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Semantic movie search on Chromia

In this course, you will build a full-stack app that converts movie plot summaries into vector embeddings and stores them on Chromia. We'll store the comprehensive movie metadata on-chain and index the embeddings using the vector_db_extension. This powerful setup allows you to perform semantic searches, letting you query by meaning and retrieve detailed results directly from the blockchain.

Key learning objectives​

  • Set up your project environment and run the backend on the Chromia testnet
  • Generate and upload vector embeddings along with movie metadata
  • Utilize natural language to search for semantically similar movies and obtain detailed results

This hands-on course introduces you to on-chain semantic search using the Carnegie Mellon University (CMU) Movie Summary Corpus and Python-based tools.

Access the complete code repository for this course here: Vector DB Movie Demo repository.