https://github.com/superlinked/superlinked-recipes

https://github.com/superlinked/superlinked-recipes

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/superlinked/superlinked-recipes
• Owner: superlinked
• Created: 2024-12-16T17:26:48.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-10-26T12:47:24.000Z (4 months ago)
• Last Synced: 2026-01-15T16:15:29.216Z (28 days ago)
• Language: Jupyter Notebook
• Size: 2.21 MB
• Stars: 10
• Watchers: 6
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Superlinked Recipes

This repository showcases projects using Superlinked for various use cases: Multi-Modal Semantic Search, Recommendation System, and RAG.

Each project is a standalone application utilizing Superlinked. A public Streamlit app is available for experimentation. Projects can be run locally or via our free-tier cloud solution with GCP. Detailed tutorials are provided for each project.

Superlinked is a framework for building scalable applications with multi-modal vector search. The core is open-source, but the Cloud solution offers additional features like batch-module, CLI, monitoring, and more.

Learn more about Superlinked:

- [Website](https://superlinked.com)

- [GitHub](https://github.com/superlinked/superlinked)

- [Documentation](https://docs.superlinked.com)

## Multi-Modal Semantic Search

Multi-Modal Semantic Search enables you to search across diverse data types by understanding the context and meaning, rather than relying solely on keywords. Superlinked supports various modalities as primary features, including [text](https://github.com/superlinked/superlinked/blob/main/notebook/feature/text_embedding.ipynb), [images](https://github.com/superlinked/superlinked/blob/main/notebook/image_search_e_commerce.ipynb), [numbers](https://github.com/superlinked/superlinked/blob/main/notebook/feature/number_embedding_minmax.ipynb), [categories](https://github.com/superlinked/superlinked/blob/main/notebook/feature/categorical_embedding.ipynb), and [recency](https://github.com/superlinked/superlinked/blob/main/notebook/feature/recency_embedding.ipynb). If you prefer to use your own embeddings, Superlinked offers a [CustomSpace](https://github.com/superlinked/superlinked/blob/main/notebook/feature/custom_space.ipynb) feature to accommodate this need.

Superlinked allows you to fine-tune the importance of different attributes for each query by adjusting [weights at query time](https://github.com/superlinked/superlinked/blob/main/notebook/feature/dynamic_parameters.ipynb), making the process straightforward and intuitive. To further simplify the experience, Superlinked offers a [Natural Language Interface](https://github.com/superlinked/superlinked/blob/main/notebook/feature/natural_language_querying.ipynb), enabling users to input their queries in plain, everyday language.

A standout feature of Superlinked is its ability to handle data objects holistically, eliminating the need for Reciprocal Rank Fusion (RRF), which significantly enhances system performance. For those who require keyword search capabilities, Superlinked also provides Hybrid Search, again without the need for RRF.

Below is a table showcasing projects built using Superlinked, demonstrating the power of multi-modal semantic search.

  

    Recipe

    Key Features

    Modalities

  

  

    

      📂 Hotel Search


      🚀 Try it now


      

    

    

        Natural Language Queries


        Multi-modal Semantic Search


    

    

        Text


        Numbers


        Categories


    

  

## Recommendation System

Recommendation Systems combine Semantic Search and personalization for relevant suggestions based on user preferences.

  

    Recipe

    Key Features

    Modalities

  

  

    

      📂 E-Commerce RecSys


      🚀 Try it now

    

    

      Item-to-item recommendations


      Item-to-user recommendations


      Collaborative filtering


    

    

      Images


      Text


      Categories


      Numbers


    

    

    

      📂 RecSys NLQ


      🚀 Try it now

    

    

      Item-to-item recommendations


      Item-to-user recommendations


      Collaborative filtering


      NLQ

    

    

      Images


      Text


      Categories


      Numbers