
Unlocking Creativity with Azure OpenAI
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In the previous section, we explored various AOAI embedding models for generating vector embeddings. After creating these vectors, it’s essential to have a database optimized for storing and managing them effectively. The key distinction between a vector database and other types of databases is its capability to handle high-dimensional data. A vector database is specifically engineered to store data as high-dimensional vectors, which are mathematical representations of various features or attributes. Each vector comprises multiple dimensions, ranging from tens to thousands, depending on the data’s complexity and detail. These vectors are usually generated by applying transformation or embedding functions to raw data sources such as text, images, audio, video, and more. This type of database enables the indexing and querying of embeddings using vector search algorithms that assess vector distance or similarity. To ensure accurate retrieval of relevant...