How Vector Databases Power AI
AI Summary
In this video, the speaker explains the concept of vector databases and how they differ from traditional databases. Rather than storing raw data like text or images directly, vector databases use an embedding model to convert this data into high-dimensional vectors, which are essentially long lists of numbers capturing the semantic meaning of the content. The speaker addresses a viewer’s question about the dimensions and corresponding scales in vector embeddings, noting that they are not predefined but learned during training. This means the dimensions are abstract numerical features without clear labels or units. While some dimensions may correlate with intuitively understandable concepts (e.g., color intensity), the focus is on the holistic vector rather than interpreting each specific number. The video emphasizes the use of measures like cosine similarity or Euclidean distance to find semantically similar results by analyzing their overall position in vector space.