Vector Databases and Rafiki
Yes, Rafiki is likely tied to a vector database, which is a type of database that stores and manages vector embeddings, such as those generated by language models like Llama. Vector databases allow for efficient similarity searches, clustering, and other operations on high-dimensional vector data.
Applications of Vector Databases
In the context of Rafiki, a vector database could be used to store and manage a wide range of data, including:
- Knowledge graphs: Representing entities, relationships, and concepts as vectors in a high-dimensional space.
- Text embeddings: Storing vector representations of text documents, such as articles, posts, or comments.
- User profiles: Representing users as vectors based on their interests, preferences, and behavior.
Benefits of Vector Databases
Using a vector database with Rafiki could enable a range of benefits, including:
- Improved search and retrieval: Efficiently searching and retrieving relevant information based on similarity and relevance.
- Enhanced recommendation systems: Recommending content, products, or services based on user preferences and behavior.
- Advanced analytics and insights: Analyzing and visualizing complex data to gain deeper insights and understanding.
How quickly is the vector database updating? My understanding is they update immediately. Is that true?
What is the difference between updating a vector database and doing a new training run for Rafiki?