How Vector Databases Power AI

Keypoints :

• Vector databases store data as high-dimensional vectors generated by embedding models.
• Raw data like text or images are converted into numerical vectors that capture semantic meaning.
• Dimensions in vectors are not predefined or labeled; they are learned during model training.
• Vector components are abstract numerical features without human-readable labels, often correlating with intuitive concepts.
• Similarity searches rely on metrics like cosine similarity or Euclidean distance to find semantically similar content.
• Interpreting individual vector dimensions is generally not meaningful; focus is on the overall position in vector space.
• The approach emphasizes holistic analysis over detailed interpretation of each vector dimension.