search would be a complex and daunting task. It would require extensive work to encode both the search query and the dataset into a comparable format, and then efficiently compare them to find the most relevant results. This involves dealing with high-dimensional vector operations, managing large datasets, and ensuring that the comparisons are both accurate and performant.
We're taking semantic search to the next level by performing vector arithmetic on embeddings. This approach allows us to manipulate the embeddings in ways that capture complex relationships and nuances in the data, bringing some truly exciting possibilities to life.
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