Definition of a vector database storing data in a vectorial representation.

Uses in finding similar items through encoded representations.

Breaking down private data into chunks for encoding.

Utilizing similarity matrices for querying vector databases.

Understanding similarity through Euclidean distance and dot product.

Distinguishing between dot product and cosine similarity.

Importance of indexing for quick data retrieval.

Descriptions of approximate nearest neighbors algorithms.

Using projection matrices to separate vectors in space.

Retrieve similar vectors based on hashed values.

Building efficient graphs for vector search.

Process of connecting vectors to nearest neighbors.

Creating layers in the graph for varied density searches.

Refining searches across multiple graph layers.

Support for metadata storage and complex queries.

Scalability and management of vector embeddings.