# Use cases and extensions · Semantic search on Chromia using Vector DB · Learn Chromia

> Your semantic search system, built on Chromia's Vector Database Extension, now keeps movie embeddings on-chain and answers real-time semantic queries from a…

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

Semantic search on Chromia using Vector DBLesson 15 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/)

15 / 15Advanced

# Use cases and extensions

Your semantic search system, built on Chromia's Vector Database Extension, now keeps movie embeddings on-chain and answers real-time semantic queries from a Python client. The next step is to build on that base.

## Real-world applications

Movies are only one example. The same vector search pipeline handles any kind of content, such as:

* Product descriptions, for semantic search in ecommerce
* News articles, for curating and retrieving stories on-chain
* Support tickets, for automated help agents
* User posts or comments, for recommendation engines
* Knowledge bases, for chatbots that use RAG

In each case, Chromia's decentralized data layer stores the data, and vector embeddings make it searchable.

## Retrieval-augmented generation (RAG)

A RAG workflow usually runs like this:

1. The user sends a query.
2. The system turns the query into an embedding.
3. The vector database returns matching results.
4. A language model takes those results as context.

Your Chromia implementation already covers steps 1 to 3. To finish the workflow, pass your semantic search results to a language model, for example behind a chat interface. The model then generates its answers from the matches you supply as context.

For a working example of a chat agent that talks to Chromia through tools, queries, and blockchain calls, see the [GOAT SDK course](https://chromia.com/learn/chromia-goat-chat-agent/introduction/). You can follow the same pattern:

* Create an embedding of the user's prompt
* Run a vector query with the Python client
* Return the results to the agent as context

The result is a retrieval-augmented agent that draws on semantic matches from data stored entirely on-chain.

## Congratulations

You now have a working decentralized semantic search system on Chromia.

The pipeline takes raw text, stores it as on-chain vectors, and serves real-time queries. It is complete, extensible, and suitable for real-world use.

## What's next?

With the pipeline in place, you can:

* Try other models or embedding strategies
* Build a chat or web front end
* Connect it to other on-chain logic or user actions
* Reuse the same semantic search structure in other domains

The foundation is done. Everything from here builds on it.

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

[← Previous**Work with Chromia through the Python client**](https://chromia.com/learn/vector-db-movie-demo/code-deep-dive/python-client/)[Course done →**Start building**](https://chromia.com/developers/#start)

On this page

* [Real-world applications](#real-world-applications)
* [Retrieval-augmented generation (RAG)](#retrieval-augmented-generation-rag)
* [🎉 Congratulations](#-congratulations)
* [What’s next?](#whats-next)