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  1. Getting started

Notebook Examples

Last updated 10 months ago

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

Hybrid Search with Hyperspace
arXiv dataset
all-MiniLM-L6-v2
Classic Search with Hyperspace
Crimes in Chicago
Hybrid Search and Hamming Distance with Hyperspace
I
Movie Recommendation with Hyperspace
The Movie Dataset
all-MiniLM-L6-v2
Matching Applications with Hyperspace
ADVEC-ML App Data
bge-small-en
E-Commerce Recommendation System with Hyperspace
Amazon Products Dataset
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