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. 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.