Chromia
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Semantic search on Chromia using Vector DB

Build a movie search engine that matches sentence embeddings via the Vector DB extension.

Advanced15 lessons3 modulesAIRell

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  1. 01Searching movies semantically on Chromia

Module 1: Project setup

  1. 02Overview
  2. 03Choose an embedding model
  3. 04Configure your Rell module
  4. 05Deploy your Rell module
  5. 06Finalize your Python environment

Module 2: Running the data pipeline

  1. 07Overview
  2. 08Preprocess movie data
  3. 09Generate sentence embeddings
  4. 10Store vectors on the blockchain
  1. 11Search the vector database

Module 4: A deep dive into the code

  1. 12Overview
  2. 13Link your data to the Vector DB with Rell
  3. 14Work with Chromia through the Python client
  1. 15Use cases and extensions