Superlinked is a Python framework for AI Engineers building high-performance search & recommendation applications that combine structured and unstructured data.
  • WHY: Improve your vector search relevance by encoding metadata together with your unstructured data into vectors. Read more at Why Superlinked
  • WHAT: A framework and a self-hostable REST API server that connects your data, vector database and backend services.
  • HOW: Construct custom data & query embedding models from pre-trained encoders from sentence-transformers, open-clip and custom encoders for numbers, timestamps and categorical data. See concepts and use cases for examples.
If you like what we do, give us a star! ⭐ Superlinked framework diagram

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Discover Superlinked

getting%20started

Getting Started

Begin your journey with Superlinked by setting up and understanding the basics.
concept

Concepts

Explore the core ideas and principles behind Superlinked’s functionality.
use%20cases

Use Cases

Learn how Superlinked can be applied to solve real-world problems.
reference

Reference

Access detailed documentation for Superlinked’s components.
changelog

Changelog

View the changelog for Superlinked.
features

Recipes

Explore real-world applications and examples.

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Resources

vectorhub

VectorHub

Free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack.
vdb%20table

Vector DB Comparison

Open-source collaborative comparison of vector databases by Superlinked.