# Module 2: Running the data pipeline · Semantic search on Chromia using Vector DB · Learn Chromia

> With your environment set up, you can move on to preparing the movie data and getting it onto the chain.

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

Semantic search on Chromia using Vector DBLesson 7 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/)

07 / 15Module 2: Running the data pipelineAdvanced

# Module 2: Running the data pipeline

With your environment set up, you can move on to preparing the movie data and getting it onto the chain.

This module walks you through downloading the dataset, cleaning and normalizing its text, and creating embeddings with the model you picked earlier. You then upload the vectors, together with the complete movie metadata, to the Chromia chain you deployed.

When you finish, your on-chain database will contain the data that semantic search needs.

Make sure `vector_demo_env` is active

From inside the `rell/` folder, run:

bash

```nolines
cd ../python
```

From the project root, run this instead:

bash

```nolines
cd python
```

Next, activate the environment:

bash

```nolines
source vector_demo_env/bin/activate
```

## In this lesson

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

[Begin lesson →](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/preprocess/)

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

[← Previous**Finalize your Python environment**](https://chromia.com/learn/vector-db-movie-demo/setup/finalize-python-env/)[Next →**Preprocess movie data**](https://chromia.com/learn/vector-db-movie-demo/data-pipeline/preprocess/)