# Searching movies semantically on Chromia · Semantic search on Chromia using Vector DB · Learn Chromia

> You will build a full-stack app that turns movie plot summaries into vector embeddings and stores them on Chromia. The full movie metadata lives on-chain,…

Source: https://chromia.com/learn/vector-db-movie-demo/introduction/

Semantic search on Chromia using Vector DBLesson 1 of 15

1. [01Searching movies semantically on Chromia](https://chromia.com/learn/vector-db-movie-demo/introduction/)

Module 1: Project setup

1. [02Overview](https://chromia.com/learn/vector-db-movie-demo/setup/)
2. [03Choose an embedding model](https://chromia.com/learn/vector-db-movie-demo/setup/embedding-model/)
3. [04Configure your Rell module](https://chromia.com/learn/vector-db-movie-demo/setup/configure.rell/)
4. [05Deploy your Rell module](https://chromia.com/learn/vector-db-movie-demo/setup/deploy-rell-module/)
5. [06Finalize your Python environment](https://chromia.com/learn/vector-db-movie-demo/setup/finalize-python-env/)

Module 2: Running the data pipeline

1. [07Overview](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/)
2. [08Preprocess movie data](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/preprocess/)
3. [09Generate sentence embeddings](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/generate-embeddings/)
4. [10Store vectors on the blockchain](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/upload-vectors/)

1. [11Search the vector database](https://chromia.com/learn/vector-db-movie-demo/search/)

Module 4: A deep dive into the code

1. [12Overview](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/)
2. [13Link your data to the Vector DB with Rell](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/rell-interface/)
3. [14Work with Chromia through the Python client](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/python-client/)

1. [15Use cases and extensions](https://chromia.com/learn/vector-db-movie-demo/use-cases/)

[Learn](https://chromia.com/learn/)[Semantic search on Chromia using Vector DB](https://chromia.com/learn/vector-db-movie-demo/)

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# Searching movies semantically on Chromia

You will build a full-stack app that turns movie plot summaries into vector embeddings and stores them on Chromia. The full movie metadata lives on-chain, and the embeddings are indexed with the `vector_db_extension`. With this in place, you can run semantic searches that match queries by meaning and return detailed results straight from the blockchain.

## Key learning objectives

* Prepare your project environment and run the backend on the Chromia testnet
* Create vector embeddings and upload them together with movie metadata
* Find semantically similar movies with natural-language queries and get detailed results back

The course is hands-on: you work with the Carnegie Mellon University (CMU) Movie Summary Corpus and Python-based tools to learn how semantic search works on-chain.

### Repository link

The full source code for this course is available in the [Vector DB Movie Demo repository](https://bitbucket.org/chromawallet/vector-db-movie-demo/src/main/).

Written for this site from Chromia’s public course. Code samples are the course’s own.

[← Course**Semantic search on Chromia using Vector DB**](https://chromia.com/learn/vector-db-movie-demo/)[Next →**Module 1: Project setup**](https://chromia.com/learn/vector-db-movie-demo/setup/)

On this page

* [Key learning objectives](#key-learning-objectives)