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highlighting vector databases

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@@ -36,7 +36,7 @@ The image above shows how a basic RAG system works. Before forwarding the questi
As your data grows, you’ll need efficient ways to identify the most relevant information for your LLM's limited memory. This is where you’ll want a proper way to store and retrieve the specific data you’ll need for your query, without needing the LLM to remember it.
Vector databases store information as vector embeddings. This format supports efficient similarity searches to retrieve relevant data for your query. For example, Qdrant is specifically designed to perform fast, even in scenarios dealing with billions of vectors.
**Vector databases** store information as vector embeddings. This format supports efficient similarity searches to retrieve relevant data for your query. For example, Qdrant is specifically designed to perform fast, even in scenarios dealing with billions of vectors.
This article will focus on RAG systems and architecture. If you’re interested in learning more about vector search, we recommend the following articles: [What is a Vector Database?](https://qdrant.tech/articles/what-is-a-vector-database/) and [What are Vector Embeddings?](https://qdrant.tech/articles/what-are-embeddings/).