Vector databases are specialized databases designed to store and manage vector data, often in the form of embeddings. These embeddings are the numerical representation of complex data types, such as text, images, sound, and video.
Crucially, these embeddings capture the semantic essence of the content, ensuring that items with similar meanings are represented by vectors that are closely positioned in the embedding space. Vector databases also support large-scale vector operations such as similarity search and comparison, enabling the next generation of AI applications.
Key Takeaways:
- Learn the basics of vector databases
- Query OpenAI’s API to retrieve vector embeddings
- How to use those embeddings to solve real business problems