Index and Query Vector Data in Redis
The lesson demonstrates how to create a vector index in Redis by defining a schema with VectorField specifying the HNSW algorithm, dimensions, and distance metric, then query embeddings using KNN for nearest-neighbor search and hybrid queries combining metadata filters with vector search, including pipelining for bulk inserts.
