Overview
Explore the core ideas and principles behind Superlinked's functionality..
Describe your data using Python classes with the @schema decorator.
Describe your vector embeddings from building blocks with Spaces.
Combine your embeddings into a queryable Index.
Define your search with dynamic parameters and weights as a Query.
Load your data using a Source.
Define your transformations with a Parser (e.g.: from
pd.DataFrame
).Run your configuration with an Executor.
Colab notebooks explaining the concepts

Categorical Embeddings
Efficiently represent and compare categorical data in vector space for similarity searches.

Combine Multiple Embeddings
Merge different types of embeddings to create a unified representation for complex objects.

Natural Language Querying
Perform similarity searches using natural language queries instead of vector representations.

Number Embedding Minmax
Embed numerical values within a specified range for effective similarity comparisons.

Number Embedding Similar
Embed numbers to find similar values based on relative closeness rather than exact matches.
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