What is the first step in the Vector Data Workflow?

The process of working with vector data typically begins with the generation of vector embeddings. This step is crucial because vector embeddings serve as the numerical representations of data, such as text or images, in a format that machine learning algorithms can understand and utilize. By converting raw data into vectors, you are enabling the system to capture the semantic meaning and relationships within the data, which is essential for any subsequent analysis or search operations.

Generating vector embeddings involves utilizing specific algorithms or models—like neural networks—to transform the input data into high-dimensional vectors. These embeddings facilitate the evaluation of similarity between items, allowing effective retrieval and analysis based on the understanding captured in these representations.

After this initial step, the following stages, such as storing the vector embeddings or conducting searches, rely on the successful generation of these embeddings. Without this foundational step, the workflow would have no numerical basis upon which further processing could occur.