Vector databases are specialized systems designed to handle the storage and retrieval of high-dimensional vector representations of unstructured, complex data — like images, text, or audio. By representing complex data as numerical vectors, these systems understand context and conceptual similarity, providing noticeably similar results to queries rather than exact matches, which enables advanced data analysis and retrieval.

As the volume of data in vector databases increases, storing and retrieving information becomes increasingly challenging. Binary quantization simplifies high-dimensional vectors into compact binary codes, reducing data size and enhancing retrieval speed. This approach improves storage efficiency and enables faster searches, allowing databases to manage larger datasets more effectively.

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