# Module 4: A deep dive into the code · Semantic search on Chromia using Vector DB · Learn Chromia

> So far you have watched the full pipeline run end to end: preprocessing, embedding, uploading, and searching vector data and metadata on-chain.

Source: https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/

Semantic search on Chromia using Vector DBLesson 12 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/)

12 / 15Module 4: A deep dive into the codeAdvanced

# Module 4: A deep dive into the code

So far you have watched the full pipeline run end to end: preprocessing, embedding, uploading, and searching vector data and metadata on-chain.

This module takes you inside the architecture and implementation. You will see how the Rell backend connects to the Vector DB extension, how complete movie metadata is kept next to the vectors, and how the Python client communicates with your Chromia chain.

You will step through the same code the demo runs. Along the way, the lessons point out where the design is flexible, so you can adapt it to your own use case.

## In this lesson

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

[Begin lesson →](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/rell-interface/)

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

[← Previous**Search the vector database**](https://chromia.com/learn/vector-db-movie-demo/search/)[Next →**Link your data to the Vector DB with Rell**](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/rell-interface/)