Notebook Examples
Last updated
Last updated
Use semantic search and metadata filtering to match academic papers. Dataset:
Embedding Model:
Vector Dimension: 384 No. Metadata Fields: 7 Query Operations: Vector search, key-value matching
Use classic search to match files of Crimes in Chicago. Dataset: No. Metadata Fields: 23 Query Operations: Key-value matching, geo-distance matching, date matching
Use metadata filtering and vector search in binary format.
Dataset:nternal by Hyperspace Embedding Model:
Randomly generated data Vector Dimension: 200 No. Metadata Fields: 6 Query Operations: Vector search, key-value matching
Use metadata filtering and semantic search to create a Movie recommendation engine.
Dataset: Embedding Model:
Vector Dimension: 384 No. Metadata Fields: 15 Query Operations: Vector search, key-value matching, aggregations
Use metadata filtering and semantic search to match applications.
Dataset: Embedding Model:
Vector Dimension: 384 No. Metadata Fields: 6 Query Operations: Vector search, key-value matching
Use keyword filtering and multi vector search to build a recommendation system for Amazon products Dataset: Embedding Model:
Vector Dimension: 384 No. Metadata Fields: 6 Query Operations: Vector search, text matching